From 7d91a84b42d1f6e8660d4d1e6fc260ca48bf5bc3 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?=E2=80=9Cdanielmdepaoli=E2=80=9D?=
 <“danielmdepaoli@gmail.com”>
Date: Sat, 26 Aug 2023 13:10:47 +0100
Subject: [PATCH] Lab Done

---
 your-code/main.ipynb | 1247 ++++++++++++++++++++++++++++++++++++++----
 1 file changed, 1134 insertions(+), 113 deletions(-)

diff --git a/your-code/main.ipynb b/your-code/main.ipynb
index a5caf8b..482ec74 100755
--- a/your-code/main.ipynb
+++ b/your-code/main.ipynb
@@ -37,13 +37,236 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [],
    "source": [
     "websites = pd.read_csv('../data/website.csv')"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "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>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>10/10/2015 18:21</td>\n",
+       "      <td>None</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>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</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>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</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>None</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          None           None   \n",
+       "1          Apache/2.4.10         15087.0          None           None   \n",
+       "2  Microsoft-HTTPAPI/2.0           324.0          None           None   \n",
+       "3                  nginx           162.0            US             AK   \n",
+       "4                   None        124140.0            US             TX   \n",
+       "\n",
+       "      WHOIS_REGDATE WHOIS_UPDATED_DATE  ...  DIST_REMOTE_TCP_PORT  REMOTE_IPS  \\\n",
+       "0  10/10/2015 18:21               None  ...                     0           2   \n",
+       "1              None               None  ...                     7           4   \n",
+       "2              None               None  ...                     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": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "websites.head()"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -65,11 +288,63 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 21,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1781, 21)"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# Your code here\n"
+    "websites.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "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": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "websites.dtypes"
    ]
   },
   {
@@ -102,20 +377,73 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
+   "execution_count": 23,
+   "metadata": {
+    "scrolled": false
+   },
    "outputs": [],
    "source": [
-    "# Your code here\n"
+    "import seaborn as sns\n",
+    "import matplotlib.pyplot as plt"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/folders/d9/783c_j055nj0b0y037qm_1zc0000gn/T/ipykernel_91760/3274975957.py:1: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
+      "  corr = np.abs(websites.corr())\n",
+      "/var/folders/d9/783c_j055nj0b0y037qm_1zc0000gn/T/ipykernel_91760/3274975957.py:4: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
+      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
+      "  mask = np.zeros_like(corr, dtype = np.bool)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1400x1400 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "corr = np.abs(websites.corr())\n",
+    "\n",
+    "#Set up mask for triangle representation\n",
+    "mask = np.zeros_like(corr, dtype = np.bool)\n",
+    "mask[np.triu_indices_from(mask)] = True\n",
+    "\n",
+    "# Set up the matplotlib figure\n",
+    "f, ax = plt.subplots(figsize = (14, 14))\n",
+    "# Generate a custom diverging colormap\n",
+    "cmap = sns.diverging_palette(220, 10, as_cmap = True)\n",
+    "# Draw the heatmap with the mask and correct aspect ratio\n",
+    "sns.heatmap(corr, mask = mask,  vmax = 1, square = True, linewidths = .5, cbar_kws = {\"shrink\" : .5}, annot = corr)\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "# Your comment here"
+    "\"type\" is highly correlated to NUMBER_SPECIAL_CHARACTERS (0.28) (followed by URL_LENGTH (0.16))\n",
+    "\n",
+    "NUMBER_SPECIAL_CHARACTERS is highly correlated to URL_LENGTH (0.92)\n",
+    "\n",
+    "CONTENT_LENGTH is not highly correlated to any other one\n",
+    "\n",
+    "DIST_REMOTE_TCP_PORT is correlated to APP_BYTES and REMOTE_APP_BYTES (both with 0.78)\n",
+    "\n",
+    "Drop APP_BYTES and REMOTE_APP_BYTES"
