diff --git a/your-code/Untitled.ipynb b/your-code/Untitled.ipynb
new file mode 100644
index 0000000..a33982a
--- /dev/null
+++ b/your-code/Untitled.ipynb
@@ -0,0 +1,55 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "77cfb0fe-ad75-47e7-81b0-8c6aec69eb68",
+   "metadata": {},
+   "source": [
+    "Defender que la tortilla de patatas debe tener cebolla es, en gran parte, una cuestión de gusto personal, pero también puedes apoyarte en algunos argumentos que apelan a la tradición y al equilibrio de sabores. Aquí te dejo algunas razones que podrías usar para defender tu postura:\r\n",
+    "\r\n",
+    "1. **Equilibrio de sabores**: La cebolla aporta una dulzura natural que contrasta perfectamente con el sabor más terroso de las patatas. Este equilibrio mejora la experiencia gastronómica, creando una tortilla más compleja y sabrosa. Sin cebolla, la tortilla puede resultar más monótona o demasiado \"sólo de patata\", sin la complejidad que la cebolla aporta.\r\n",
+    "\r\n",
+    "2. **Tradición y evolución**: Aunque hay varias versiones de la tortilla de patatas, la inclusión de cebolla es común en muchas recetas tradicionales, especialmente en la cocina española de la península. La combinación de patatas y cebolla se ha consolidado como la versión clásica de la tortilla. De hecho, hay muchos chefs y expertos en gastronomía que defienden esta variante como la más auténtica.\r\n",
+    "\r\n",
+    "3. **Textura y jugosidad**: La cebolla no solo aporta sabor, sino también una textura diferente. Al cocinarse, se carameliza un poco, lo que contribuye a la jugosidad de la tortilla y evita que quede demasiado seca. La cebolla ayuda a que el plato tenga una mayor suavidad y una sensación más jugosa al comerlo.\r\n",
+    "\r\n",
+    "4. **Una cuestión de sabor completo**: Sin cebolla, la tortilla tiende a centrarse únicamente en el sabor de las patatas y el huevo, pero con cebolla, tienes una gama más amplia de sabores. El dulzor natural de la cebolla suaviza la fuerza del aceite de oliva y puede incluso realzar el sabor de las patatas y los huevos.\r\n",
+    "\r\n",
+    "5. **Cultura y preferencia popular**: En muchas regiones de España, la tortilla con cebolla es la versión más popular y apreciada. De hecho, existen incluso concursos de tortilla de patatas, y la de cebolla suele ser la que más recibe elogios. Si bien el debate entre la tortilla con y sin cebolla es muy pasional, hay un fuerte consenso en que la cebolla es un ingrediente que mejora la tortilla.\r\n",
+    "\r\n",
+    "6. **Versatilidad**: La tortilla de patatas con cebolla tiene una versatilidad mayor en cuanto a los maridajes. Puede acompañar mejor a otros alimentos y tener una presencia más destacada en una comida, ya sea en una tapa, como plato principal, o incluso en bocadillo.\r\n",
+    "\r\n",
+    "Por supuesto, la tortilla de patatas con cebolla no es la única versión válida, pero con estos argumentos puedes hacer valer tu preferencia y explicar por qué es una opción tan deliciosa y equilibrada."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b0d7e736-d1d5-49a0-94ca-6a124a3bd70a",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/your-code/main.ipynb b/your-code/main.ipynb
index a5caf8b..a892312 100755
--- a/your-code/main.ipynb
+++ b/your-code/main.ipynb
@@ -12,7 +12,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -37,11 +37,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
-    "websites = pd.read_csv('../data/website.csv')"
+    "websites = pd.read_csv(r'C:\\Users\\hugoe\\Desktop\\LABS\\lab-supervised-learning\\data/website.csv')"
    ]
   },
   {
@@ -65,599 +65,10135 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>URL</th>\n",
+       "      <th>URL_LENGTH</th>\n",
+       "      <th>NUMBER_SPECIAL_CHARACTERS</th>\n",
+       "      <th>CHARSET</th>\n",
+       "      <th>SERVER</th>\n",
+       "      <th>CONTENT_LENGTH</th>\n",
+       "      <th>WHOIS_COUNTRY</th>\n",
+       "      <th>WHOIS_STATEPRO</th>\n",
+       "      <th>WHOIS_REGDATE</th>\n",
+       "      <th>WHOIS_UPDATED_DATE</th>\n",
+       "      <th>...</th>\n",
+       "      <th>DIST_REMOTE_TCP_PORT</th>\n",
+       "      <th>REMOTE_IPS</th>\n",
+       "      <th>APP_BYTES</th>\n",
+       "      <th>SOURCE_APP_PACKETS</th>\n",
+       "      <th>REMOTE_APP_PACKETS</th>\n",
+       "      <th>SOURCE_APP_BYTES</th>\n",
+       "      <th>REMOTE_APP_BYTES</th>\n",
+       "      <th>APP_PACKETS</th>\n",
+       "      <th>DNS_QUERY_TIMES</th>\n",
+       "      <th>Type</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>M0_109</td>\n",
+       "      <td>16</td>\n",
+       "      <td>7</td>\n",
+       "      <td>iso-8859-1</td>\n",
+       "      <td>nginx</td>\n",
+       "      <td>263.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>10/10/2015 18:21</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>700</td>\n",
+       "      <td>9</td>\n",
+       "      <td>10</td>\n",
+       "      <td>1153</td>\n",
+       "      <td>832</td>\n",
+       "      <td>9</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>B0_2314</td>\n",
+       "      <td>16</td>\n",
+       "      <td>6</td>\n",
+       "      <td>UTF-8</td>\n",
+       "      <td>Apache/2.4.10</td>\n",
+       "      <td>15087.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>7</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1230</td>\n",
+       "      <td>17</td>\n",
+       "      <td>19</td>\n",
+       "      <td>1265</td>\n",
+       "      <td>1230</td>\n",
+       "      <td>17</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>B0_911</td>\n",
+       "      <td>16</td>\n",
+       "      <td>6</td>\n",
+       "      <td>us-ascii</td>\n",
+       "      <td>Microsoft-HTTPAPI/2.0</td>\n",
+       "      <td>324.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>B0_113</td>\n",
+       "      <td>17</td>\n",
+       "      <td>6</td>\n",
+       "      <td>ISO-8859-1</td>\n",
+       "      <td>nginx</td>\n",
+       "      <td>162.0</td>\n",
+       "      <td>US</td>\n",
+       "      <td>AK</td>\n",
+       "      <td>7/10/1997 4:00</td>\n",
+       "      <td>12/09/2013 0:45</td>\n",
+       "      <td>...</td>\n",
+       "      <td>22</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3812</td>\n",
+       "      <td>39</td>\n",
+       "      <td>37</td>\n",
+       "      <td>18784</td>\n",
+       "      <td>4380</td>\n",
+       "      <td>39</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>B0_403</td>\n",
+       "      <td>17</td>\n",
+       "      <td>6</td>\n",
+       "      <td>UTF-8</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>124140.0</td>\n",
+       "      <td>US</td>\n",
+       "      <td>TX</td>\n",
+       "      <td>12/05/1996 0:00</td>\n",
+       "      <td>11/04/2017 0:00</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2</td>\n",
+       "      <td>5</td>\n",
+       "      <td>4278</td>\n",
+       "      <td>61</td>\n",
+       "      <td>62</td>\n",
+       "      <td>129889</td>\n",
+       "      <td>4586</td>\n",
+       "      <td>61</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 21 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       URL  URL_LENGTH  NUMBER_SPECIAL_CHARACTERS     CHARSET  \\\n",
+       "0   M0_109          16                          7  iso-8859-1   \n",
+       "1  B0_2314          16                          6       UTF-8   \n",
+       "2   B0_911          16                          6    us-ascii   \n",
+       "3   B0_113          17                          6  ISO-8859-1   \n",
+       "4   B0_403          17                          6       UTF-8   \n",
+       "\n",
+       "                  SERVER  CONTENT_LENGTH WHOIS_COUNTRY WHOIS_STATEPRO  \\\n",
+       "0                  nginx           263.0           NaN            NaN   \n",
+       "1          Apache/2.4.10         15087.0           NaN            NaN   \n",
+       "2  Microsoft-HTTPAPI/2.0           324.0           NaN            NaN   \n",
+       "3                  nginx           162.0            US             AK   \n",
+       "4                    NaN        124140.0            US             TX   \n",
+       "\n",
+       "      WHOIS_REGDATE WHOIS_UPDATED_DATE  ...  DIST_REMOTE_TCP_PORT  REMOTE_IPS  \\\n",
+       "0  10/10/2015 18:21                NaN  ...                     0           2   \n",
+       "1               NaN                NaN  ...                     7           4   \n",
+       "2               NaN                NaN  ...                     0           0   \n",
+       "3    7/10/1997 4:00    12/09/2013 0:45  ...                    22           3   \n",
+       "4   12/05/1996 0:00    11/04/2017 0:00  ...                     2           5   \n",
+       "\n",
+       "   APP_BYTES  SOURCE_APP_PACKETS  REMOTE_APP_PACKETS  SOURCE_APP_BYTES  \\\n",
+       "0        700                   9                  10              1153   \n",
+       "1       1230                  17                  19              1265   \n",
+       "2          0                   0                   0                 0   \n",
+       "3       3812                  39                  37             18784   \n",
+       "4       4278                  61                  62            129889   \n",
+       "\n",
+       "   REMOTE_APP_BYTES  APP_PACKETS  DNS_QUERY_TIMES  Type  \n",
+       "0               832            9              2.0     1  \n",
+       "1              1230           17              0.0     0  \n",
+       "2                 0            0              0.0     0  \n",
+       "3              4380           39              8.0     0  \n",
+       "4              4586           61              4.0     0  \n",
+       "\n",
+       "[5 rows x 21 columns]"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Your code here\n",
+    "websites.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1781, 21)"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "websites.shape\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "URL                           object\n",
+       "URL_LENGTH                     int64\n",
+       "NUMBER_SPECIAL_CHARACTERS      int64\n",
+       "CHARSET                       object\n",
+       "SERVER                        object\n",
+       "CONTENT_LENGTH               float64\n",
+       "WHOIS_COUNTRY                 object\n",
+       "WHOIS_STATEPRO                object\n",
+       "WHOIS_REGDATE                 object\n",
+       "WHOIS_UPDATED_DATE            object\n",
+       "TCP_CONVERSATION_EXCHANGE      int64\n",
+       "DIST_REMOTE_TCP_PORT           int64\n",
+       "REMOTE_IPS                     int64\n",
+       "APP_BYTES                      int64\n",
+       "SOURCE_APP_PACKETS             int64\n",
+       "REMOTE_APP_PACKETS             int64\n",
+       "SOURCE_APP_BYTES               int64\n",
+       "REMOTE_APP_BYTES               int64\n",
+       "APP_PACKETS                    int64\n",
+       "DNS_QUERY_TIMES              float64\n",
+       "Type                           int64\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "websites.dtypes\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "URL                            0\n",
+       "URL_LENGTH                     0\n",
+       "NUMBER_SPECIAL_CHARACTERS      0\n",
+       "CHARSET                        7\n",
+       "SERVER                       176\n",
+       "CONTENT_LENGTH               812\n",
+       "WHOIS_COUNTRY                306\n",
+       "WHOIS_STATEPRO               362\n",
+       "WHOIS_REGDATE                127\n",
+       "WHOIS_UPDATED_DATE           139\n",
+       "TCP_CONVERSATION_EXCHANGE      0\n",
+       "DIST_REMOTE_TCP_PORT           0\n",
+       "REMOTE_IPS                     0\n",
+       "APP_BYTES                      0\n",
+       "SOURCE_APP_PACKETS             0\n",
+       "REMOTE_APP_PACKETS             0\n",
+       "SOURCE_APP_BYTES               0\n",
+       "REMOTE_APP_BYTES               0\n",
+       "APP_PACKETS                    0\n",
+       "DNS_QUERY_TIMES                1\n",
+       "Type                           0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "websites.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['URL', 'URL_LENGTH', 'NUMBER_SPECIAL_CHARACTERS', 'CHARSET', 'SERVER',\n",
+       "       'CONTENT_LENGTH', 'WHOIS_COUNTRY', 'WHOIS_STATEPRO', 'WHOIS_REGDATE',\n",
+       "       'WHOIS_UPDATED_DATE', 'TCP_CONVERSATION_EXCHANGE',\n",
+       "       'DIST_REMOTE_TCP_PORT', 'REMOTE_IPS', 'APP_BYTES', 'SOURCE_APP_PACKETS',\n",
+       "       'REMOTE_APP_PACKETS', 'SOURCE_APP_BYTES', 'REMOTE_APP_BYTES',\n",
+       "       'APP_PACKETS', 'DNS_QUERY_TIMES', 'Type'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "websites.columns\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Type\n",
+       "0    1565\n",
+       "1     216\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "websites['Type'].value_counts()\n",
+    "websites['Type'] = websites['Type'].map({'benign': 0, 'malicious': 1})\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "websites['Type'] = websites['Type'].map({'benign': 0, 'malicious': 1})\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Series([], Name: count, dtype: int64)"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "websites['Type'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['URL', 'CHARSET', 'SERVER', 'WHOIS_COUNTRY', 'WHOIS_STATEPRO',\n",
+       "       'WHOIS_REGDATE', 'WHOIS_UPDATED_DATE'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "categorical_columns = websites.select_dtypes(include=['object', 'category']).columns\n",
+    "categorical_columns\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>URL_LENGTH</th>\n",
+       "      <th>NUMBER_SPECIAL_CHARACTERS</th>\n",
+       "      <th>CONTENT_LENGTH</th>\n",
+       "      <th>TCP_CONVERSATION_EXCHANGE</th>\n",
+       "      <th>DIST_REMOTE_TCP_PORT</th>\n",
+       "      <th>REMOTE_IPS</th>\n",
+       "      <th>APP_BYTES</th>\n",
+       "      <th>SOURCE_APP_PACKETS</th>\n",
+       "      <th>REMOTE_APP_PACKETS</th>\n",
+       "      <th>SOURCE_APP_BYTES</th>\n",
+       "      <th>REMOTE_APP_BYTES</th>\n",
+       "      <th>APP_PACKETS</th>\n",
+       "      <th>DNS_QUERY_TIMES</th>\n",
+       "      <th>Type</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>1781.000000</td>\n",
+       "      <td>1781.000000</td>\n",
+       "      <td>969.000000</td>\n",
+       "      <td>1781.000000</td>\n",
+       "      <td>1781.000000</td>\n",
+       "      <td>1781.000000</td>\n",
+       "      <td>1.781000e+03</td>\n",
+       "      <td>1781.000000</td>\n",
+       "      <td>1781.000000</td>\n",
+       "      <td>1.781000e+03</td>\n",
+       "      <td>1.781000e+03</td>\n",
+       "      <td>1781.000000</td>\n",
+       "      <td>1780.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>56.961258</td>\n",
+       "      <td>11.111735</td>\n",
+       "      <td>11726.927761</td>\n",
+       "      <td>16.261089</td>\n",
+       "      <td>5.472768</td>\n",
+       "      <td>3.060640</td>\n",
+       "      <td>2.982339e+03</td>\n",
+       "      <td>18.540146</td>\n",
+       "      <td>18.746210</td>\n",
+       "      <td>1.589255e+04</td>\n",
+       "      <td>3.155599e+03</td>\n",
+       "      <td>18.540146</td>\n",
+       "      <td>2.263483</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>27.555586</td>\n",
+       "      <td>4.549896</td>\n",
+       "      <td>36391.809051</td>\n",
+       "      <td>40.500975</td>\n",
+       "      <td>21.807327</td>\n",
+       "      <td>3.386975</td>\n",
+       "      <td>5.605057e+04</td>\n",
+       "      <td>41.627173</td>\n",
+       "      <td>46.397969</td>\n",
+       "      <td>6.986193e+04</td>\n",
+       "      <td>5.605378e+04</td>\n",
+       "      <td>41.627173</td>\n",
+       "      <td>2.930853</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>16.000000</td>\n",
+       "      <td>5.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000e+00</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000e+00</td>\n",
+       "      <td>0.000000e+00</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>39.000000</td>\n",
+       "      <td>8.000000</td>\n",
+       "      <td>324.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000e+00</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000e+00</td>\n",
+       "      <td>0.000000e+00</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>49.000000</td>\n",
+       "      <td>10.000000</td>\n",
+       "      <td>1853.000000</td>\n",
+       "      <td>7.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>6.720000e+02</td>\n",
+       "      <td>8.000000</td>\n",
+       "      <td>9.000000</td>\n",
+       "      <td>5.790000e+02</td>\n",
+       "      <td>7.350000e+02</td>\n",
+       "      <td>8.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>68.000000</td>\n",
+       "      <td>13.000000</td>\n",
+       "      <td>11323.000000</td>\n",
+       "      <td>22.000000</td>\n",
+       "      <td>5.000000</td>\n",
+       "      <td>5.000000</td>\n",
+       "      <td>2.328000e+03</td>\n",
+       "      <td>26.000000</td>\n",
+       "      <td>25.000000</td>\n",
+       "      <td>9.806000e+03</td>\n",
+       "      <td>2.701000e+03</td>\n",
+       "      <td>26.000000</td>\n",
+       "      <td>4.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>249.000000</td>\n",
+       "      <td>43.000000</td>\n",
+       "      <td>649263.000000</td>\n",
+       "      <td>1194.000000</td>\n",
+       "      <td>708.000000</td>\n",
+       "      <td>17.000000</td>\n",
+       "      <td>2.362906e+06</td>\n",
+       "      <td>1198.000000</td>\n",
+       "      <td>1284.000000</td>\n",
+       "      <td>2.060012e+06</td>\n",
+       "      <td>2.362906e+06</td>\n",
+       "      <td>1198.000000</td>\n",
+       "      <td>20.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        URL_LENGTH  NUMBER_SPECIAL_CHARACTERS  CONTENT_LENGTH  \\\n",
+       "count  1781.000000                1781.000000      969.000000   \n",
+       "mean     56.961258                  11.111735    11726.927761   \n",
+       "std      27.555586                   4.549896    36391.809051   \n",
+       "min      16.000000                   5.000000        0.000000   \n",
+       "25%      39.000000                   8.000000      324.000000   \n",
+       "50%      49.000000                  10.000000     1853.000000   \n",
+       "75%      68.000000                  13.000000    11323.000000   \n",
+       "max     249.000000                  43.000000   649263.000000   \n",
+       "\n",
+       "       TCP_CONVERSATION_EXCHANGE  DIST_REMOTE_TCP_PORT   REMOTE_IPS  \\\n",
+       "count                1781.000000           1781.000000  1781.000000   \n",
+       "mean                   16.261089              5.472768     3.060640   \n",
+       "std                    40.500975             21.807327     3.386975   \n",
+       "min                     0.000000              0.000000     0.000000   \n",
+       "25%                     0.000000              0.000000     0.000000   \n",
+       "50%                     7.000000              0.000000     2.000000   \n",
+       "75%                    22.000000              5.000000     5.000000   \n",
+       "max                  1194.000000            708.000000    17.000000   \n",
+       "\n",
+       "          APP_BYTES  SOURCE_APP_PACKETS  REMOTE_APP_PACKETS  SOURCE_APP_BYTES  \\\n",
+       "count  1.781000e+03         1781.000000         1781.000000      1.781000e+03   \n",
+       "mean   2.982339e+03           18.540146           18.746210      1.589255e+04   \n",
+       "std    5.605057e+04           41.627173           46.397969      6.986193e+04   \n",
+       "min    0.000000e+00            0.000000            0.000000      0.000000e+00   \n",
+       "25%    0.000000e+00            0.000000            0.000000      0.000000e+00   \n",
+       "50%    6.720000e+02            8.000000            9.000000      5.790000e+02   \n",
+       "75%    2.328000e+03           26.000000           25.000000      9.806000e+03   \n",
+       "max    2.362906e+06         1198.000000         1284.000000      2.060012e+06   \n",
+       "\n",
+       "       REMOTE_APP_BYTES  APP_PACKETS  DNS_QUERY_TIMES  Type  \n",
+       "count      1.781000e+03  1781.000000      1780.000000   0.0  \n",
+       "mean       3.155599e+03    18.540146         2.263483   NaN  \n",
+       "std        5.605378e+04    41.627173         2.930853   NaN  \n",
+       "min        0.000000e+00     0.000000         0.000000   NaN  \n",
+       "25%        0.000000e+00     0.000000         0.000000   NaN  \n",
+       "50%        7.350000e+02     8.000000         0.000000   NaN  \n",
+       "75%        2.701000e+03    26.000000         4.000000   NaN  \n",
+       "max        2.362906e+06  1198.000000        20.000000   NaN  "
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "websites.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Your comment here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Next, evaluate if the columns in this dataset are strongly correlated.\n",
+    "\n",
+    "If our dataset has strongly correlated columns, we need to choose certain ML algorithms instead of others. We need to evaluate this for our dataset now.\n",
+    "\n",
+    "Luckily, most of the columns in this dataset are ordinal which makes things a lot easier for us. In the next cells below, evaluate the level of collinearity of the data.\n",
+    "\n",
+    "We provide some general directions for you to consult in order to complete this step:\n",
+    "\n",
+    "1. You will create a correlation matrix using the numeric columns in the dataset.\n",
+    "\n",
+    "1. Create a heatmap using `seaborn` to visualize which columns have high collinearity.\n",
+    "\n",
+    "1. Comment on which columns you might need to remove due to high collinearity."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Your code here\n",
+    "import seaborn as sns\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['URL_LENGTH', 'NUMBER_SPECIAL_CHARACTERS', 'CONTENT_LENGTH',\n",
+       "       'TCP_CONVERSATION_EXCHANGE', 'DIST_REMOTE_TCP_PORT', 'REMOTE_IPS',\n",
+       "       'APP_BYTES', 'SOURCE_APP_PACKETS', 'REMOTE_APP_PACKETS',\n",
+       "       'SOURCE_APP_BYTES', 'REMOTE_APP_BYTES', 'APP_PACKETS',\n",
+       "       'DNS_QUERY_TIMES', 'Type'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "numeric_columns = websites.select_dtypes(include=['float64', 'int64']).columns\n",
+    "numeric_columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>URL_LENGTH</th>\n",
+       "      <th>NUMBER_SPECIAL_CHARACTERS</th>\n",
+       "      <th>CONTENT_LENGTH</th>\n",
+       "      <th>TCP_CONVERSATION_EXCHANGE</th>\n",
+       "      <th>DIST_REMOTE_TCP_PORT</th>\n",
+       "      <th>REMOTE_IPS</th>\n",
+       "      <th>APP_BYTES</th>\n",
+       "      <th>SOURCE_APP_PACKETS</th>\n",
+       "      <th>REMOTE_APP_PACKETS</th>\n",
+       "      <th>SOURCE_APP_BYTES</th>\n",
+       "      <th>REMOTE_APP_BYTES</th>\n",
+       "      <th>APP_PACKETS</th>\n",
+       "      <th>DNS_QUERY_TIMES</th>\n",
+       "      <th>Type</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>URL_LENGTH</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.917986</td>\n",
+       "      <td>0.129745</td>\n",
+       "      <td>-0.038407</td>\n",
+       "      <td>-0.039839</td>\n",
+       "      <td>-0.046367</td>\n",
+       "      <td>-0.026446</td>\n",
+       "      <td>-0.042264</td>\n",
+       "      <td>-0.033779</td>\n",
+       "      <td>-0.014857</td>\n",
+       "      <td>-0.026686</td>\n",
+       "      <td>-0.042264</td>\n",
+       "      <td>-0.068582</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>NUMBER_SPECIAL_CHARACTERS</th>\n",
+       "      <td>0.917986</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.214225</td>\n",
+       "      <td>-0.037459</td>\n",
+       "      <td>-0.042619</td>\n",
+       "      <td>-0.047103</td>\n",
+       "      <td>-0.023914</td>\n",
+       "      <td>-0.040096</td>\n",
+       "      <td>-0.030597</td>\n",
+       "      <td>-0.014376</td>\n",
+       "      <td>-0.024098</td>\n",
+       "      <td>-0.040096</td>\n",
+       "      <td>-0.050048</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>CONTENT_LENGTH</th>\n",
+       "      <td>0.129745</td>\n",
+       "      <td>0.214225</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.078451</td>\n",
+       "      <td>-0.000381</td>\n",
+       "      <td>0.004774</td>\n",
+       "      <td>0.051202</td>\n",
+       "      <td>0.074142</td>\n",
+       "      <td>0.091077</td>\n",
+       "      <td>0.100094</td>\n",
+       "      <td>0.047595</td>\n",
+       "      <td>0.074142</td>\n",
+       "      <td>-0.045644</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TCP_CONVERSATION_EXCHANGE</th>\n",
+       "      <td>-0.038407</td>\n",
+       "      <td>-0.037459</td>\n",
+       "      <td>0.078451</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.555188</td>\n",
+       "      <td>0.331080</td>\n",
+       "      <td>0.457320</td>\n",
+       "      <td>0.997796</td>\n",
+       "      <td>0.990848</td>\n",
+       "      <td>0.865580</td>\n",
+       "      <td>0.458702</td>\n",
+       "      <td>0.997796</td>\n",
+       "      <td>0.349832</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DIST_REMOTE_TCP_PORT</th>\n",
+       "      <td>-0.039839</td>\n",
+       "      <td>-0.042619</td>\n",
+       "      <td>-0.000381</td>\n",
+       "      <td>0.555188</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.210188</td>\n",
+       "      <td>0.780238</td>\n",
+       "      <td>0.558612</td>\n",
+       "      <td>0.591188</td>\n",
+       "      <td>0.313359</td>\n",
+       "      <td>0.781212</td>\n",
+       "      <td>0.558612</td>\n",
+       "      <td>0.259942</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>REMOTE_IPS</th>\n",
+       "      <td>-0.046367</td>\n",
+       "      <td>-0.047103</td>\n",
+       "      <td>0.004774</td>\n",
+       "      <td>0.331080</td>\n",
+       "      <td>0.210188</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.023126</td>\n",
+       "      <td>0.361104</td>\n",
+       "      <td>0.304683</td>\n",
+       "      <td>0.171651</td>\n",
+       "      <td>0.025324</td>\n",
+       "      <td>0.361104</td>\n",
+       "      <td>0.548189</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>APP_BYTES</th>\n",
+       "      <td>-0.026446</td>\n",
+       "      <td>-0.023914</td>\n",
+       "      <td>0.051202</td>\n",
+       "      <td>0.457320</td>\n",
+       "      <td>0.780238</td>\n",
+       "      <td>0.023126</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.445822</td>\n",
+       "      <td>0.468999</td>\n",
+       "      <td>0.074464</td>\n",
+       "      <td>0.999992</td>\n",
+       "      <td>0.445822</td>\n",
+       "      <td>0.012221</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>SOURCE_APP_PACKETS</th>\n",
+       "      <td>-0.042264</td>\n",
+       "      <td>-0.040096</td>\n",
+       "      <td>0.074142</td>\n",
+       "      <td>0.997796</td>\n",
+       "      <td>0.558612</td>\n",
+       "      <td>0.361104</td>\n",
+       "      <td>0.445822</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.989285</td>\n",
+       "      <td>0.857495</td>\n",
+       "      <td>0.447448</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.410843</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>REMOTE_APP_PACKETS</th>\n",
+       "      <td>-0.033779</td>\n",
+       "      <td>-0.030597</td>\n",
+       "      <td>0.091077</td>\n",
+       "      <td>0.990848</td>\n",
+       "      <td>0.591188</td>\n",
+       "      <td>0.304683</td>\n",
+       "      <td>0.468999</td>\n",
+       "      <td>0.989285</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.880555</td>\n",
+       "      <td>0.470401</td>\n",
+       "      <td>0.989285</td>\n",
+       "      <td>0.355716</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>SOURCE_APP_BYTES</th>\n",
+       "      <td>-0.014857</td>\n",
+       "      <td>-0.014376</td>\n",
+       "      <td>0.100094</td>\n",
+       "      <td>0.865580</td>\n",
+       "      <td>0.313359</td>\n",
+       "      <td>0.171651</td>\n",
+       "      <td>0.074464</td>\n",
+       "      <td>0.857495</td>\n",
+       "      <td>0.880555</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.075328</td>\n",
+       "      <td>0.857495</td>\n",
+       "      <td>0.215285</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>REMOTE_APP_BYTES</th>\n",
+       "      <td>-0.026686</td>\n",
+       "      <td>-0.024098</td>\n",
+       "      <td>0.047595</td>\n",
+       "      <td>0.458702</td>\n",