    ]
   },
   {
@@ -133,11 +461,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Your code here\n"
+    "websites.drop(columns = [\"NUMBER_SPECIAL_CHARACTERS\", \"APP_PACKETS\", \"SOURCE_APP_PACKETS\", \"REMOTE_APP_PACKETS\"], inplace = True)"
    ]
   },
   {
@@ -151,11 +479,44 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 27,
    "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Print heatmap again\n"
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/folders/d9/783c_j055nj0b0y037qm_1zc0000gn/T/ipykernel_91760/271723498.py:1: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
+      "  corr = np.abs(websites.corr())\n",
+      "/var/folders/d9/783c_j055nj0b0y037qm_1zc0000gn/T/ipykernel_91760/271723498.py:3: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
+      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
+      "  mask = np.zeros_like(corr, dtype = np.bool)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1400x1400 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "corr = np.abs(websites.corr())\n",
+    "\n",
+    "mask = np.zeros_like(corr, dtype = np.bool)\n",
+    "mask[np.triu_indices_from(mask)] = True\n",
+    "\n",
+    "f, ax = plt.subplots(figsize = (14, 14))\n",
+    "\n",
+    "cmap = sns.diverging_palette(220, 10, as_cmap = True)\n",
+    "\n",
+    "sns.heatmap(corr, mask = mask,  vmax = 1, square = True, linewidths = .5, cbar_kws = {\"shrink\" : .5}, annot = corr)\n",
+    "\n",
+    "plt.show()"
    ]
   },
   {
@@ -169,11 +530,39 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 28,
    "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Your code here\n"
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "URL                            0\n",
+       "URL_LENGTH                     0\n",
+       "CHARSET                        0\n",
+       "SERVER                         1\n",
+       "CONTENT_LENGTH               812\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_BYTES               0\n",
+       "REMOTE_APP_BYTES               0\n",
+       "DNS_QUERY_TIMES                1\n",
+       "Type                           0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "websites.isna().sum()"
    ]
   },
   {
@@ -187,11 +576,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Your code here\n"
+    "websites.drop(columns = [\"CONTENT_LENGTH\"], inplace = True)\n",
+    "\n",
+    "websites = websites.dropna(axis = 0)"
    ]
   },
   {
@@ -214,11 +605,38 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 30,
    "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Examine missing values in each column\n"
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "URL                          0\n",
+       "URL_LENGTH                   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_BYTES             0\n",
+       "REMOTE_APP_BYTES             0\n",
+       "DNS_QUERY_TIMES              0\n",
+       "Type                         0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "websites.isna().sum()\n"
    ]
   },
   {
@@ -256,11 +674,77 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 31,
    "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Your code here\n"
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/folders/d9/783c_j055nj0b0y037qm_1zc0000gn/T/ipykernel_91760/2231414693.py:1: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  websites[\"WHOIS_COUNTRY\"] = websites[\"WHOIS_COUNTRY\"].str.replace(\"Cyprus\",\"CY\")\n",
+      "/var/folders/d9/783c_j055nj0b0y037qm_1zc0000gn/T/ipykernel_91760/2231414693.py:2: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  websites[\"WHOIS_COUNTRY\"] = websites[\"WHOIS_COUNTRY\"].str.replace(\"us\",\"US\")\n",
+      "/var/folders/d9/783c_j055nj0b0y037qm_1zc0000gn/T/ipykernel_91760/2231414693.py:3: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  websites[\"WHOIS_COUNTRY\"] = websites[\"WHOIS_COUNTRY\"].str.replace(\"se\",\"SE\")\n",
+      "/var/folders/d9/783c_j055nj0b0y037qm_1zc0000gn/T/ipykernel_91760/2231414693.py:4: FutureWarning: The default value of regex will change from True to False in a future version.\n",
+      "  websites[\"WHOIS_COUNTRY\"] = websites[\"WHOIS_COUNTRY\"].str.replace(\"\\[u'GB'; u'UK'\\]\",\"UK\")\n",
+      "/var/folders/d9/783c_j055nj0b0y037qm_1zc0000gn/T/ipykernel_91760/2231414693.py:4: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  websites[\"WHOIS_COUNTRY\"] = websites[\"WHOIS_COUNTRY\"].str.replace(\"\\[u'GB'; u'UK'\\]\",\"UK\")\n",