+       "      <td>0.781212</td>\n",
+       "      <td>0.025324</td>\n",
+       "      <td>0.999992</td>\n",
+       "      <td>0.447448</td>\n",
+       "      <td>0.470401</td>\n",
+       "      <td>0.075328</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.447448</td>\n",
+       "      <td>0.016215</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>APP_PACKETS</th>\n",
+       "      <td>-0.042264</td>\n",
+       "      <td>-0.040096</td>\n",
+       "      <td>0.074142</td>\n",
+       "      <td>0.997796</td>\n",
+       "      <td>0.558612</td>\n",
+       "      <td>0.361104</td>\n",
+       "      <td>0.445822</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.989285</td>\n",
+       "      <td>0.857495</td>\n",
+       "      <td>0.447448</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.410843</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DNS_QUERY_TIMES</th>\n",
+       "      <td>-0.068582</td>\n",
+       "      <td>-0.050048</td>\n",
+       "      <td>-0.045644</td>\n",
+       "      <td>0.349832</td>\n",
+       "      <td>0.259942</td>\n",
+       "      <td>0.548189</td>\n",
+       "      <td>0.012221</td>\n",
+       "      <td>0.410843</td>\n",
+       "      <td>0.355716</td>\n",
+       "      <td>0.215285</td>\n",
+       "      <td>0.016215</td>\n",
+       "      <td>0.410843</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Type</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                           URL_LENGTH  NUMBER_SPECIAL_CHARACTERS  \\\n",
+       "URL_LENGTH                   1.000000                   0.917986   \n",
+       "NUMBER_SPECIAL_CHARACTERS    0.917986                   1.000000   \n",
+       "CONTENT_LENGTH               0.129745                   0.214225   \n",
+       "TCP_CONVERSATION_EXCHANGE   -0.038407                  -0.037459   \n",
+       "DIST_REMOTE_TCP_PORT        -0.039839                  -0.042619   \n",
+       "REMOTE_IPS                  -0.046367                  -0.047103   \n",
+       "APP_BYTES                   -0.026446                  -0.023914   \n",
+       "SOURCE_APP_PACKETS          -0.042264                  -0.040096   \n",
+       "REMOTE_APP_PACKETS          -0.033779                  -0.030597   \n",
+       "SOURCE_APP_BYTES            -0.014857                  -0.014376   \n",
+       "REMOTE_APP_BYTES            -0.026686                  -0.024098   \n",
+       "APP_PACKETS                 -0.042264                  -0.040096   \n",
+       "DNS_QUERY_TIMES             -0.068582                  -0.050048   \n",
+       "Type                              NaN                        NaN   \n",
+       "\n",
+       "                           CONTENT_LENGTH  TCP_CONVERSATION_EXCHANGE  \\\n",
+       "URL_LENGTH                       0.129745                  -0.038407   \n",
+       "NUMBER_SPECIAL_CHARACTERS        0.214225                  -0.037459   \n",
+       "CONTENT_LENGTH                   1.000000                   0.078451   \n",
+       "TCP_CONVERSATION_EXCHANGE        0.078451                   1.000000   \n",
+       "DIST_REMOTE_TCP_PORT            -0.000381                   0.555188   \n",
+       "REMOTE_IPS                       0.004774                   0.331080   \n",
+       "APP_BYTES                        0.051202                   0.457320   \n",
+       "SOURCE_APP_PACKETS               0.074142                   0.997796   \n",
+       "REMOTE_APP_PACKETS               0.091077                   0.990848   \n",
+       "SOURCE_APP_BYTES                 0.100094                   0.865580   \n",
+       "REMOTE_APP_BYTES                 0.047595                   0.458702   \n",
+       "APP_PACKETS                      0.074142                   0.997796   \n",
+       "DNS_QUERY_TIMES                 -0.045644                   0.349832   \n",
+       "Type                                  NaN                        NaN   \n",
+       "\n",
+       "                           DIST_REMOTE_TCP_PORT  REMOTE_IPS  APP_BYTES  \\\n",
+       "URL_LENGTH                            -0.039839   -0.046367  -0.026446   \n",
+       "NUMBER_SPECIAL_CHARACTERS             -0.042619   -0.047103  -0.023914   \n",
+       "CONTENT_LENGTH                        -0.000381    0.004774   0.051202   \n",
+       "TCP_CONVERSATION_EXCHANGE              0.555188    0.331080   0.457320   \n",
+       "DIST_REMOTE_TCP_PORT                   1.000000    0.210188   0.780238   \n",
+       "REMOTE_IPS                             0.210188    1.000000   0.023126   \n",
+       "APP_BYTES                              0.780238    0.023126   1.000000   \n",
+       "SOURCE_APP_PACKETS                     0.558612    0.361104   0.445822   \n",
+       "REMOTE_APP_PACKETS                     0.591188    0.304683   0.468999   \n",
+       "SOURCE_APP_BYTES                       0.313359    0.171651   0.074464   \n",
+       "REMOTE_APP_BYTES                       0.781212    0.025324   0.999992   \n",
+       "APP_PACKETS                            0.558612    0.361104   0.445822   \n",
+       "DNS_QUERY_TIMES                        0.259942    0.548189   0.012221   \n",
+       "Type                                        NaN         NaN        NaN   \n",
+       "\n",
+       "                           SOURCE_APP_PACKETS  REMOTE_APP_PACKETS  \\\n",
+       "URL_LENGTH                          -0.042264           -0.033779   \n",
+       "NUMBER_SPECIAL_CHARACTERS           -0.040096           -0.030597   \n",
+       "CONTENT_LENGTH                       0.074142            0.091077   \n",
+       "TCP_CONVERSATION_EXCHANGE            0.997796            0.990848   \n",
+       "DIST_REMOTE_TCP_PORT                 0.558612            0.591188   \n",
+       "REMOTE_IPS                           0.361104            0.304683   \n",
+       "APP_BYTES                            0.445822            0.468999   \n",
+       "SOURCE_APP_PACKETS                   1.000000            0.989285   \n",
+       "REMOTE_APP_PACKETS                   0.989285            1.000000   \n",
+       "SOURCE_APP_BYTES                     0.857495            0.880555   \n",
+       "REMOTE_APP_BYTES                     0.447448            0.470401   \n",
+       "APP_PACKETS                          1.000000            0.989285   \n",
+       "DNS_QUERY_TIMES                      0.410843            0.355716   \n",
+       "Type                                      NaN                 NaN   \n",
+       "\n",
+       "                           SOURCE_APP_BYTES  REMOTE_APP_BYTES  APP_PACKETS  \\\n",
+       "URL_LENGTH                        -0.014857         -0.026686    -0.042264   \n",
+       "NUMBER_SPECIAL_CHARACTERS         -0.014376         -0.024098    -0.040096   \n",
+       "CONTENT_LENGTH                     0.100094          0.047595     0.074142   \n",
+       "TCP_CONVERSATION_EXCHANGE          0.865580          0.458702     0.997796   \n",
+       "DIST_REMOTE_TCP_PORT               0.313359          0.781212     0.558612   \n",
+       "REMOTE_IPS                         0.171651          0.025324     0.361104   \n",
+       "APP_BYTES                          0.074464          0.999992     0.445822   \n",
+       "SOURCE_APP_PACKETS                 0.857495          0.447448     1.000000   \n",
+       "REMOTE_APP_PACKETS                 0.880555          0.470401     0.989285   \n",
+       "SOURCE_APP_BYTES                   1.000000          0.075328     0.857495   \n",
+       "REMOTE_APP_BYTES                   0.075328          1.000000     0.447448   \n",
+       "APP_PACKETS                        0.857495          0.447448     1.000000   \n",
+       "DNS_QUERY_TIMES                    0.215285          0.016215     0.410843   \n",
+       "Type                                    NaN               NaN          NaN   \n",
+       "\n",
+       "                           DNS_QUERY_TIMES  Type  \n",
+       "URL_LENGTH                       -0.068582   NaN  \n",
+       "NUMBER_SPECIAL_CHARACTERS        -0.050048   NaN  \n",
+       "CONTENT_LENGTH                   -0.045644   NaN  \n",
+       "TCP_CONVERSATION_EXCHANGE         0.349832   NaN  \n",
+       "DIST_REMOTE_TCP_PORT              0.259942   NaN  \n",
+       "REMOTE_IPS                        0.548189   NaN  \n",
+       "APP_BYTES                         0.012221   NaN  \n",
+       "SOURCE_APP_PACKETS                0.410843   NaN  \n",
+       "REMOTE_APP_PACKETS                0.355716   NaN  \n",
+       "SOURCE_APP_BYTES                  0.215285   NaN  \n",
+       "REMOTE_APP_BYTES                  0.016215   NaN  \n",
+       "APP_PACKETS                       0.410843   NaN  \n",
+       "DNS_QUERY_TIMES                   1.000000   NaN  \n",
+       "Type                                   NaN   NaN  "
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "correlation_matrix = websites[numeric_columns].corr()\n",
+    "correlation_matrix"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x800 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(12, 8))\n",
+    "sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', fmt='.2f', linewidths=0.5, cbar=True)\n",
+    "plt.title(\"Matriz de Correlación de las Columnas Numéricas\", fontsize=16)\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Your comment here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Challenge 2 - Remove Column Collinearity.\n",
+    "\n",
+    "From the heatmap you created, you should have seen at least 3 columns that can be removed due to high collinearity. Remove these columns from the dataset.\n",
+    "\n",
+    "Note that you should remove as few columns as you can. You don't have to remove all the columns at once. But instead, try removing one column, then produce the heatmap again to determine if additional columns should be removed. As long as the dataset no longer contains columns that are correlated for over 90%, you can stop. Also, keep in mind when two columns have high collinearity, you only need to remove one of them but not both.\n",
+    "\n",
+    "In the cells below, remove as few columns as you can to eliminate the high collinearity in the dataset. Make sure to comment on your way so that the instructional team can learn about your thinking process which allows them to give feedback. At the end, print the heatmap again."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Your code here\n",
+    "websites_cleaned = websites.copy()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "highly_correlated_pairs = []\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for col in correlation_matrix.columns:\n",
+    "    for row in correlation_matrix.index:\n",
+    "        if abs(correlation_matrix.loc[row, col]) > 0.9 and row != col:\n",
+    "            highly_correlated_pairs.append((row, col))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[('NUMBER_SPECIAL_CHARACTERS', 'URL_LENGTH'),\n",
+       " ('URL_LENGTH', 'NUMBER_SPECIAL_CHARACTERS'),\n",
+       " ('SOURCE_APP_PACKETS', 'TCP_CONVERSATION_EXCHANGE'),\n",
+       " ('REMOTE_APP_PACKETS', 'TCP_CONVERSATION_EXCHANGE'),\n",
+       " ('APP_PACKETS', 'TCP_CONVERSATION_EXCHANGE'),\n",
+       " ('REMOTE_APP_BYTES', 'APP_BYTES'),\n",
+       " ('TCP_CONVERSATION_EXCHANGE', 'SOURCE_APP_PACKETS'),\n",
+       " ('REMOTE_APP_PACKETS', 'SOURCE_APP_PACKETS'),\n",
+       " ('APP_PACKETS', 'SOURCE_APP_PACKETS'),\n",
+       " ('TCP_CONVERSATION_EXCHANGE', 'REMOTE_APP_PACKETS'),\n",
+       " ('SOURCE_APP_PACKETS', 'REMOTE_APP_PACKETS'),\n",
+       " ('APP_PACKETS', 'REMOTE_APP_PACKETS'),\n",
+       " ('APP_BYTES', 'REMOTE_APP_BYTES'),\n",
+       " ('TCP_CONVERSATION_EXCHANGE', 'APP_PACKETS'),\n",
+       " ('SOURCE_APP_PACKETS', 'APP_PACKETS'),\n",
+       " ('REMOTE_APP_PACKETS', 'APP_PACKETS')]"
+      ]
+     },
+     "execution_count": 55,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "highly_correlated_pairs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "websites_cleaned = websites_cleaned.drop(columns=['NUMBER_SPECIAL_CHARACTERS', 'URL_LENGTH', 'CONTENT_LENGTH'])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['URL', 'CHARSET', 'SERVER', 'WHOIS_COUNTRY', 'WHOIS_STATEPRO',\n",
+       "       'WHOIS_REGDATE', 'WHOIS_UPDATED_DATE', 'TCP_CONVERSATION_EXCHANGE',\n",
+       "       'DIST_REMOTE_TCP_PORT', 'REMOTE_IPS', 'APP_BYTES', 'SOURCE_APP_PACKETS',\n",
+       "       'REMOTE_APP_PACKETS', 'SOURCE_APP_BYTES', 'REMOTE_APP_BYTES',\n",
+       "       'APP_PACKETS', 'DNS_QUERY_TIMES', 'Type'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "websites_cleaned.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "URL                           object\n",
+       "CHARSET                       object\n",
+       "SERVER                        object\n",
+       "WHOIS_COUNTRY                 object\n",
+       "WHOIS_STATEPRO                object\n",
+       "WHOIS_REGDATE                 object\n",
+       "WHOIS_UPDATED_DATE            object\n",
+       "TCP_CONVERSATION_EXCHANGE      int64\n",
+       "DIST_REMOTE_TCP_PORT           int64\n",
+       "REMOTE_IPS                     int64\n",
+       "APP_BYTES                      int64\n",
+       "SOURCE_APP_PACKETS             int64\n",
+       "REMOTE_APP_PACKETS             int64\n",
+       "SOURCE_APP_BYTES               int64\n",
+       "REMOTE_APP_BYTES               int64\n",
+       "APP_PACKETS                    int64\n",
+       "DNS_QUERY_TIMES              float64\n",
+       "Type                         float64\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 65,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "websites_cleaned.dtypes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['TCP_CONVERSATION_EXCHANGE', 'DIST_REMOTE_TCP_PORT', 'REMOTE_IPS',\n",
+       "       'APP_BYTES', 'SOURCE_APP_PACKETS', 'REMOTE_APP_PACKETS',\n",
+       "       'SOURCE_APP_BYTES', 'REMOTE_APP_BYTES', 'APP_PACKETS',\n",
+       "       'DNS_QUERY_TIMES', 'Type'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 69,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "numeric_columns_cleaned = websites_cleaned.select_dtypes(include=['float64', 'int64']).columns\n",
+    "numeric_columns_cleaned "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>TCP_CONVERSATION_EXCHANGE</th>\n",
+       "      <th>DIST_REMOTE_TCP_PORT</th>\n",
+       "      <th>REMOTE_IPS</th>\n",
+       "      <th>APP_BYTES</th>\n",
+       "      <th>SOURCE_APP_PACKETS</th>\n",
+       "      <th>REMOTE_APP_PACKETS</th>\n",
+       "      <th>SOURCE_APP_BYTES</th>\n",
+       "      <th>REMOTE_APP_BYTES</th>\n",
+       "      <th>APP_PACKETS</th>\n",
+       "      <th>DNS_QUERY_TIMES</th>\n",
+       "      <th>Type</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>TCP_CONVERSATION_EXCHANGE</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.555188</td>\n",
+       "      <td>0.331080</td>\n",
+       "      <td>0.457320</td>\n",
+       "      <td>0.997796</td>\n",
+       "      <td>0.990848</td>\n",
+       "      <td>0.865580</td>\n",
+       "      <td>0.458702</td>\n",
+       "      <td>0.997796</td>\n",
+       "      <td>0.349832</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DIST_REMOTE_TCP_PORT</th>\n",
+       "      <td>0.555188</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.210188</td>\n",
+       "      <td>0.780238</td>\n",
+       "      <td>0.558612</td>\n",
+       "      <td>0.591188</td>\n",
+       "      <td>0.313359</td>\n",
+       "      <td>0.781212</td>\n",
+       "      <td>0.558612</td>\n",
+       "      <td>0.259942</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>REMOTE_IPS</th>\n",
+       "      <td>0.331080</td>\n",
+       "      <td>0.210188</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.023126</td>\n",
+       "      <td>0.361104</td>\n",
+       "      <td>0.304683</td>\n",
+       "      <td>0.171651</td>\n",
+       "      <td>0.025324</td>\n",
+       "      <td>0.361104</td>\n",
+       "      <td>0.548189</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>APP_BYTES</th>\n",
+       "      <td>0.457320</td>\n",
+       "      <td>0.780238</td>\n",
+       "      <td>0.023126</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.445822</td>\n",
+       "      <td>0.468999</td>\n",
+       "      <td>0.074464</td>\n",
+       "      <td>0.999992</td>\n",
+       "      <td>0.445822</td>\n",
+       "      <td>0.012221</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>SOURCE_APP_PACKETS</th>\n",
+       "      <td>0.997796</td>\n",
+       "      <td>0.558612</td>\n",
+       "      <td>0.361104</td>\n",
+       "      <td>0.445822</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.989285</td>\n",
+       "      <td>0.857495</td>\n",
+       "      <td>0.447448</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.410843</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>REMOTE_APP_PACKETS</th>\n",
+       "      <td>0.990848</td>\n",
+       "      <td>0.591188</td>\n",
+       "      <td>0.304683</td>\n",
+       "      <td>0.468999</td>\n",
+       "      <td>0.989285</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.880555</td>\n",
+       "      <td>0.470401</td>\n",
+       "      <td>0.989285</td>\n",
+       "      <td>0.355716</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>SOURCE_APP_BYTES</th>\n",
+       "      <td>0.865580</td>\n",
+       "      <td>0.313359</td>\n",
+       "      <td>0.171651</td>\n",
+       "      <td>0.074464</td>\n",
+       "      <td>0.857495</td>\n",
+       "      <td>0.880555</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.075328</td>\n",
+       "      <td>0.857495</td>\n",
+       "      <td>0.215285</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>REMOTE_APP_BYTES</th>\n",
+       "      <td>0.458702</td>\n",
+       "      <td>0.781212</td>\n",
+       "      <td>0.025324</td>\n",
+       "      <td>0.999992</td>\n",
+       "      <td>0.447448</td>\n",
+       "      <td>0.470401</td>\n",
+       "      <td>0.075328</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.447448</td>\n",
+       "      <td>0.016215</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>APP_PACKETS</th>\n",
+       "      <td>0.997796</td>\n",
+       "      <td>0.558612</td>\n",
+       "      <td>0.361104</td>\n",
+       "      <td>0.445822</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.989285</td>\n",
+       "      <td>0.857495</td>\n",
+       "      <td>0.447448</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.410843</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DNS_QUERY_TIMES</th>\n",
+       "      <td>0.349832</td>\n",
+       "      <td>0.259942</td>\n",
+       "      <td>0.548189</td>\n",
+       "      <td>0.012221</td>\n",
+       "      <td>0.410843</td>\n",
+       "      <td>0.355716</td>\n",
+       "      <td>0.215285</td>\n",
+       "      <td>0.016215</td>\n",
+       "      <td>0.410843</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Type</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                           TCP_CONVERSATION_EXCHANGE  DIST_REMOTE_TCP_PORT  \\\n",
+       "TCP_CONVERSATION_EXCHANGE                   1.000000              0.555188   \n",
+       "DIST_REMOTE_TCP_PORT                        0.555188              1.000000   \n",
+       "REMOTE_IPS                                  0.331080              0.210188   \n",
+       "APP_BYTES                                   0.457320              0.780238   \n",
+       "SOURCE_APP_PACKETS                          0.997796              0.558612   \n",
+       "REMOTE_APP_PACKETS                          0.990848              0.591188   \n",
+       "SOURCE_APP_BYTES                            0.865580              0.313359   \n",
+       "REMOTE_APP_BYTES                            0.458702              0.781212   \n",
+       "APP_PACKETS                                 0.997796              0.558612   \n",
+       "DNS_QUERY_TIMES                             0.349832              0.259942   \n",
+       "Type                                             NaN                   NaN   \n",
+       "\n",
+       "                           REMOTE_IPS  APP_BYTES  SOURCE_APP_PACKETS  \\\n",
+       "TCP_CONVERSATION_EXCHANGE    0.331080   0.457320            0.997796   \n",
+       "DIST_REMOTE_TCP_PORT         0.210188   0.780238            0.558612   \n",
+       "REMOTE_IPS                   1.000000   0.023126            0.361104   \n",
+       "APP_BYTES                    0.023126   1.000000            0.445822   \n",
+       "SOURCE_APP_PACKETS           0.361104   0.445822            1.000000   \n",
+       "REMOTE_APP_PACKETS           0.304683   0.468999            0.989285   \n",
+       "SOURCE_APP_BYTES             0.171651   0.074464            0.857495   \n",
+       "REMOTE_APP_BYTES             0.025324   0.999992            0.447448   \n",
+       "APP_PACKETS                  0.361104   0.445822            1.000000   \n",
+       "DNS_QUERY_TIMES              0.548189   0.012221            0.410843   \n",
+       "Type                              NaN        NaN                 NaN   \n",
+       "\n",
+       "                           REMOTE_APP_PACKETS  SOURCE_APP_BYTES  \\\n",
+       "TCP_CONVERSATION_EXCHANGE            0.990848          0.865580   \n",
+       "DIST_REMOTE_TCP_PORT                 0.591188          0.313359   \n",
+       "REMOTE_IPS                           0.304683          0.171651   \n",
+       "APP_BYTES                            0.468999          0.074464   \n",
+       "SOURCE_APP_PACKETS                   0.989285          0.857495   \n",
+       "REMOTE_APP_PACKETS                   1.000000          0.880555   \n",
+       "SOURCE_APP_BYTES                     0.880555          1.000000   \n",
+       "REMOTE_APP_BYTES                     0.470401          0.075328   \n",
+       "APP_PACKETS                          0.989285          0.857495   \n",
+       "DNS_QUERY_TIMES                      0.355716          0.215285   \n",
+       "Type                                      NaN               NaN   \n",
+       "\n",
+       "                           REMOTE_APP_BYTES  APP_PACKETS  DNS_QUERY_TIMES  \\\n",
+       "TCP_CONVERSATION_EXCHANGE          0.458702     0.997796         0.349832   \n",
+       "DIST_REMOTE_TCP_PORT               0.781212     0.558612         0.259942   \n",
+       "REMOTE_IPS                         0.025324     0.361104         0.548189   \n",
+       "APP_BYTES                          0.999992     0.445822         0.012221   \n",
+       "SOURCE_APP_PACKETS                 0.447448     1.000000         0.410843   \n",
+       "REMOTE_APP_PACKETS                 0.470401     0.989285         0.355716   \n",
+       "SOURCE_APP_BYTES                   0.075328     0.857495         0.215285   \n",
+       "REMOTE_APP_BYTES                   1.000000     0.447448         0.016215   \n",
+       "APP_PACKETS                        0.447448     1.000000         0.410843   \n",
+       "DNS_QUERY_TIMES                    0.016215     0.410843         1.000000   \n",
+       "Type                                    NaN          NaN              NaN   \n",
+       "\n",
+       "                           Type  \n",
+       "TCP_CONVERSATION_EXCHANGE   NaN  \n",
+       "DIST_REMOTE_TCP_PORT        NaN  \n",
+       "REMOTE_IPS                  NaN  \n",
+       "APP_BYTES                   NaN  \n",
+       "SOURCE_APP_PACKETS          NaN  \n",
+       "REMOTE_APP_PACKETS          NaN  \n",
+       "SOURCE_APP_BYTES            NaN  \n",
+       "REMOTE_APP_BYTES            NaN  \n",
+       "APP_PACKETS                 NaN  \n",
+       "DNS_QUERY_TIMES             NaN  \n",
+       "Type                        NaN  "
+      ]
+     },
+     "execution_count": 71,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "correlation_matrix_cleaned = websites_cleaned[numeric_columns_cleaned].corr()\n",
+    "correlation_matrix_cleaned"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Your comment here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x800 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Print heatmap again\n",
+    "plt.figure(figsize=(12, 8))\n",
+    "sns.heatmap(correlation_matrix_cleaned, annot=True, cmap='coolwarm', fmt='.2f', linewidths=0.5, cbar=True)\n",
+    "plt.title(\"Matriz de Correlación después de eliminar columnas\", fontsize=16)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Challenge 3 - Handle Missing Values\n",
+    "\n",
+    "The next step would be handling missing values. **We start by examining the number of missing values in each column, which you will do in the next cell.**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "URL                             0\n",
+       "CHARSET                         7\n",
+       "SERVER                        176\n",
+       "WHOIS_COUNTRY                 306\n",
+       "WHOIS_STATEPRO                362\n",
+       "WHOIS_REGDATE                 127\n",
+       "WHOIS_UPDATED_DATE            139\n",
+       "TCP_CONVERSATION_EXCHANGE       0\n",
+       "DIST_REMOTE_TCP_PORT            0\n",
+       "REMOTE_IPS                      0\n",
+       "APP_BYTES                       0\n",
+       "SOURCE_APP_PACKETS              0\n",
+       "REMOTE_APP_PACKETS              0\n",
+       "SOURCE_APP_BYTES                0\n",
+       "REMOTE_APP_BYTES                0\n",
+       "APP_PACKETS                     0\n",
+       "DNS_QUERY_TIMES                 1\n",
+       "Type                         1781\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 75,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Your code here\n",
+    "missing_values = websites_cleaned.isnull().sum()\n",
+    "missing_values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "URL                            0.000000\n",
+       "CHARSET                        0.393038\n",
+       "SERVER                         9.882089\n",
+       "WHOIS_COUNTRY                 17.181359\n",
+       "WHOIS_STATEPRO                20.325660\n",
+       "WHOIS_REGDATE                  7.130825\n",
+       "WHOIS_UPDATED_DATE             7.804604\n",
+       "TCP_CONVERSATION_EXCHANGE      0.000000\n",
+       "DIST_REMOTE_TCP_PORT           0.000000\n",
+       "REMOTE_IPS                     0.000000\n",
+       "APP_BYTES                      0.000000\n",
+       "SOURCE_APP_PACKETS             0.000000\n",
+       "REMOTE_APP_PACKETS             0.000000\n",
+       "SOURCE_APP_BYTES               0.000000\n",
+       "REMOTE_APP_BYTES               0.000000\n",
+       "APP_PACKETS                    0.000000\n",
+       "DNS_QUERY_TIMES                0.056148\n",
+       "Type                         100.000000\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 77,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "missing_percentage = (missing_values / len(websites_cleaned)) * 100\n",
+    "missing_percentage"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If you remember in the previous labs, we drop a column if the column contains a high proportion of missing values. After dropping those problematic columns, we drop the rows with missing values.\n",
+    "\n",
+    "#### In the cells below, handle the missing values from the dataset. Remember to comment the rationale of your decisions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "URL                          False\n",
+       "CHARSET                      False\n",
+       "SERVER                       False\n",
+       "WHOIS_COUNTRY                False\n",
+       "WHOIS_STATEPRO               False\n",
+       "WHOIS_REGDATE                False\n",
+       "WHOIS_UPDATED_DATE           False\n",
+       "TCP_CONVERSATION_EXCHANGE    False\n",
+       "DIST_REMOTE_TCP_PORT         False\n",
+       "REMOTE_IPS                   False\n",
+       "APP_BYTES                    False\n",
+       "SOURCE_APP_PACKETS           False\n",
+       "REMOTE_APP_PACKETS           False\n",
+       "SOURCE_APP_BYTES             False\n",
+       "REMOTE_APP_BYTES             False\n",
+       "APP_PACKETS                  False\n",
+       "DNS_QUERY_TIMES              False\n",
+       "Type                          True\n",
+       "dtype: bool"
+      ]
+     },
+     "execution_count": 82,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Your code here\n",
+    "threshold = 0.5  # Definimos el umbral como 50%\n",
+    "missing_percentage = (websites_cleaned.isnull().sum() / len(websites_cleaned)) > threshold\n",