+      "/var/folders/d9/783c_j055nj0b0y037qm_1zc0000gn/T/ipykernel_91760/2231414693.py:5: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  websites[\"WHOIS_COUNTRY\"] = websites[\"WHOIS_COUNTRY\"].str.replace(\"GB\",\"UK\")\n",
+      "/var/folders/d9/783c_j055nj0b0y037qm_1zc0000gn/T/ipykernel_91760/2231414693.py:6: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  websites[\"WHOIS_COUNTRY\"] = websites[\"WHOIS_COUNTRY\"].str.replace(\"United Kingdom\",\"UK\")\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "array(['None', 'US', 'SC', 'UK', 'RU', 'AU', 'CA', 'PA', 'SE', 'IN', 'LU',\n",
+       "       'TH', 'FR', 'NL', 'UG', 'JP', 'CN', 'SI', 'IL', 'ru', 'KY', 'AT',\n",
+       "       'CZ', 'PH', 'BE', 'NO', 'TR', 'LV', 'DE', 'ES', 'BR', 'KR', 'HK',\n",
+       "       'UA', 'CH', 'BS', 'PK', 'IT', 'CY', 'BY', 'AE', 'IE', 'UY', 'KG'],\n",
+       "      dtype=object)"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "websites[\"WHOIS_COUNTRY\"] = websites[\"WHOIS_COUNTRY\"].str.replace(\"Cyprus\",\"CY\") \n",
+    "websites[\"WHOIS_COUNTRY\"] = websites[\"WHOIS_COUNTRY\"].str.replace(\"us\",\"US\")\n",
+    "websites[\"WHOIS_COUNTRY\"] = websites[\"WHOIS_COUNTRY\"].str.replace(\"se\",\"SE\")\n",
+    "websites[\"WHOIS_COUNTRY\"] = websites[\"WHOIS_COUNTRY\"].str.replace(\"\\[u'GB'; u'UK'\\]\",\"UK\")\n",
+    "websites[\"WHOIS_COUNTRY\"] = websites[\"WHOIS_COUNTRY\"].str.replace(\"GB\",\"UK\")\n",
+    "websites[\"WHOIS_COUNTRY\"] = websites[\"WHOIS_COUNTRY\"].str.replace(\"United Kingdom\",\"UK\")\n",
+    "\n",
+    "websites[\"WHOIS_COUNTRY\"].unique()"
    ]
   },
   {
@@ -278,11 +762,40 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 32,
    "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Your code here\n"
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/folders/d9/783c_j055nj0b0y037qm_1zc0000gn/T/ipykernel_91760/1277553622.py:9: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
+      "  ax.set_xticklabels(country_counts.index, rotation=90)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "country_counts = websites['WHOIS_COUNTRY'].value_counts()\n",
+    "\n",
+    "fig, ax = plt.subplots(figsize=(10, 6))\n",
+    "\n",
+    "ax.bar(country_counts.index, country_counts.values)\n",
+    "\n",
+    "ax.set_xlabel('Country')\n",
+    "ax.set_ylabel('Count')\n",
+    "ax.set_xticklabels(country_counts.index, rotation=90)\n",
+    "\n",
+    "plt.show()"
    ]
   },
   {
@@ -294,13 +807,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 33,
    "metadata": {
     "scrolled": true
    },
-   "outputs": [],
-   "source": [
-    "# Your code here\n"
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/folders/d9/783c_j055nj0b0y037qm_1zc0000gn/T/ipykernel_91760/1433082138.py:3: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  websites[\"WHOIS_COUNTRY\"] = websites[\"WHOIS_COUNTRY\"].apply(lambda x: x if x in top_countries else \"OTHER\")\n"
+     ]
+    }
+   ],
+   "source": [
+    "top_countries = [\"US\",\"None\",\"CA\",\"ES\",\"AU\",\"PA\",\"GB\",\"UK\",\"JP\",\"IN\"]\n",
+    "\n",
+    "websites[\"WHOIS_COUNTRY\"] = websites[\"WHOIS_COUNTRY\"].apply(lambda x: x if x in top_countries else \"OTHER\")"
    ]
   },
   {
@@ -316,11 +844,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 34,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/folders/d9/783c_j055nj0b0y037qm_1zc0000gn/T/ipykernel_91760/638224779.py:1: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  websites.drop(['WHOIS_STATEPRO', 'WHOIS_REGDATE', 'WHOIS_UPDATED_DATE'], axis = 1, inplace = True)\n"
+     ]
+    }
+   ],
    "source": [
-    "# Your code here\n"
+    "websites.drop(['WHOIS_STATEPRO', 'WHOIS_REGDATE', 'WHOIS_UPDATED_DATE'], axis = 1, inplace = True)"
    ]
   },
   {
@@ -334,11 +874,35 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 35,
    "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Your code here\n"
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "URL                           object\n",
+       "URL_LENGTH                     int64\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_BYTES               int64\n",
+       "REMOTE_APP_BYTES               int64\n",
+       "DNS_QUERY_TIMES              float64\n",
+       "Type                           int64\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "websites.dtypes"
    ]
   },
   {
@@ -350,11 +914,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 38,
    "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Your code here\n"
+   "outputs": [
+    {