+    "missing_percentage"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 84,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "columns_to_drop = missing_percentage[missing_percentage].index\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 86,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "websites_cleaned.drop(columns=columns_to_drop, inplace=True)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 88,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "websites_cleaned.dropna(inplace=True)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 90,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1257, 17)"
+      ]
+     },
+     "execution_count": 90,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "websites_cleaned.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Your comment here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Again, examine the number of missing values in each column. \n",
+    "\n",
+    "If all cleaned, proceed. Otherwise, go back and do more cleaning."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 92,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "URL                          0\n",
+       "CHARSET                      0\n",
+       "SERVER                       0\n",
+       "WHOIS_COUNTRY                0\n",
+       "WHOIS_STATEPRO               0\n",
+       "WHOIS_REGDATE                0\n",
+       "WHOIS_UPDATED_DATE           0\n",
+       "TCP_CONVERSATION_EXCHANGE    0\n",
+       "DIST_REMOTE_TCP_PORT         0\n",
+       "REMOTE_IPS                   0\n",
+       "APP_BYTES                    0\n",
+       "SOURCE_APP_PACKETS           0\n",
+       "REMOTE_APP_PACKETS           0\n",
+       "SOURCE_APP_BYTES             0\n",
+       "REMOTE_APP_BYTES             0\n",
+       "APP_PACKETS                  0\n",
+       "DNS_QUERY_TIMES              0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 92,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Examine missing values in each column\n",
+    "missing_values_after_cleaning = websites_cleaned.isnull().sum()\n",
+    "missing_values_after_cleaning"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 94,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "URL                          0.0\n",
+       "CHARSET                      0.0\n",
+       "SERVER                       0.0\n",
+       "WHOIS_COUNTRY                0.0\n",
+       "WHOIS_STATEPRO               0.0\n",
+       "WHOIS_REGDATE                0.0\n",
+       "WHOIS_UPDATED_DATE           0.0\n",
+       "TCP_CONVERSATION_EXCHANGE    0.0\n",
+       "DIST_REMOTE_TCP_PORT         0.0\n",
+       "REMOTE_IPS                   0.0\n",
+       "APP_BYTES                    0.0\n",
+       "SOURCE_APP_PACKETS           0.0\n",
+       "REMOTE_APP_PACKETS           0.0\n",
+       "SOURCE_APP_BYTES             0.0\n",
+       "REMOTE_APP_BYTES             0.0\n",
+       "APP_PACKETS                  0.0\n",
+       "DNS_QUERY_TIMES              0.0\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 94,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "missing_percentage_after_cleaning = (missing_values_after_cleaning / len(websites_cleaned)) * 100\n",
+    "missing_percentage_after_cleaning"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Challenge 4 - Handle `WHOIS_*` Categorical Data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "There are several categorical columns we need to handle. These columns are:\n",
+    "\n",
+    "* `URL`\n",
+    "* `CHARSET`\n",
+    "* `SERVER`\n",
+    "* `WHOIS_COUNTRY`\n",
+    "* `WHOIS_STATEPRO`\n",
+    "* `WHOIS_REGDATE`\n",
+    "* `WHOIS_UPDATED_DATE`\n",
+    "\n",
+    "How to handle string columns is always case by case. Let's start by working on `WHOIS_COUNTRY`. Your steps are:\n",
+    "\n",
+    "1. List out the unique values of `WHOIS_COUNTRY`.\n",
+    "1. Consolidate the country values with consistent country codes. For example, the following values refer to the same country and should use consistent country code:\n",
+    "    * `CY` and `Cyprus`\n",
+    "    * `US` and `us`\n",
+    "    * `SE` and `se`\n",
+    "    * `GB`, `United Kingdom`, and `[u'GB'; u'UK']`\n",
+    "\n",
+    "#### In the cells below, fix the country values as intructed above."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 97,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['US', 'SC', 'RU', 'AU', 'CA', 'PA', 'IN', 'GB', \"[u'GB'; u'UK']\",\n",
+       "       'NL', 'UG', 'JP', 'CN', 'UK', 'SI', 'ru', 'KY', 'AT', 'CZ', 'PH',\n",
+       "       'LV', 'TR', 'ES', 'us', 'HK', 'UA', 'CH', 'BS', 'PK', 'IL', 'DE',\n",
+       "       'SE', 'IT', 'NO', 'BE', 'BY', 'AE', 'IE', 'UY', 'KG'], dtype=object)"
+      ]
+     },
+     "execution_count": 97,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Your code here\n",
+    "unique_countries = websites_cleaned['WHOIS_COUNTRY'].unique()\n",
+    "unique_countries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 103,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "country_mapping = {\n",
+    "    'CY': 'Cyprus', 'cy': 'Cyprus',\n",
+    "    'US': 'United States', 'us': 'United States',\n",
+    "    'RU': 'Russia', 'ru': 'Russia',\n",
+    "    'AU': 'Australia', \n",
+    "    'CA': 'Canada', \n",
+    "    'PA': 'Panama', \n",
+    "    'IN': 'India', \n",
+    "    'GB': 'United Kingdom', 'GB;': 'United Kingdom', 'UK': 'United Kingdom', \n",
+    "    \"[u'GB'; u'UK']\": 'United Kingdom', \n",
+    "    'NL': 'Netherlands', \n",
+    "    'UG': 'Uganda', \n",
+    "    'JP': 'Japan', \n",
+    "    'CN': 'China', \n",
+    "    'SI': 'Slovenia', \n",
+    "    'KY': 'Cayman Islands', \n",
+    "    'AT': 'Austria', \n",
+    "    'CZ': 'Czech Republic', \n",
+    "    'PH': 'Philippines', \n",
+    "    'LV': 'Latvia', \n",
+    "    'TR': 'Turkey', \n",
+    "    'ES': 'Spain', \n",
+    "    'HK': 'Hong Kong', \n",
+    "    'UA': 'Ukraine', \n",
+    "    'CH': 'Switzerland', \n",
+    "    'BS': 'Bahamas', \n",
+    "    'PK': 'Pakistan', \n",
+    "    'IL': 'Israel', \n",
+    "    'DE': 'Germany', \n",
+    "    'SE': 'Sweden', \n",
+    "    'IT': 'Italy', \n",
+    "    'NO': 'Norway', \n",
+    "    'BE': 'Belgium', \n",
+    "    'BY': 'Belarus', \n",
+    "    'AE': 'United Arab Emirates', \n",
+    "    'IE': 'Ireland', \n",
+    "    'UY': 'Uruguay', \n",
+    "    'KG': 'Kyrgyzstan'\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 105,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['United States', 'SC', 'Russia', 'Australia', 'Canada', 'Panama',\n",
+       "       'India', 'United Kingdom', 'Netherlands', 'Uganda', 'Japan',\n",
+       "       'China', 'Slovenia', 'Cayman Islands', 'Austria', 'Czech Republic',\n",
+       "       'Philippines', 'Latvia', 'Turkey', 'Spain', 'Hong Kong', 'Ukraine',\n",
+       "       'Switzerland', 'Bahamas', 'Pakistan', 'Israel', 'Germany',\n",
+       "       'Sweden', 'Italy', 'Norway', 'Belgium', 'Belarus',\n",
+       "       'United Arab Emirates', 'Ireland', 'Uruguay', 'Kyrgyzstan'],\n",
+       "      dtype=object)"
+      ]
+     },
+     "execution_count": 105,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "websites_cleaned['WHOIS_COUNTRY'] = websites_cleaned['WHOIS_COUNTRY'].replace(country_mapping)\n",
+    "websites_cleaned['WHOIS_COUNTRY'].unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Since we have fixed the country values, can we convert this column to ordinal now?\n",
+    "\n",
+    "Not yet. If you reflect on the previous labs how we handle categorical columns, you probably remember we ended up dropping a lot of those columns because there are too many unique values. Too many unique values in a column is not desirable in machine learning because it makes prediction inaccurate. But there are workarounds under certain conditions. One of the fixable conditions is:\n",
+    "\n",
+    "#### If a limited number of values account for the majority of data, we can retain these top values and re-label all other rare values.\n",
+    "\n",
+    "The `WHOIS_COUNTRY` column happens to be this case. You can verify it by print a bar chart of the `value_counts` in the next cell to verify:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 107,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 111,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "WHOIS_COUNTRY\n",
+       "United States           960\n",
+       "Canada                   75\n",
+       "Spain                    63\n",
+       "Australia                26\n",
+       "United Kingdom           22\n",
+       "Panama                   21\n",
+       "Japan                    10\n",
+       "India                     8\n",
+       "Czech Republic            8\n",
+       "China                     8\n",
+       "Russia                    6\n",
+       "Netherlands               5\n",
+       "Switzerland               5\n",
+       "Bahamas                   4\n",
+       "Austria                   4\n",
+       "Cayman Islands            3\n",
+       "Philippines               3\n",
+       "SC                        3\n",
+       "Uruguay                   2\n",
+       "Ukraine                   2\n",
+       "Kyrgyzstan                2\n",
+       "Hong Kong                 2\n",
+       "Slovenia                  2\n",
+       "Latvia                    1\n",
+       "Pakistan                  1\n",
+       "Israel                    1\n",
+       "Germany                   1\n",
+       "Sweden                    1\n",
+       "Italy                     1\n",
+       "Norway                    1\n",
+       "Belgium                   1\n",
+       "Belarus                   1\n",
+       "United Arab Emirates      1\n",
+       "Ireland                   1\n",
+       "Uganda                    1\n",
+       "Turkey                    1\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 111,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "country_counts = websites_cleaned['WHOIS_COUNTRY'].value_counts()\n",
+    "country_counts"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 113,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(10, 6))\n",
+    "country_counts.plot(kind='bar', color='skyblue')\n",
+    "plt.title('Distribución de los valores en WHOIS_COUNTRY')\n",
+    "plt.xlabel('Países')\n",
+    "plt.ylabel('Frecuencia')\n",
+    "plt.xticks(rotation=90)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 115,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "WHOIS_COUNTRY\n",
+       "United States     960\n",
+       "Canada             75\n",
+       "Spain              63\n",
+       "Australia          26\n",
+       "United Kingdom     22\n",
+       "Panama             21\n",
+       "Japan              10\n",
+       "India               8\n",
+       "Czech Republic      8\n",
+       "China               8\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 115,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "country_counts.head(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 119,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['Bahamas', 'Austria', 'Cayman Islands', 'Philippines', 'SC', 'Uruguay',\n",
+       "       'Ukraine', 'Kyrgyzstan', 'Hong Kong', 'Slovenia', 'Latvia', 'Pakistan',\n",
+       "       'Israel', 'Germany', 'Sweden', 'Italy', 'Norway', 'Belgium', 'Belarus',\n",
+       "       'United Arab Emirates', 'Ireland', 'Uganda', 'Turkey'],\n",
+       "      dtype='object', name='WHOIS_COUNTRY')"
+      ]
+     },
+     "execution_count": 119,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "threshold = 5\n",
+    "rare_countries = country_counts[country_counts < threshold].index\n",
+    "rare_countries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "websites_cleaned['WHOIS_COUNTRY'] = websites_cleaned['WHOIS_COUNTRY'].replace(rare_countries, 'Other')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 121,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "WHOIS_COUNTRY\n",
+       "United States           960\n",
+       "Canada                   75\n",
+       "Spain                    63\n",
+       "Australia                26\n",
+       "United Kingdom           22\n",
+       "Panama                   21\n",
+       "Japan                    10\n",
+       "India                     8\n",
+       "Czech Republic            8\n",
+       "China                     8\n",
+       "Russia                    6\n",
+       "Netherlands               5\n",
+       "Switzerland               5\n",
+       "Bahamas                   4\n",
+       "Austria                   4\n",
+       "Cayman Islands            3\n",
+       "Philippines               3\n",
+       "SC                        3\n",
+       "Uruguay                   2\n",
+       "Ukraine                   2\n",
+       "Kyrgyzstan                2\n",
+       "Hong Kong                 2\n",
+       "Slovenia                  2\n",
+       "Latvia                    1\n",
+       "Pakistan                  1\n",
+       "Israel                    1\n",
+       "Germany                   1\n",
+       "Sweden                    1\n",
+       "Italy                     1\n",
+       "Norway                    1\n",
+       "Belgium                   1\n",
+       "Belarus                   1\n",
+       "United Arab Emirates      1\n",
+       "Ireland                   1\n",
+       "Uganda                    1\n",
+       "Turkey                    1\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 121,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "websites_cleaned['WHOIS_COUNTRY'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### After verifying, now let's keep the top 10 values of the column and re-label other columns with `OTHER`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 123,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['United States', 'Canada', 'Spain', 'Australia', 'United Kingdom',\n",
+       "       'Panama', 'Japan', 'India', 'Czech Republic', 'China'],\n",
+       "      dtype='object', name='WHOIS_COUNTRY')"
+      ]
+     },
+     "execution_count": 123,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Your code here\n",
+    "top_10_countries = websites_cleaned['WHOIS_COUNTRY'].value_counts().nlargest(10).index\n",
+    "top_10_countries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "websites_cleaned['WHOIS_COUNTRY'] = websites_cleaned['WHOIS_COUNTRY'].apply(lambda x: x if x in top_10_countries else 'OTHER')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 125,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "WHOIS_COUNTRY\n",
+       "United States           960\n",
+       "Canada                   75\n",
+       "Spain                    63\n",
+       "Australia                26\n",
+       "United Kingdom           22\n",
+       "Panama                   21\n",
+       "Japan                    10\n",
+       "India                     8\n",
+       "Czech Republic            8\n",
+       "China                     8\n",
+       "Russia                    6\n",
+       "Netherlands               5\n",
+       "Switzerland               5\n",
+       "Bahamas                   4\n",
+       "Austria                   4\n",
+       "Cayman Islands            3\n",
+       "Philippines               3\n",
+       "SC                        3\n",
+       "Uruguay                   2\n",
+       "Ukraine                   2\n",
+       "Kyrgyzstan                2\n",
+       "Hong Kong                 2\n",
+       "Slovenia                  2\n",
+       "Latvia                    1\n",
+       "Pakistan                  1\n",
+       "Israel                    1\n",
+       "Germany                   1\n",
+       "Sweden                    1\n",
+       "Italy                     1\n",
+       "Norway                    1\n",
+       "Belgium                   1\n",
+       "Belarus                   1\n",
+       "United Arab Emirates      1\n",
+       "Ireland                   1\n",
+       "Uganda                    1\n",
+       "Turkey                    1\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 125,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "websites_cleaned['WHOIS_COUNTRY'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now since `WHOIS_COUNTRY` has been re-labelled, we don't need `WHOIS_STATEPRO` any more because the values of the states or provinces may not be relevant any more. We'll drop this column.\n",
+    "\n",
+    "In addition, we will also drop `WHOIS_REGDATE` and `WHOIS_UPDATED_DATE`. These are the registration and update dates of the website domains. Not of our concerns.\n",
+    "\n",
+    "#### In the next cell, drop `['WHOIS_STATEPRO', 'WHOIS_REGDATE', 'WHOIS_UPDATED_DATE']`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 127,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['URL', 'CHARSET', 'SERVER', 'WHOIS_COUNTRY',\n",
+       "       'TCP_CONVERSATION_EXCHANGE', 'DIST_REMOTE_TCP_PORT', 'REMOTE_IPS',\n",
+       "       'APP_BYTES', 'SOURCE_APP_PACKETS', 'REMOTE_APP_PACKETS',\n",
+       "       'SOURCE_APP_BYTES', 'REMOTE_APP_BYTES', 'APP_PACKETS',\n",
+       "       'DNS_QUERY_TIMES'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 127,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Your code here\n",
+    "websites_cleaned = websites_cleaned.drop(['WHOIS_STATEPRO', 'WHOIS_REGDATE', 'WHOIS_UPDATED_DATE'], axis=1)\n",
+    "websites_cleaned.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Challenge 5 - Handle Remaining Categorical Data & Convert to Ordinal\n",
+    "\n",
+    "Now print the `dtypes` of the data again. Besides `WHOIS_COUNTRY` which we already fixed, there should be 3 categorical columns left: `URL`, `CHARSET`, and `SERVER`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 129,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "URL                           object\n",
+       "CHARSET                       object\n",
+       "SERVER                        object\n",
+       "WHOIS_COUNTRY                 object\n",
+       "TCP_CONVERSATION_EXCHANGE      int64\n",
+       "DIST_REMOTE_TCP_PORT           int64\n",
+       "REMOTE_IPS                     int64\n",
+       "APP_BYTES                      int64\n",
+       "SOURCE_APP_PACKETS             int64\n",
+       "REMOTE_APP_PACKETS             int64\n",
+       "SOURCE_APP_BYTES               int64\n",
+       "REMOTE_APP_BYTES               int64\n",
+       "APP_PACKETS                    int64\n",
+       "DNS_QUERY_TIMES              float64\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 129,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Your code here\n",
+    "websites_cleaned.dtypes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 133,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "URL                           object\n",
+       "CHARSET                        int64\n",
+       "SERVER                         int64\n",
+       "WHOIS_COUNTRY                 object\n",
+       "TCP_CONVERSATION_EXCHANGE      int64\n",
+       "DIST_REMOTE_TCP_PORT           int64\n",
+       "REMOTE_IPS                     int64\n",
+       "APP_BYTES                      int64\n",
+       "SOURCE_APP_PACKETS             int64\n",
+       "REMOTE_APP_PACKETS             int64\n",
+       "SOURCE_APP_BYTES               int64\n",
+       "REMOTE_APP_BYTES               int64\n",
+       "APP_PACKETS                    int64\n",
+       "DNS_QUERY_TIMES              float64\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 133,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "websites_cleaned['CHARSET'] = pd.factorize(websites_cleaned['CHARSET'])[0]\n",
+    "websites_cleaned['SERVER'] = pd.factorize(websites_cleaned['SERVER'])[0]\n",
+    "websites_cleaned.dtypes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 135,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>CHARSET</th>\n",
+       "      <th>SERVER</th>\n",
+       "      <th>WHOIS_COUNTRY</th>\n",
+       "      <th>TCP_CONVERSATION_EXCHANGE</th>\n",
+       "      <th>DIST_REMOTE_TCP_PORT</th>\n",
+       "      <th>REMOTE_IPS</th>\n",
+       "      <th>APP_BYTES</th>\n",
+       "      <th>SOURCE_APP_PACKETS</th>\n",
+       "      <th>REMOTE_APP_PACKETS</th>\n",
+       "      <th>SOURCE_APP_BYTES</th>\n",
+       "      <th>REMOTE_APP_BYTES</th>\n",
+       "      <th>APP_PACKETS</th>\n",
+       "      <th>DNS_QUERY_TIMES</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>United States</td>\n",
+       "      <td>31</td>\n",
+       "      <td>22</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3812</td>\n",
+       "      <td>39</td>\n",
+       "      <td>37</td>\n",
+       "      <td>18784</td>\n",
+       "      <td>4380</td>\n",
+       "      <td>39</td>\n",
+       "      <td>8.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>SC</td>\n",
+       "      <td>11</td>\n",
+       "      <td>6</td>\n",
+       "      <td>9</td>\n",
+       "      <td>894</td>\n",
+       "      <td>11</td>\n",
+       "      <td>13</td>\n",
+       "      <td>838</td>\n",
+       "      <td>894</td>\n",
+       "      <td>11</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>United States</td>\n",
+       "      <td>12</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1189</td>\n",
+       "      <td>14</td>\n",
+       "      <td>13</td>\n",
+       "      <td>8559</td>\n",
+       "      <td>1327</td>\n",
+       "      <td>14</td>\n",
+       "      <td>2.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
+       "      <td>United States</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>4</td>\n",
+       "      <td>3</td>\n",
+       "      <td>United States</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1776</th>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Spain</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>186</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1777</th>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Spain</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>124</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1778</th>\n",
+       "      <td>4</td>\n",
+       "      <td>192</td>\n",
+       "      <td>United States</td>\n",
+       "      <td>83</td>\n",
+       "      <td>2</td>\n",
+       "      <td>6</td>\n",
+       "      <td>6631</td>\n",
+       "      <td>87</td>\n",
+       "      <td>89</td>\n",
+       "      <td>132181</td>\n",
+       "      <td>6945</td>\n",
+       "      <td>87</td>\n",
+       "      <td>4.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1779</th>\n",
+       "      <td>0</td>\n",
+       "      <td>8</td>\n",
+       "      <td>United States</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1780</th>\n",
+       "      <td>4</td>\n",
+       "      <td>90</td>\n",
+       "      <td>United States</td>\n",
+       "      <td>19</td>\n",
+       "      <td>6</td>\n",
+       "      <td>11</td>\n",
+       "      <td>2314</td>\n",
+       "      <td>25</td>\n",
+       "      <td>28</td>\n",
+       "      <td>3039</td>\n",
+       "      <td>2776</td>\n",
+       "      <td>25</td>\n",
+       "      <td>6.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1257 rows × 13 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      CHARSET  SERVER  WHOIS_COUNTRY  TCP_CONVERSATION_EXCHANGE  \\\n",
+       "3           0       0  United States                         31   \n",
+       "5           1       0             SC                         11   \n",
+       "6           2       1  United States                         12   \n",
+       "7           3       2  United States                          0   \n",
+       "10          4       3  United States                          0   \n",
+       "...       ...     ...            ...                        ...   \n",
+       "1776        1       3          Spain                          0   \n",
+       "1777        1       3          Spain                          0   \n",
+       "1778        4     192  United States                         83   \n",
+       "1779        0       8  United States                          0   \n",
+       "1780        4      90  United States                         19   \n",
+       "\n",
+       "      DIST_REMOTE_TCP_PORT  REMOTE_IPS  APP_BYTES  SOURCE_APP_PACKETS  \\\n",
+       "3                       22           3       3812                  39   \n",
+       "5                        6           9        894                  11   \n",
+       "6                        0           3       1189                  14   \n",
+       "7                        0           0          0                   0   \n",
+       "10                       0           0          0                   0   \n",
+       "...                    ...         ...        ...                 ...   \n",
+       "1776                     0           0          0                   0   \n",
+       "1777                     0           0          0                   0   \n",
+       "1778                     2           6       6631                  87   \n",
+       "1779                     0           0          0                   0   \n",
+       "1780                     6          11       2314                  25   \n",
+       "\n",
+       "      REMOTE_APP_PACKETS  SOURCE_APP_BYTES  REMOTE_APP_BYTES  APP_PACKETS  \\\n",
+       "3                     37             18784              4380           39   \n",
+       "5                     13               838               894           11   \n",
+       "6                     13              8559              1327           14   \n",
+       "7                      0                 0                 0            0   \n",
+       "10                     0                 0                 0            0   \n",
+       "...                  ...               ...               ...          ...   \n",
+       "1776                   3               186                 0            0   \n",
+       "1777                   2               124                 0            0   \n",
+       "1778                  89            132181              6945           87   \n",
+       "1779                   0                 0                 0            0   \n",
+       "1780                  28              3039              2776           25   \n",
+       "\n",
+       "      DNS_QUERY_TIMES  \n",
+       "3                 8.0  \n",
+       "5                 0.0  \n",
+       "6                 2.0  \n",
+       "7                 0.0  \n",
+       "10                0.0  \n",
+       "...               ...  \n",
+       "1776              0.0  \n",
+       "1777              0.0  \n",
+       "1778              4.0  \n",
+       "1779              0.0  \n",
+       "1780              6.0  \n",
+       "\n",
+       "[1257 rows x 13 columns]"
+      ]
+     },
+     "execution_count": 135,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "websites_cleaned = websites_cleaned.drop(columns=['URL'])\n",
+    "websites_cleaned "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 137,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.preprocessing import MinMaxScaler\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 139,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "numeric_columns = websites_cleaned.select_dtypes(include=['int64', 'float64']).columns\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 141,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "scaler = MinMaxScaler()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 143,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "websites_cleaned[numeric_columns] = scaler.fit_transform(websites_cleaned[numeric_columns])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 145,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>CHARSET</th>\n",
+       "      <th>SERVER</th>\n",
+       "      <th>WHOIS_COUNTRY</th>\n",
+       "      <th>TCP_CONVERSATION_EXCHANGE</th>\n",
+       "      <th>DIST_REMOTE_TCP_PORT</th>\n",
+       "      <th>REMOTE_IPS</th>\n",
+       "      <th>APP_BYTES</th>\n",
+       "      <th>SOURCE_APP_PACKETS</th>\n",
+       "      <th>REMOTE_APP_PACKETS</th>\n",
+       "      <th>SOURCE_APP_BYTES</th>\n",
+       "      <th>REMOTE_APP_BYTES</th>\n",
+       "      <th>APP_PACKETS</th>\n",
+       "      <th>DNS_QUERY_TIMES</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>United States</td>\n",
+       "      <td>0.025963</td>\n",
+       "      <td>0.031073</td>\n",
+       "      <td>0.1875</td>\n",
+       "      <td>0.001613</td>\n",
+       "      <td>0.032554</td>\n",
+       "      <td>0.028816</td>\n",
+       "      <td>0.009118</td>\n",
+       "      <td>0.001854</td>\n",
+       "      <td>0.032554</td>\n",
+       "      <td>0.571429</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>0.166667</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>SC</td>\n",
+       "      <td>0.009213</td>\n",
+       "      <td>0.008475</td>\n",
+       "      <td>0.5625</td>\n",
+       "      <td>0.000378</td>\n",
+       "      <td>0.009182</td>\n",
+       "      <td>0.010125</td>\n",
+       "      <td>0.000407</td>\n",
+       "      <td>0.000378</td>\n",
+       "      <td>0.009182</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>0.333333</td>\n",
+       "      <td>0.005208</td>\n",
+       "      <td>United States</td>\n",
+       "      <td>0.010050</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.1875</td>\n",
+       "      <td>0.000503</td>\n",
+       "      <td>0.011686</td>\n",
+       "      <td>0.010125</td>\n",
+       "      <td>0.004155</td>\n",
+       "      <td>0.000562</td>\n",
+       "      <td>0.011686</td>\n",
+       "      <td>0.142857</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>0.010417</td>\n",
+       "      <td>United States</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>0.666667</td>\n",
+       "      <td>0.015625</td>\n",
+       "      <td>United States</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     CHARSET    SERVER  WHOIS_COUNTRY  TCP_CONVERSATION_EXCHANGE  \\\n",
+       "3   0.000000  0.000000  United States                   0.025963   \n",
+       "5   0.166667  0.000000             SC                   0.009213   \n",
+       "6   0.333333  0.005208  United States                   0.010050   \n",
+       "7   0.500000  0.010417  United States                   0.000000   \n",
+       "10  0.666667  0.015625  United States                   0.000000   \n",
+       "\n",
+       "    DIST_REMOTE_TCP_PORT  REMOTE_IPS  APP_BYTES  SOURCE_APP_PACKETS  \\\n",
+       "3               0.031073      0.1875   0.001613            0.032554   \n",
+       "5               0.008475      0.5625   0.000378            0.009182   \n",
+       "6               0.000000      0.1875   0.000503            0.011686   \n",
+       "7               0.000000      0.0000   0.000000            0.000000   \n",
+       "10              0.000000      0.0000   0.000000            0.000000   \n",
+       "\n",
+       "    REMOTE_APP_PACKETS  SOURCE_APP_BYTES  REMOTE_APP_BYTES  APP_PACKETS  \\\n",
+       "3             0.028816          0.009118          0.001854     0.032554   \n",
+       "5             0.010125          0.000407          0.000378     0.009182   \n",
+       "6             0.010125          0.004155          0.000562     0.011686   \n",
+       "7             0.000000          0.000000          0.000000     0.000000   \n",
+       "10            0.000000          0.000000          0.000000     0.000000   \n",
+       "\n",
+       "    DNS_QUERY_TIMES  \n",
+       "3          0.571429  \n",
+       "5          0.000000  \n",
+       "6          0.142857  \n",
+       "7          0.000000  \n",
+       "10         0.000000  "
+      ]
+     },
+     "execution_count": 145,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "websites_cleaned.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 147,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.preprocessing import StandardScaler\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 149,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "scaler = StandardScaler()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 151,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "websites_cleaned[numeric_columns] = scaler.fit_transform(websites_cleaned[numeric_columns])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 153,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>CHARSET</th>\n",
+       "      <th>SERVER</th>\n",
+       "      <th>WHOIS_COUNTRY</th>\n",
+       "      <th>TCP_CONVERSATION_EXCHANGE</th>\n",
+       "      <th>DIST_REMOTE_TCP_PORT</th>\n",
+       "      <th>REMOTE_IPS</th>\n",
+       "      <th>APP_BYTES</th>\n",
+       "      <th>SOURCE_APP_PACKETS</th>\n",
+       "      <th>REMOTE_APP_PACKETS</th>\n",
+       "      <th>SOURCE_APP_BYTES</th>\n",
+       "      <th>REMOTE_APP_BYTES</th>\n",
+       "      <th>APP_PACKETS</th>\n",
+       "      <th>DNS_QUERY_TIMES</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>-1.137986</td>\n",
+       "      <td>-0.689157</td>\n",
+       "      <td>United States</td>\n",
+       "      <td>0.335029</td>\n",
+       "      <td>0.696619</td>\n",
+       "      <td>0.050815</td>\n",
+       "      <td>0.005376</td>\n",
+       "      <td>0.458169</td>\n",
+       "      <td>0.363993</td>\n",
+       "      <td>0.032058</td>\n",
+       "      <td>0.011598</td>\n",
+       "      <td>0.458169</td>\n",
+       "      <td>2.193922</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>-0.459083</td>\n",
+       "      <td>-0.689157</td>\n",
+       "      <td>SC</td>\n",
+       "      <td>-0.107568</td>\n",
+       "      <td>0.027769</td>\n",
+       "      <td>1.816943</td>\n",
+       "      <td>-0.038389</td>\n",
+       "      <td>-0.148837</td>\n",
+       "      <td>-0.104218</td>\n",
+       "      <td>-0.209305</td>\n",
+       "      <td>-0.040686</td>\n",
+       "      <td>-0.148837</td>\n",
+       "      <td>-0.727047</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>0.219820</td>\n",
+       "      <td>-0.667713</td>\n",
+       "      <td>United States</td>\n",
+       "      <td>-0.085439</td>\n",
+       "      <td>-0.223050</td>\n",
+       "      <td>0.050815</td>\n",
+       "      <td>-0.033965</td>\n",
+       "      <td>-0.083801</td>\n",
+       "      <td>-0.104218</td>\n",
+       "      <td>-0.105462</td>\n",
+       "      <td>-0.034191</td>\n",
+       "      <td>-0.083801</td>\n",
+       "      <td>0.003195</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>0.898723</td>\n",
+       "      <td>-0.646268</td>\n",
+       "      <td>United States</td>\n",
+       "      <td>-0.350997</td>\n",
+       "      <td>-0.223050</td>\n",
+       "      <td>-0.832248</td>\n",
+       "      <td>-0.051798</td>\n",
+       "      <td>-0.387304</td>\n",
+       "      <td>-0.357832</td>\n",
+       "      <td>-0.220576</td>\n",
+       "      <td>-0.054094</td>\n",
+       "      <td>-0.387304</td>\n",
+       "      <td>-0.727047</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>1.577626</td>\n",
+       "      <td>-0.624824</td>\n",
+       "      <td>United States</td>\n",
+       "      <td>-0.350997</td>\n",
+       "      <td>-0.223050</td>\n",
+       "      <td>-0.832248</td>\n",
+       "      <td>-0.051798</td>\n",
+       "      <td>-0.387304</td>\n",
+       "      <td>-0.357832</td>\n",
+       "      <td>-0.220576</td>\n",
+       "      <td>-0.054094</td>\n",
+       "      <td>-0.387304</td>\n",
+       "      <td>-0.727047</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     CHARSET    SERVER  WHOIS_COUNTRY  TCP_CONVERSATION_EXCHANGE  \\\n",
+       "3  -1.137986 -0.689157  United States                   0.335029   \n",
+       "5  -0.459083 -0.689157             SC                  -0.107568   \n",
+       "6   0.219820 -0.667713  United States                  -0.085439   \n",
+       "7   0.898723 -0.646268  United States                  -0.350997   \n",
+       "10  1.577626 -0.624824  United States                  -0.350997   \n",
+       "\n",
+       "    DIST_REMOTE_TCP_PORT  REMOTE_IPS  APP_BYTES  SOURCE_APP_PACKETS  \\\n",
+       "3               0.696619    0.050815   0.005376            0.458169   \n",
+       "5               0.027769    1.816943  -0.038389           -0.148837   \n",
+       "6              -0.223050    0.050815  -0.033965           -0.083801   \n",
+       "7              -0.223050   -0.832248  -0.051798           -0.387304   \n",
+       "10             -0.223050   -0.832248  -0.051798           -0.387304   \n",
+       "\n",
+       "    REMOTE_APP_PACKETS  SOURCE_APP_BYTES  REMOTE_APP_BYTES  APP_PACKETS  \\\n",
+       "3             0.363993          0.032058          0.011598     0.458169   \n",
+       "5            -0.104218         -0.209305         -0.040686    -0.148837   \n",
+       "6            -0.104218         -0.105462         -0.034191    -0.083801   \n",
+       "7            -0.357832         -0.220576         -0.054094    -0.387304   \n",
+       "10           -0.357832         -0.220576         -0.054094    -0.387304   \n",
+       "\n",
+       "    DNS_QUERY_TIMES  \n",
+       "3          2.193922  \n",
+       "5         -0.727047  \n",
+       "6          0.003195  \n",
+       "7         -0.727047  \n",
+       "10        -0.727047  "
+      ]
+     },
+     "execution_count": 153,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "websites_cleaned.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 155,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "CHARSET                      float64\n",
+       "SERVER                       float64\n",
+       "WHOIS_COUNTRY                 object\n",
+       "TCP_CONVERSATION_EXCHANGE    float64\n",
+       "DIST_REMOTE_TCP_PORT         float64\n",
+       "REMOTE_IPS                   float64\n",
+       "APP_BYTES                    float64\n",
+       "SOURCE_APP_PACKETS           float64\n",
+       "REMOTE_APP_PACKETS           float64\n",
+       "SOURCE_APP_BYTES             float64\n",
+       "REMOTE_APP_BYTES             float64\n",
+       "APP_PACKETS                  float64\n",
+       "DNS_QUERY_TIMES              float64\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 155,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "websites_cleaned.dtypes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 157,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>CHARSET</th>\n",
+       "      <th>SERVER</th>\n",
+       "      <th>WHOIS_COUNTRY</th>\n",
+       "      <th>TCP_CONVERSATION_EXCHANGE</th>\n",
+       "      <th>DIST_REMOTE_TCP_PORT</th>\n",
+       "      <th>REMOTE_IPS</th>\n",
+       "      <th>APP_BYTES</th>\n",
+       "      <th>SOURCE_APP_PACKETS</th>\n",
+       "      <th>REMOTE_APP_PACKETS</th>\n",
+       "      <th>SOURCE_APP_BYTES</th>\n",
+       "      <th>REMOTE_APP_BYTES</th>\n",
+       "      <th>APP_PACKETS</th>\n",
+       "      <th>DNS_QUERY_TIMES</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>-1.137986</td>\n",
+       "      <td>-0.689157</td>\n",
+       "      <td>United States</td>\n",
+       "      <td>0.335029</td>\n",
+       "      <td>0.696619</td>\n",
+       "      <td>0.050815</td>\n",
+       "      <td>0.005376</td>\n",
+       "      <td>0.458169</td>\n",
+       "      <td>0.363993</td>\n",
+       "      <td>0.032058</td>\n",
+       "      <td>0.011598</td>\n",
+       "      <td>0.458169</td>\n",
+       "      <td>2.193922</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>-0.459083</td>\n",
+       "      <td>-0.689157</td>\n",
+       "      <td>SC</td>\n",
+       "      <td>-0.107568</td>\n",
+       "      <td>0.027769</td>\n",
+       "      <td>1.816943</td>\n",
+       "      <td>-0.038389</td>\n",
+       "      <td>-0.148837</td>\n",
+       "      <td>-0.104218</td>\n",
+       "      <td>-0.209305</td>\n",
+       "      <td>-0.040686</td>\n",
+       "      <td>-0.148837</td>\n",
+       "      <td>-0.727047</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>0.219820</td>\n",
+       "      <td>-0.667713</td>\n",
+       "      <td>United States</td>\n",
+       "      <td>-0.085439</td>\n",
+       "      <td>-0.223050</td>\n",
+       "      <td>0.050815</td>\n",
+       "      <td>-0.033965</td>\n",
+       "      <td>-0.083801</td>\n",
+       "      <td>-0.104218</td>\n",
+       "      <td>-0.105462</td>\n",
+       "      <td>-0.034191</td>\n",
+       "      <td>-0.083801</td>\n",
+       "      <td>0.003195</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>0.898723</td>\n",
+       "      <td>-0.646268</td>\n",
+       "      <td>United States</td>\n",
+       "      <td>-0.350997</td>\n",
+       "      <td>-0.223050</td>\n",
+       "      <td>-0.832248</td>\n",
+       "      <td>-0.051798</td>\n",
+       "      <td>-0.387304</td>\n",
+       "      <td>-0.357832</td>\n",
+       "      <td>-0.220576</td>\n",
+       "      <td>-0.054094</td>\n",
+       "      <td>-0.387304</td>\n",
+       "      <td>-0.727047</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>1.577626</td>\n",
+       "      <td>-0.624824</td>\n",
+       "      <td>United States</td>\n",
+       "      <td>-0.350997</td>\n",
+       "      <td>-0.223050</td>\n",
+       "      <td>-0.832248</td>\n",
+       "      <td>-0.051798</td>\n",
+       "      <td>-0.387304</td>\n",
+       "      <td>-0.357832</td>\n",
+       "      <td>-0.220576</td>\n",
+       "      <td>-0.054094</td>\n",
+       "      <td>-0.387304</td>\n",
+       "      <td>-0.727047</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     CHARSET    SERVER  WHOIS_COUNTRY  TCP_CONVERSATION_EXCHANGE  \\\n",
+       "3  -1.137986 -0.689157  United States                   0.335029   \n",
+       "5  -0.459083 -0.689157             SC                  -0.107568   \n",
+       "6   0.219820 -0.667713  United States                  -0.085439   \n",
+       "7   0.898723 -0.646268  United States                  -0.350997   \n",
+       "10  1.577626 -0.624824  United States                  -0.350997   \n",
+       "\n",
+       "    DIST_REMOTE_TCP_PORT  REMOTE_IPS  APP_BYTES  SOURCE_APP_PACKETS  \\\n",
+       "3               0.696619    0.050815   0.005376            0.458169   \n",
+       "5               0.027769    1.816943  -0.038389           -0.148837   \n",
+       "6              -0.223050    0.050815  -0.033965           -0.083801   \n",
+       "7              -0.223050   -0.832248  -0.051798           -0.387304   \n",
+       "10             -0.223050   -0.832248  -0.051798           -0.387304   \n",
+       "\n",
+       "    REMOTE_APP_PACKETS  SOURCE_APP_BYTES  REMOTE_APP_BYTES  APP_PACKETS  \\\n",
+       "3             0.363993          0.032058          0.011598     0.458169   \n",
+       "5            -0.104218         -0.209305         -0.040686    -0.148837   \n",
+       "6            -0.104218         -0.105462         -0.034191    -0.083801   \n",
+       "7            -0.357832         -0.220576         -0.054094    -0.387304   \n",
+       "10           -0.357832         -0.220576         -0.054094    -0.387304   \n",
+       "\n",
+       "    DNS_QUERY_TIMES  \n",
+       "3          2.193922  \n",
+       "5         -0.727047  \n",
+       "6          0.003195  \n",
+       "7         -0.727047  \n",
+       "10        -0.727047  "
+      ]
+     },
+     "execution_count": 157,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "websites_cleaned.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### `URL` is easy. We'll simply drop it because it has too many unique values that there's no way for us to consolidate."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 161,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['CHARSET', 'SERVER', 'WHOIS_COUNTRY', 'TCP_CONVERSATION_EXCHANGE',\n",
+       "       'DIST_REMOTE_TCP_PORT', 'REMOTE_IPS', 'APP_BYTES', 'SOURCE_APP_PACKETS',\n",
+       "       'REMOTE_APP_PACKETS', 'SOURCE_APP_BYTES', 'REMOTE_APP_BYTES',\n",
+       "       'APP_PACKETS', 'DNS_QUERY_TIMES'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 161,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Your code here\n",
+    "websites_cleaned.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Print the unique value counts of `CHARSET`. You see there are only a few unique values. So we can keep it as it is."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 163,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "CHARSET\n",
+       "-0.459083    520\n",
+       " 1.577626    279\n",
+       "-1.137986    275\n",
+       " 0.898723     98\n",
+       " 0.219820     83\n",
+       " 2.256528      1\n",
+       " 2.935431      1\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 163,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Your code here\n",
+    "char_count = websites_cleaned['CHARSET'].value_counts()\n",
+    "char_count"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`SERVER` is a little more complicated. Print its unique values and think about how you can consolidate those values.\n",
+    "\n",
+    "#### Before you think of your own solution, don't read the instructions that come next."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 165,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "SERVER\n",
+       "-0.624824    311\n",
+       "-0.689157    144\n",
+       "-0.517601     80\n",
+       "-0.646268     76\n",
+       "-0.324601     49\n",
+       "            ... \n",
+       "-0.002934      1\n",
+       " 0.082844      1\n",
+       " 1.583956      1\n",
+       " 1.562512      1\n",
+       " 3.428180      1\n",
+       "Name: count, Length: 193, dtype: int64"
+      ]
+     },
+     "execution_count": 165,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Your code here\n",
+    "websites_cleaned['SERVER'].value_counts()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 171,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dtype('float64')"
+      ]
+     },
+     "execution_count": 171,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "websites_cleaned['SERVER'] = websites_cleaned['SERVER'].astype(float)\n",
+    "websites_cleaned['SERVER'].dtype"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 173,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['CHARSET', 'SERVER', 'WHOIS_COUNTRY', 'TCP_CONVERSATION_EXCHANGE',\n",
+       "       'DIST_REMOTE_TCP_PORT', 'REMOTE_IPS', 'APP_BYTES', 'SOURCE_APP_PACKETS',\n",
+       "       'REMOTE_APP_PACKETS', 'SOURCE_APP_BYTES', 'REMOTE_APP_BYTES',\n",
+       "       'APP_PACKETS', 'DNS_QUERY_TIMES'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 173,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "websites_cleaned.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 175,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "CHARSET                      0\n",
+       "SERVER                       0\n",
+       "WHOIS_COUNTRY                0\n",
+       "TCP_CONVERSATION_EXCHANGE    0\n",
+       "DIST_REMOTE_TCP_PORT         0\n",
+       "REMOTE_IPS                   0\n",
+       "APP_BYTES                    0\n",
+       "SOURCE_APP_PACKETS           0\n",
+       "REMOTE_APP_PACKETS           0\n",
+       "SOURCE_APP_BYTES             0\n",
+       "REMOTE_APP_BYTES             0\n",
+       "APP_PACKETS                  0\n",
+       "DNS_QUERY_TIMES              0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 175,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "websites_cleaned.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 177,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "CHARSET                      float64\n",
+       "SERVER                       float64\n",
+       "WHOIS_COUNTRY                 object\n",
+       "TCP_CONVERSATION_EXCHANGE    float64\n",
+       "DIST_REMOTE_TCP_PORT         float64\n",
+       "REMOTE_IPS                   float64\n",
+       "APP_BYTES                    float64\n",
+       "SOURCE_APP_PACKETS           float64\n",
+       "REMOTE_APP_PACKETS           float64\n",
+       "SOURCE_APP_BYTES             float64\n",
+       "REMOTE_APP_BYTES             float64\n",
+       "APP_PACKETS                  float64\n",
+       "DNS_QUERY_TIMES              float64\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 177,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "websites_cleaned.dtypes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 179,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([-6.89157201e-01, -6.67712736e-01, -6.46268271e-01, -6.24823806e-01,\n",
+       "       -6.03379341e-01, -5.81934877e-01, -5.60490412e-01, -5.39045947e-01,\n",
+       "       -5.17601482e-01, -4.96157017e-01, -4.74712552e-01, -4.53268088e-01,\n",
+       "       -4.31823623e-01, -4.10379158e-01, -3.88934693e-01, -3.67490228e-01,\n",
+       "       -3.46045763e-01, -3.24601299e-01, -3.03156834e-01, -2.81712369e-01,\n",
+       "       -2.60267904e-01, -2.38823439e-01, -2.17378974e-01, -1.95934510e-01,\n",
+       "       -1.74490045e-01, -1.53045580e-01, -1.31601115e-01, -1.10156650e-01,\n",
+       "       -8.87121855e-02, -6.72677206e-02, -4.58232558e-02, -2.43787910e-02,\n",
+       "       -2.93432613e-03,  1.85101387e-02,  3.99546035e-02,  6.13990684e-02,\n",
+       "        8.28435332e-02,  1.04287998e-01,  1.25732463e-01,  1.47176928e-01,\n",
+       "        1.68621393e-01,  1.90065857e-01,  2.11510322e-01,  2.32954787e-01,\n",
+       "        2.54399252e-01,  2.75843717e-01,  2.97288182e-01,  3.18732646e-01,\n",
+       "        3.40177111e-01,  3.61621576e-01,  3.83066041e-01,  4.04510506e-01,\n",
+       "        4.25954971e-01,  4.47399435e-01,  4.68843900e-01,  4.90288365e-01,\n",
+       "        5.11732830e-01,  5.33177295e-01,  5.54621760e-01,  5.76066224e-01,\n",
+       "        5.97510689e-01,  6.18955154e-01,  6.40399619e-01,  6.61844084e-01,\n",
+       "        6.83288549e-01,  7.04733013e-01,  7.26177478e-01,  7.47621943e-01,\n",
+       "        7.69066408e-01,  7.90510873e-01,  8.11955338e-01,  8.33399802e-01,\n",
+       "        8.54844267e-01,  8.76288732e-01,  8.97733197e-01,  9.19177662e-01,\n",
+       "        9.40622127e-01,  9.62066591e-01,  9.83511056e-01,  1.00495552e+00,\n",
+       "        1.02639999e+00,  1.04784445e+00,  1.06928892e+00,  1.09073338e+00,\n",
+       "        1.11217785e+00,  1.13362231e+00,  1.15506677e+00,  1.17651124e+00,\n",
+       "        1.19795570e+00,  1.21940017e+00,  1.24084463e+00,  1.26228910e+00,\n",
+       "        1.28373356e+00,  1.30517803e+00,  1.32662249e+00,  1.34806696e+00,\n",
+       "        1.36951142e+00,  1.39095589e+00,  1.41240035e+00,  1.43384482e+00,\n",
+       "        1.45528928e+00,  1.47673375e+00,  1.49817821e+00,  1.51962268e+00,\n",
+       "        1.54106714e+00,  1.56251161e+00,  1.58395607e+00,  1.60540054e+00,\n",
+       "        1.62684500e+00,  1.64828947e+00,  1.66973393e+00,  1.69117840e+00,\n",
+       "        1.71262286e+00,  1.73406733e+00,  1.75551179e+00,  1.77695625e+00,\n",
+       "        1.79840072e+00,  1.81984518e+00,  1.84128965e+00,  1.86273411e+00,\n",
+       "        1.88417858e+00,  1.90562304e+00,  1.92706751e+00,  1.94851197e+00,\n",
+       "        1.96995644e+00,  1.99140090e+00,  2.01284537e+00,  2.03428983e+00,\n",
+       "        2.05573430e+00,  2.07717876e+00,  2.09862323e+00,  2.12006769e+00,\n",
+       "        2.14151216e+00,  2.16295662e+00,  2.18440109e+00,  2.20584555e+00,\n",
+       "        2.22729002e+00,  2.24873448e+00,  2.27017895e+00,  2.29162341e+00,\n",
+       "        2.31306788e+00,  2.33451234e+00,  2.35595681e+00,  2.37740127e+00,\n",
+       "        2.39884574e+00,  2.42029020e+00,  2.44173466e+00,  2.46317913e+00,\n",
+       "        2.48462359e+00,  2.50606806e+00,  2.52751252e+00,  2.54895699e+00,\n",
+       "        2.57040145e+00,  2.59184592e+00,  2.61329038e+00,  2.63473485e+00,\n",
+       "        2.65617931e+00,  2.67762378e+00,  2.69906824e+00,  2.72051271e+00,\n",
+       "        2.74195717e+00,  2.76340164e+00,  2.78484610e+00,  2.80629057e+00,\n",
+       "        2.82773503e+00,  2.84917950e+00,  2.87062396e+00,  2.89206843e+00,\n",
+       "        2.91351289e+00,  2.93495736e+00,  2.95640182e+00,  2.97784629e+00,\n",
+       "        2.99929075e+00,  3.02073522e+00,  3.04217968e+00,  3.06362414e+00,\n",
+       "        3.08506861e+00,  3.10651307e+00,  3.12795754e+00,  3.14940200e+00,\n",
+       "        3.17084647e+00,  3.19229093e+00,  3.21373540e+00,  3.23517986e+00,\n",
+       "        3.25662433e+00,  3.27806879e+00,  3.29951326e+00,  3.32095772e+00,\n",
+       "        3.34240219e+00,  3.36384665e+00,  3.38529112e+00,  3.40673558e+00,\n",
+       "        3.42818005e+00])"
+      ]
+     },
+     "execution_count": 179,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "websites_cleaned['SERVER'].unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 181,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "plt.hist(websites_cleaned['SERVER'], bins=30, edgecolor='black')\n",
+    "plt.title('Distribución de los valores de SERVER')\n",
+    "plt.xlabel('Valor de SERVER')\n",
+    "plt.ylabel('Frecuencia')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 183,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "      SERVER SERVER_CONSOLIDATED\n",
+      "3  -0.689157                Bajo\n",
+      "5  -0.689157                Bajo\n",
+      "6  -0.667713                Bajo\n",
+      "7  -0.646268                Bajo\n",
+      "10 -0.624824                Bajo\n"
+     ]
+    }
+   ],
+   "source": [
+    "bins = [-float('inf'), -0.5, 0, 0.5, float('inf')]  \n",
+    "labels = ['Bajo', 'Medio-bajo', 'Medio-alto', 'Alto']\n",
+    "\n",
+    "websites_cleaned['SERVER_CONSOLIDATED'] = pd.cut(websites_cleaned['SERVER'], bins=bins, labels=labels)\n",
+    "\n",
+    "print(websites_cleaned[['SERVER', 'SERVER_CONSOLIDATED']].head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 185,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "SERVER_CONSOLIDATED\n",
+       "Bajo          669\n",
+       "Alto          298\n",
+       "Medio-bajo    241\n",
+       "Medio-alto     49\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 185,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "websites_cleaned['SERVER_CONSOLIDATED'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![Think Hard](../images/think-hard.jpg)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Your comment here\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Although there are so many unique values in the `SERVER` column, there are actually only 3 main server types: `Microsoft`, `Apache`, and `nginx`. Just check if each `SERVER` value contains any of those server types and re-label them. For `SERVER` values that don't contain any of those substrings, label with `Other`.\n",
+    "\n",
+    "At the end, your `SERVER` column should only contain 4 unique values: `Microsoft`, `Apache`, `nginx`, and `Other`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 187,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Your code here\n",
+    "import pandas as pd\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 189,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def label_server(row):\n",
+    "    if 'Microsoft' in str(row):\n",
+    "        return 'Microsoft'\n",
+    "    elif 'Apache' in str(row):\n",
+    "        return 'Apache'\n",
+    "    elif 'nginx' in str(row):\n",
+    "        return 'nginx'\n",
+    "    else:\n",
+    "        return 'Other'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 197,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3       Other\n",
+       "5       Other\n",
+       "6       Other\n",
+       "7       Other\n",
+       "10      Other\n",
+       "        ...  \n",
+       "1776    Other\n",
+       "1777    Other\n",
+       "1778    Other\n",
+       "1779    Other\n",
+       "1780    Other\n",
+       "Name: SERVER, Length: 1257, dtype: object"
+      ]
+     },
+     "execution_count": 197,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "websites_cleaned['SERVER'] = websites_cleaned['SERVER'].apply(label_server)\n",
+    "websites_cleaned['SERVER'] "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 193,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['Other'], dtype=object)"
+      ]
+     },
+     "execution_count": 193,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "websites_cleaned['SERVER'].unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 195,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "SERVER\n",
+       "Other    1257\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 195,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Count `SERVER` value counts here\n",
+    "websites_cleaned['SERVER'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "OK, all our categorical data are fixed now. **Let's convert them to ordinal data using Pandas' `get_dummies` function ([documentation](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.get_dummies.html)). Also, assign the data with dummy values to a new variable `website_dummy`.**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 199,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>CHARSET</th>\n",
+       "      <th>WHOIS_COUNTRY</th>\n",
+       "      <th>TCP_CONVERSATION_EXCHANGE</th>\n",
+       "      <th>DIST_REMOTE_TCP_PORT</th>\n",
+       "      <th>REMOTE_IPS</th>\n",
+       "      <th>APP_BYTES</th>\n",
+       "      <th>SOURCE_APP_PACKETS</th>\n",
+       "      <th>REMOTE_APP_PACKETS</th>\n",
+       "      <th>SOURCE_APP_BYTES</th>\n",
+       "      <th>REMOTE_APP_BYTES</th>\n",
+       "      <th>APP_PACKETS</th>\n",
+       "      <th>DNS_QUERY_TIMES</th>\n",
+       "      <th>SERVER_CONSOLIDATED</th>\n",
+       "      <th>SERVER_Other</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>-1.137986</td>\n",
+       "      <td>United States</td>\n",
+       "      <td>0.335029</td>\n",
+       "      <td>0.696619</td>\n",
+       "      <td>0.050815</td>\n",