+     "ename": "KeyError",
+     "evalue": "\"['URL'] not found in axis\"",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[38], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mwebsites\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdrop\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcolumns\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mURL\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minplace\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/anaconda3/lib/python3.10/site-packages/pandas/util/_decorators.py:331\u001b[0m, in \u001b[0;36mdeprecate_nonkeyword_arguments.<locals>.decorate.<locals>.wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    325\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(args) \u001b[38;5;241m>\u001b[39m num_allow_args:\n\u001b[1;32m    326\u001b[0m     warnings\u001b[38;5;241m.\u001b[39mwarn(\n\u001b[1;32m    327\u001b[0m         msg\u001b[38;5;241m.\u001b[39mformat(arguments\u001b[38;5;241m=\u001b[39m_format_argument_list(allow_args)),\n\u001b[1;32m    328\u001b[0m         \u001b[38;5;167;01mFutureWarning\u001b[39;00m,\n\u001b[1;32m    329\u001b[0m         stacklevel\u001b[38;5;241m=\u001b[39mfind_stack_level(),\n\u001b[1;32m    330\u001b[0m     )\n\u001b[0;32m--> 331\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/anaconda3/lib/python3.10/site-packages/pandas/core/frame.py:5399\u001b[0m, in \u001b[0;36mDataFrame.drop\u001b[0;34m(self, labels, axis, index, columns, level, inplace, errors)\u001b[0m\n\u001b[1;32m   5251\u001b[0m \u001b[38;5;129m@deprecate_nonkeyword_arguments\u001b[39m(version\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, allowed_args\u001b[38;5;241m=\u001b[39m[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mself\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlabels\u001b[39m\u001b[38;5;124m\"\u001b[39m])\n\u001b[1;32m   5252\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mdrop\u001b[39m(  \u001b[38;5;66;03m# type: ignore[override]\u001b[39;00m\n\u001b[1;32m   5253\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   5260\u001b[0m     errors: IgnoreRaise \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mraise\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m   5261\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m DataFrame \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m   5262\u001b[0m     \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m   5263\u001b[0m \u001b[38;5;124;03m    Drop specified labels from rows or columns.\u001b[39;00m\n\u001b[1;32m   5264\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   5397\u001b[0m \u001b[38;5;124;03m            weight  1.0     0.8\u001b[39;00m\n\u001b[1;32m   5398\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m-> 5399\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdrop\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   5400\u001b[0m \u001b[43m        \u001b[49m\u001b[43mlabels\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlabels\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   5401\u001b[0m \u001b[43m        \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   5402\u001b[0m \u001b[43m        \u001b[49m\u001b[43mindex\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mindex\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   5403\u001b[0m \u001b[43m        \u001b[49m\u001b[43mcolumns\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcolumns\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   5404\u001b[0m \u001b[43m        \u001b[49m\u001b[43mlevel\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlevel\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   5405\u001b[0m \u001b[43m        \u001b[49m\u001b[43minplace\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minplace\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   5406\u001b[0m \u001b[43m        \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merrors\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   5407\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/anaconda3/lib/python3.10/site-packages/pandas/util/_decorators.py:331\u001b[0m, in \u001b[0;36mdeprecate_nonkeyword_arguments.<locals>.decorate.<locals>.wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    325\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(args) \u001b[38;5;241m>\u001b[39m num_allow_args:\n\u001b[1;32m    326\u001b[0m     warnings\u001b[38;5;241m.\u001b[39mwarn(\n\u001b[1;32m    327\u001b[0m         msg\u001b[38;5;241m.