+       "      <td>0.005376</td>\n",
+       "      <td>0.458169</td>\n",
+       "      <td>0.363993</td>\n",
+       "      <td>0.032058</td>\n",
+       "      <td>0.011598</td>\n",
+       "      <td>0.458169</td>\n",
+       "      <td>2.193922</td>\n",
+       "      <td>Bajo</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>-0.459083</td>\n",
+       "      <td>SC</td>\n",
+       "      <td>-0.107568</td>\n",
+       "      <td>0.027769</td>\n",
+       "      <td>1.816943</td>\n",
+       "      <td>-0.038389</td>\n",
+       "      <td>-0.148837</td>\n",
+       "      <td>-0.104218</td>\n",
+       "      <td>-0.209305</td>\n",
+       "      <td>-0.040686</td>\n",
+       "      <td>-0.148837</td>\n",
+       "      <td>-0.727047</td>\n",
+       "      <td>Bajo</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>0.219820</td>\n",
+       "      <td>United States</td>\n",
+       "      <td>-0.085439</td>\n",
+       "      <td>-0.223050</td>\n",
+       "      <td>0.050815</td>\n",
+       "      <td>-0.033965</td>\n",
+       "      <td>-0.083801</td>\n",
+       "      <td>-0.104218</td>\n",
+       "      <td>-0.105462</td>\n",
+       "      <td>-0.034191</td>\n",
+       "      <td>-0.083801</td>\n",
+       "      <td>0.003195</td>\n",
+       "      <td>Bajo</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>0.898723</td>\n",
+       "      <td>United States</td>\n",
+       "      <td>-0.350997</td>\n",
+       "      <td>-0.223050</td>\n",
+       "      <td>-0.832248</td>\n",
+       "      <td>-0.051798</td>\n",
+       "      <td>-0.387304</td>\n",
+       "      <td>-0.357832</td>\n",
+       "      <td>-0.220576</td>\n",
+       "      <td>-0.054094</td>\n",
+       "      <td>-0.387304</td>\n",
+       "      <td>-0.727047</td>\n",
+       "      <td>Bajo</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>1.577626</td>\n",
+       "      <td>United States</td>\n",
+       "      <td>-0.350997</td>\n",
+       "      <td>-0.223050</td>\n",
+       "      <td>-0.832248</td>\n",
+       "      <td>-0.051798</td>\n",
+       "      <td>-0.387304</td>\n",
+       "      <td>-0.357832</td>\n",
+       "      <td>-0.220576</td>\n",
+       "      <td>-0.054094</td>\n",
+       "      <td>-0.387304</td>\n",
+       "      <td>-0.727047</td>\n",
+       "      <td>Bajo</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1776</th>\n",
+       "      <td>-0.459083</td>\n",
+       "      <td>Spain</td>\n",
+       "      <td>-0.350997</td>\n",
+       "      <td>-0.223050</td>\n",
+       "      <td>-0.832248</td>\n",
+       "      <td>-0.051798</td>\n",
+       "      <td>-0.387304</td>\n",
+       "      <td>-0.299305</td>\n",
+       "      <td>-0.218074</td>\n",
+       "      <td>-0.054094</td>\n",
+       "      <td>-0.387304</td>\n",
+       "      <td>-0.727047</td>\n",
+       "      <td>Bajo</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1777</th>\n",
+       "      <td>-0.459083</td>\n",
+       "      <td>Spain</td>\n",
+       "      <td>-0.350997</td>\n",
+       "      <td>-0.223050</td>\n",
+       "      <td>-0.832248</td>\n",
+       "      <td>-0.051798</td>\n",
+       "      <td>-0.387304</td>\n",
+       "      <td>-0.318814</td>\n",
+       "      <td>-0.218908</td>\n",
+       "      <td>-0.054094</td>\n",
+       "      <td>-0.387304</td>\n",
+       "      <td>-0.727047</td>\n",
+       "      <td>Bajo</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1778</th>\n",
+       "      <td>1.577626</td>\n",
+       "      <td>United States</td>\n",
+       "      <td>1.485782</td>\n",
+       "      <td>-0.139444</td>\n",
+       "      <td>0.933879</td>\n",
+       "      <td>0.047657</td>\n",
+       "      <td>1.498751</td>\n",
+       "      <td>1.378450</td>\n",
+       "      <td>1.557184</td>\n",
+       "      <td>0.050069</td>\n",
+       "      <td>1.498751</td>\n",
+       "      <td>0.733438</td>\n",
+       "      <td>Alto</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1779</th>\n",
+       "      <td>-1.137986</td>\n",
+       "      <td>United States</td>\n",
+       "      <td>-0.350997</td>\n",
+       "      <td>-0.223050</td>\n",
+       "      <td>-0.832248</td>\n",
+       "      <td>-0.051798</td>\n",
+       "      <td>-0.387304</td>\n",
+       "      <td>-0.357832</td>\n",
+       "      <td>-0.220576</td>\n",
+       "      <td>-0.054094</td>\n",
+       "      <td>-0.387304</td>\n",
+       "      <td>-0.727047</td>\n",
+       "      <td>Bajo</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1780</th>\n",
+       "      <td>1.577626</td>\n",
+       "      <td>United States</td>\n",
+       "      <td>0.069471</td>\n",
+       "      <td>0.027769</td>\n",
+       "      <td>2.405652</td>\n",
+       "      <td>-0.017091</td>\n",
+       "      <td>0.154666</td>\n",
+       "      <td>0.188414</td>\n",
+       "      <td>-0.179703</td>\n",
+       "      <td>-0.012459</td>\n",
+       "      <td>0.154666</td>\n",
+       "      <td>1.463680</td>\n",
+       "      <td>Alto</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1257 rows × 14 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       CHARSET  WHOIS_COUNTRY  TCP_CONVERSATION_EXCHANGE  \\\n",
+       "3    -1.137986  United States                   0.335029   \n",
+       "5    -0.459083             SC                  -0.107568   \n",
+       "6     0.219820  United States                  -0.085439   \n",
+       "7     0.898723  United States                  -0.350997   \n",
+       "10    1.577626  United States                  -0.350997   \n",
+       "...        ...            ...                        ...   \n",
+       "1776 -0.459083          Spain                  -0.350997   \n",
+       "1777 -0.459083          Spain                  -0.350997   \n",
+       "1778  1.577626  United States                   1.485782   \n",
+       "1779 -1.137986  United States                  -0.350997   \n",
+       "1780  1.577626  United States                   0.069471   \n",
+       "\n",
+       "      DIST_REMOTE_TCP_PORT  REMOTE_IPS  APP_BYTES  SOURCE_APP_PACKETS  \\\n",
+       "3                 0.696619    0.050815   0.005376            0.458169   \n",
+       "5                 0.027769    1.816943  -0.038389           -0.148837   \n",
+       "6                -0.223050    0.050815  -0.033965           -0.083801   \n",
+       "7                -0.223050   -0.832248  -0.051798           -0.387304   \n",
+       "10               -0.223050   -0.832248  -0.051798           -0.387304   \n",
+       "...                    ...         ...        ...                 ...   \n",
+       "1776             -0.223050   -0.832248  -0.051798           -0.387304   \n",
+       "1777             -0.223050   -0.832248  -0.051798           -0.387304   \n",
+       "1778             -0.139444    0.933879   0.047657            1.498751   \n",
+       "1779             -0.223050   -0.832248  -0.051798           -0.387304   \n",
+       "1780              0.027769    2.405652  -0.017091            0.154666   \n",
+       "\n",
+       "      REMOTE_APP_PACKETS  SOURCE_APP_BYTES  REMOTE_APP_BYTES  APP_PACKETS  \\\n",
+       "3               0.363993          0.032058          0.011598     0.458169   \n",
+       "5              -0.104218         -0.209305         -0.040686    -0.148837   \n",
+       "6              -0.104218         -0.105462         -0.034191    -0.083801   \n",
+       "7              -0.357832         -0.220576         -0.054094    -0.387304   \n",
+       "10             -0.357832         -0.220576         -0.054094    -0.387304   \n",
+       "...                  ...               ...               ...          ...   \n",
+       "1776           -0.299305         -0.218074         -0.054094    -0.387304   \n",
+       "1777           -0.318814         -0.218908         -0.054094    -0.387304   \n",
+       "1778            1.378450          1.557184          0.050069     1.498751   \n",
+       "1779           -0.357832         -0.220576         -0.054094    -0.387304   \n",
+       "1780            0.188414         -0.179703         -0.012459     0.154666   \n",
+       "\n",
+       "      DNS_QUERY_TIMES SERVER_CONSOLIDATED  SERVER_Other  \n",
+       "3            2.193922                Bajo          True  \n",
+       "5           -0.727047                Bajo          True  \n",
+       "6            0.003195                Bajo          True  \n",
+       "7           -0.727047                Bajo          True  \n",
+       "10          -0.727047                Bajo          True  \n",
+       "...               ...                 ...           ...  \n",
+       "1776        -0.727047                Bajo          True  \n",
+       "1777        -0.727047                Bajo          True  \n",
+       "1778         0.733438                Alto          True  \n",
+       "1779        -0.727047                Bajo          True  \n",
+       "1780         1.463680                Alto          True  \n",
+       "\n",
+       "[1257 rows x 14 columns]"
+      ]
+     },
+     "execution_count": 199,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Your code here\n",
+    "website_dummy = pd.get_dummies(websites_cleaned, columns=['SERVER'])\n",
+    "website_dummy"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now, inspect `website_dummy` to make sure the data and types are intended - there shouldn't be any categorical columns at this point."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 201,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "CHARSET                       float64\n",
+       "WHOIS_COUNTRY                  object\n",
+       "TCP_CONVERSATION_EXCHANGE     float64\n",
+       "DIST_REMOTE_TCP_PORT          float64\n",
+       "REMOTE_IPS                    float64\n",
+       "APP_BYTES                     float64\n",
+       "SOURCE_APP_PACKETS            float64\n",
+       "REMOTE_APP_PACKETS            float64\n",
+       "SOURCE_APP_BYTES              float64\n",
+       "REMOTE_APP_BYTES              float64\n",
+       "APP_PACKETS                   float64\n",
+       "DNS_QUERY_TIMES               float64\n",
+       "SERVER_CONSOLIDATED          category\n",
+       "SERVER_Other                     bool\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 201,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# Your code here\n"
+    "# Your code here\n",
+    "website_dummy.dtypes"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 203,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>CHARSET</th>\n",
+       "      <th>TCP_CONVERSATION_EXCHANGE</th>\n",
+       "      <th>DIST_REMOTE_TCP_PORT</th>\n",
+       "      <th>REMOTE_IPS</th>\n",
+       "      <th>APP_BYTES</th>\n",
+       "      <th>SOURCE_APP_PACKETS</th>\n",
+       "      <th>REMOTE_APP_PACKETS</th>\n",
+       "      <th>SOURCE_APP_BYTES</th>\n",
+       "      <th>REMOTE_APP_BYTES</th>\n",
+       "      <th>APP_PACKETS</th>\n",
+       "      <th>...</th>\n",
+       "      <th>WHOIS_COUNTRY_Spain</th>\n",
+       "      <th>WHOIS_COUNTRY_Sweden</th>\n",
+       "      <th>WHOIS_COUNTRY_Switzerland</th>\n",
+       "      <th>WHOIS_COUNTRY_Turkey</th>\n",
+       "      <th>WHOIS_COUNTRY_Uganda</th>\n",
+       "      <th>WHOIS_COUNTRY_Ukraine</th>\n",
+       "      <th>WHOIS_COUNTRY_United Arab Emirates</th>\n",
+       "      <th>WHOIS_COUNTRY_United Kingdom</th>\n",
+       "      <th>WHOIS_COUNTRY_United States</th>\n",
+       "      <th>WHOIS_COUNTRY_Uruguay</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>-1.137986</td>\n",
+       "      <td>0.335029</td>\n",
+       "      <td>0.696619</td>\n",
+       "      <td>0.050815</td>\n",
+       "      <td>0.005376</td>\n",
+       "      <td>0.458169</td>\n",
+       "      <td>0.363993</td>\n",
+       "      <td>0.032058</td>\n",
+       "      <td>0.011598</td>\n",
+       "      <td>0.458169</td>\n",
+       "      <td>...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>-0.459083</td>\n",
+       "      <td>-0.107568</td>\n",
+       "      <td>0.027769</td>\n",
+       "      <td>1.816943</td>\n",
+       "      <td>-0.038389</td>\n",
+       "      <td>-0.148837</td>\n",
+       "      <td>-0.104218</td>\n",
+       "      <td>-0.209305</td>\n",
+       "      <td>-0.040686</td>\n",
+       "      <td>-0.148837</td>\n",
+       "      <td>...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>0.219820</td>\n",
+       "      <td>-0.085439</td>\n",
+       "      <td>-0.223050</td>\n",
+       "      <td>0.050815</td>\n",
+       "      <td>-0.033965</td>\n",
+       "      <td>-0.083801</td>\n",
+       "      <td>-0.104218</td>\n",
+       "      <td>-0.105462</td>\n",
+       "      <td>-0.034191</td>\n",
+       "      <td>-0.083801</td>\n",
+       "      <td>...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>0.898723</td>\n",
+       "      <td>-0.350997</td>\n",
+       "      <td>-0.223050</td>\n",
+       "      <td>-0.832248</td>\n",
+       "      <td>-0.051798</td>\n",
+       "      <td>-0.387304</td>\n",
+       "      <td>-0.357832</td>\n",
+       "      <td>-0.220576</td>\n",
+       "      <td>-0.054094</td>\n",
+       "      <td>-0.387304</td>\n",
+       "      <td>...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>1.577626</td>\n",
+       "      <td>-0.350997</td>\n",
+       "      <td>-0.223050</td>\n",
+       "      <td>-0.832248</td>\n",
+       "      <td>-0.051798</td>\n",
+       "      <td>-0.387304</td>\n",
+       "      <td>-0.357832</td>\n",
+       "      <td>-0.220576</td>\n",
+       "      <td>-0.054094</td>\n",
+       "      <td>-0.387304</td>\n",
+       "      <td>...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1776</th>\n",
+       "      <td>-0.459083</td>\n",
+       "      <td>-0.350997</td>\n",
+       "      <td>-0.223050</td>\n",
+       "      <td>-0.832248</td>\n",
+       "      <td>-0.051798</td>\n",
+       "      <td>-0.387304</td>\n",
+       "      <td>-0.299305</td>\n",
+       "      <td>-0.218074</td>\n",
+       "      <td>-0.054094</td>\n",
+       "      <td>-0.387304</td>\n",
+       "      <td>...</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1777</th>\n",
+       "      <td>-0.459083</td>\n",
+       "      <td>-0.350997</td>\n",
+       "      <td>-0.223050</td>\n",
+       "      <td>-0.832248</td>\n",
+       "      <td>-0.051798</td>\n",
+       "      <td>-0.387304</td>\n",
+       "      <td>-0.318814</td>\n",
+       "      <td>-0.218908</td>\n",
+       "      <td>-0.054094</td>\n",
+       "      <td>-0.387304</td>\n",
+       "      <td>...</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1778</th>\n",
+       "      <td>1.577626</td>\n",
+       "      <td>1.485782</td>\n",
+       "      <td>-0.139444</td>\n",
+       "      <td>0.933879</td>\n",
+       "      <td>0.047657</td>\n",
+       "      <td>1.498751</td>\n",
+       "      <td>1.378450</td>\n",
+       "      <td>1.557184</td>\n",
+       "      <td>0.050069</td>\n",
+       "      <td>1.498751</td>\n",
+       "      <td>...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1779</th>\n",
+       "      <td>-1.137986</td>\n",
+       "      <td>-0.350997</td>\n",
+       "      <td>-0.223050</td>\n",
+       "      <td>-0.832248</td>\n",
+       "      <td>-0.051798</td>\n",
+       "      <td>-0.387304</td>\n",
+       "      <td>-0.357832</td>\n",
+       "      <td>-0.220576</td>\n",
+       "      <td>-0.054094</td>\n",
+       "      <td>-0.387304</td>\n",
+       "      <td>...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1780</th>\n",
+       "      <td>1.577626</td>\n",
+       "      <td>0.069471</td>\n",
+       "      <td>0.027769</td>\n",
+       "      <td>2.405652</td>\n",
+       "      <td>-0.017091</td>\n",
+       "      <td>0.154666</td>\n",
+       "      <td>0.188414</td>\n",
+       "      <td>-0.179703</td>\n",
+       "      <td>-0.012459</td>\n",
+       "      <td>0.154666</td>\n",
+       "      <td>...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1257 rows × 48 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       CHARSET  TCP_CONVERSATION_EXCHANGE  DIST_REMOTE_TCP_PORT  REMOTE_IPS  \\\n",
+       "3    -1.137986                   0.335029              0.696619    0.050815   \n",
+       "5    -0.459083                  -0.107568              0.027769    1.816943   \n",
+       "6     0.219820                  -0.085439             -0.223050    0.050815   \n",
+       "7     0.898723                  -0.350997             -0.223050   -0.832248   \n",
+       "10    1.577626                  -0.350997             -0.223050   -0.832248   \n",
+       "...        ...                        ...                   ...         ...   \n",
+       "1776 -0.459083                  -0.350997             -0.223050   -0.832248   \n",
+       "1777 -0.459083                  -0.350997             -0.223050   -0.832248   \n",
+       "1778  1.577626                   1.485782             -0.139444    0.933879   \n",
+       "1779 -1.137986                  -0.350997             -0.223050   -0.832248   \n",
+       "1780  1.577626                   0.069471              0.027769    2.405652   \n",
+       "\n",
+       "      APP_BYTES  SOURCE_APP_PACKETS  REMOTE_APP_PACKETS  SOURCE_APP_BYTES  \\\n",
+       "3      0.005376            0.458169            0.363993          0.032058   \n",
+       "5     -0.038389           -0.148837           -0.104218         -0.209305   \n",
+       "6     -0.033965           -0.083801           -0.104218         -0.105462   \n",
+       "7     -0.051798           -0.387304           -0.357832         -0.220576   \n",
+       "10    -0.051798           -0.387304           -0.357832         -0.220576   \n",
+       "...         ...                 ...                 ...               ...   \n",
+       "1776  -0.051798           -0.387304           -0.299305         -0.218074   \n",
+       "1777  -0.051798           -0.387304           -0.318814         -0.218908   \n",
+       "1778   0.047657            1.498751            1.378450          1.557184   \n",
+       "1779  -0.051798           -0.387304           -0.357832         -0.220576   \n",
+       "1780  -0.017091            0.154666            0.188414         -0.179703   \n",
+       "\n",
+       "      REMOTE_APP_BYTES  APP_PACKETS  ...  WHOIS_COUNTRY_Spain  \\\n",
+       "3             0.011598     0.458169  ...                False   \n",
+       "5            -0.040686    -0.148837  ...                False   \n",
+       "6            -0.034191    -0.083801  ...                False   \n",
+       "7            -0.054094    -0.387304  ...                False   \n",
+       "10           -0.054094    -0.387304  ...                False   \n",
+       "...                ...          ...  ...                  ...   \n",
+       "1776         -0.054094    -0.387304  ...                 True   \n",
+       "1777         -0.054094    -0.387304  ...                 True   \n",
+       "1778          0.050069     1.498751  ...                False   \n",
+       "1779         -0.054094    -0.387304  ...                False   \n",
+       "1780         -0.012459     0.154666  ...                False   \n",
+       "\n",
+       "     WHOIS_COUNTRY_Sweden  WHOIS_COUNTRY_Switzerland  WHOIS_COUNTRY_Turkey  \\\n",
+       "3                   False                      False                 False   \n",
+       "5                   False                      False                 False   \n",
+       "6                   False                      False                 False   \n",
+       "7                   False                      False                 False   \n",
+       "10                  False                      False                 False   \n",
+       "...                   ...                        ...                   ...   \n",
+       "1776                False                      False                 False   \n",
+       "1777                False                      False                 False   \n",
+       "1778                False                      False                 False   \n",
+       "1779                False                      False                 False   \n",
+       "1780                False                      False                 False   \n",
+       "\n",
+       "      WHOIS_COUNTRY_Uganda  WHOIS_COUNTRY_Ukraine  \\\n",
+       "3                    False                  False   \n",
+       "5                    False                  False   \n",
+       "6                    False                  False   \n",
+       "7                    False                  False   \n",
+       "10                   False                  False   \n",
+       "...                    ...                    ...   \n",
+       "1776                 False                  False   \n",
+       "1777                 False                  False   \n",
+       "1778                 False                  False   \n",
+       "1779                 False                  False   \n",
+       "1780                 False                  False   \n",
+       "\n",
+       "      WHOIS_COUNTRY_United Arab Emirates  WHOIS_COUNTRY_United Kingdom  \\\n",
+       "3                                  False                         False   \n",
+       "5                                  False                         False   \n",
+       "6                                  False                         False   \n",
+       "7                                  False                         False   \n",
+       "10                                 False                         False   \n",
+       "...                                  ...                           ...   \n",
+       "1776                               False                         False   \n",
+       "1777                               False                         False   \n",
+       "1778                               False                         False   \n",
+       "1779                               False                         False   \n",
+       "1780                               False                         False   \n",
+       "\n",
+       "      WHOIS_COUNTRY_United States  WHOIS_COUNTRY_Uruguay  \n",
+       "3                            True                  False  \n",
+       "5                           False                  False  \n",
+       "6                            True                  False  \n",
+       "7                            True                  False  \n",
+       "10                           True                  False  \n",
+       "...                           ...                    ...  \n",
+       "1776                        False                  False  \n",
+       "1777                        False                  False  \n",
+       "1778                         True                  False  \n",
+       "1779                         True                  False  \n",
+       "1780                         True                  False  \n",
+       "\n",
+       "[1257 rows x 48 columns]"
+      ]
+     },
+     "execution_count": 203,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# Your comment here"
+    "website_dummy = pd.get_dummies(website_dummy, columns=['WHOIS_COUNTRY'], drop_first=True)\n",
+    "website_dummy "
    ]
   },
   {
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": 207,
    "metadata": {},
+   "outputs": [],
    "source": [
-    "#### Next, evaluate if the columns in this dataset are strongly correlated.\n",
-    "\n",
-    "If our dataset has strongly correlated columns, we need to choose certain ML algorithms instead of others. We need to evaluate this for our dataset now.\n",
-    "\n",
-    "Luckily, most of the columns in this dataset are ordinal which makes things a lot easier for us. In the next cells below, evaluate the level of collinearity of the data.\n",
-    "\n",
-    "We provide some general directions for you to consult in order to complete this step:\n",
-    "\n",
-    "1. You will create a correlation matrix using the numeric columns in the dataset.\n",
+    "from sklearn.preprocessing import LabelEncoder\n",
     "\n",
-    "1. Create a heatmap using `seaborn` to visualize which columns have high collinearity.\n",
     "\n",
-    "1. Comment on which columns you might need to remove due to high collinearity."
+    "le = LabelEncoder()\n",
+    "website_dummy['SERVER_CONSOLIDATED'] = le.fit_transform(website_dummy['SERVER_CONSOLIDATED'])"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 209,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Your code here\n"
+    "website_dummy['SERVER_Other'] = website_dummy['SERVER_Other'].astype(int)\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 211,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "CHARSET                               float64\n",
+       "TCP_CONVERSATION_EXCHANGE             float64\n",
+       "DIST_REMOTE_TCP_PORT                  float64\n",
+       "REMOTE_IPS                            float64\n",
+       "APP_BYTES                             float64\n",
+       "SOURCE_APP_PACKETS                    float64\n",
+       "REMOTE_APP_PACKETS                    float64\n",
+       "SOURCE_APP_BYTES                      float64\n",
+       "REMOTE_APP_BYTES                      float64\n",
+       "APP_PACKETS                           float64\n",
+       "DNS_QUERY_TIMES                       float64\n",
+       "SERVER_CONSOLIDATED                     int32\n",
+       "SERVER_Other                            int32\n",
+       "WHOIS_COUNTRY_Austria                    bool\n",
+       "WHOIS_COUNTRY_Bahamas                    bool\n",
+       "WHOIS_COUNTRY_Belarus                    bool\n",
+       "WHOIS_COUNTRY_Belgium                    bool\n",
+       "WHOIS_COUNTRY_Canada                     bool\n",
+       "WHOIS_COUNTRY_Cayman Islands             bool\n",
+       "WHOIS_COUNTRY_China                      bool\n",
+       "WHOIS_COUNTRY_Czech Republic             bool\n",
+       "WHOIS_COUNTRY_Germany                    bool\n",
+       "WHOIS_COUNTRY_Hong Kong                  bool\n",
+       "WHOIS_COUNTRY_India                      bool\n",
+       "WHOIS_COUNTRY_Ireland                    bool\n",
+       "WHOIS_COUNTRY_Israel                     bool\n",
+       "WHOIS_COUNTRY_Italy                      bool\n",
+       "WHOIS_COUNTRY_Japan                      bool\n",
+       "WHOIS_COUNTRY_Kyrgyzstan                 bool\n",
+       "WHOIS_COUNTRY_Latvia                     bool\n",
+       "WHOIS_COUNTRY_Netherlands                bool\n",
+       "WHOIS_COUNTRY_Norway                     bool\n",
+       "WHOIS_COUNTRY_Pakistan                   bool\n",
+       "WHOIS_COUNTRY_Panama                     bool\n",
+       "WHOIS_COUNTRY_Philippines                bool\n",
+       "WHOIS_COUNTRY_Russia                     bool\n",
+       "WHOIS_COUNTRY_SC                         bool\n",
+       "WHOIS_COUNTRY_Slovenia                   bool\n",
+       "WHOIS_COUNTRY_Spain                      bool\n",
+       "WHOIS_COUNTRY_Sweden                     bool\n",
+       "WHOIS_COUNTRY_Switzerland                bool\n",
+       "WHOIS_COUNTRY_Turkey                     bool\n",
+       "WHOIS_COUNTRY_Uganda                     bool\n",
+       "WHOIS_COUNTRY_Ukraine                    bool\n",
+       "WHOIS_COUNTRY_United Arab Emirates       bool\n",
+       "WHOIS_COUNTRY_United Kingdom             bool\n",
+       "WHOIS_COUNTRY_United States              bool\n",
+       "WHOIS_COUNTRY_Uruguay                    bool\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 211,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# Your comment here"
+    "website_dummy.dtypes"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Challenge 2 - Remove Column Collinearity.\n",
-    "\n",
-    "From the heatmap you created, you should have seen at least 3 columns that can be removed due to high collinearity. Remove these columns from the dataset.\n",
-    "\n",
-    "Note that you should remove as few columns as you can. You don't have to remove all the columns at once. But instead, try removing one column, then produce the heatmap again to determine if additional columns should be removed. As long as the dataset no longer contains columns that are correlated for over 90%, you can stop. Also, keep in mind when two columns have high collinearity, you only need to remove one of them but not both.\n",
+    "# Challenge 6 - Modeling, Prediction, and Evaluation\n",
     "\n",
-    "In the cells below, remove as few columns as you can to eliminate the high collinearity in the dataset. Make sure to comment on your way so that the instructional team can learn about your thinking process which allows them to give feedback. At the end, print the heatmap again."