\u001b[39mformat(arguments\u001b[38;5;241m=\u001b[39m_format_argument_list(allow_args)),\n\u001b[1;32m    328\u001b[0m         \u001b[38;5;167;01mFutureWarning\u001b[39;00m,\n\u001b[1;32m    329\u001b[0m         stacklevel\u001b[38;5;241m=\u001b[39mfind_stack_level(),\n\u001b[1;32m    330\u001b[0m     )\n\u001b[0;32m--> 331\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/anaconda3/lib/python3.10/site-packages/pandas/core/generic.py:4505\u001b[0m, in \u001b[0;36mNDFrame.drop\u001b[0;34m(self, labels, axis, index, columns, level, inplace, errors)\u001b[0m\n\u001b[1;32m   4503\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m axis, labels \u001b[38;5;129;01min\u001b[39;00m axes\u001b[38;5;241m.\u001b[39mitems():\n\u001b[1;32m   4504\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m labels \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m-> 4505\u001b[0m         obj \u001b[38;5;241m=\u001b[39m \u001b[43mobj\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_drop_axis\u001b[49m\u001b[43m(\u001b[49m\u001b[43mlabels\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlevel\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlevel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merrors\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   4507\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m inplace:\n\u001b[1;32m   4508\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_update_inplace(obj)\n",
+      "File \u001b[0;32m~/anaconda3/lib/python3.10/site-packages/pandas/core/generic.py:4546\u001b[0m, in \u001b[0;36mNDFrame._drop_axis\u001b[0;34m(self, labels, axis, level, errors, only_slice)\u001b[0m\n\u001b[1;32m   4544\u001b[0m         new_axis \u001b[38;5;241m=\u001b[39m axis\u001b[38;5;241m.\u001b[39mdrop(labels, level\u001b[38;5;241m=\u001b[39mlevel, errors\u001b[38;5;241m=\u001b[39merrors)\n\u001b[1;32m   4545\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 4546\u001b[0m         new_axis \u001b[38;5;241m=\u001b[39m \u001b[43maxis\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdrop\u001b[49m\u001b[43m(\u001b[49m\u001b[43mlabels\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merrors\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   4547\u001b[0m     indexer \u001b[38;5;241m=\u001b[39m axis\u001b[38;5;241m.\u001b[39mget_indexer(new_axis)\n\u001b[1;32m   4549\u001b[0m \u001b[38;5;66;03m# Case for non-unique axis\u001b[39;00m\n\u001b[1;32m   4550\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n",
+      "File \u001b[0;32m~/anaconda3/lib/python3.10/site-packages/pandas/core/indexes/base.py:6934\u001b[0m, in \u001b[0;36mIndex.drop\u001b[0;34m(self, labels, errors)\u001b[0m\n\u001b[1;32m   6932\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m mask\u001b[38;5;241m.\u001b[39many():\n\u001b[1;32m   6933\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m errors \u001b[38;5;241m!=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mignore\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[0;32m-> 6934\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mlist\u001b[39m(labels[mask])\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m not found in axis\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m   6935\u001b[0m     indexer \u001b[38;5;241m=\u001b[39m indexer[\u001b[38;5;241m~\u001b[39mmask]\n\u001b[1;32m   6936\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdelete(indexer)\n",
+      "\u001b[0;31mKeyError\u001b[0m: \"['URL'] not found in axis\""
+     ]
+    }
+   ],
+   "source": [
+    "websites.drop(columns = [\"URL\"], inplace = True)"
    ]
   },
   {
@@ -366,11 +948,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 39,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['iso-8859-1', 'UTF-8', 'us-ascii', 'ISO-8859-1', 'utf-8', 'None',\n",
+       "       'windows-1251', 'ISO-8859', 'windows-1252'], dtype=object)"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# Your code here"
+    "websites[\"CHARSET\"].unique()"
    ]
   },
   {
@@ -384,11 +978,33 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Your code here\n"
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Apache                                                                             385\n",
+       "nginx                                                                              211\n",
+       "None                                                                               175\n",
+       "Microsoft-HTTPAPI/2.0                                                              113\n",
+       "cloudflare-nginx                                                                    94\n",
+       "                                                                                  ... \n",
+       "Apache/2.2.29 (Unix) mod_ssl/2.2.29 OpenSSL/1.0.1e-fips DAV/2 mod_bwlimited/1.4      1\n",
+       "gunicorn/19.7.1                                                                      1\n",
+       "Apache/2.2.31 (Unix) mod_ssl/2.2.31 OpenSSL/0.9.8e-fips-rhel5 mod_bwlimited/1.4      1\n",
+       "Apache/1.3.37 (Unix) mod_perl/1.29 mod_ssl/2.8.28 OpenSSL/0.9.7e-p1                  1\n",
+       "Apache/2.2.16 (Debian)                                                               1\n",
+       "Name: SERVER, Length: 239, dtype: int64"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "websites[\"SERVER\"].value_counts()"
    ]
   },
   {
@@ -418,22 +1034,60 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Your code here\n"
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/folders/d9/783c_j055nj0b0y037qm_1zc0000gn/T/ipykernel_91760/3764248375.py:11: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  websites[\"SERVER\"] = websites.apply(relabel_server, axis = 1)\n"
+     ]
+    }
+   ],
+   "source": [
+    "def relabel_server(row):\n",
+    "    if \"microsoft\" in row[\"SERVER\"].lower():\n",
+    "        return \"Microsoft\"\n",
+    "    if \"apache\" in row[\"SERVER\"].lower():\n",
+    "        return \"Apache\"\n",
+    "    if \"nginx\" in row[\"SERVER\"].lower():\n",
+    "        return \"nginx\"\n",
+    "    else:\n",
+    "        return \"Other\"\n",
+    "\n",
+    "websites[\"SERVER\"] = websites.apply(relabel_server, axis = 1)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 42,
    "metadata": {
     "scrolled": false
    },
-   "outputs": [],
-   "source": [
-    "# Count `SERVER` value counts here\n"
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Apache       642\n",
+       "Other        503\n",
+       "nginx        436\n",
+       "Microsoft    198\n",
+       "Name: SERVER, dtype: int64"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "websites[\"SERVER\"].value_counts()"
    ]
   },
   {
@@ -445,11 +1099,226 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Your code here\n"
+   "execution_count": 43,
+   "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>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_BYTES</th>\n",
+       "      <th>REMOTE_APP_BYTES</th>\n",
+       "      <th>DNS_QUERY_TIMES</th>\n",
+       "      <th>Type</th>\n",
+       "      <th>CHARSET_ISO-8859</th>\n",
+       "      <th>...</th>\n",
+       "      <th>WHOIS_COUNTRY_AU</th>\n",
+       "      <th>WHOIS_COUNTRY_CA</th>\n",
+       "      <th>WHOIS_COUNTRY_ES</th>\n",
+       "      <th>WHOIS_COUNTRY_IN</th>\n",
+       "      <th>WHOIS_COUNTRY_JP</th>\n",
+       "      <th>WHOIS_COUNTRY_None</th>\n",
+       "      <th>WHOIS_COUNTRY_OTHER</th>\n",
+       "      <th>WHOIS_COUNTRY_PA</th>\n",
+       "      <th>WHOIS_COUNTRY_UK</th>\n",
+       "      <th>WHOIS_COUNTRY_US</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>16</td>\n",
+       "      <td>7</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>700</td>\n",
+       "      <td>1153</td>\n",
+       "      <td>832</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</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>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>16</td>\n",
+       "      <td>17</td>\n",
+       "      <td>7</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1230</td>\n",
+       "      <td>1265</td>\n",
+       "      <td>1230</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</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>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>16</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",
+       "      <td>0</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>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>17</td>\n",
+       "      <td>31</td>\n",
+       "      <td>22</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3812</td>\n",
+       "      <td>18784</td>\n",
+       "      <td>4380</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</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</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>17</td>\n",
+       "      <td>57</td>\n",
+       "      <td>2</td>\n",
+       "      <td>5</td>\n",
+       "      <td>4278</td>\n",
+       "      <td>129889</td>\n",
+       "      <td>4586</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</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</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 32 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   URL_LENGTH  TCP_CONVERSATION_EXCHANGE  DIST_REMOTE_TCP_PORT  REMOTE_IPS  \\\n",
+       "0          16                          7                     0           2   \n",
+       "1          16                         17                     7           4   \n",
+       "2          16                          0                     0           0   \n",
+       "3          17                         31                    22           3   \n",
+       "4          17                         57                     2           5   \n",
+       "\n",
+       "   APP_BYTES  SOURCE_APP_BYTES  REMOTE_APP_BYTES  DNS_QUERY_TIMES  Type  \\\n",
+       "0        700              1153               832              2.0     1   \n",
+       "1       1230              1265              1230              0.0     0   \n",
+       "2          0                 0                 0              0.0     0   \n",
+       "3       3812             18784              4380              8.0     0   \n",
+       "4       4278            129889              4586              4.0     0   \n",
+       "\n",
+       "   CHARSET_ISO-8859  ...  WHOIS_COUNTRY_AU  WHOIS_COUNTRY_CA  \\\n",
+       "0                 0  ...                 0                 0   \n",
+       "1                 0  ...                 0                 0   \n",
+       "2                 0  ...                 0                 0   \n",
+       "3                 0  ...                 0                 0   \n",
+       "4                 0  ...                 0                 0   \n",
+       "\n",
+       "   WHOIS_COUNTRY_ES  WHOIS_COUNTRY_IN  WHOIS_COUNTRY_JP  WHOIS_COUNTRY_None  \\\n",
+       "0                 0                 0                 0                   1   \n",
+       "1                 0                 0                 0                   1   \n",
+       "2                 0                 0                 0                   1   \n",
+       "3                 0                 0                 0                   0   \n",
+       "4                 0                 0                 0                   0   \n",
+       "\n",
+       "   WHOIS_COUNTRY_OTHER  WHOIS_COUNTRY_PA  WHOIS_COUNTRY_UK  WHOIS_COUNTRY_US  \n",
+       "0                    0                 0                 0                 0  \n",
+       "1                    0                 0                 0                 0  \n",
+       "2                    0                 0                 0                 0  \n",
+       "3                    0                 0                 0                 1  \n",
+       "4                    0                 0                 0                 1  \n",
+       "\n",
+       "[5 rows x 32 columns]"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "website_dummy = pd.get_dummies(websites)\n",
+    "website_dummy.head()"
    ]
   },
   {
@@ -461,11 +1330,54 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Your code here\n"
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "URL_LENGTH                     int64\n",
+       "TCP_CONVERSATION_EXCHANGE      int64\n",
+       "DIST_REMOTE_TCP_PORT           int64\n",
+       "REMOTE_IPS                     int64\n",
+       "APP_BYTES                      int64\n",
+       "SOURCE_APP_BYTES               int64\n",
+       "REMOTE_APP_BYTES               int64\n",
+       "DNS_QUERY_TIMES              float64\n",
+       "Type                           int64\n",
+       "CHARSET_ISO-8859               uint8\n",
+       "CHARSET_ISO-8859-1             uint8\n",
+       "CHARSET_None                   uint8\n",
+       "CHARSET_UTF-8                  uint8\n",
+       "CHARSET_iso-8859-1             uint8\n",
+       "CHARSET_us-ascii               uint8\n",
+       "CHARSET_utf-8                  uint8\n",
+       "CHARSET_windows-1251           uint8\n",
+       "CHARSET_windows-1252           uint8\n",
+       "SERVER_Apache                  uint8\n",
+       "SERVER_Microsoft               uint8\n",
+       "SERVER_Other                   uint8\n",
+       "SERVER_nginx                   uint8\n",
+       "WHOIS_COUNTRY_AU               uint8\n",
+       "WHOIS_COUNTRY_CA               uint8\n",
+       "WHOIS_COUNTRY_ES               uint8\n",
+       "WHOIS_COUNTRY_IN               uint8\n",
+       "WHOIS_COUNTRY_JP               uint8\n",
+       "WHOIS_COUNTRY_None             uint8\n",
+       "WHOIS_COUNTRY_OTHER            uint8\n",
+       "WHOIS_COUNTRY_PA               uint8\n",
+       "WHOIS_COUNTRY_UK               uint8\n",
+       "WHOIS_COUNTRY_US               uint8\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "website_dummy.dtypes"
    ]
   },
   {
@@ -479,13 +1391,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 45,
    "metadata": {},
    "outputs": [],
    "source": [
     "from sklearn.model_selection import train_test_split\n",
     "\n",
-    "# Your code here:\n"
+    "features = website_dummy.drop([\"Type\"], axis = 1)\n",
+    "target = website_dummy[\"Type\"]\n",
+    "\n",
+    "X_train, X_test, y_train, y_test = train_test_split(features, target, test_size = 0.2)"
    ]
   },
   {
@@ -499,12 +1414,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 46,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Your code here:\n",
-    "\n"
+    "from sklearn.linear_model import LogisticRegression\n",
+    "\n",
+    "model = LogisticRegression()"
    ]
   },
   {
@@ -516,12 +1432,39 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Your code here:\n",
-    "\n"
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/danieldepaoli/anaconda3/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: 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 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression()</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-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div></div></div>"
+      ],
+      "text/plain": [
+       "LogisticRegression()"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model.fit(X_train, y_train)"
    ]
   },
   {
@@ -533,12 +1476,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Your code here:\n",
-    "\n"
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[300   8]\n",
+      " [ 36  12]]\n",
+      "0.8764044943820225\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import confusion_matrix, accuracy_score\n",
+    "\n",
+    "y_pred = model.predict(X_test)\n",
+    "\n",
+    "print(confusion_matrix(y_test,y_pred))\n",
+    "print(accuracy_score(y_test,y_pred))"
    ]
   },
   {
@@ -569,12 +1526,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Your code here:\n",
-    "\n"
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" 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-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</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": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.neighbors import KNeighborsClassifier\n",
+    "\n",
+    "model = KNeighborsClassifier(n_neighbors = 3)\n",
+    "model.fit(X_train, y_train)"
    ]
   },
   {
@@ -586,12 +1559,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Your code here:\n",
-    "\n"
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[303   5]\n",
+      " [ 15  33]]\n",
+      "0.9438202247191011\n"
+     ]
+    }
+   ],
+   "source": [
+    "y_pred = model.predict(X_test)\n",
+    "\n",
+    "print(confusion_matrix(y_test,y_pred))\n",
+    "print(accuracy_score(y_test,y_pred))"
    ]
   },
   {
@@ -605,12 +1590,48 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 51,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" 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-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier()</pre></div></div></div></div></div>"
+      ],
+      "text/plain": [
+       "KNeighborsClassifier()"
+      ]
+     },
+     "execution_count": 51,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# Your code here:\n",
-    "\n"
+    "model = KNeighborsClassifier(n_neighbors = 5)\n",
+    "model.fit(X_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[301   7]\n",
+      " [ 19  29]]\n",
+      "0.9269662921348315\n"
+     ]
+    }
+   ],
+   "source": [
+    "y_pred = model.predict(X_test)\n",
+    "\n",
+    "print(confusion_matrix(y_test,y_pred))\n",
+    "print(accuracy_score(y_test,y_pred))"
    ]
   },
   {
@@ -622,7 +1643,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 53,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -655,9 +1676,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "ironhack-3.7",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
-   "name": "ironhack-3.7"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
@@ -669,7 +1690,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.3"
+   "version": "3.10.9"
   }
  },
  "nbformat": 4,