+    "We'll start off this section by splitting the data to train and test. **Name your 4 variables `X_train`, `X_test`, `y_train`, and `y_test`. Select 80% of the data for training and 20% for testing.**"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 219,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Your code here\n"
+    "from sklearn.model_selection import train_test_split\n",
+    "\n",
+    "# Your code here:\n",
+    "X = website_dummy.drop(['SERVER_Other'], axis=1) \n",
+    "y = website_dummy['SERVER_Other']\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 221,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(       CHARSET  TCP_CONVERSATION_EXCHANGE  DIST_REMOTE_TCP_PORT  REMOTE_IPS  \\\n",
+       " 1043 -0.459083                  -0.350997             -0.223050   -0.832248   \n",
+       " 323  -1.137986                   0.047341             -0.139444    1.522588   \n",
+       " 1481 -0.459083                  -0.350997             -0.223050   -0.832248   \n",
+       " 1655  1.577626                  -0.240348             -0.139444    0.050815   \n",
+       " 572  -0.459083                   0.224380             -0.223050    1.522588   \n",
+       " ...        ...                        ...                   ...         ...   \n",
+       " 1521  1.577626                  -0.328867             -0.181247   -0.537894   \n",
+       " 1583 -0.459083                  -0.063309             -0.097640    0.933879   \n",
+       " 1626 -0.459083                  -0.350997             -0.223050   -0.832248   \n",
+       " 1276 -0.459083                  -0.019049             -0.139444    0.639525   \n",
+       " 1622 -0.459083                  -0.262477             -0.097640   -0.243539   \n",
+       " \n",
+       "       APP_BYTES  SOURCE_APP_PACKETS  REMOTE_APP_PACKETS  SOURCE_APP_BYTES  \\\n",
+       " 1043  -0.051798           -0.387304           -0.357832         -0.220576   \n",
+       " 323   -0.030320            0.046272            0.168905         -0.192157   \n",
+       " 1481  -0.051798           -0.387304           -0.357832         -0.220576   \n",
+       " 1655  -0.044689           -0.278910           -0.299305         -0.218101   \n",
+       " 572   -0.011947            0.263060            0.324976          0.242448   \n",
+       " ...         ...                 ...                 ...               ...   \n",
+       " 1521  -0.050448           -0.365625           -0.260288         -0.214981   \n",
+       " 1583  -0.027935           -0.018764           -0.065200         -0.194538   \n",
+       " 1626  -0.051798           -0.387304           -0.299305         -0.218074   \n",
+       " 1276  -0.033230           -0.018764           -0.182253         -0.210354   \n",
+       " 1622  -0.045319           -0.300588           -0.260288         -0.216407   \n",
+       " \n",
+       "       REMOTE_APP_BYTES  APP_PACKETS  ...  WHOIS_COUNTRY_Spain  \\\n",
+       " 1043         -0.054094    -0.387304  ...                False   \n",
+       " 323          -0.030517     0.046272  ...                False   \n",
+       " 1481         -0.054094    -0.387304  ...                False   \n",
+       " 1655         -0.046985    -0.278910  ...                False   \n",
+       " 572          -0.009234     0.263060  ...                False   \n",
+       " ...                ...          ...  ...                  ...   \n",
+       " 1521         -0.052744    -0.365625  ...                False   \n",
+       " 1583         -0.025132    -0.018764  ...                False   \n",
+       " 1626         -0.054094    -0.387304  ...                 True   \n",
+       " 1276         -0.032647    -0.018764  ...                False   \n",
+       " 1622         -0.047615    -0.300588  ...                False   \n",
+       " \n",
+       "       WHOIS_COUNTRY_Sweden  WHOIS_COUNTRY_Switzerland  WHOIS_COUNTRY_Turkey  \\\n",
+       " 1043                 False                      False                 False   \n",
+       " 323                  False                      False                 False   \n",
+       " 1481                 False                      False                 False   \n",
+       " 1655                 False                      False                 False   \n",
+       " 572                  False                      False                 False   \n",
+       " ...                    ...                        ...                   ...   \n",
+       " 1521                 False                      False                 False   \n",
+       " 1583                 False                      False                 False   \n",
+       " 1626                 False                      False                 False   \n",
+       " 1276                 False                      False                 False   \n",
+       " 1622                 False                      False                 False   \n",
+       " \n",
+       "       WHOIS_COUNTRY_Uganda  WHOIS_COUNTRY_Ukraine  \\\n",
+       " 1043                 False                  False   \n",
+       " 323                  False                  False   \n",
+       " 1481                 False                  False   \n",
+       " 1655                 False                  False   \n",
+       " 572                  False                  False   \n",
+       " ...                    ...                    ...   \n",
+       " 1521                 False                  False   \n",
+       " 1583                 False                  False   \n",
+       " 1626                 False                  False   \n",
+       " 1276                 False                  False   \n",
+       " 1622                 False                  False   \n",
+       " \n",
+       "       WHOIS_COUNTRY_United Arab Emirates  WHOIS_COUNTRY_United Kingdom  \\\n",
+       " 1043                               False                         False   \n",
+       " 323                                False                         False   \n",
+       " 1481                               False                         False   \n",
+       " 1655                               False                         False   \n",
+       " 572                                False                         False   \n",
+       " ...                                  ...                           ...   \n",
+       " 1521                               False                         False   \n",
+       " 1583                               False                         False   \n",
+       " 1626                               False                         False   \n",
+       " 1276                               False                         False   \n",
+       " 1622                               False                         False   \n",
+       " \n",
+       "       WHOIS_COUNTRY_United States  WHOIS_COUNTRY_Uruguay  \n",
+       " 1043                        False                  False  \n",
+       " 323                         False                  False  \n",
+       " 1481                         True                  False  \n",
+       " 1655                        False                  False  \n",
+       " 572                          True                  False  \n",
+       " ...                           ...                    ...  \n",
+       " 1521                         True                  False  \n",
+       " 1583                        False                  False  \n",
+       " 1626                        False                  False  \n",
+       " 1276                        False                  False  \n",
+       " 1622                         True                  False  \n",
+       " \n",
+       " [1005 rows x 47 columns],\n",
+       "        CHARSET  TCP_CONVERSATION_EXCHANGE  DIST_REMOTE_TCP_PORT  REMOTE_IPS  \\\n",
+       " 646   1.577626                   1.485782             -0.139444    0.050815   \n",
+       " 160  -0.459083                  -0.306737             -0.139444   -0.537894   \n",
+       " 91   -0.459083                  -0.350997             -0.223050   -0.832248   \n",
+       " 104   1.577626                  -0.350997             -0.223050   -0.832248   \n",
+       " 1554 -0.459083                   0.025211              0.153178   -0.243539   \n",
+       " ...        ...                        ...                   ...         ...   \n",
+       " 246  -1.137986                   0.224380             -0.223050    0.050815   \n",
+       " 713   1.577626                   3.743027             -0.223050   -0.243539   \n",
+       " 595  -0.459083                  -0.350997             -0.223050   -0.832248   \n",
+       " 388   1.577626                  -0.350997             -0.223050   -0.832248   \n",
+       " 1031 -0.459083                  -0.350997             -0.223050   -0.832248   \n",
+       " \n",
+       "       APP_BYTES  SOURCE_APP_PACKETS  REMOTE_APP_PACKETS  SOURCE_APP_BYTES  \\\n",
+       " 646    0.038313            1.498751            1.729608          1.760069   \n",
+       " 160   -0.047659           -0.343946           -0.279797         -0.217214   \n",
+       " 91    -0.051798           -0.387304           -0.357832         -0.220576   \n",
+       " 104   -0.051798           -0.387304           -0.357832         -0.220576   \n",
+       " 1554  -0.004943            0.111309            0.051853         -0.091959   \n",
+       " ...         ...                 ...                 ...               ...   \n",
+       " 246    0.001732            0.306418            0.246940         -0.075994   \n",
+       " 713    0.155692            3.666631            4.772978          6.346959   \n",
+       " 595   -0.051798           -0.387304           -0.357832         -0.220576   \n",
+       " 388   -0.051798           -0.387304           -0.357832         -0.220576   \n",
+       " 1031  -0.051798           -0.387304           -0.357832         -0.220576   \n",
+       " \n",
+       "       REMOTE_APP_BYTES  APP_PACKETS  ...  WHOIS_COUNTRY_Spain  \\\n",
+       " 646           0.040575     1.498751  ...                False   \n",
+       " 160          -0.049955    -0.343946  ...                False   \n",
+       " 91           -0.054094    -0.387304  ...                False   \n",
+       " 104          -0.054094    -0.387304  ...                False   \n",
+       " 1554         -0.000640     0.111309  ...                False   \n",
+       " ...                ...          ...  ...                  ...   \n",
+       " 246           0.006454     0.306418  ...                False   \n",
+       " 713           0.155762     3.666631  ...                False   \n",
+       " 595          -0.054094    -0.387304  ...                False   \n",
+       " 388          -0.054094    -0.387304  ...                False   \n",
+       " 1031         -0.054094    -0.387304  ...                False   \n",
+       " \n",
+       "       WHOIS_COUNTRY_Sweden  WHOIS_COUNTRY_Switzerland  WHOIS_COUNTRY_Turkey  \\\n",
+       " 646                  False                      False                 False   \n",
+       " 160                  False                      False                 False   \n",
+       " 91                   False                      False                 False   \n",
+       " 104                  False                      False                 False   \n",
+       " 1554                 False                      False                 False   \n",
+       " ...                    ...                        ...                   ...   \n",
+       " 246                  False                      False                 False   \n",
+       " 713                  False                      False                 False   \n",
+       " 595                  False                      False                 False   \n",
+       " 388                  False                      False                 False   \n",
+       " 1031                 False                      False                 False   \n",
+       " \n",
+       "       WHOIS_COUNTRY_Uganda  WHOIS_COUNTRY_Ukraine  \\\n",
+       " 646                  False                  False   \n",
+       " 160                  False                  False   \n",
+       " 91                   False                  False   \n",
+       " 104                  False                  False   \n",
+       " 1554                 False                  False   \n",
+       " ...                    ...                    ...   \n",
+       " 246                  False                  False   \n",
+       " 713                  False                  False   \n",
+       " 595                  False                  False   \n",
+       " 388                  False                  False   \n",
+       " 1031                 False                  False   \n",
+       " \n",
+       "       WHOIS_COUNTRY_United Arab Emirates  WHOIS_COUNTRY_United Kingdom  \\\n",
+       " 646                                False                         False   \n",
+       " 160                                False                         False   \n",
+       " 91                                 False                         False   \n",
+       " 104                                False                          True   \n",
+       " 1554                               False                         False   \n",
+       " ...                                  ...                           ...   \n",
+       " 246                                False                         False   \n",
+       " 713                                False                         False   \n",
+       " 595                                False                         False   \n",
+       " 388                                False                         False   \n",
+       " 1031                               False                         False   \n",
+       " \n",
+       "       WHOIS_COUNTRY_United States  WHOIS_COUNTRY_Uruguay  \n",
+       " 646                          True                  False  \n",
+       " 160                          True                  False  \n",
+       " 91                           True                  False  \n",
+       " 104                         False                  False  \n",
+       " 1554                         True                  False  \n",
+       " ...                           ...                    ...  \n",
+       " 246                         False                  False  \n",
+       " 713                         False                  False  \n",
+       " 595                         False                  False  \n",
+       " 388                          True                  False  \n",
+       " 1031                        False                  False  \n",
+       " \n",
+       " [252 rows x 47 columns],\n",
+       " 1043    1\n",
+       " 323     1\n",
+       " 1481    1\n",
+       " 1655    1\n",
+       " 572     1\n",
+       "        ..\n",
+       " 1521    1\n",
+       " 1583    1\n",
+       " 1626    1\n",
+       " 1276    1\n",
+       " 1622    1\n",
+       " Name: SERVER_Other, Length: 1005, dtype: int32,\n",
+       " 646     1\n",
+       " 160     1\n",
+       " 91      1\n",
+       " 104     1\n",
+       " 1554    1\n",
+       "        ..\n",
+       " 246     1\n",
+       " 713     1\n",
+       " 595     1\n",
+       " 388     1\n",
+       " 1031    1\n",
+       " Name: SERVER_Other, Length: 252, dtype: int32)"
+      ]
+     },
+     "execution_count": 221,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# Your comment here"
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
+    "X_train, X_test, y_train, y_test"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
-   "source": [
-    "# Print heatmap again\n"
-   ]
+   "source": []
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Challenge 3 - Handle Missing Values\n",
+    "#### In this lab, we will try two different models and compare our results.\n",
     "\n",
-    "The next step would be handling missing values. **We start by examining the number of missing values in each column, which you will do in the next cell.**"
+    "The first model we will use in this lab is logistic regression. We have previously learned about logistic regression as a classification algorithm. In the cell below, load `LogisticRegression` from scikit-learn and initialize the model."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 225,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "SERVER_Other\n",
+       "1    1005\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 225,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# Your code here\n"
+    "y_train.value_counts()"
    ]
   },
   {
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": 227,
    "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "SERVER_Other\n",
+       "1    1257\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 227,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "If you remember in the previous labs, we drop a column if the column contains a high proportion of missing values. After dropping those problematic columns, we drop the rows with missing values.\n",
-    "\n",
-    "#### In the cells below, handle the missing values from the dataset. Remember to comment the rationale of your decisions."
+    "y.value_counts()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 231,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "SERVER_CONSOLIDATED\n",
+       "1    669\n",
+       "0    298\n",
+       "3    241\n",
+       "2     49\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 231,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# Your code here\n"
+    "y = website_dummy['SERVER_CONSOLIDATED']\n",
+    "y.value_counts()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 233,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Your comment here"
+    "# Your code here:\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "X = website_dummy.drop(columns=['SERVER_CONSOLIDATED'])  # Asegúrate de que 'SERVER_CONSOLIDATED' no esté en X\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42, stratify=y)\n"
    ]
   },
   {
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": 235,
    "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\hugoe\\anaconda3\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
+      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "Please also refer to the documentation for alternative solver options:\n",
+      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
+      "  n_iter_i = _check_optimize_result(\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-1 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-1 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-1 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-1 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-1 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 1ex;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;LogisticRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression(random_state=42)</pre></div> </div></div></div></div>"
+      ],
+      "text/plain": [
+       "LogisticRegression(random_state=42)"
+      ]
+     },
+     "execution_count": 235,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "#### Again, examine the number of missing values in each column. \n",
-    "\n",
-    "If all cleaned, proceed. Otherwise, go back and do more cleaning."
+    "from sklearn.linear_model import LogisticRegression\n",
+    "logreg = LogisticRegression(random_state=42)\n",
+    "logreg.fit(X_train, y_train)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 239,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([1, 3, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
+       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1,\n",
+       "       1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
+       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1,\n",
+       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 3, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
+       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
+       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
+       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1,\n",
+       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 3, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
+       "       1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
+       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1,\n",
+       "       3, 1, 1, 1, 1, 1, 1, 1, 1, 0])"
+      ]
+     },
+     "execution_count": 239,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# Examine missing values in each column\n"
+    "y_pred = logreg.predict(X_test)\n",
+    "y_pred"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Challenge 4 - Handle `WHOIS_*` Categorical Data"
+    "Next, fit the model to our training data. We have already separated our data into 4 parts. Use those in your model."
    ]
   },
   {
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": 241,
    "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\hugoe\\anaconda3\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
+      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "Please also refer to the documentation for alternative solver options:\n",
+      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
+      "  n_iter_i = _check_optimize_result(\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-2 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-2 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-2 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-2 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-2 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 1ex;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-2 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;LogisticRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression(random_state=42)</pre></div> </div></div></div></div>"
+      ],
+      "text/plain": [
+       "LogisticRegression(random_state=42)"
+      ]
+     },
+     "execution_count": 241,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "There are several categorical columns we need to handle. These columns are:\n",
-    "\n",
-    "* `URL`\n",
-    "* `CHARSET`\n",
-    "* `SERVER`\n",
-    "* `WHOIS_COUNTRY`\n",
-    "* `WHOIS_STATEPRO`\n",
-    "* `WHOIS_REGDATE`\n",
-    "* `WHOIS_UPDATED_DATE`\n",
-    "\n",
-    "How to handle string columns is always case by case. Let's start by working on `WHOIS_COUNTRY`. Your steps are:\n",
-    "\n",
-    "1. List out the unique values of `WHOIS_COUNTRY`.\n",
-    "1. Consolidate the country values with consistent country codes. For example, the following values refer to the same country and should use consistent country code:\n",
-    "    * `CY` and `Cyprus`\n",
-    "    * `US` and `us`\n",
-    "    * `SE` and `se`\n",
-    "    * `GB`, `United Kingdom`, and `[u'GB'; u'UK']`\n",
-    "\n",
-    "#### In the cells below, fix the country values as intructed above."
+    "# Your code here:\n",
+    "logreg.fit(X_train, y_train)\n",
+    "\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
-   "source": [
-    "# Your code here\n"
-   ]
+   "source": []
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Since we have fixed the country values, can we convert this column to ordinal now?\n",
-    "\n",
-    "Not yet. If you reflect on the previous labs how we handle categorical columns, you probably remember we ended up dropping a lot of those columns because there are too many unique values. Too many unique values in a column is not desirable in machine learning because it makes prediction inaccurate. But there are workarounds under certain conditions. One of the fixable conditions is:\n",
-    "\n",
-    "#### If a limited number of values account for the majority of data, we can retain these top values and re-label all other rare values.\n",
-    "\n",
-    "The `WHOIS_COUNTRY` column happens to be this case. You can verify it by print a bar chart of the `value_counts` in the next cell to verify:"
+    "finally, import `confusion_matrix` and `accuracy_score` from `sklearn.metrics` and fit our testing data. Assign the fitted data to `y_pred` and print the confusion matrix as well as the accuracy score"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 243,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[  6,  53,   0,   1],\n",
+       "       [  1, 130,   0,   3],\n",
+       "       [  0,  10,   0,   0],\n",
+       "       [  2,  46,   0,   0]], dtype=int64)"
+      ]
+     },
+     "execution_count": 243,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# Your code here\n"
+    "# Your code here:\n",
+    "from sklearn.metrics import confusion_matrix, accuracy_score\n",
+    "y_pred = logreg.predict(X_test)\n",
+    "\n",
+    "conf_matrix = confusion_matrix(y_test, y_pred)\n",
+    "conf_matrix"
    ]
   },
   {
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": 245,
    "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.5396825396825397"
+      ]
+     },
+     "execution_count": 245,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "#### After verifying, now let's keep the top 10 values of the column and re-label other columns with `OTHER`."
+    "accuracy = accuracy_score(y_test, y_pred)\n",
+    "accuracy"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {
-    "scrolled": true
-   },
+   "execution_count": null,
+   "metadata": {},
    "outputs": [],
-   "source": [
-    "# Your code here\n"
-   ]
+   "source": []
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Now since `WHOIS_COUNTRY` has been re-labelled, we don't need `WHOIS_STATEPRO` any more because the values of the states or provinces may not be relevant any more. We'll drop this column.\n",
-    "\n",
-    "In addition, we will also drop `WHOIS_REGDATE` and `WHOIS_UPDATED_DATE`. These are the registration and update dates of the website domains. Not of our concerns.\n",
-    "\n",
-    "#### In the next cell, drop `['WHOIS_STATEPRO', 'WHOIS_REGDATE', 'WHOIS_UPDATED_DATE']`."
+    "What are your thoughts on the performance of the model? Write your conclusions below."
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 17,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "# Your code here\n"
+    " Your conclusions here:\n",
+    "\n",
+    " \n",
+    "En general, aunque el modelo tiene una precisión del 54%, lo que es un punto de partida, hay margen de mejora. Especialmente, se debe trabajar en diferenciar mejor las clases 2 y 3, explorar técnicas para balancear las clases, y probar otros modelos para mejorar la precisión general.\n"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Challenge 5 - Handle Remaining Categorical Data & Convert to Ordinal\n",
+    "#### Our second algorithm is is K-Nearest Neighbors. \n",
     "\n",
-    "Now print the `dtypes` of the data again. Besides `WHOIS_COUNTRY` which we already fixed, there should be 3 categorical columns left: `URL`, `CHARSET`, and `SERVER`."
+    "Though is it not required, we will fit a model using the training data and then test the performance of the model using the testing data. Start by loading `KNeighborsClassifier` from scikit-learn and then initializing and fitting the model. We'll start off with a model where k=3."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 251,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Your code here\n"
+    "# Your code here:\n",
+    "from sklearn.neighbors import KNeighborsClassifier\n",
+    "from sklearn.metrics import confusion_matrix, accuracy_score\n"
    ]
   },
   {
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": 253,
    "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-3 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-3 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-3 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-3 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-3 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-3 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-3 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-3 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-3 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-3 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-3 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 1ex;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-3 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-3 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier(n_neighbors=3)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">&nbsp;&nbsp;KNeighborsClassifier<a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.neighbors.KNeighborsClassifier.html\">?<span>Documentation for KNeighborsClassifier</span></a><span class=\"sk-estimator-doc-link \">i<span>Not fitted</span></span></label><div class=\"sk-toggleable__content \"><pre>KNeighborsClassifier(n_neighbors=3)</pre></div> </div></div></div></div>"
+      ],
+      "text/plain": [
+       "KNeighborsClassifier(n_neighbors=3)"
+      ]
+     },
+     "execution_count": 253,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "#### `URL` is easy. We'll simply drop it because it has too many unique values that there's no way for us to consolidate."
+    "knn = KNeighborsClassifier(n_neighbors=3)\n",
+    "knn"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 255,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-4 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-4 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-4 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-4 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-4 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-4 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-4 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-4 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-4 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-4 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-4 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 1ex;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-4 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-4 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier(n_neighbors=3)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;KNeighborsClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.neighbors.KNeighborsClassifier.html\">?<span>Documentation for KNeighborsClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>KNeighborsClassifier(n_neighbors=3)</pre></div> </div></div></div></div>"
+      ],
+      "text/plain": [
+       "KNeighborsClassifier(n_neighbors=3)"
+      ]
+     },
+     "execution_count": 255,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# Your code here\n"
+    "knn.fit(X_train, y_train)\n"
    ]
   },
   {
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": 259,
    "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0,\n",
+       "       1, 3, 1, 1, 1, 1, 1, 1, 0, 0, 0, 3, 0, 1, 0, 1, 0, 1, 1, 1, 3, 1,\n",
+       "       1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 3, 1,\n",
+       "       1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1,\n",
+       "       1, 1, 0, 1, 0, 1, 0, 3, 3, 1, 1, 3, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1,\n",
+       "       1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 3, 1, 3, 0, 1, 0, 0, 1, 1, 1,\n",
+       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 3, 3, 3, 0, 1, 1, 1, 0, 3, 1, 0,\n",
+       "       0, 1, 3, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1,\n",
+       "       1, 1, 3, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
+       "       1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 3, 0, 0, 1, 1, 1, 1, 1, 1,\n",
+       "       1, 0, 0, 1, 3, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 3, 1,\n",
+       "       1, 3, 0, 1, 1, 3, 1, 1, 1, 0])"
+      ]
+     },
+     "execution_count": 259,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "#### Print the unique value counts of `CHARSET`. You see there are only a few unique values. So we can keep it as it is."
+    "y_pred_knn = knn.predict(X_test)\n",
+    "y_pred_knn"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 261,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 18,  40,   0,   2],\n",
+       "       [ 20, 107,   0,   7],\n",
+       "       [  3,   6,   0,   1],\n",
+       "       [ 11,  27,   0,  10]], dtype=int64)"
+      ]
+     },
+     "execution_count": 261,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "conf_matrix_knn = confusion_matrix(y_test, y_pred_knn)\n",
+    "conf_matrix_knn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 263,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.5357142857142857"
+      ]
+     },
+     "execution_count": 263,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# Your code here"
+    "accuracy_knn = accuracy_score(y_test, y_pred_knn)\n",
+    "accuracy_knn "
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "`SERVER` is a little more complicated. Print its unique values and think about how you can consolidate those values.\n",
-    "\n",
-    "#### Before you think of your own solution, don't read the instructions that come next."
+    "To test your model, compute the predicted values for the testing sample and print the confusion matrix as well as the accuracy score."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 265,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Your code here\n"
+    "# Your code here:\n",
+    "from sklearn.metrics import confusion_matrix, accuracy_score"
    ]
   },
   {
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": 267,
    "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 18,  40,   0,   2],\n",
+       "       [ 20, 107,   0,   7],\n",
+       "       [  3,   6,   0,   1],\n",
+       "       [ 11,  27,   0,  10]], dtype=int64)"
+      ]
+     },
+     "execution_count": 267,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "![Think Hard](../images/think-hard.jpg)"
+    "conf_matrix_knn = confusion_matrix(y_test, y_pred_knn)\n",
+    "conf_matrix_knn"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 269,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.5357142857142857"
+      ]
+     },
+     "execution_count": 269,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# Your comment here\n"
+    "accuracy_knn = accuracy_score(y_test, y_pred_knn)\n",
+    "accuracy_knn "
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Although there are so many unique values in the `SERVER` column, there are actually only 3 main server types: `Microsoft`, `Apache`, and `nginx`. Just check if each `SERVER` value contains any of those server types and re-label them. For `SERVER` values that don't contain any of those substrings, label with `Other`.\n",
+    "#### We'll create another K-Nearest Neighbors model with k=5. \n",
     "\n",
-    "At the end, your `SERVER` column should only contain 4 unique values: `Microsoft`, `Apache`, `nginx`, and `Other`."
+    "Initialize and fit the model below and print the confusion matrix and the accuracy score."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 271,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Your code here\n"
+    "# Your code here:\n",
+    "from sklearn.neighbors import KNeighborsClassifier\n",
+    "from sklearn.metrics import confusion_matrix, accuracy_score\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [],
+   "execution_count": 273,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-5 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-5 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-5 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-5 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-5 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-5 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-5 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-5 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-5 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-5 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-5 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 1ex;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-5 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-5 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">&nbsp;&nbsp;KNeighborsClassifier<a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.neighbors.KNeighborsClassifier.html\">?<span>Documentation for KNeighborsClassifier</span></a><span class=\"sk-estimator-doc-link \">i<span>Not fitted</span></span></label><div class=\"sk-toggleable__content \"><pre>KNeighborsClassifier()</pre></div> </div></div></div></div>"
+      ],
+      "text/plain": [
+       "KNeighborsClassifier()"
+      ]
+     },
+     "execution_count": 273,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# Count `SERVER` value counts here\n"
+    "knn_5 = KNeighborsClassifier(n_neighbors=5)\n",
+    "knn_5"
    ]
   },
   {
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": 275,
    "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-6 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-6 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-6 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-6 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-6 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-6 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-6 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-6 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-6 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-6 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-6 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 1ex;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-6 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-6 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" checked><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;KNeighborsClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.neighbors.KNeighborsClassifier.html\">?<span>Documentation for KNeighborsClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>KNeighborsClassifier()</pre></div> </div></div></div></div>"
+      ],
+      "text/plain": [
+       "KNeighborsClassifier()"
+      ]
+     },
+     "execution_count": 275,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "OK, all our categorical data are fixed now. **Let's convert them to ordinal data using Pandas' `get_dummies` function ([documentation](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.get_dummies.html)). Also, assign the data with dummy values to a new variable `website_dummy`.**"
+    "knn_5.fit(X_train, y_train)\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 277,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Your code here\n"
+    "y_pred_knn_5 = knn_5.predict(X_test)\n"
    ]
   },
   {
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": 279,
    "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 16,  43,   0,   1],\n",
+       "       [ 17, 114,   0,   3],\n",
+       "       [  2,   7,   0,   1],\n",
+       "       [  9,  31,   0,   8]], dtype=int64)"
+      ]
+     },
+     "execution_count": 279,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "Now, inspect `website_dummy` to make sure the data and types are intended - there shouldn't be any categorical columns at this point."
+    "conf_matrix_knn_5 = confusion_matrix(y_test, y_pred_knn_5)\n",
+    "conf_matrix_knn_5"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 281,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.5476190476190477"
+      ]
+     },
+     "execution_count": 281,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# Your code here\n"
+    "accuracy_knn_5 = accuracy_score(y_test, y_pred_knn_5)\n",
+    "accuracy_knn_5 "
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Challenge 6 - Modeling, Prediction, and Evaluation\n",
-    "\n",
-    "We'll start off this section by splitting the data to train and test. **Name your 4 variables `X_train`, `X_test`, `y_train`, and `y_test`. Select 80% of the data for training and 20% for testing.**"
+    "Did you see an improvement in the confusion matrix when increasing k to 5? Did you see an improvement in the accuracy score? Write your conclusions below."
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 29,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "from sklearn.model_selection import train_test_split\n",
+    " Your conclusions here:\n",
     "\n",
-    "# Your code here:\n"
+    "Mejora en el accuracy: Al aumentar k de 3 a 5, el modelo muestra una ligera mejora en el accuracy score (de 0.5397 a 0.5476).\r\n",
+    "\r\n",
+    "Mejora en la matriz de confusión: Con k =5, se observa una distribución más equilibrada de las predicciones incorrectas, especialmente para la clase 1, con menos errores que con k=3.\r\n",
+    "\r\n",
+    "Tamaño del cambio: La mejora es pequeña, lo que sugiere que un cambio de k =3 a k=5 tiene un impacto moderado en el rendimiento del modelo.\r\n",
+    "\r\n",
+    "En resumen, el modelo con k=5 tiene un desempeño ligeramente mejor que con k=3, pero la diferencia no es significativa. Se podría experimentar con valores más altos de k o probar otros modelos para obtener mejoras adicionales. para obtener mejoras adicionales."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "#### In this lab, we will try two different models and compare our results.\n",
+    "# Bonus Challenge - Feature Scaling\n",
     "\n",
-    "The first model we will use in this lab is logistic regression. We have previously learned about logistic regression as a classification algorithm. In the cell below, load `LogisticRegression` from scikit-learn and initialize the model."
+    "Problem-solving in machine learning is iterative. You can improve your model prediction with various techniques (there is a sweetspot for the time you spend and the improvement you receive though). Now you've completed only one iteration of ML analysis. There are more iterations you can conduct to make improvements. In order to be able to do that, you will need deeper knowledge in statistics and master more data analysis techniques. In this bootcamp, we don't have time to achieve that advanced goal. But you will make constant efforts after the bootcamp to eventually get there.\n",
+    "\n",
+    "However, now we do want you to learn one of the advanced techniques which is called *feature scaling*. The idea of feature scaling is to standardize/normalize the range of independent variables or features of the data. This can make the outliers more apparent so that you can remove them. This step needs to happen during Challenge 6 after you split the training and test data because you don't want to split the data again which makes it impossible to compare your results with and without feature scaling. For general concepts about feature scaling, click [here](https://en.wikipedia.org/wiki/Feature_scaling). To read deeper, click [here](https://medium.com/greyatom/why-how-and-when-to-scale-your-features-4b30ab09db5e).\n",
+    "\n",
+    "In the next cell, attempt to improve your model prediction accuracy by means of feature scaling. A library you can utilize is `sklearn.preprocessing.RobustScaler` ([documentation](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.RobustScaler.html)). You'll use the `RobustScaler` to fit and transform your `X_train`, then transform `X_test`. You will use logistic regression to fit and predict your transformed data and obtain the accuracy score in the same way. Compare the accuracy score with your normalized data with the previous accuracy data. Is there an improvement?"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 291,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Your code here:\n",
-    "\n"
+    "# Your code here\n",
+    "from sklearn.preprocessing import RobustScaler\n",
+    "from sklearn.linear_model import LogisticRegression\n",
+    "from sklearn.metrics import accuracy_score"
    ]
   },
   {
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": 293,
    "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-7 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-7 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-7 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-7 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-7 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-7 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-7 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-7 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-7 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-7 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-7 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-7 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-7 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-7 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 1ex;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-7 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-7 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-7\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RobustScaler()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" checked><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">&nbsp;&nbsp;RobustScaler<a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.preprocessing.RobustScaler.html\">?<span>Documentation for RobustScaler</span></a><span class=\"sk-estimator-doc-link \">i<span>Not fitted</span></span></label><div class=\"sk-toggleable__content \"><pre>RobustScaler()</pre></div> </div></div></div></div>"
+      ],
+      "text/plain": [
+       "RobustScaler()"
+      ]
+     },
+     "execution_count": 293,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "Next, fit the model to our training data. We have already separated our data into 4 parts. Use those in your model."
+    "scaler = RobustScaler()\n",
+    "scaler"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 295,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Your code here:\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "finally, import `confusion_matrix` and `accuracy_score` from `sklearn.metrics` and fit our testing data. Assign the fitted data to `y_pred` and print the confusion matrix as well as the accuracy score"
+    "X_train_scaled = scaler.fit_transform(X_train)\n",
+    "X_test_scaled = scaler.transform(X_test)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 297,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-8 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-8 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-8 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-8 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-8 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-8 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-8 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-8 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-8 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-8 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-8 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-8 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-8 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-8 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 1ex;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-8 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-8 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-8\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" checked><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">&nbsp;&nbsp;LogisticRegression<a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a><span class=\"sk-estimator-doc-link \">i<span>Not fitted</span></span></label><div class=\"sk-toggleable__content \"><pre>LogisticRegression(random_state=42)</pre></div> </div></div></div></div>"
+      ],
+      "text/plain": [
+       "LogisticRegression(random_state=42)"
+      ]
+     },
+     "execution_count": 297,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# Your code here:\n",
-    "\n"
+    "logreg = LogisticRegression(random_state=42)\n",
+    "logreg"
    ]
   },
   {
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": 299,
    "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\hugoe\\anaconda3\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
+      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "Please also refer to the documentation for alternative solver options:\n",
+      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
+      "  n_iter_i = _check_optimize_result(\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-9 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-9 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-9 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-9 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-9 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-9 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-9 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-9 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-9 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-9 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-9 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-9 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-9 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-9 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-9 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-9 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-9 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-9 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-9 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-9 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-9 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-9 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-9 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-9 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-9 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-9 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-9 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-9 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-9 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-9 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-9 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-9 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-9 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-9 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-9 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-9 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-9 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-9 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 1ex;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-9 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-9 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-9 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-9 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-9\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" checked><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;LogisticRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression(random_state=42)</pre></div> </div></div></div></div>"
+      ],
+      "text/plain": [
+       "LogisticRegression(random_state=42)"
+      ]
+     },
+     "execution_count": 299,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "What are your thoughts on the performance of the model? Write your conclusions below."
+    "logreg.fit(X_train_scaled, y_train)\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 301,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Your conclusions here:\n",
-    "\n"
+    "y_pred_scaled = logreg.predict(X_test_scaled)\n"
    ]
   },
   {
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": 303,
    "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.5396825396825397"
+      ]
+     },
+     "execution_count": 303,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "#### Our second algorithm is is K-Nearest Neighbors. \n",
-    "\n",
-    "Though is it not required, we will fit a model using the training data and then test the performance of the model using the testing data. Start by loading `KNeighborsClassifier` from scikit-learn and then initializing and fitting the model. We'll start off with a model where k=3."
+    "accuracy_scaled = accuracy_score(y_test, y_pred_scaled)\n",
+    "accuracy_scaled"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 305,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Your code here:\n",
-    "\n"
+    "from sklearn.metrics import confusion_matrix, accuracy_score\n"
    ]
   },
   {
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": 307,
    "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[  6,  54,   0,   0],\n",
+       "       [  2, 130,   0,   2],\n",
+       "       [  0,  10,   0,   0],\n",
+       "       [  2,  46,   0,   0]], dtype=int64)"
+      ]
+     },
+     "execution_count": 307,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "To test your model, compute the predicted values for the testing sample and print the confusion matrix as well as the accuracy score."
+    "conf_matrix_scaled = confusion_matrix(y_test, y_pred_scaled)\n",
+    "conf_matrix_scaled"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 309,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.5396825396825397"
+      ]
+     },
+     "execution_count": 309,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# Your code here:\n",
-    "\n"
+    "accuracy_scaled = accuracy_score(y_test, y_pred_scaled)\n",
+    "accuracy_scaled"
    ]
   },
   {
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": 313,
    "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\hugoe\\anaconda3\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
+      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "Please also refer to the documentation for alternative solver options:\n",
+      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
+      "  n_iter_i = _check_optimize_result(\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-10 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-10 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-10 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-10 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-10 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-10 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-10 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-10 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-10 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-10 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-10 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-10 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-10 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-10 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-10 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-10 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-10 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-10 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-10 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-10 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-10 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-10 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-10 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-10 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-10 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-10 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-10 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-10 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-10 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-10 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-10 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-10 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-10 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-10 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-10 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-10 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-10 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-10 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 1ex;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-10 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-10 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-10 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-10 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-10\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-10\" type=\"checkbox\" checked><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;LogisticRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression(random_state=42)</pre></div> </div></div></div></div>"
+      ],
+      "text/plain": [
+       "LogisticRegression(random_state=42)"
+      ]
+     },
+     "execution_count": 313,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "#### We'll create another K-Nearest Neighbors model with k=5. \n",
-    "\n",
-    "Initialize and fit the model below and print the confusion matrix and the accuracy score."
+    "logreg_no_scaling = LogisticRegression(random_state=42)\n",
+    "logreg_no_scaling.fit(X_train, y_train)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 315,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Your code here:\n",
-    "\n"
+    "y_pred_no_scaling = logreg_no_scaling.predict(X_test)\n"
    ]
   },
   {
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": 317,
    "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[  6,  53,   0,   1],\n",
+       "       [  1, 130,   0,   3],\n",
+       "       [  0,  10,   0,   0],\n",
+       "       [  2,  46,   0,   0]], dtype=int64)"
+      ]
+     },
+     "execution_count": 317,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "Did you see an improvement in the confusion matrix when increasing k to 5? Did you see an improvement in the accuracy score? Write your conclusions below."
+    "conf_matrix_no_scaling = confusion_matrix(y_test, y_pred_no_scaling)\n",
+    "conf_matrix_no_scaling"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 319,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.5396825396825397"
+      ]
+     },
+     "execution_count": 319,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# Your conclusions here:\n",
-    "\n"
+    "accuracy_no_scaling = accuracy_score(y_test, y_pred_no_scaling)\n",
+    "accuracy_no_scaling"
    ]
   },
   {
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": 321,
    "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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UlBQAQJEiRXLU//fffwcAREZGGrUPGjQIAEzGglSpUgX169dX7meeWtS0aVOULVvWpP3s2bMmy+zXr5/yt0ajQb9+/ZCeno6NGzcq7ba2tsrft2/fRnJyMho1aoS9e/eazC8gIABVqlR5wTN9Mk5ix44duHr1apaP7969G9evX0efPn2Mvi9s1aoVvL29sxwX8/HHHxvdb9SoUZbP+WkbN25Eeno6Bg4cCK32n3/Hnj17wsHB4aXG3yxatAi1a9dGhQoVADz5P2jVqpXRaZIGgwErV65EmzZtULt2bZN5ZJ7299tvv8HX1xft27fPto+5bGxsEBERYdL+9OudmpqKmzdvolGjRrh//z6OHz8OANi3bx/OnTuHgQMHmoy/eLqesLAwXL16FXFxcUrbokWLYGtriw4dOjy3vubNm2P79u1o27YtDhw4gLFjxyI4OBhubm5GI9lXrlwJg8GAIUOGGL2GT9di7uuc1bpZtmwZKleuDG9vb9y8eVO5NW3aFACU55i5Pv7v//4PBoPhuc/xabdu3VLOVHlWgwYNoNVqlbEbCQkJKFSoEPz9/VG4cGFUr14dCQkJymMAjMY55MZHH31k9Fo2atQIGRkZuHDhwnOnCw0NxZ49e3DmzBmlbcmSJbCxsUG7du0AAI6OjgCA9evX4/79+2bV9fQ4D+DJ83Vzc0PZsmXh7e2N4sWLW3xdZLVv+fvvv5V9vLn77rzQo0cPrFu3Dk2aNEF8fDxGjBiBRo0aoWLFiti2bZvS77fffoNGo0F0dLTJPJ5+vZ/eDyQnJ+PmzZsICAjA2bNnkZycnKsaX7SPXrduHQoVKoSePXsqbVqtFn379s12nsWKFTP7dOcCHTwcHBwAPNl558SFCxeg1WqVN6pMLi4uKFq0qMkG/3S4AP7ZmN3d3bNsv337tlG7Vqs1GZDzxhtvAIDRKVxr1qxBvXr1oNfrUbx4cTg5OWHGjBlZ/vN5eXm96GkCeHKK4OHDh+Hu7o46depg6NChRv+Amc+1UqVKJtN6e3ubrAu9Xg8nJyejtmLFipk852dltxydTody5cq9cCebnTt37uD3339HQEAATp8+rdwaNmyI3bt34+TJkwCAGzduICUlBT4+Ps+d35kzZ17Yx1xubm4mg9AA4MiRI2jfvj0cHR3h4OAAJycnZeBi5mue+abyopqaN2+O0qVLK2HLYDDgl19+Qbt27XIUyP39/bF8+XLcvn0bO3fuRFRUFFJTU9GxY0ccPXpUqUWr1T438Jr7Ome1bk6dOoUjR47AycnJ6Ja5zWQO4AsNDUXDhg3x4YcfwtnZGZ07d8bSpUtzHEJExKStaNGiqFq1qtEbas2aNZU3hwYNGhg9ptPpUKdOnRwtLzvP7l8yA9GLtql3330XWq0WS5YsUZ7PsmXL0LJlS2Wf6OXlhcjISMyZMwclS5ZEcHAwpk+fnqM3NB8fHxQtWtTo+TZs2BDAkzfO+vXrGz3m7u5u8lzM9aJ1Ye6+O68EBwdj/fr1uHPnDrZs2YK+ffviwoULaN26tfL/eebMGbi6uqJ48eLPnVdCQgKCgoJgb2+PokWLwsnJSTklOTfBIyf76AsXLqB06dImA7mfXa9PExGzP3wV+ODh6uqKw4cPmzVdTldidqPUs2vPaof2Ilu3bkXbtm2h1+vxww8/4Pfff8eGDRvQtWvXLOf3dEp+nk6dOuHs2bOYOnUqXF1dMW7cOFStWhV//PGH2TUC2T/n/LJs2TKkpaVhwoQJqFixonLL/ERkyYtDZcru/yYjIyPL9qxeqzt37iAgIAAHDhzA8OHDsXr1amzYsAFjxowBALM+wQNPXpeuXbvit99+w8OHDxEXF4erV68anYGREzqdDv7+/hg1ahRmzJiBR48eYdmyZWbNwxxZrRuDwYBq1aphw4YNWd769OmjTLtlyxZs3LgR3bp1w8GDBxEaGormzZtn+1oAQPHixaHRaLJ9Y3/zzTdx5MgR3LlzBwkJCcpZGsCT4LFz5048evQI8fHx8PPze+kzC3K7H3F1dUWjRo2wdOlSAMBff/2FixcvmlzHZsKECTh48CAGDx6MBw8eYMCAAahatSouX7783PlrtVrUr18f27Ztg4hkuS7i4+ORnp6OXbt2vfTRDiDn6yK3Rx+B7LfT3LCzs0OjRo0wbdo0fP3117h9+7ZZ+9YzZ86gWbNmuHnzJiZOnIi1a9diw4YN+PTTTwGYvx8A8m4fffv2bZQsWdKsaQp08ACA1q1b48yZM9i+ffsL+3p4eMBgMODUqVNG7UlJSbhz5w48PDwsWpvBYDD5KiLzk7inpyeAJ4fl9Ho91q9fjx49eqBly5YICgqyyPJLly6NPn36YOXKlTh37hxKlCiBkSNHAoDyXE+cOGEy3YkTJyy2LrJbTnp6Os6dO5fr5SxatAg+Pj5YtmyZyS0oKEi5oI+TkxMcHBxeGE7Lly//wj6Zn8Lu3Llj1G7Op63Nmzfj77//xoIFC/DJJ5+gdevWCAoKMjn8X758eQDIUagOCwtDSkoKVq9ejUWLFsHJyQnBwcE5rulZmV9JXbt2TanFYDAoR0CyYonXuXz58rh16xaaNWuGoKAgk9vTR1O0Wi2aNWuGiRMn4ujRoxg5ciQ2bdpk9JXTs6ytrVG+fHmTi4FlevPNNyEi2LhxI/bt26d8ygeevNk+ePAAa9euxdmzZ3P0Zvsyb5IvEhoaigMHDuDEiRNYsmQJ7OzsjE5/zVStWjV8/fXX2LJlC7Zu3YorV65g5syZL5z/m2++iVu3bmHVqlW4fv26ybo4c+YMfv/9dzx48ECVdWHOvrtYsWIm22h6erry/2ypmjJltb1cvXrV5CJsT1u9ejXS0tKwatUq9OrVC2+//TaCgoJy/MEytzw8PHDt2jWTr99Onz6d7TTnzp1D5cqVzVpOgQ8en3/+Oezt7fHhhx8iKSnJ5PEzZ85gypQpAIC3334bADB58mSjPhMnTgTwZHyDpU2bNk35W0Qwbdo0FCpUCM2aNQPwJKVqNBqjNH7+/PmXuspfRkaGyaG6UqVKwdXVFWlpaQCebCylSpXCzJkzlTYA+OOPP3Ds2DGLrYugoCDodDp8//33Rp9e5s6di+Tk5Fwt59KlS9iyZQs6deqEjh07mtwiIiJw+vRp7NixA1qtFiEhIVi9enWWl6jOrKlDhw44cOAAVqxYkW2fzDCwZcsW5bGMjIwcX2EV+OdTydPrIj09HT/88INRv1q1asHLywuTJ0822Yk++ymwevXqqF69OubMmYPffvsNnTt3ztF1FOLi4rL8dJ35fXrmG31ISAi0Wi2GDx9u8kksc3pLvM6dOnXClStXMHv2bJPHHjx4gHv37gFAljv0GjVqAIDR/3JW6tevn+2lyjPfQCdOnIhHjx4Zfcr39PRE6dKlMXbsWKO+z2Nvb2/y2llKhw4dYGVlhV9++QXLli1D69atYW9vrzyekpKCx48fG01TrVo1aLXaF64j4J/nN2bMGNjZ2SnrFwDq1KkDa2trVdeFOfvu8uXLG22jADBr1iyTIx6Z6yundcXGxmbZ/uz20qFDB4gIhg0bZtI3c9vIaj+QnJyM+fPn56iW3AoODsajR4+MtjGDwYDp06dn2T85ORlnzpwx2hZyouBdxeUZ5cuXx88//4zQ0FBUrlwZYWFh8PHxQXp6OrZt24Zly5ahe/fuAABfX1+Eh4dj1qxZyiHvnTt34qeffkJISAgCAwMtWpter8e6desQHh6OunXr4o8//sDatWsxePBg5bu4Vq1aYeLEiXjrrbfQtWtXXL9+HdOnT0eFChVw8ODBXC03NTUVZcqUQceOHeHr64vChQtj48aN2LVrFyZMmAAAKFSoEMaMGYOIiAgEBASgS5cuSEpKwpQpU+Dp6akc8ntZTk5OiIqKwrBhw/DWW2+hbdu2OHHiBH744Qf4+/ub/ZUAAPz8888QEbRt2zbLx99++21YW1tj0aJFqFu3LkaNGoU///wTAQEB+Oijj1C5cmVcu3YNy5YtQ3x8PIoWLYr//Oc/+PXXX/Huu++iR48e8PPzUz7xzZw5E76+vqhatSrq1auHqKgo3Lp1C8WLF8fixYtNdvDP06BBAxQrVgzh4eEYMGAANBoNYmJiTAKAVqvFjBkz0KZNG9SoUQMREREoXbo0jh8/jiNHjmD9+vVG/cPCwvDZZ58BQI7Xaf/+/XH//n20b98e3t7eyjazZMkSeHp6KoM/K1SogK+++koZTPfOO+/AxsYGu3btgqurK0aPHm2R17lbt25YunQpPv74Y8TFxaFhw4bIyMjA8ePHsXTpUqxfvx61a9fG8OHDsWXLFrRq1QoeHh64fv06fvjhB5QpU+aFb4Lt2rVDTEwMTp48qYwdyVS2bFm4u7tj+/bt8PT0hKurq9HjDRo0UAYOPn0EIDt+fn6YMWMGvv32W1SoUAGlSpVSBsq+rFKlSiEwMBATJ05EamqqydcsmzZtQr9+/fDuu+/ijTfewOPHjxETEwMrK6sXDjoGnoQLnU6H7du3o0mTJkZB1s7ODr6+vti+fTuKFi2ao7FRfn5+2LhxIyZOnAhXV1d4eXmZ9Tsg5uy7P/zwQ3z88cfo0KEDmjdvjgMHDmD9+vUmXxfUqFEDVlZWGDNmDJKTk2FjY4OmTZuiVKlSWdbQrl07eHl5oU2bNihfvjzu3buHjRs3YvXq1fD391eOOAUGBqJbt274/vvvcerUKbz11lswGAzYunUrAgMD0a9fP7Ro0QI6nQ5t2rRBr169cPfuXcyePRulSpUyOTJjSSEhIahTpw4GDRqE06dPw9vbG6tWrVLC/LNHgTZu3AgRUQYt55hZ58D8i508eVJ69uwpnp6eotPppEiRItKwYUOZOnWq0QVmHj16JMOGDRMvLy8pVKiQuLu7P/cCYs/C/7+I19MyT28aN26c0pbVBcScnZ0lOjra5PSruXPnSsWKFcXGxka8vb1l/vz5WZ6SltWyn34s8xSytLQ0+c9//iO+vr5SpEgRsbe3F19f3yyvubFkyRKpWbOm2NjYSPHixZ97AbFnZVVjdqZNmybe3t5SqFAhcXZ2lt69e5ucJprT02mrVasmZcuWfW6fJk2aSKlSpZTLyF+4cEHCwsLEyclJbGxspFy5ctK3b1+jC4j9/fff0q9fP3Fzc1MuXhUeHi43b95U+pw5c0aCgoLExsZGnJ2dZfDgwbJhw4YsT6etWrVqlrUlJCRIvXr1xNbWVlxdXZXTWJ+dh4hIfHy8NG/eXHkdq1evbnQtgEzXrl0TKysreeONN567Xp72xx9/SI8ePcTb21sKFy6sXD69f//+kpSUZNJ/3rx5yv9KsWLFJCAgQDZs2GDUJyev8/PWTXp6uowZM0aqVq2qLMfPz0+GDRsmycnJIiISGxsr7dq1E1dXV9HpdOLq6ipdunSRkydPvvA5p6WlScmSJZVrvzyrS5cuAkC6du1q8tjEiROVa+k8K6vTaRMTE6VVq1ZSpEgRQRYXEHv29O6sTst+ntmzZwsAKVKkiMklys+ePSs9evSQ8uXLi16vl+LFi0tgYKBs3LgxR/MWeXLaLv7/dS6elXkNjKevZZIpq/3C8ePHpXHjxmJrayvI4gJiz27zWa3PnO67MzIy5IsvvlAuzBYcHCynT582OZ1W5Mk6LFeunFhZWb1w3f/yyy/SuXNnKV++vNja2oper5cqVarIV199ZfJTDY8fP5Zx48aJt7e3cgHHli1bGl3AcdWqVVK9enXR6/Xi6ekpY8aMUS4K+PTzNucCYs/K6rW4ceOGdO3aVbmAWPfu3ZWfDVi8eLFR39DQUHnzzTezXSfZ0YjkYsQjvbTu3bvj119/xd27d/O7FHoN3Lx5E6VLl8aQIUPwzTff5Hc5r7QRI0Zg/vz5OHXq1Cs3aJooP6xcuRLt27dHfHy8cjQvMTERXl5eWLx4sdlHPAr8GA8ievJT6xkZGejWrVt+l/LK+/TTT3H37l0sXrw4v0shUt2zv9aekZGBqVOnwsHBAbVq1VLaJ0+ejGrVqpn/NQtegzEeRK+zTZs2KWd1hISEKGdLUfYKFy6s+o/gEb0q+vfvjwcPHqB+/fpIS0vD8uXLsW3bNowaNcrorJrvvvsu18tg8CAqwIYPH45t27ahYcOGmDp1an6XQ0SvuKZNm2LChAlYs2YNHj58iAoVKmDq1KlGV9l+WRzjQURERKrhGA8iIiJSDYMHERERqea1G+NhMBhw9epVFClSJE8vWUxERFTQiAhSU1Ph6upq8mvUOfXaBY+rV6+a/HosERER5dylS5dQpkyZXE372gWPzJ8Cv3TpkvIT0URERPRiKSkpcHd3V95Lc+O1Cx6ZX684ODgweBAREeXCywxV4OBSIiIiUg2DBxEREamGwYOIiIhUw+BBREREqmHwICIiItUweBAREZFqGDyIiIhINQweREREpBoGDyIiIlINgwcRERGphsGDiIiIVMPgQURERKp57X4kLq+8xO/lEP3riOR3BbmnGcaNlV4fEv3qbaw84kFERESqYfAgIiIi1TB4EBERkWoYPIiIiEg1DB5ERESkGgYPIiIiUg2DBxEREamGwYOIiIhUw+BBREREqmHwICIiItUweBAREZFqGDyIiIhINQweREREpBoGDyIiIlINgwcRERGphsGDiIiIVPNKBI/p06fD09MTer0edevWxc6dO7Ptu2DBAmg0GqObXq9XsVoiIiLKrXwPHkuWLEFkZCSio6Oxd+9e+Pr6Ijg4GNevX892GgcHB1y7dk25XbhwQcWKiYiIKLfyPXhMnDgRPXv2REREBKpUqYKZM2fCzs4O8+bNy3YajUYDFxcX5ebs7KxixURERJRb+Ro80tPTsWfPHgQFBSltWq0WQUFB2L59e7bT3b17Fx4eHnB3d0e7du1w5MgRNcolIiKil5SvwePmzZvIyMgwOWLh7OyMxMTELKepVKkS5s2bh//7v//DwoULYTAY0KBBA1y+fDnL/mlpaUhJSTG6ERERUf7I969azFW/fn2EhYWhRo0aCAgIwPLly+Hk5IQff/wxy/6jR4+Go6OjcnN3d1e5YiIiIsqUr8GjZMmSsLKyQlJSklF7UlISXFxccjSPQoUKoWbNmjh9+nSWj0dFRSE5OVm5Xbp06aXrJiIiotzJ1+Ch0+ng5+eH2NhYpc1gMCA2Nhb169fP0TwyMjJw6NAhlC5dOsvHbWxs4ODgYHQjIiKi/GGd3wVERkYiPDwctWvXRp06dTB58mTcu3cPERERAICwsDC4ublh9OjRAIDhw4ejXr16qFChAu7cuYNx48bhwoUL+PDDD/PzaRAREVEO5HvwCA0NxY0bNzBkyBAkJiaiRo0aWLdunTLg9OLFi9Bq/zkwc/v2bfTs2ROJiYkoVqwY/Pz8sG3bNlSpUiW/ngIRERHlkEZEJL+LUFNKSgocHR2RnJxs0a9dNBqLzYrolfdv3mtohnFjpdeHRFt2Y7XEe+i/7qwWIiIi+vdi8CAiIiLVMHgQERGRahg8iIiISDUMHkRERKQaBg8iIiJSDYMHERERqYbBg4iIiFTD4EFERESqYfAgIiIi1TB4EBERkWoYPIiIiEg1DB5ERESkGgYPIiIiUg2DBxEREamGwYOIiIhUw+BBREREqmHwICIiItUweBAREZFqGDyIiIhINQweREREpBoGDyIiIlINgwcRERGphsGDiIiIVMPgQURERKph8CAiIiLVMHgQERGRahg8iIiISDUMHkRERKQaBg8iIiJSDYMHERERqYbBg4iIiFTD4EFERESqYfAgIiIi1TB4EBERkWoYPIiIiEg1DB5ERESkGgYPIiIiUg2DBxEREamGwYOIiIhUw+BBREREqmHwICIiItUweBAREZFqXongMX36dHh6ekKv16Nu3brYuXNnjqZbvHgxNBoNQkJC8rZAIiIisoh8Dx5LlixBZGQkoqOjsXfvXvj6+iI4OBjXr19/7nTnz5/HZ599hkaNGqlUKREREb2sfA8eEydORM+ePREREYEqVapg5syZsLOzw7x587KdJiMjA++99x6GDRuGcuXKqVgtERERvYx8DR7p6enYs2cPgoKClDatVougoCBs37492+mGDx+OUqVK4YMPPlCjTCIiIrIQ6/xc+M2bN5GRkQFnZ2ejdmdnZxw/fjzLaeLj4zF37lzs378/R8tIS0tDWlqacj8lJSXX9RIREdHLyfevWsyRmpqKbt26Yfbs2ShZsmSOphk9ejQcHR2Vm7u7ex5XSURERNnJ1yMeJUuWhJWVFZKSkozak5KS4OLiYtL/zJkzOH/+PNq0aaO0GQwGAIC1tTVOnDiB8uXLG00TFRWFyMhI5X5KSgrDBxERUT7J1+Ch0+ng5+eH2NhY5ZRYg8GA2NhY9OvXz6S/t7c3Dh06ZNT29ddfIzU1FVOmTMkyUNjY2MDGxiZP6iciIiLz5GvwAIDIyEiEh4ejdu3aqFOnDiZPnox79+4hIiICABAWFgY3NzeMHj0aer0ePj4+RtMXLVoUAEzaiYiI6NWT78EjNDQUN27cwJAhQ5CYmIgaNWpg3bp1yoDTixcvQqv9Vw1FISIiomxoRETyuwg1paSkwNHREcnJyXBwcLDYfDUai82K6JX3b95raIZxY6XXh0RbdmO1xHsoDyUQERGRahg8iIiISDUMHkRERKQaBg8iIiJSDYMHERERqealgsfDhw8tVQcRERG9BswOHgaDASNGjICbmxsKFy6Ms2fPAgC++eYbzJ071+IFEhERUcFhdvD49ttvsWDBAowdOxY6nU5p9/HxwZw5cyxaHBERERUsZgeP//73v5g1axbee+89WFlZKe2+vr7Z/pQ9EREREZCL4HHlyhVUqFDBpN1gMODRo0cWKYqIiIgKJrODR5UqVbB161aT9l9//RU1a9a0SFFERERUMJn9I3FDhgxBeHg4rly5AoPBgOXLl+PEiRP473//izVr1uRFjURERFRAmH3Eo127dli9ejU2btwIe3t7DBkyBMeOHcPq1avRvHnzvKiRiIiICgizjng8fvwYo0aNQo8ePbBhw4a8qomIiIgKKLOOeFhbW2Ps2LF4/PhxXtVDREREBZjZX7U0a9YM//vf//KiFiIiIirgzB5c2rJlS3z55Zc4dOgQ/Pz8YG9vb/R427ZtLVYcERERFSwaERFzJtBqsz9IotFokJGR8dJF5aWUlBQ4OjoiOTkZDg4OFpuvRmOxWRG98szba7xaNMO4sdLrQ6Itu7Fa4j3U7CMeBoMhVwsiIiIieqlfpyUiIiIyR66Cx//+9z+0adMGFSpUQIUKFdC2bdssr2ZKRERE9DSzg8fChQsRFBQEOzs7DBgwAAMGDICtrS2aNWuGn3/+OS9qJCIiogLC7MGllStXxkcffYRPP/3UqH3ixImYPXs2jh07ZtECLY2DS4leHgeXEv07vIqDS80+4nH27Fm0adPGpL1t27Y4d+5croogIiKi14PZwcPd3R2xsbEm7Rs3boS7u7tFiiIiIqKCyezTaQcNGoQBAwZg//79aNCgAQAgISEBCxYswJQpUyxeIBERERUcZgeP3r17w8XFBRMmTMDSpUsBPBn3sWTJErRr187iBRIREVHBYXbwAID27dujffv2lq6FiIiICjizx3js2rULO3bsMGnfsWMHdu/ebZGiiIiIqGAyO3j07dsXly5dMmm/cuUK+vbta5GiiIiIqGAyO3gcPXoUtWrVMmmvWbMmjh49apGiiIiIqGAyO3jY2NggKSnJpP3atWuwts7VkBEiIiJ6TZgdPFq0aIGoqCgkJycrbXfu3MHgwYPRvHlzixZHREREBYvZhyjGjx+Pxo0bw8PDAzVr1gQA7N+/H87OzoiJibF4gURERFRwmB083NzccPDgQSxatAgHDhyAra0tIiIi0KVLFxQqVCgvaiQiIqICIleDMuzt7fHRRx9ZuhYiIiIq4HI8xuPkyZPYuXOnUVtsbCwCAwNRp04djBo1yuLFERERUcGS4+DxxRdfYM2aNcr9c+fOoU2bNtDpdKhfvz5Gjx6NyZMn50WNREREVEDk+KuW3bt34/PPP1fuL1q0CG+88QbWr18PAKhevTqmTp2KgQMHWrxIIiIiKhhyfMTj5s2bKFOmjHI/Li4Obdq0Ue43adIE58+ft2hxREREVLDkOHgUL14c165dAwAYDAbs3r0b9erVUx5PT0+HiFi+QiIiIiowchw8mjRpghEjRuDSpUuYPHkyDAYDmjRpojx+9OhReHp65kGJREREVFDkeIzHyJEj0bx5c3h4eMDKygrff/897O3tlcdjYmLQtGnTPCmSiIiICoYcBw9PT08cO3YMR44cgZOTE1xdXY0eHzZsmNEYECIiIqJnmfVbLdbW1vD19TUJHQDg6+uLEiVK5KqI6dOnw9PTE3q9HnXr1jW5XsjTli9fjtq1a6No0aKwt7dHjRo1eKl2IiKifwmzfyTO0pYsWYLIyEhER0dj79698PX1RXBwMK5fv55l/+LFi+Orr77C9u3bcfDgQURERCAiIkI5rZeIiIheXRrJ51NR6tatC39/f0ybNg3AkzNm3N3d0b9/f3z55Zc5mketWrXQqlUrjBgx4oV9U1JS4OjoiOTkZDg4OLxU7U/TaCw2K6JX3r/5BDbNMG6s9PqQaMturJZ4D83XIx7p6enYs2cPgoKClDatVougoCBs3779hdOLCGJjY3HixAk0btw4yz5paWlISUkxuhEREVH+yNfgcfPmTWRkZMDZ2dmo3dnZGYmJidlOl5ycjMKFC0On06FVq1aYOnUqmjdvnmXf0aNHw9HRUbm5u7tb9DkQERFRzpkdPDw9PTF8+HBcvHgxL+rJkSJFimD//v3YtWsXRo4cicjISGzevDnLvlFRUUhOTlZuly5dUrdYIiIiUpgdPAYOHIjly5ejXLlyaN68ORYvXoy0tLRcLbxkyZKwsrJCUlKSUXtSUhJcXFyynU6r1aJChQqoUaMGBg0ahI4dO2L06NFZ9rWxsYGDg4PRjYiIiPJHroLH/v37sXPnTlSuXBn9+/dH6dKl0a9fP+zdu9eseel0Ovj5+SE2NlZpMxgMiI2NRf369XM8H4PBkOvwQ0REROrJ9RiPWrVq4fvvv8fVq1cRHR2NOXPmwN/fHzVq1MC8efNy/LstkZGRmD17Nn766SccO3YMvXv3xr179xAREQEACAsLQ1RUlNJ/9OjR2LBhA86ePYtjx45hwoQJiImJwfvvv5/bp0JEREQqyfGVS5/16NEjrFixAvPnz8eGDRtQr149fPDBB7h8+TIGDx6MjRs34ueff37hfEJDQ3Hjxg0MGTIEiYmJqFGjBtatW6cMOL148SK02n/y0b1799CnTx9cvnwZtra28Pb2xsKFCxEaGprbp0JEREQqMfs6Hnv37sX8+fPxyy+/QKvVIiwsDB9++CG8vb2VPocPH4a/vz8ePHhg8YJfFq/jQfTyeB0Pon+HV/E6HmYf8fD390fz5s0xY8YMhISEoFChQiZ9vLy80Llz51wVRERERAWX2cHj7Nmz8PDweG4fe3t7zJ8/P9dFERERUcFk9uDS69evY8eOHSbtO3bswO7duy1SFBERERVMZgePvn37ZnkRritXrqBv374WKYqIiIgKJrODx9GjR1GrVi2T9po1a+Lo0aMWKYqIiIgKJrODh42NjcmVRgHg2rVrsLbO9dm5RERE9BowO3i0aNFC+f2TTHfu3MHgwYOz/aE2IiIiIiAXZ7WMHz8ejRs3hoeHB2rWrAkA2L9/P5ydnRETE2PxAomIiKjgMDt4uLm54eDBg1i0aBEOHDgAW1tbREREoEuXLlle04OIiIgoU64GZdjb2+Ojjz6ydC1ERERUwOV6NOjRo0dx8eJFpKenG7W3bdv2pYsiIiKigilXVy5t3749Dh06BI1Go/wKreb//1hJRkaGZSskIiKiAsPss1o++eQTeHl54fr167Czs8ORI0ewZcsW1K5dG5s3b86DEomIiKigMPuIx/bt27Fp0yaULFkSWq0WWq0Wb775JkaPHo0BAwZg3759eVEnERERFQBmH/HIyMhAkSJFAAAlS5bE1atXAQAeHh44ceKEZasjIiKiAsXsIx4+Pj44cOAAvLy8ULduXYwdOxY6nQ6zZs1CuXLl8qJGIiIiKiDMDh5ff/017t27BwAYPnw4WrdujUaNGqFEiRJYsmSJxQskIiKigsPs4BEcHKz8XaFCBRw/fhy3bt1CsWLFlDNbiIiIiLJi1hiPR48ewdraGocPHzZqL168OEMHERERvZBZwaNQoUIoW7Ysr9VBREREuWL2WS1fffUVBg8ejFu3buVFPURERFSAmT3GY9q0aTh9+jRcXV3h4eEBe3t7o8f37t1rseKIiIioYDE7eISEhORBGURERPQ6MDt4REdH50UdRERE9Bowe4wHERERUW6ZfcRDq9U+99RZnvFCRERE2TE7eKxYscLo/qNHj7Bv3z789NNPGDZsmMUKIyIiooLH7ODRrl07k7aOHTuiatWqWLJkCT744AOLFEZEREQFj8XGeNSrVw+xsbGWmh0REREVQBYJHg8ePMD3338PNzc3S8yOiIiICiizv2p59sfgRASpqamws7PDwoULLVocERERFSxmB49JkyYZBQ+tVgsnJyfUrVsXxYoVs2hxREREVLCYHTy6d++eB2UQERHR68DsMR7z58/HsmXLTNqXLVuGn376ySJFERERUcFkdvAYPXo0SpYsadJeqlQpjBo1yiJFERERUcFkdvC4ePEivLy8TNo9PDxw8eJFixRFREREBZPZwaNUqVI4ePCgSfuBAwdQokQJixRFREREBZPZwaNLly4YMGAA4uLikJGRgYyMDGzatAmffPIJOnfunBc1EhERUQFh9lktI0aMwPnz59GsWTNYWz+Z3GAwICwsjGM8iIiI6LnMDh46nQ5LlizBt99+i/3798PW1hbVqlWDh4dHXtRHREREBYjZwSNTxYoVUbFiRUvWQkRERAWc2WM8OnTogDFjxpi0jx07Fu+++65FiiIiIqKCyezgsWXLFrz99tsm7S1btsSWLVssUhQREREVTGYHj7t370Kn05m0FypUCCkpKbkqYvr06fD09IRer0fdunWxc+fObPvOnj0bjRo1QrFixVCsWDEEBQU9tz8RERG9OswOHtWqVcOSJUtM2hcvXowqVaqYXcCSJUsQGRmJ6Oho7N27F76+vggODsb169ez7L9582Z06dIFcXFx2L59O9zd3dGiRQtcuXLF7GUTERGRujQiIuZMsHr1arzzzjvo2rUrmjZtCgCIjY3FL7/8gmXLliEkJMSsAurWrQt/f39MmzYNwJNTc93d3dG/f398+eWXL5w+IyMDxYoVw7Rp0xAWFvbC/ikpKXB0dERycjIcHBzMqvV5nvrBXqICz7y9xqtFM4wbK70+JNqyG6sl3kPNPuLRpk0brFy5EqdPn0afPn0waNAgXL58GRs3bjQ7dKSnp2PPnj0ICgr6pyCtFkFBQdi+fXuO5nH//n08evQIxYsXN2vZREREpL5cnU7bqlUrtGrVyqT98OHD8PHxyfF8bt68iYyMDDg7Oxu1Ozs74/jx4zmaxxdffAFXV1ej8PK0tLQ0pKWlKfdzOw6FiIiIXp7ZRzyelZqailmzZqFOnTrw9fW1RE059t1332Hx4sVYsWIF9Hp9ln1Gjx4NR0dH5ebu7q5qjURERPSPXAePLVu2ICwsDKVLl8b48ePRtGlT/PXXX2bNo2TJkrCyskJSUpJRe1JSElxcXJ477fjx4/Hdd9/hzz//RPXq1bPtFxUVheTkZOV26dIls2okIiIiyzHrq5bExEQsWLAAc+fORUpKCjp16oS0tDSsXLkyV2e06HQ6+Pn5ITY2VhkfYjAYEBsbi379+mU73dixYzFy5EisX78etWvXfu4ybGxsYGNjY3ZtREREZHk5PuLRpk0bVKpUCQcPHsTkyZNx9epVTJ069aULiIyMxOzZs/HTTz/h2LFj6N27N+7du4eIiAgAQFhYGKKiopT+Y8aMwTfffIN58+bB09MTiYmJSExMxN27d1+6FiIiIspbOT7i8ccff2DAgAHo3bu3RX+jJTQ0FDdu3MCQIUOQmJiIGjVqYN26dcqA04sXL0Kr/ScfzZgxA+np6ejYsaPRfKKjozF06FCL1UVERESWl+PgER8fj7lz58LPzw+VK1dGt27d0LlzZ4sU0a9fv2y/Wtm8ebPR/fPnz1tkmURERKS+HH/VUq9ePcyePRvXrl1Dr169sHjxYri6usJgMGDDhg1ITU3NyzqJiIioADD7rBZ7e3v06NED8fHxOHToEAYNGoTvvvsOpUqVQtu2bfOiRiIiIiogXuo6HpUqVcLYsWNx+fJl/PLLL5aqiYiIiAqol76AGABYWVkhJCQEq1atssTsiIiIqICySPAgIiIiygkGDyIiIlINgwcRERGphsGDiIiIVMPgQURERKph8CAiIiLVMHgQERGRahg8iIiISDUMHkRERKQaBg8iIiJSDYMHERERqYbBg4iIiFTD4EFERESqYfAgIiIi1TB4EBERkWoYPIiIiEg1DB5ERESkGgYPIiIiUg2DBxEREamGwYOIiIhUw+BBREREqmHwICIiItUweBAREZFqGDyIiIhINQweREREpBoGDyIiIlINgwcRERGphsGDiIiIVMPgQURERKph8CAiIiLVMHgQERGRahg8iIiISDUMHkRERKQaBg8iIiJSDYMHERERqYbBg4iIiFTD4EFERESqYfAgIiIi1TB4EBERkWryPXhMnz4dnp6e0Ov1qFu3Lnbu3Jlt3yNHjqBDhw7w9PSERqPB5MmT1SuUiIiIXlq+Bo8lS5YgMjIS0dHR2Lt3L3x9fREcHIzr169n2f/+/fsoV64cvvvuO7i4uKhcLREREb2sfA0eEydORM+ePREREYEqVapg5syZsLOzw7x587Ls7+/vj3HjxqFz586wsbFRuVoiIiJ6WfkWPNLT07Fnzx4EBQX9U4xWi6CgIGzfvj2/yiIiIqI8ZJ1fC7558yYyMjLg7Oxs1O7s7Izjx49bbDlpaWlIS0tT7qekpFhs3kRERGSefB9cmtdGjx4NR0dH5ebu7p7fJREREb228i14lCxZElZWVkhKSjJqT0pKsujA0aioKCQnJyu3S5cuWWzeREREZJ58Cx46nQ5+fn6IjY1V2gwGA2JjY1G/fn2LLcfGxgYODg5GNyIiIsof+TbGAwAiIyMRHh6O2rVro06dOpg8eTLu3buHiIgIAEBYWBjc3NwwevRoAE8GpB49elT5+8qVK9i/fz8KFy6MChUq5NvzICIiopzJ1+ARGhqKGzduYMiQIUhMTESNGjWwbt06ZcDpxYsXodX+c1Dm6tWrqFmzpnJ//PjxGD9+PAICArB582a1yyciIiIzaURE8rsINaWkpMDR0RHJyckW/dpFo7HYrIheef/mvYZmGDdWen1ItGU3Vku8hxb4s1qIiIjo1cHgQURERKph8CAiIiLVMHgQERGRahg8iIiISDUMHkRERKQaBg8iIiJSDYMHERERqYbBg4iIiFTD4EFERESqYfAgIiIi1TB4EBERkWoYPIiIiEg1DB5ERESkGgYPIiIiUg2DBxEREamGwYOIiIhUw+BBREREqmHwICIiItUweBAREZFqGDyIiIhINQweREREpBoGDyIiIlINgwcRERGphsGDiIiIVMPgQURERKph8CAiIiLVMHgQERGRahg8iIiISDUMHkRERKQaBg8iIiJSDYMHERERqYbBg4iIiFTD4EFERESqYfAgIiIi1TB4EBERkWoYPIiIiEg1DB5ERESkGgYPIiIiUg2DBxEREamGwYOIiIhUw+BBREREqmHwICIiItUweBAREZFqXongMX36dHh6ekKv16Nu3brYuXPnc/svW7YM3t7e0Ov1qFatGn7//XeVKiUiIqKXke/BY8mSJYiMjER0dDT27t0LX19fBAcH4/r161n237ZtG7p06YIPPvgA+/btQ0hICEJCQnD48GGVKyciIiJzaURE8rOAunXrwt/fH9OmTQMAGAwGuLu7o3///vjyyy9N+oeGhuLevXtYs2aN0lavXj3UqFEDM2fOfOHyUlJS4OjoiOTkZDg4OFjseWg0FpsV0Ssvf/caL0czjBsrvT4k2rIbqyXeQ60tWpGZ0tPTsWfPHkRFRSltWq0WQUFB2L59e5bTbN++HZGRkUZtwcHBWLlyZZb909LSkJaWptxPTk4G8GTlEVHu/Ks3n4f5XQCReiz9Xpc5v5c5ZpGvwePmzZvIyMiAs7OzUbuzszOOHz+e5TSJiYlZ9k9MTMyy/+jRozFs2DCTdnd391xWTUSOjvldARHlhON3ebOxpqamwjGXO4J8DR5qiIqKMjpCYjAYcOvWLZQoUQIafj/yr5aSkgJ3d3dcunTJol+bEZFlcVstOEQEqampcHV1zfU88jV4lCxZElZWVkhKSjJqT0pKgouLS5bTuLi4mNXfxsYGNjY2Rm1FixbNfdH0ynFwcODOjOhfgNtqwZDbIx2Z8vWsFp1OBz8/P8TGxiptBoMBsbGxqF+/fpbT1K9f36g/AGzYsCHb/kRERPTqyPevWiIjIxEeHo7atWujTp06mDx5Mu7du4eIiAgAQFhYGNzc3DB69GgAwCeffIKAgABMmDABrVq1wuLFi7F7927MmjUrP58GERER5UC+B4/Q0FDcuHEDQ4YMQWJiImrUqIF169YpA0gvXrwIrfafAzMNGjTAzz//jK+//hqDBw9GxYoVsXLlSvj4+OTXU6B8YmNjg+joaJOv0ojo1cJtlZ6W79fxICIiotdHvl+5lIiIiF4fDB5ERESkGgYPIiIiUg2DBz3X5s2bodFocOfOnef28/T0xOTJk1Wp6VWh0WiUS/WfP38eGo0G+/fvz9eaiHLi375dN2nSBAMHDlTuv6p1UtYYPF4TM2fORJEiRfD48WOl7e7duyhUqBCaNGli1Ddzp3TmzBk0aNAA165dUy4Ys2DBgny9AFtOdzAHDhxA27ZtUapUKej1enh6eiI0NDTbXz1+We7u7rh27RrPriJVvYrb9Y0bN9C7d2+ULVsWNjY2cHFxQXBwMBISEiwy/6zs2rULH330UZ7NnyyLweM1ERgYiLt372L37t1K29atW+Hi4oIdO3bg4cN/fjkrLi4OZcuWRfny5aHT6eDi4vKvurz8jRs30KxZMxQvXhzr16/HsWPHMH/+fLi6uuLevXt5skwrKyu4uLjA2jrfz1Cn18iruF136NAB+/btw08//YSTJ09i1apVaNKkCf7++2+LLyuTk5MT7Ozs8mz+ZFkMHq+JSpUqoXTp0ti8ebPStnnzZrRr1w5eXl7466+/jNoDAwOVvzMPyW7evBkRERFITk6GRqOBRqPB0KFDlenu37+PHj16oEiRIihbtqzJRd0OHTqEpk2bwtbWFiVKlMBHH32Eu3fvKo8/e/gUAEJCQtC9e3fl8QsXLuDTTz9Vlp+VhIQEJCcnY86cOahZsya8vLwQGBiISZMmwcvLS+l35MgRtG7dGg4ODihSpAgaNWqEM2fOAHjyCap58+YoWbIkHB0dERAQgL1792a7fp/9qiVzvcXGxqJ27dqws7NDgwYNcOLECaPpvv32W5QqVQpFihTBhx9+iC+//BI1atTIdjlET3sVtuun3blzB1u3bsWYMWMQGBgIDw8P1KlTB1FRUWjbtq1Rv169esHZ2Rl6vR4+Pj5Ys2YNAODvv/9Gly5d4ObmBjs7O1SrVg2//PLLc9fDs0dCNRoN5syZg/bt28POzg4VK1bEqlWrjKZZtWoVKlasCL1ej8DAQPz00085+vqJXh6Dx2skMDAQcXFxyv24uDg0adIEAQEBSvuDBw+wY8cOZQf1tAYNGmDy5MlwcHDAtWvXcO3aNXz22WfK4xMmTEDt2rWxb98+9OnTB71791beaO/du4fg4GAUK1YMu3btwrJly7Bx40b069cvx/UvX74cZcqUwfDhw5XlZ8XFxQWPHz/GihUrsv3p5itXrqBx48awsbHBpk2bsGfPHvTo0UM5ZJ2amorw8HDEx8fjr7/+QsWKFfH2228jNTU1x/UCwFdffYUJEyZg9+7dsLa2Ro8ePZTHFi1ahJEjR2LMmDHYs2cPypYtixkzZpg1f6L83K6fVbhwYRQuXBgrV65EWlpaln0MBgNatmyJhIQELFy4EEePHsV3330HKysrAMDDhw/h5+eHtWvX4vDhw/joo4/QrVs37Ny506z1MmzYMHTq1AkHDx7E22+/jffeew+3bt0CAJw7dw4dO3ZESEgIDhw4gF69euGrr74ya/70EoReG7NnzxZ7e3t59OiRpKSkiLW1tVy/fl1+/vlnady4sYiIxMbGCgC5cOGCiIjExcUJALl9+7aIiMyfP18cHR1N5u3h4SHvv/++ct9gMEipUqVkxowZIiIya9YsKVasmNy9e1fps3btWtFqtZKYmCgiIgEBAfLJJ58Yzbddu3YSHh5utJxJkya98LkOHjxYrK2tpXjx4vLWW2/J2LFjleWIiERFRYmXl5ekp6e/cF4iIhkZGVKkSBFZvXq10gZAVqxYISIi586dEwCyb98+EflnvW3cuNHo+QKQBw8eiIhI3bp1pW/fvkbLadiwofj6+uaoJiKR/N2us/Lrr79KsWLFRK/XS4MGDSQqKkoOHDigPL5+/XrRarVy4sSJHD/HVq1ayaBBg5T7z+4rnt0vAJCvv/5auX/37l0BIH/88YeIiHzxxRfi4+NjtIyvvvrKaJ1Q3uERj9dIkyZNcO/ePezatQtbt27FG2+8AScnJwQEBCjfB2/evBnlypVD2bJlzZ5/9erVlb81Gg1cXFyUwZzHjh2Dr68v7O3tlT4NGzaEwWDI9tPTyxg5ciQSExMxc+ZMVK1aFTNnzoS3tzcOHToEANi/fz8aNWqEQoUKZTl9UlISevbsiYoVK8LR0REODg64e/cuLl68aFYdT6+T0qVLA4CyTk6cOIE6deoY9X/2PtGL5Od2nZUOHTrg6tWrWLVqFd566y1s3rwZtWrVwoIFCwA82fbKlCmDN954I8vpMzIyMGLECFSrVg3FixdH4cKFsX79+pfa9uzt7eHg4GC07fn7+xv157anHgaP10iFChVQpkwZxMXFIS4uDgEBAQAAV1dXuLu7Y9u2bYiLi0PTpk1zNf9n38Q1Gg0MBkOOp9dqtSZfjTx69ChXtQBAiRIl8O6772L8+PE4duwYXF1dMX78eACAra3tc6cNDw/H/v37MWXKFGzbtg379+9HiRIlkJ6eblYNT6+TzDEp5qwTohd5FbdrvV6P5s2b45tvvsG2bdvQvXt3REdHA3jxtjdu3DhMmTIFX3zxBeLi4rB//34EBwe/1LaX07pJHQwer5nAwEBs3rwZmzdvNjrdrnHjxvjjjz+wc+fOLL8HzqTT6ZCRkWH2citXrowDBw4YnVWSkJAArVaLSpUqAXgyMv3pcRsZGRk4fPiwRZav0+lQvnx5ZfnVq1fH1q1bsw02CQkJGDBgAN5++21UrVoVNjY2uHnzptnLfZ5KlSph165dRm3P3ifKifzarnOqSpUqRtve5cuXcfLkySz7JiQkoF27dnj//ffh6+uLcuXKZds3typVqmR0JhDAbU9NDB6vmcDAQMTHx2P//v3KJyMACAgIwI8//oj09PTn7qA8PT1x9+5dxMbG4ubNm7h//36Olvvee+9Br9cjPDwchw8fRlxcHPr3749u3bopv0TctGlTrF27FmvXrsXx48fRu3dvkxHmnp6e2LJlC65cuZJtEFizZg3ef/99rFmzBidPnsSJEycwfvx4/P7772jXrh0AoF+/fkhJSUHnzp2xe/dunDp1CjExMcrXPhUrVkRMTAyOHTuGHTt24L333nvhJzVz9e/fH3PnzsVPP/2EU6dO4dtvv8XBgwf/Vacu06shv7brZ/39999o2rQpFi5ciIMHD+LcuXNYtmwZxo4dq2x7AQEBaNy4MTp06IANGzbg3Llz+OOPP7Bu3ToAT7a9DRs2YNu2bTh27Bh69eqFpKSkXNWTnV69euH48eP44osvcPLkSSxdulT5KojbX95j8HjNBAYG4sGDB6hQoYLyhg882RmkpqYqp+dlp0GDBvj4448RGhoKJycnjB07NkfLtbOzw/r163Hr1i34+/ujY8eOaNasGaZNm6b06dGjB8LDwxEWFoaAgACUK1fOZGc5fPhwnD9/HuXLl4eTk1OWy6pSpQrs7OwwaNAg1KhRA/Xq1cPSpUsxZ84cdOvWDcCTr2E2bdqEu3fvIiAgAH5+fpg9e7ZyeHbu3Lm4ffs2atWqhW7dumHAgAEoVapUjp5rTr333nuIiorCZ599hlq1auHcuXPo3r079Hq9RZdDBV9+bdfPKly4MOrWrYtJkyahcePG8PHxwTfffIOePXsabeu//fYb/P390aVLF1SpUgWff/65csTl66+/Rq1atRAcHIwmTZrAxcUFISEhuaonO15eXvj111+xfPlyVK9eHTNmzFDOarGxsbHossiURp79Up2I8k3z5s3h4uKCmJiY/C6F6LUycuRIzJw5E5cuXcrvUgo8XmaRKJ/cv38fM2fORHBwMKysrPDLL79g48aN2LBhQ36XRlTg/fDDD/D390eJEiWQkJCAcePGmXVdIco9Bg+ifKLRaPD7779j5MiRePjwISpVqoTffvsNQUFB+V0aUYGXOa7q1q1bKFu2LAYNGoSoqKj8Luu1wK9aiIiISDUcXEpERESqYfAgIiIi1TB4EBERkWoYPIiIiEg1DB5ERESkGgYPIiIiUg2DBxEREamGwYOIiIhUw+BBREREqvl/qw6tk+Q5ioQAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 600x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "# Bonus Challenge - Feature Scaling\n",
+    "import matplotlib.pyplot as plt\n",
     "\n",
-    "Problem-solving in machine learning is iterative. You can improve your model prediction with various techniques (there is a sweetspot for the time you spend and the improvement you receive though). Now you've completed only one iteration of ML analysis. There are more iterations you can conduct to make improvements. In order to be able to do that, you will need deeper knowledge in statistics and master more data analysis techniques. In this bootcamp, we don't have time to achieve that advanced goal. But you will make constant efforts after the bootcamp to eventually get there.\n",
+    "accuracies = [accuracy_no_scaling, accuracy_scaled]\n",
     "\n",
-    "However, now we do want you to learn one of the advanced techniques which is called *feature scaling*. The idea of feature scaling is to standardize/normalize the range of independent variables or features of the data. This can make the outliers more apparent so that you can remove them. This step needs to happen during Challenge 6 after you split the training and test data because you don't want to split the data again which makes it impossible to compare your results with and without feature scaling. For general concepts about feature scaling, click [here](https://en.wikipedia.org/wiki/Feature_scaling). To read deeper, click [here](https://medium.com/greyatom/why-how-and-when-to-scale-your-features-4b30ab09db5e).\n",
+    "plt.figure(figsize=(6, 4))  \n",
+    "plt.bar(['Without Scaling', 'With Scaling'], accuracies, color=['blue', 'green'])\n",
     "\n",
-    "In the next cell, attempt to improve your model prediction accuracy by means of feature scaling. A library you can utilize is `sklearn.preprocessing.RobustScaler` ([documentation](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.RobustScaler.html)). You'll use the `RobustScaler` to fit and transform your `X_train`, then transform `X_test`. You will use logistic regression to fit and predict your transformed data and obtain the accuracy score in the same way. Compare the accuracy score with your normalized data with the previous accuracy data. Is there an improvement?"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Your code here"
+    "plt.title('Comparison of Accuracy Scores (With vs Without Scaling)')\n",
+    "plt.ylabel('Accuracy Score')\n",
+    "\n",
+    "plt.show()"
    ]
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "ironhack-3.7",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
-   "name": "ironhack-3.7"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
@@ -669,9 +10205,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.3"
+   "version": "3.12.4"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }