diff --git a/210677_Nitin_1.ipynb b/210677_Nitin_1.ipynb
new file mode 100644
index 0000000..23a6842
--- /dev/null
+++ b/210677_Nitin_1.ipynb
@@ -0,0 +1,4790 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "6ed95c96",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "78e4bd65",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dataset=pd.read_csv(\"dataset.csv\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "056a8ef0",
+   "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",
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+       "\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>CUST_ID</th>\n",
+       "      <th>BALANCE</th>\n",
+       "      <th>BALANCE_FREQUENCY</th>\n",
+       "      <th>PURCHASES</th>\n",
+       "      <th>ONEOFF_PURCHASES</th>\n",
+       "      <th>INSTALLMENTS_PURCHASES</th>\n",
+       "      <th>CASH_ADVANCE</th>\n",
+       "      <th>PURCHASES_FREQUENCY</th>\n",
+       "      <th>ONEOFF_PURCHASES_FREQUENCY</th>\n",
+       "      <th>PURCHASES_INSTALLMENTS_FREQUENCY</th>\n",
+       "      <th>CASH_ADVANCE_FREQUENCY</th>\n",
+       "      <th>CASH_ADVANCE_TRX</th>\n",
+       "      <th>PURCHASES_TRX</th>\n",
+       "      <th>CREDIT_LIMIT</th>\n",
+       "      <th>PAYMENTS</th>\n",
+       "      <th>MINIMUM_PAYMENTS</th>\n",
+       "      <th>PRC_FULL_PAYMENT</th>\n",
+       "      <th>TENURE</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
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+       "      <td>0.000000</td>\n",
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+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
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+       "      <td>1072.340217</td>\n",
+       "      <td>0.222222</td>\n",
+       "      <td>12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
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+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
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+       "      <td>12</td>\n",
+       "      <td>7500.0</td>\n",
+       "      <td>622.066742</td>\n",
+       "      <td>627.284787</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>C10004</td>\n",
+       "      <td>1666.670542</td>\n",
+       "      <td>0.636364</td>\n",
+       "      <td>1499.00</td>\n",
+       "      <td>1499.00</td>\n",
+       "      <td>0.00</td>\n",
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+       "      <td>0.000000</td>\n",
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+       "      <td>1</td>\n",
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+       "      <td>NaN</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>C10005</td>\n",
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+       "      <td>1.000000</td>\n",
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+       "      <td>0.000000</td>\n",
+       "      <td>12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
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+       "    <tr>\n",
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+       "      <td>0.833333</td>\n",
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+       "      <td>1000.0</td>\n",
+       "      <td>325.594462</td>\n",
+       "      <td>48.886365</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
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+       "      <td>C19187</td>\n",
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+       "      <td>1.000000</td>\n",
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+       "      <td>NaN</td>\n",
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+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
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+       "      <td>23.398673</td>\n",
+       "      <td>0.833333</td>\n",
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+       "      <td>0.00</td>\n",
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+       "      <td>82.418369</td>\n",
+       "      <td>0.250000</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8948</th>\n",
+       "      <td>C19189</td>\n",
+       "      <td>13.457564</td>\n",
+       "      <td>0.833333</td>\n",
+       "      <td>0.00</td>\n",
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+       "      <td>55.755628</td>\n",
+       "      <td>0.250000</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8949</th>\n",
+       "      <td>C19190</td>\n",
+       "      <td>372.708075</td>\n",
+       "      <td>0.666667</td>\n",
+       "      <td>1093.25</td>\n",
+       "      <td>1093.25</td>\n",
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+       "      <td>127.040008</td>\n",
+       "      <td>0.666667</td>\n",
+       "      <td>0.666667</td>\n",
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+       "      <td>2</td>\n",
+       "      <td>23</td>\n",
+       "      <td>1200.0</td>\n",
+       "      <td>63.165404</td>\n",
+       "      <td>88.288956</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>8950 rows × 18 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     CUST_ID      BALANCE  BALANCE_FREQUENCY  PURCHASES  ONEOFF_PURCHASES  \\\n",
+       "0     C10001    40.900749           0.818182      95.40              0.00   \n",
+       "1     C10002  3202.467416           0.909091       0.00              0.00   \n",
+       "2     C10003  2495.148862           1.000000     773.17            773.17   \n",
+       "3     C10004  1666.670542           0.636364    1499.00           1499.00   \n",
+       "4     C10005   817.714335           1.000000      16.00             16.00   \n",
+       "...      ...          ...                ...        ...               ...   \n",
+       "8945  C19186    28.493517           1.000000     291.12              0.00   \n",
+       "8946  C19187    19.183215           1.000000     300.00              0.00   \n",
+       "8947  C19188    23.398673           0.833333     144.40              0.00   \n",
+       "8948  C19189    13.457564           0.833333       0.00              0.00   \n",
+       "8949  C19190   372.708075           0.666667    1093.25           1093.25   \n",
+       "\n",
+       "      INSTALLMENTS_PURCHASES  CASH_ADVANCE  PURCHASES_FREQUENCY  \\\n",
+       "0                      95.40      0.000000             0.166667   \n",
+       "1                       0.00   6442.945483             0.000000   \n",
+       "2                       0.00      0.000000             1.000000   \n",
+       "3                       0.00    205.788017             0.083333   \n",
+       "4                       0.00      0.000000             0.083333   \n",
+       "...                      ...           ...                  ...   \n",
+       "8945                  291.12      0.000000             1.000000   \n",
+       "8946                  300.00      0.000000             1.000000   \n",
+       "8947                  144.40      0.000000             0.833333   \n",
+       "8948                    0.00     36.558778             0.000000   \n",
+       "8949                    0.00    127.040008             0.666667   \n",
+       "\n",
+       "      ONEOFF_PURCHASES_FREQUENCY  PURCHASES_INSTALLMENTS_FREQUENCY  \\\n",
+       "0                       0.000000                          0.083333   \n",
+       "1                       0.000000                          0.000000   \n",
+       "2                       1.000000                          0.000000   \n",
+       "3                       0.083333                          0.000000   \n",
+       "4                       0.083333                          0.000000   \n",
+       "...                          ...                               ...   \n",
+       "8945                    0.000000                          0.833333   \n",
+       "8946                    0.000000                          0.833333   \n",
+       "8947                    0.000000                          0.666667   \n",
+       "8948                    0.000000                          0.000000   \n",
+       "8949                    0.666667                          0.000000   \n",
+       "\n",
+       "      CASH_ADVANCE_FREQUENCY  CASH_ADVANCE_TRX  PURCHASES_TRX  CREDIT_LIMIT  \\\n",
+       "0                   0.000000                 0              2        1000.0   \n",
+       "1                   0.250000                 4              0        7000.0   \n",
+       "2                   0.000000                 0             12        7500.0   \n",
+       "3                   0.083333                 1              1        7500.0   \n",
+       "4                   0.000000                 0              1        1200.0   \n",
+       "...                      ...               ...            ...           ...   \n",
+       "8945                0.000000                 0              6        1000.0   \n",
+       "8946                0.000000                 0              6        1000.0   \n",
+       "8947                0.000000                 0              5        1000.0   \n",
+       "8948                0.166667                 2              0         500.0   \n",
+       "8949                0.333333                 2             23        1200.0   \n",
+       "\n",
+       "         PAYMENTS  MINIMUM_PAYMENTS  PRC_FULL_PAYMENT  TENURE  \n",
+       "0      201.802084        139.509787          0.000000      12  \n",
+       "1     4103.032597       1072.340217          0.222222      12  \n",
+       "2      622.066742        627.284787          0.000000      12  \n",
+       "3        0.000000               NaN          0.000000      12  \n",
+       "4      678.334763        244.791237          0.000000      12  \n",
+       "...           ...               ...               ...     ...  \n",
+       "8945   325.594462         48.886365          0.500000       6  \n",
+       "8946   275.861322               NaN          0.000000       6  \n",
+       "8947    81.270775         82.418369          0.250000       6  \n",
+       "8948    52.549959         55.755628          0.250000       6  \n",
+       "8949    63.165404         88.288956          0.000000       6  \n",
+       "\n",
+       "[8950 rows x 18 columns]"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "8ea0b3ef",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Columns names:\n",
+      "['CUST_ID', 'BALANCE', 'BALANCE_FREQUENCY', 'PURCHASES', 'ONEOFF_PURCHASES', 'INSTALLMENTS_PURCHASES', 'CASH_ADVANCE', 'PURCHASES_FREQUENCY', 'ONEOFF_PURCHASES_FREQUENCY', 'PURCHASES_INSTALLMENTS_FREQUENCY', 'CASH_ADVANCE_FREQUENCY', 'CASH_ADVANCE_TRX', 'PURCHASES_TRX', 'CREDIT_LIMIT', 'PAYMENTS', 'MINIMUM_PAYMENTS', 'PRC_FULL_PAYMENT', 'TENURE']\n"
+     ]
+    }
+   ],
+   "source": [
+    "columns_names=dataset.columns.tolist()\n",
+    "print(\"Columns names:\")\n",
+    "print(columns_names)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "87e985a2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(8950, 18)"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dataset.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "b1dae2f3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
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+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
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+       "\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>CUST_ID</th>\n",
+       "      <th>BALANCE</th>\n",
+       "      <th>BALANCE_FREQUENCY</th>\n",
+       "      <th>PURCHASES</th>\n",
+       "      <th>ONEOFF_PURCHASES</th>\n",
+       "      <th>INSTALLMENTS_PURCHASES</th>\n",
+       "      <th>CASH_ADVANCE</th>\n",
+       "      <th>PURCHASES_FREQUENCY</th>\n",
+       "      <th>ONEOFF_PURCHASES_FREQUENCY</th>\n",
+       "      <th>PURCHASES_INSTALLMENTS_FREQUENCY</th>\n",
+       "      <th>CASH_ADVANCE_FREQUENCY</th>\n",
+       "      <th>CASH_ADVANCE_TRX</th>\n",
+       "      <th>PURCHASES_TRX</th>\n",
+       "      <th>CREDIT_LIMIT</th>\n",
+       "      <th>PAYMENTS</th>\n",
+       "      <th>MINIMUM_PAYMENTS</th>\n",
+       "      <th>PRC_FULL_PAYMENT</th>\n",
+       "      <th>TENURE</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>C10001</td>\n",
+       "      <td>40.900749</td>\n",
+       "      <td>0.818182</td>\n",
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+       "      <td>139.509787</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>C10002</td>\n",
+       "      <td>3202.467416</td>\n",
+       "      <td>0.909091</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>6442.945483</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.250000</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7000.0</td>\n",
+       "      <td>4103.032597</td>\n",
+       "      <td>1072.340217</td>\n",
+       "      <td>0.222222</td>\n",
+       "      <td>12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>C10003</td>\n",
+       "      <td>2495.148862</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>773.17</td>\n",
+       "      <td>773.17</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>12</td>\n",
+       "      <td>7500.0</td>\n",
+       "      <td>622.066742</td>\n",
+       "      <td>627.284787</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>C10004</td>\n",
+       "      <td>1666.670542</td>\n",
+       "      <td>0.636364</td>\n",
+       "      <td>1499.00</td>\n",
+       "      <td>1499.00</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>205.788017</td>\n",
+       "      <td>0.083333</td>\n",
+       "      <td>0.083333</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.083333</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>7500.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>C10005</td>\n",
+       "      <td>817.714335</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>16.00</td>\n",
+       "      <td>16.00</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.083333</td>\n",
+       "      <td>0.083333</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1200.0</td>\n",
+       "      <td>678.334763</td>\n",
+       "      <td>244.791237</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>12</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  CUST_ID      BALANCE  BALANCE_FREQUENCY  PURCHASES  ONEOFF_PURCHASES  \\\n",
+       "0  C10001    40.900749           0.818182      95.40              0.00   \n",
+       "1  C10002  3202.467416           0.909091       0.00              0.00   \n",
+       "2  C10003  2495.148862           1.000000     773.17            773.17   \n",
+       "3  C10004  1666.670542           0.636364    1499.00           1499.00   \n",
+       "4  C10005   817.714335           1.000000      16.00             16.00   \n",
+       "\n",
+       "   INSTALLMENTS_PURCHASES  CASH_ADVANCE  PURCHASES_FREQUENCY  \\\n",
+       "0                    95.4      0.000000             0.166667   \n",
+       "1                     0.0   6442.945483             0.000000   \n",
+       "2                     0.0      0.000000             1.000000   \n",
+       "3                     0.0    205.788017             0.083333   \n",
+       "4                     0.0      0.000000             0.083333   \n",
+       "\n",
+       "   ONEOFF_PURCHASES_FREQUENCY  PURCHASES_INSTALLMENTS_FREQUENCY  \\\n",
+       "0                    0.000000                          0.083333   \n",
+       "1                    0.000000                          0.000000   \n",
+       "2                    1.000000                          0.000000   \n",
+       "3                    0.083333                          0.000000   \n",
+       "4                    0.083333                          0.000000   \n",
+       "\n",
+       "   CASH_ADVANCE_FREQUENCY  CASH_ADVANCE_TRX  PURCHASES_TRX  CREDIT_LIMIT  \\\n",
+       "0                0.000000                 0              2        1000.0   \n",
+       "1                0.250000                 4              0        7000.0   \n",
+       "2                0.000000                 0             12        7500.0   \n",
+       "3                0.083333                 1              1        7500.0   \n",
+       "4                0.000000                 0              1        1200.0   \n",
+       "\n",
+       "      PAYMENTS  MINIMUM_PAYMENTS  PRC_FULL_PAYMENT  TENURE  \n",
+       "0   201.802084        139.509787          0.000000      12  \n",
+       "1  4103.032597       1072.340217          0.222222      12  \n",
+       "2   622.066742        627.284787          0.000000      12  \n",
+       "3     0.000000               NaN          0.000000      12  \n",
+       "4   678.334763        244.791237          0.000000      12  "
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dataset.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "044f7e5e",
+   "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>BALANCE</th>\n",
+       "      <th>BALANCE_FREQUENCY</th>\n",
+       "      <th>PURCHASES</th>\n",
+       "      <th>ONEOFF_PURCHASES</th>\n",
+       "      <th>INSTALLMENTS_PURCHASES</th>\n",
+       "      <th>CASH_ADVANCE</th>\n",
+       "      <th>PURCHASES_FREQUENCY</th>\n",
+       "      <th>ONEOFF_PURCHASES_FREQUENCY</th>\n",
+       "      <th>PURCHASES_INSTALLMENTS_FREQUENCY</th>\n",
+       "      <th>CASH_ADVANCE_FREQUENCY</th>\n",
+       "      <th>CASH_ADVANCE_TRX</th>\n",
+       "      <th>PURCHASES_TRX</th>\n",
+       "      <th>CREDIT_LIMIT</th>\n",
+       "      <th>PAYMENTS</th>\n",
+       "      <th>MINIMUM_PAYMENTS</th>\n",
+       "      <th>PRC_FULL_PAYMENT</th>\n",
+       "      <th>TENURE</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>8950.000000</td>\n",
+       "      <td>8950.000000</td>\n",
+       "      <td>8950.000000</td>\n",
+       "      <td>8950.000000</td>\n",
+       "      <td>8950.000000</td>\n",
+       "      <td>8950.000000</td>\n",
+       "      <td>8950.000000</td>\n",
+       "      <td>8950.000000</td>\n",
+       "      <td>8950.000000</td>\n",
+       "      <td>8950.000000</td>\n",
+       "      <td>8950.000000</td>\n",
+       "      <td>8950.000000</td>\n",
+       "      <td>8949.000000</td>\n",
+       "      <td>8950.000000</td>\n",
+       "      <td>8637.000000</td>\n",
+       "      <td>8950.000000</td>\n",
+       "      <td>8950.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>1564.474828</td>\n",
+       "      <td>0.877271</td>\n",
+       "      <td>1003.204834</td>\n",
+       "      <td>592.437371</td>\n",
+       "      <td>411.067645</td>\n",
+       "      <td>978.871112</td>\n",
+       "      <td>0.490351</td>\n",
+       "      <td>0.202458</td>\n",
+       "      <td>0.364437</td>\n",
+       "      <td>0.135144</td>\n",
+       "      <td>3.248827</td>\n",
+       "      <td>14.709832</td>\n",
+       "      <td>4494.449450</td>\n",
+       "      <td>1733.143852</td>\n",
+       "      <td>864.206542</td>\n",
+       "      <td>0.153715</td>\n",
+       "      <td>11.517318</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>2081.531879</td>\n",
+       "      <td>0.236904</td>\n",
+       "      <td>2136.634782</td>\n",
+       "      <td>1659.887917</td>\n",
+       "      <td>904.338115</td>\n",
+       "      <td>2097.163877</td>\n",
+       "      <td>0.401371</td>\n",
+       "      <td>0.298336</td>\n",
+       "      <td>0.397448</td>\n",
+       "      <td>0.200121</td>\n",
+       "      <td>6.824647</td>\n",
+       "      <td>24.857649</td>\n",
+       "      <td>3638.815725</td>\n",
+       "      <td>2895.063757</td>\n",
+       "      <td>2372.446607</td>\n",
+       "      <td>0.292499</td>\n",
+       "      <td>1.338331</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\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",
+       "      <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>50.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.019163</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>6.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>128.281915</td>\n",
+       "      <td>0.888889</td>\n",
+       "      <td>39.635000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.083333</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1600.000000</td>\n",
+       "      <td>383.276166</td>\n",
+       "      <td>169.123707</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>12.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>873.385231</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>361.280000</td>\n",
+       "      <td>38.000000</td>\n",
+       "      <td>89.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>0.083333</td>\n",
+       "      <td>0.166667</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>7.000000</td>\n",
+       "      <td>3000.000000</td>\n",
+       "      <td>856.901546</td>\n",
+       "      <td>312.343947</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>12.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>2054.140036</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1110.130000</td>\n",
+       "      <td>577.405000</td>\n",
+       "      <td>468.637500</td>\n",
+       "      <td>1113.821139</td>\n",
+       "      <td>0.916667</td>\n",
+       "      <td>0.300000</td>\n",
+       "      <td>0.750000</td>\n",
+       "      <td>0.222222</td>\n",
+       "      <td>4.000000</td>\n",
+       "      <td>17.000000</td>\n",
+       "      <td>6500.000000</td>\n",
+       "      <td>1901.134317</td>\n",
+       "      <td>825.485459</td>\n",
+       "      <td>0.142857</td>\n",
+       "      <td>12.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>19043.138560</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>49039.570000</td>\n",
+       "      <td>40761.250000</td>\n",
+       "      <td>22500.000000</td>\n",
+       "      <td>47137.211760</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.500000</td>\n",
+       "      <td>123.000000</td>\n",
+       "      <td>358.000000</td>\n",
+       "      <td>30000.000000</td>\n",
+       "      <td>50721.483360</td>\n",
+       "      <td>76406.207520</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>12.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            BALANCE  BALANCE_FREQUENCY     PURCHASES  ONEOFF_PURCHASES  \\\n",
+       "count   8950.000000        8950.000000   8950.000000       8950.000000   \n",
+       "mean    1564.474828           0.877271   1003.204834        592.437371   \n",
+       "std     2081.531879           0.236904   2136.634782       1659.887917   \n",
+       "min        0.000000           0.000000      0.000000          0.000000   \n",
+       "25%      128.281915           0.888889     39.635000          0.000000   \n",
+       "50%      873.385231           1.000000    361.280000         38.000000   \n",
+       "75%     2054.140036           1.000000   1110.130000        577.405000   \n",
+       "max    19043.138560           1.000000  49039.570000      40761.250000   \n",
+       "\n",
+       "       INSTALLMENTS_PURCHASES  CASH_ADVANCE  PURCHASES_FREQUENCY  \\\n",
+       "count             8950.000000   8950.000000          8950.000000   \n",
+       "mean               411.067645    978.871112             0.490351   \n",
+       "std                904.338115   2097.163877             0.401371   \n",
+       "min                  0.000000      0.000000             0.000000   \n",
+       "25%                  0.000000      0.000000             0.083333   \n",
+       "50%                 89.000000      0.000000             0.500000   \n",
+       "75%                468.637500   1113.821139             0.916667   \n",
+       "max              22500.000000  47137.211760             1.000000   \n",
+       "\n",
+       "       ONEOFF_PURCHASES_FREQUENCY  PURCHASES_INSTALLMENTS_FREQUENCY  \\\n",
+       "count                 8950.000000                       8950.000000   \n",
+       "mean                     0.202458                          0.364437   \n",
+       "std                      0.298336                          0.397448   \n",
+       "min                      0.000000                          0.000000   \n",
+       "25%                      0.000000                          0.000000   \n",
+       "50%                      0.083333                          0.166667   \n",
+       "75%                      0.300000                          0.750000   \n",
+       "max                      1.000000                          1.000000   \n",
+       "\n",
+       "       CASH_ADVANCE_FREQUENCY  CASH_ADVANCE_TRX  PURCHASES_TRX  CREDIT_LIMIT  \\\n",
+       "count             8950.000000       8950.000000    8950.000000   8949.000000   \n",
+       "mean                 0.135144          3.248827      14.709832   4494.449450   \n",
+       "std                  0.200121          6.824647      24.857649   3638.815725   \n",
+       "min                  0.000000          0.000000       0.000000     50.000000   \n",
+       "25%                  0.000000          0.000000       1.000000   1600.000000   \n",
+       "50%                  0.000000          0.000000       7.000000   3000.000000   \n",
+       "75%                  0.222222          4.000000      17.000000   6500.000000   \n",
+       "max                  1.500000        123.000000     358.000000  30000.000000   \n",
+       "\n",
+       "           PAYMENTS  MINIMUM_PAYMENTS  PRC_FULL_PAYMENT       TENURE  \n",
+       "count   8950.000000       8637.000000       8950.000000  8950.000000  \n",
+       "mean    1733.143852        864.206542          0.153715    11.517318  \n",
+       "std     2895.063757       2372.446607          0.292499     1.338331  \n",
+       "min        0.000000          0.019163          0.000000     6.000000  \n",
+       "25%      383.276166        169.123707          0.000000    12.000000  \n",
+       "50%      856.901546        312.343947          0.000000    12.000000  \n",
+       "75%     1901.134317        825.485459          0.142857    12.000000  \n",
+       "max    50721.483360      76406.207520          1.000000    12.000000  "
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dataset.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "cfa3beb2",
+   "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>BALANCE</th>\n",
+       "      <th>BALANCE_FREQUENCY</th>\n",
+       "      <th>PURCHASES</th>\n",
+       "      <th>ONEOFF_PURCHASES</th>\n",
+       "      <th>INSTALLMENTS_PURCHASES</th>\n",
+       "      <th>CASH_ADVANCE</th>\n",
+       "      <th>PURCHASES_FREQUENCY</th>\n",
+       "      <th>ONEOFF_PURCHASES_FREQUENCY</th>\n",
+       "      <th>PURCHASES_INSTALLMENTS_FREQUENCY</th>\n",
+       "      <th>CASH_ADVANCE_FREQUENCY</th>\n",
+       "      <th>CASH_ADVANCE_TRX</th>\n",
+       "      <th>PURCHASES_TRX</th>\n",
+       "      <th>CREDIT_LIMIT</th>\n",
+       "      <th>PAYMENTS</th>\n",
+       "      <th>MINIMUM_PAYMENTS</th>\n",
+       "      <th>PRC_FULL_PAYMENT</th>\n",
+       "      <th>TENURE</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>BALANCE</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.322412</td>\n",
+       "      <td>0.181261</td>\n",
+       "      <td>0.164350</td>\n",
+       "      <td>0.126469</td>\n",
+       "      <td>0.496692</td>\n",
+       "      <td>-0.077944</td>\n",
+       "      <td>0.073166</td>\n",
+       "      <td>-0.063186</td>\n",
+       "      <td>0.449218</td>\n",
+       "      <td>0.385152</td>\n",
+       "      <td>0.154338</td>\n",
+       "      <td>0.531283</td>\n",
+       "      <td>0.322802</td>\n",
+       "      <td>0.398684</td>\n",
+       "      <td>-0.318959</td>\n",
+       "      <td>0.072692</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>BALANCE_FREQUENCY</th>\n",
+       "      <td>0.322412</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.133674</td>\n",
+       "      <td>0.104323</td>\n",
+       "      <td>0.124292</td>\n",
+       "      <td>0.099388</td>\n",
+       "      <td>0.229715</td>\n",
+       "      <td>0.202415</td>\n",
+       "      <td>0.176079</td>\n",
+       "      <td>0.191873</td>\n",
+       "      <td>0.141555</td>\n",
+       "      <td>0.189626</td>\n",
+       "      <td>0.095843</td>\n",
+       "      <td>0.065008</td>\n",
+       "      <td>0.132569</td>\n",
+       "      <td>-0.095082</td>\n",
+       "      <td>0.119776</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>PURCHASES</th>\n",
+       "      <td>0.181261</td>\n",
+       "      <td>0.133674</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.916845</td>\n",
+       "      <td>0.679896</td>\n",
+       "      <td>-0.051474</td>\n",
+       "      <td>0.393017</td>\n",
+       "      <td>0.498430</td>\n",
+       "      <td>0.315567</td>\n",
+       "      <td>-0.120143</td>\n",
+       "      <td>-0.067175</td>\n",
+       "      <td>0.689561</td>\n",
+       "      <td>0.356963</td>\n",
+       "      <td>0.603264</td>\n",
+       "      <td>0.093860</td>\n",
+       "      <td>0.180379</td>\n",
+       "      <td>0.086288</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ONEOFF_PURCHASES</th>\n",
+       "      <td>0.164350</td>\n",
+       "      <td>0.104323</td>\n",
+       "      <td>0.916845</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.330622</td>\n",
+       "      <td>-0.031326</td>\n",
+       "      <td>0.264937</td>\n",
+       "      <td>0.524891</td>\n",
+       "      <td>0.127729</td>\n",
+       "      <td>-0.082628</td>\n",
+       "      <td>-0.046212</td>\n",
+       "      <td>0.545523</td>\n",
+       "      <td>0.319724</td>\n",
+       "      <td>0.567292</td>\n",
+       "      <td>0.048755</td>\n",
+       "      <td>0.132763</td>\n",
+       "      <td>0.064150</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>INSTALLMENTS_PURCHASES</th>\n",
+       "      <td>0.126469</td>\n",
+       "      <td>0.124292</td>\n",
+       "      <td>0.679896</td>\n",
+       "      <td>0.330622</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-0.064244</td>\n",
+       "      <td>0.442418</td>\n",
+       "      <td>0.214042</td>\n",
+       "      <td>0.511351</td>\n",
+       "      <td>-0.132318</td>\n",
+       "      <td>-0.073999</td>\n",
+       "      <td>0.628108</td>\n",
+       "      <td>0.256499</td>\n",
+       "      <td>0.384084</td>\n",
+       "      <td>0.132172</td>\n",
+       "      <td>0.182569</td>\n",
+       "      <td>0.086143</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>CASH_ADVANCE</th>\n",
+       "      <td>0.496692</td>\n",
+       "      <td>0.099388</td>\n",
+       "      <td>-0.051474</td>\n",
+       "      <td>-0.031326</td>\n",
+       "      <td>-0.064244</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-0.215507</td>\n",
+       "      <td>-0.086754</td>\n",
+       "      <td>-0.177070</td>\n",
+       "      <td>0.628522</td>\n",
+       "      <td>0.656498</td>\n",
+       "      <td>-0.075850</td>\n",
+       "      <td>0.303985</td>\n",
+       "      <td>0.453238</td>\n",
+       "      <td>0.140107</td>\n",
+       "      <td>-0.152935</td>\n",
+       "      <td>-0.068312</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>PURCHASES_FREQUENCY</th>\n",
+       "      <td>-0.077944</td>\n",
+       "      <td>0.229715</td>\n",
+       "      <td>0.393017</td>\n",
+       "      <td>0.264937</td>\n",
+       "      <td>0.442418</td>\n",
+       "      <td>-0.215507</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.501343</td>\n",
+       "      <td>0.862934</td>\n",
+       "      <td>-0.308478</td>\n",
+       "      <td>-0.203478</td>\n",
+       "      <td>0.568430</td>\n",
+       "      <td>0.119788</td>\n",
+       "      <td>0.103464</td>\n",
+       "      <td>0.003030</td>\n",
+       "      <td>0.305802</td>\n",
+       "      <td>0.061506</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ONEOFF_PURCHASES_FREQUENCY</th>\n",
+       "      <td>0.073166</td>\n",
+       "      <td>0.202415</td>\n",
+       "      <td>0.498430</td>\n",
+       "      <td>0.524891</td>\n",
+       "      <td>0.214042</td>\n",
+       "      <td>-0.086754</td>\n",
+       "      <td>0.501343</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.142329</td>\n",
+       "      <td>-0.111716</td>\n",
+       "      <td>-0.069088</td>\n",
+       "      <td>0.544869</td>\n",
+       "      <td>0.295038</td>\n",
+       "      <td>0.243537</td>\n",
+       "      <td>-0.030327</td>\n",
+       "      <td>0.157531</td>\n",
+       "      <td>0.082466</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>PURCHASES_INSTALLMENTS_FREQUENCY</th>\n",
+       "      <td>-0.063186</td>\n",
+       "      <td>0.176079</td>\n",
+       "      <td>0.315567</td>\n",
+       "      <td>0.127729</td>\n",
+       "      <td>0.511351</td>\n",
+       "      <td>-0.177070</td>\n",
+       "      <td>0.862934</td>\n",
+       "      <td>0.142329</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-0.262958</td>\n",
+       "      <td>-0.169207</td>\n",
+       "      <td>0.529975</td>\n",
+       "      <td>0.060755</td>\n",
+       "      <td>0.085551</td>\n",
+       "      <td>0.030073</td>\n",
+       "      <td>0.250087</td>\n",
+       "      <td>0.073275</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>CASH_ADVANCE_FREQUENCY</th>\n",
+       "      <td>0.449218</td>\n",
+       "      <td>0.191873</td>\n",
+       "      <td>-0.120143</td>\n",
+       "      <td>-0.082628</td>\n",
+       "      <td>-0.132318</td>\n",
+       "      <td>0.628522</td>\n",
+       "      <td>-0.308478</td>\n",
+       "      <td>-0.111716</td>\n",
+       "      <td>-0.262958</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.799561</td>\n",
+       "      <td>-0.131168</td>\n",
+       "      <td>0.132616</td>\n",
+       "      <td>0.183192</td>\n",
+       "      <td>0.098838</td>\n",
+       "      <td>-0.249773</td>\n",
+       "      <td>-0.133372</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>CASH_ADVANCE_TRX</th>\n",
+       "      <td>0.385152</td>\n",
+       "      <td>0.141555</td>\n",
+       "      <td>-0.067175</td>\n",
+       "      <td>-0.046212</td>\n",
+       "      <td>-0.073999</td>\n",
+       "      <td>0.656498</td>\n",
+       "      <td>-0.203478</td>\n",
+       "      <td>-0.069088</td>\n",
+       "      <td>-0.169207</td>\n",
+       "      <td>0.799561</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-0.066157</td>\n",
+       "      <td>0.149700</td>\n",
+       "      <td>0.255278</td>\n",
+       "      <td>0.109739</td>\n",
+       "      <td>-0.169784</td>\n",
+       "      <td>-0.043421</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>PURCHASES_TRX</th>\n",
+       "      <td>0.154338</td>\n",
+       "      <td>0.189626</td>\n",
+       "      <td>0.689561</td>\n",
+       "      <td>0.545523</td>\n",
+       "      <td>0.628108</td>\n",
+       "      <td>-0.075850</td>\n",
+       "      <td>0.568430</td>\n",
+       "      <td>0.544869</td>\n",
+       "      <td>0.529975</td>\n",
+       "      <td>-0.131168</td>\n",
+       "      <td>-0.066157</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.272882</td>\n",
+       "      <td>0.370832</td>\n",
+       "      <td>0.096333</td>\n",
+       "      <td>0.162066</td>\n",
+       "      <td>0.121874</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>CREDIT_LIMIT</th>\n",
+       "      <td>0.531283</td>\n",
+       "      <td>0.095843</td>\n",
+       "      <td>0.356963</td>\n",
+       "      <td>0.319724</td>\n",
+       "      <td>0.256499</td>\n",
+       "      <td>0.303985</td>\n",
+       "      <td>0.119788</td>\n",
+       "      <td>0.295038</td>\n",
+       "      <td>0.060755</td>\n",
+       "      <td>0.132616</td>\n",
+       "      <td>0.149700</td>\n",
+       "      <td>0.272882</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.421861</td>\n",
+       "      <td>0.126671</td>\n",
+       "      <td>0.055672</td>\n",
+       "      <td>0.139167</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>PAYMENTS</th>\n",
+       "      <td>0.322802</td>\n",
+       "      <td>0.065008</td>\n",
+       "      <td>0.603264</td>\n",
+       "      <td>0.567292</td>\n",
+       "      <td>0.384084</td>\n",
+       "      <td>0.453238</td>\n",
+       "      <td>0.103464</td>\n",
+       "      <td>0.243537</td>\n",
+       "      <td>0.085551</td>\n",
+       "      <td>0.183192</td>\n",
+       "      <td>0.255278</td>\n",
+       "      <td>0.370832</td>\n",
+       "      <td>0.421861</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.126651</td>\n",
+       "      <td>0.112138</td>\n",
+       "      <td>0.106136</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>MINIMUM_PAYMENTS</th>\n",
+       "      <td>0.398684</td>\n",
+       "      <td>0.132569</td>\n",
+       "      <td>0.093860</td>\n",
+       "      <td>0.048755</td>\n",
+       "      <td>0.132172</td>\n",
+       "      <td>0.140107</td>\n",
+       "      <td>0.003030</td>\n",
+       "      <td>-0.030327</td>\n",
+       "      <td>0.030073</td>\n",
+       "      <td>0.098838</td>\n",
+       "      <td>0.109739</td>\n",
+       "      <td>0.096333</td>\n",
+       "      <td>0.126671</td>\n",
+       "      <td>0.126651</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-0.140379</td>\n",
+       "      <td>0.059444</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>PRC_FULL_PAYMENT</th>\n",
+       "      <td>-0.318959</td>\n",
+       "      <td>-0.095082</td>\n",
+       "      <td>0.180379</td>\n",
+       "      <td>0.132763</td>\n",
+       "      <td>0.182569</td>\n",
+       "      <td>-0.152935</td>\n",
+       "      <td>0.305802</td>\n",
+       "      <td>0.157531</td>\n",
+       "      <td>0.250087</td>\n",
+       "      <td>-0.249773</td>\n",
+       "      <td>-0.169784</td>\n",
+       "      <td>0.162066</td>\n",
+       "      <td>0.055672</td>\n",
+       "      <td>0.112138</td>\n",
+       "      <td>-0.140379</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-0.016486</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TENURE</th>\n",
+       "      <td>0.072692</td>\n",
+       "      <td>0.119776</td>\n",
+       "      <td>0.086288</td>\n",
+       "      <td>0.064150</td>\n",
+       "      <td>0.086143</td>\n",
+       "      <td>-0.068312</td>\n",
+       "      <td>0.061506</td>\n",
+       "      <td>0.082466</td>\n",
+       "      <td>0.073275</td>\n",
+       "      <td>-0.133372</td>\n",
+       "      <td>-0.043421</td>\n",
+       "      <td>0.121874</td>\n",
+       "      <td>0.139167</td>\n",
+       "      <td>0.106136</td>\n",
+       "      <td>0.059444</td>\n",
+       "      <td>-0.016486</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                   BALANCE  BALANCE_FREQUENCY  PURCHASES  \\\n",
+       "BALANCE                           1.000000           0.322412   0.181261   \n",
+       "BALANCE_FREQUENCY                 0.322412           1.000000   0.133674   \n",
+       "PURCHASES                         0.181261           0.133674   1.000000   \n",
+       "ONEOFF_PURCHASES                  0.164350           0.104323   0.916845   \n",
+       "INSTALLMENTS_PURCHASES            0.126469           0.124292   0.679896   \n",
+       "CASH_ADVANCE                      0.496692           0.099388  -0.051474   \n",
+       "PURCHASES_FREQUENCY              -0.077944           0.229715   0.393017   \n",
+       "ONEOFF_PURCHASES_FREQUENCY        0.073166           0.202415   0.498430   \n",
+       "PURCHASES_INSTALLMENTS_FREQUENCY -0.063186           0.176079   0.315567   \n",
+       "CASH_ADVANCE_FREQUENCY            0.449218           0.191873  -0.120143   \n",
+       "CASH_ADVANCE_TRX                  0.385152           0.141555  -0.067175   \n",
+       "PURCHASES_TRX                     0.154338           0.189626   0.689561   \n",
+       "CREDIT_LIMIT                      0.531283           0.095843   0.356963   \n",
+       "PAYMENTS                          0.322802           0.065008   0.603264   \n",
+       "MINIMUM_PAYMENTS                  0.398684           0.132569   0.093860   \n",
+       "PRC_FULL_PAYMENT                 -0.318959          -0.095082   0.180379   \n",
+       "TENURE                            0.072692           0.119776   0.086288   \n",
+       "\n",
+       "                                  ONEOFF_PURCHASES  INSTALLMENTS_PURCHASES  \\\n",
+       "BALANCE                                   0.164350                0.126469   \n",
+       "BALANCE_FREQUENCY                         0.104323                0.124292   \n",
+       "PURCHASES                                 0.916845                0.679896   \n",
+       "ONEOFF_PURCHASES                          1.000000                0.330622   \n",
+       "INSTALLMENTS_PURCHASES                    0.330622                1.000000   \n",
+       "CASH_ADVANCE                             -0.031326               -0.064244   \n",
+       "PURCHASES_FREQUENCY                       0.264937                0.442418   \n",
+       "ONEOFF_PURCHASES_FREQUENCY                0.524891                0.214042   \n",
+       "PURCHASES_INSTALLMENTS_FREQUENCY          0.127729                0.511351   \n",
+       "CASH_ADVANCE_FREQUENCY                   -0.082628               -0.132318   \n",
+       "CASH_ADVANCE_TRX                         -0.046212               -0.073999   \n",
+       "PURCHASES_TRX                             0.545523                0.628108   \n",
+       "CREDIT_LIMIT                              0.319724                0.256499   \n",
+       "PAYMENTS                                  0.567292                0.384084   \n",
+       "MINIMUM_PAYMENTS                          0.048755                0.132172   \n",
+       "PRC_FULL_PAYMENT                          0.132763                0.182569   \n",
+       "TENURE                                    0.064150                0.086143   \n",
+       "\n",
+       "                                  CASH_ADVANCE  PURCHASES_FREQUENCY  \\\n",
+       "BALANCE                               0.496692            -0.077944   \n",
+       "BALANCE_FREQUENCY                     0.099388             0.229715   \n",
+       "PURCHASES                            -0.051474             0.393017   \n",
+       "ONEOFF_PURCHASES                     -0.031326             0.264937   \n",
+       "INSTALLMENTS_PURCHASES               -0.064244             0.442418   \n",
+       "CASH_ADVANCE                          1.000000            -0.215507   \n",
+       "PURCHASES_FREQUENCY                  -0.215507             1.000000   \n",
+       "ONEOFF_PURCHASES_FREQUENCY           -0.086754             0.501343   \n",
+       "PURCHASES_INSTALLMENTS_FREQUENCY     -0.177070             0.862934   \n",
+       "CASH_ADVANCE_FREQUENCY                0.628522            -0.308478   \n",
+       "CASH_ADVANCE_TRX                      0.656498            -0.203478   \n",
+       "PURCHASES_TRX                        -0.075850             0.568430   \n",
+       "CREDIT_LIMIT                          0.303985             0.119788   \n",
+       "PAYMENTS                              0.453238             0.103464   \n",
+       "MINIMUM_PAYMENTS                      0.140107             0.003030   \n",
+       "PRC_FULL_PAYMENT                     -0.152935             0.305802   \n",
+       "TENURE                               -0.068312             0.061506   \n",
+       "\n",
+       "                                  ONEOFF_PURCHASES_FREQUENCY  \\\n",
+       "BALANCE                                             0.073166   \n",
+       "BALANCE_FREQUENCY                                   0.202415   \n",
+       "PURCHASES                                           0.498430   \n",
+       "ONEOFF_PURCHASES                                    0.524891   \n",
+       "INSTALLMENTS_PURCHASES                              0.214042   \n",
+       "CASH_ADVANCE                                       -0.086754   \n",
+       "PURCHASES_FREQUENCY                                 0.501343   \n",
+       "ONEOFF_PURCHASES_FREQUENCY                          1.000000   \n",
+       "PURCHASES_INSTALLMENTS_FREQUENCY                    0.142329   \n",
+       "CASH_ADVANCE_FREQUENCY                             -0.111716   \n",
+       "CASH_ADVANCE_TRX                                   -0.069088   \n",
+       "PURCHASES_TRX                                       0.544869   \n",
+       "CREDIT_LIMIT                                        0.295038   \n",
+       "PAYMENTS                                            0.243537   \n",
+       "MINIMUM_PAYMENTS                                   -0.030327   \n",
+       "PRC_FULL_PAYMENT                                    0.157531   \n",
+       "TENURE                                              0.082466   \n",
+       "\n",
+       "                                  PURCHASES_INSTALLMENTS_FREQUENCY  \\\n",
+       "BALANCE                                                  -0.063186   \n",
+       "BALANCE_FREQUENCY                                         0.176079   \n",
+       "PURCHASES                                                 0.315567   \n",
+       "ONEOFF_PURCHASES                                          0.127729   \n",
+       "INSTALLMENTS_PURCHASES                                    0.511351   \n",
+       "CASH_ADVANCE                                             -0.177070   \n",
+       "PURCHASES_FREQUENCY                                       0.862934   \n",
+       "ONEOFF_PURCHASES_FREQUENCY                                0.142329   \n",
+       "PURCHASES_INSTALLMENTS_FREQUENCY                          1.000000   \n",
+       "CASH_ADVANCE_FREQUENCY                                   -0.262958   \n",
+       "CASH_ADVANCE_TRX                                         -0.169207   \n",
+       "PURCHASES_TRX                                             0.529975   \n",
+       "CREDIT_LIMIT                                              0.060755   \n",
+       "PAYMENTS                                                  0.085551   \n",
+       "MINIMUM_PAYMENTS                                          0.030073   \n",
+       "PRC_FULL_PAYMENT                                          0.250087   \n",
+       "TENURE                                                    0.073275   \n",
+       "\n",
+       "                                  CASH_ADVANCE_FREQUENCY  CASH_ADVANCE_TRX  \\\n",
+       "BALANCE                                         0.449218          0.385152   \n",
+       "BALANCE_FREQUENCY                               0.191873          0.141555   \n",
+       "PURCHASES                                      -0.120143         -0.067175   \n",
+       "ONEOFF_PURCHASES                               -0.082628         -0.046212   \n",
+       "INSTALLMENTS_PURCHASES                         -0.132318         -0.073999   \n",
+       "CASH_ADVANCE                                    0.628522          0.656498   \n",
+       "PURCHASES_FREQUENCY                            -0.308478         -0.203478   \n",
+       "ONEOFF_PURCHASES_FREQUENCY                     -0.111716         -0.069088   \n",
+       "PURCHASES_INSTALLMENTS_FREQUENCY               -0.262958         -0.169207   \n",
+       "CASH_ADVANCE_FREQUENCY                          1.000000          0.799561   \n",
+       "CASH_ADVANCE_TRX                                0.799561          1.000000   \n",
+       "PURCHASES_TRX                                  -0.131168         -0.066157   \n",
+       "CREDIT_LIMIT                                    0.132616          0.149700   \n",
+       "PAYMENTS                                        0.183192          0.255278   \n",
+       "MINIMUM_PAYMENTS                                0.098838          0.109739   \n",
+       "PRC_FULL_PAYMENT                               -0.249773         -0.169784   \n",
+       "TENURE                                         -0.133372         -0.043421   \n",
+       "\n",
+       "                                  PURCHASES_TRX  CREDIT_LIMIT  PAYMENTS  \\\n",
+       "BALANCE                                0.154338      0.531283  0.322802   \n",
+       "BALANCE_FREQUENCY                      0.189626      0.095843  0.065008   \n",
+       "PURCHASES                              0.689561      0.356963  0.603264   \n",
+       "ONEOFF_PURCHASES                       0.545523      0.319724  0.567292   \n",
+       "INSTALLMENTS_PURCHASES                 0.628108      0.256499  0.384084   \n",
+       "CASH_ADVANCE                          -0.075850      0.303985  0.453238   \n",
+       "PURCHASES_FREQUENCY                    0.568430      0.119788  0.103464   \n",
+       "ONEOFF_PURCHASES_FREQUENCY             0.544869      0.295038  0.243537   \n",
+       "PURCHASES_INSTALLMENTS_FREQUENCY       0.529975      0.060755  0.085551   \n",
+       "CASH_ADVANCE_FREQUENCY                -0.131168      0.132616  0.183192   \n",
+       "CASH_ADVANCE_TRX                      -0.066157      0.149700  0.255278   \n",
+       "PURCHASES_TRX                          1.000000      0.272882  0.370832   \n",
+       "CREDIT_LIMIT                           0.272882      1.000000  0.421861   \n",
+       "PAYMENTS                               0.370832      0.421861  1.000000   \n",
+       "MINIMUM_PAYMENTS                       0.096333      0.126671  0.126651   \n",
+       "PRC_FULL_PAYMENT                       0.162066      0.055672  0.112138   \n",
+       "TENURE                                 0.121874      0.139167  0.106136   \n",
+       "\n",
+       "                                  MINIMUM_PAYMENTS  PRC_FULL_PAYMENT    TENURE  \n",
+       "BALANCE                                   0.398684         -0.318959  0.072692  \n",
+       "BALANCE_FREQUENCY                         0.132569         -0.095082  0.119776  \n",
+       "PURCHASES                                 0.093860          0.180379  0.086288  \n",
+       "ONEOFF_PURCHASES                          0.048755          0.132763  0.064150  \n",
+       "INSTALLMENTS_PURCHASES                    0.132172          0.182569  0.086143  \n",
+       "CASH_ADVANCE                              0.140107         -0.152935 -0.068312  \n",
+       "PURCHASES_FREQUENCY                       0.003030          0.305802  0.061506  \n",
+       "ONEOFF_PURCHASES_FREQUENCY               -0.030327          0.157531  0.082466  \n",
+       "PURCHASES_INSTALLMENTS_FREQUENCY          0.030073          0.250087  0.073275  \n",
+       "CASH_ADVANCE_FREQUENCY                    0.098838         -0.249773 -0.133372  \n",
+       "CASH_ADVANCE_TRX                          0.109739         -0.169784 -0.043421  \n",
+       "PURCHASES_TRX                             0.096333          0.162066  0.121874  \n",
+       "CREDIT_LIMIT                              0.126671          0.055672  0.139167  \n",
+       "PAYMENTS                                  0.126651          0.112138  0.106136  \n",
+       "MINIMUM_PAYMENTS                          1.000000         -0.140379  0.059444  \n",
+       "PRC_FULL_PAYMENT                         -0.140379          1.000000 -0.016486  \n",
+       "TENURE                                    0.059444         -0.016486  1.000000  "
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dataset.corr()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "7f58bee4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "5df4f00f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import seaborn as sns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "64419e9e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Correlation between different fearures')"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1224x1224 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "correlation = dataset.corr()\n",
+    "plt.figure(figsize=(17,17))\n",
+    "sns.heatmap(correlation, vmax=1, square=True,annot=True,cmap='cubehelix')\n",
+    "\n",
+    "plt.title('Correlation between different fearures')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "a893c6e9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Count of zeros in column  CUST_ID  is :  0\n",
+      "Count of zeros in column  BALANCE  is :  80\n",
+      "Count of zeros in column  BALANCE_FREQUENCY  is :  80\n",
+      "Count of zeros in column  PURCHASES  is :  2044\n",
+      "Count of zeros in column  ONEOFF_PURCHASES  is :  4302\n",
+      "Count of zeros in column  INSTALLMENTS_PURCHASES  is :  3916\n",
+      "Count of zeros in column  CASH_ADVANCE  is :  4628\n",
+      "Count of zeros in column  PURCHASES_FREQUENCY  is :  2043\n",
+      "Count of zeros in column  ONEOFF_PURCHASES_FREQUENCY  is :  4302\n",
+      "Count of zeros in column  PURCHASES_INSTALLMENTS_FREQUENCY  is :  3915\n",
+      "Count of zeros in column  CASH_ADVANCE_FREQUENCY  is :  4628\n",
+      "Count of zeros in column  CASH_ADVANCE_TRX  is :  4628\n",
+      "Count of zeros in column  PURCHASES_TRX  is :  2044\n",
+      "Count of zeros in column  CREDIT_LIMIT  is :  0\n",
+      "Count of zeros in column  PAYMENTS  is :  240\n",
+      "Count of zeros in column  MINIMUM_PAYMENTS  is :  0\n",
+      "Count of zeros in column  PRC_FULL_PAYMENT  is :  5903\n",
+      "Count of zeros in column  TENURE  is :  0\n"
+     ]
+    }
+   ],
+   "source": [
+    "for column_name in dataset.columns:\n",
+    "    column = dataset[column_name]\n",
+    "    # Get the count of Zeros in column \n",
+    "    count = (column == 0).sum()\n",
+    "    print('Count of zeros in column ', column_name, ' is : ', count)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "7ee28cc1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dataset.drop(dataset.columns[[0,4,5,6,7,8,9,10,11,12,13,16]], axis = 1,inplace = True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "1893b54a",
+   "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>BALANCE</th>\n",
+       "      <th>BALANCE_FREQUENCY</th>\n",
+       "      <th>PURCHASES</th>\n",
+       "      <th>PAYMENTS</th>\n",
+       "      <th>MINIMUM_PAYMENTS</th>\n",
+       "      <th>TENURE</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>40.900749</td>\n",
+       "      <td>0.818182</td>\n",
+       "      <td>95.40</td>\n",
+       "      <td>201.802084</td>\n",
+       "      <td>139.509787</td>\n",
+       "      <td>12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>3202.467416</td>\n",
+       "      <td>0.909091</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>4103.032597</td>\n",
+       "      <td>1072.340217</td>\n",
+       "      <td>12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2495.148862</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>773.17</td>\n",
+       "      <td>622.066742</td>\n",
+       "      <td>627.284787</td>\n",
+       "      <td>12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1666.670542</td>\n",
+       "      <td>0.636364</td>\n",
+       "      <td>1499.00</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>817.714335</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>16.00</td>\n",
+       "      <td>678.334763</td>\n",
+       "      <td>244.791237</td>\n",
+       "      <td>12</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",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8945</th>\n",
+       "      <td>28.493517</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>291.12</td>\n",
+       "      <td>325.594462</td>\n",
+       "      <td>48.886365</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8946</th>\n",
+       "      <td>19.183215</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>300.00</td>\n",
+       "      <td>275.861322</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8947</th>\n",
+       "      <td>23.398673</td>\n",
+       "      <td>0.833333</td>\n",
+       "      <td>144.40</td>\n",
+       "      <td>81.270775</td>\n",
+       "      <td>82.418369</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8948</th>\n",
+       "      <td>13.457564</td>\n",
+       "      <td>0.833333</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>52.549959</td>\n",
+       "      <td>55.755628</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8949</th>\n",
+       "      <td>372.708075</td>\n",
+       "      <td>0.666667</td>\n",
+       "      <td>1093.25</td>\n",
+       "      <td>63.165404</td>\n",
+       "      <td>88.288956</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>8950 rows × 6 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          BALANCE  BALANCE_FREQUENCY  PURCHASES     PAYMENTS  \\\n",
+       "0       40.900749           0.818182      95.40   201.802084   \n",
+       "1     3202.467416           0.909091       0.00  4103.032597   \n",
+       "2     2495.148862           1.000000     773.17   622.066742   \n",
+       "3     1666.670542           0.636364    1499.00     0.000000   \n",
+       "4      817.714335           1.000000      16.00   678.334763   \n",
+       "...           ...                ...        ...          ...   \n",
+       "8945    28.493517           1.000000     291.12   325.594462   \n",
+       "8946    19.183215           1.000000     300.00   275.861322   \n",
+       "8947    23.398673           0.833333     144.40    81.270775   \n",
+       "8948    13.457564           0.833333       0.00    52.549959   \n",
+       "8949   372.708075           0.666667    1093.25    63.165404   \n",
+       "\n",
+       "      MINIMUM_PAYMENTS  TENURE  \n",
+       "0           139.509787      12  \n",
+       "1          1072.340217      12  \n",
+       "2           627.284787      12  \n",
+       "3                  NaN      12  \n",
+       "4           244.791237      12  \n",
+       "...                ...     ...  \n",
+       "8945         48.886365       6  \n",
+       "8946               NaN       6  \n",
+       "8947         82.418369       6  \n",
+       "8948         55.755628       6  \n",
+       "8949         88.288956       6  \n",
+       "\n",
+       "[8950 rows x 6 columns]"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "58fcf2a9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "BALANCE                0\n",
+      "BALANCE_FREQUENCY      0\n",
+      "PURCHASES              0\n",
+      "PAYMENTS               0\n",
+      "MINIMUM_PAYMENTS     313\n",
+      "TENURE                 0\n",
+      "dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "# count of null values in each column\n",
+    "print(dataset.isnull().sum())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "f6ac9e29",
+   "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>BALANCE</th>\n",
+       "      <th>BALANCE_FREQUENCY</th>\n",
+       "      <th>PURCHASES</th>\n",
+       "      <th>PAYMENTS</th>\n",
+       "      <th>MINIMUM_PAYMENTS</th>\n",
+       "      <th>TENURE</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>40.900749</td>\n",
+       "      <td>0.818182</td>\n",
+       "      <td>95.40</td>\n",
+       "      <td>201.802084</td>\n",
+       "      <td>139.509787</td>\n",
+       "      <td>12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>3202.467416</td>\n",
+       "      <td>0.909091</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>4103.032597</td>\n",
+       "      <td>1072.340217</td>\n",
+       "      <td>12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2495.148862</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>773.17</td>\n",
+       "      <td>622.066742</td>\n",
+       "      <td>627.284787</td>\n",
+       "      <td>12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1666.670542</td>\n",
+       "      <td>0.636364</td>\n",
+       "      <td>1499.00</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>864.206542</td>\n",
+       "      <td>12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>817.714335</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>16.00</td>\n",
+       "      <td>678.334763</td>\n",
+       "      <td>244.791237</td>\n",
+       "      <td>12</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",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8945</th>\n",
+       "      <td>28.493517</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>291.12</td>\n",
+       "      <td>325.594462</td>\n",
+       "      <td>48.886365</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8946</th>\n",
+       "      <td>19.183215</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>300.00</td>\n",
+       "      <td>275.861322</td>\n",
+       "      <td>864.206542</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8947</th>\n",
+       "      <td>23.398673</td>\n",
+       "      <td>0.833333</td>\n",
+       "      <td>144.40</td>\n",
+       "      <td>81.270775</td>\n",
+       "      <td>82.418369</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8948</th>\n",
+       "      <td>13.457564</td>\n",
+       "      <td>0.833333</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>52.549959</td>\n",
+       "      <td>55.755628</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8949</th>\n",
+       "      <td>372.708075</td>\n",
+       "      <td>0.666667</td>\n",
+       "      <td>1093.25</td>\n",
+       "      <td>63.165404</td>\n",
+       "      <td>88.288956</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>8950 rows × 6 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          BALANCE  BALANCE_FREQUENCY  PURCHASES     PAYMENTS  \\\n",
+       "0       40.900749           0.818182      95.40   201.802084   \n",
+       "1     3202.467416           0.909091       0.00  4103.032597   \n",
+       "2     2495.148862           1.000000     773.17   622.066742   \n",
+       "3     1666.670542           0.636364    1499.00     0.000000   \n",
+       "4      817.714335           1.000000      16.00   678.334763   \n",
+       "...           ...                ...        ...          ...   \n",
+       "8945    28.493517           1.000000     291.12   325.594462   \n",
+       "8946    19.183215           1.000000     300.00   275.861322   \n",
+       "8947    23.398673           0.833333     144.40    81.270775   \n",
+       "8948    13.457564           0.833333       0.00    52.549959   \n",
+       "8949   372.708075           0.666667    1093.25    63.165404   \n",
+       "\n",
+       "      MINIMUM_PAYMENTS  TENURE  \n",
+       "0           139.509787      12  \n",
+       "1          1072.340217      12  \n",
+       "2           627.284787      12  \n",
+       "3           864.206542      12  \n",
+       "4           244.791237      12  \n",
+       "...                ...     ...  \n",
+       "8945         48.886365       6  \n",
+       "8946        864.206542       6  \n",
+       "8947         82.418369       6  \n",
+       "8948         55.755628       6  \n",
+       "8949         88.288956       6  \n",
+       "\n",
+       "[8950 rows x 6 columns]"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dataset.fillna(dataset.mean())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "bef16a61",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import seaborn as sns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "ab5cdd9d",
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Dell\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
+      "  warnings.warn(msg, FutureWarning)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:ylabel='Density'>"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig , ax = plt.subplots(figsize=(10,8) )\n",
+    "sns.distplot(dataset)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "id": "ec8d39bb",
+   "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>BALANCE</th>\n",
+       "      <th>BALANCE_FREQUENCY</th>\n",
+       "      <th>PURCHASES</th>\n",
+       "      <th>PAYMENTS</th>\n",
+       "      <th>MINIMUM_PAYMENTS</th>\n",
+       "      <th>TENURE</th>\n",
+       "      <th>A_Sqrt</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>6.395369</td>\n",
+       "      <td>0.904534</td>\n",
+       "      <td>9.767292</td>\n",
+       "      <td>14.205706</td>\n",
+       "      <td>11.811426</td>\n",
+       "      <td>3.464102</td>\n",
+       "      <td>2.528907</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>56.590347</td>\n",
+       "      <td>0.953463</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>64.054919</td>\n",
+       "      <td>32.746606</td>\n",
+       "      <td>3.464102</td>\n",
+       "      <td>7.522656</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>49.951465</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>27.805935</td>\n",
+       "      <td>24.941266</td>\n",
+       "      <td>25.045654</td>\n",
+       "      <td>3.464102</td>\n",
+       "      <td>7.067635</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>40.824877</td>\n",
+       "      <td>0.797724</td>\n",
+       "      <td>38.716921</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3.464102</td>\n",
+       "      <td>6.389435</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>28.595705</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>4.000000</td>\n",
+       "      <td>26.044861</td>\n",
+       "      <td>15.645806</td>\n",
+       "      <td>3.464102</td>\n",
+       "      <td>5.347495</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     BALANCE  BALANCE_FREQUENCY  PURCHASES   PAYMENTS  MINIMUM_PAYMENTS  \\\n",
+       "0   6.395369           0.904534   9.767292  14.205706         11.811426   \n",
+       "1  56.590347           0.953463   0.000000  64.054919         32.746606   \n",
+       "2  49.951465           1.000000  27.805935  24.941266         25.045654   \n",
+       "3  40.824877           0.797724  38.716921   0.000000               NaN   \n",
+       "4  28.595705           1.000000   4.000000  26.044861         15.645806   \n",
+       "\n",
+       "     TENURE    A_Sqrt  \n",
+       "0  3.464102  2.528907  \n",
+       "1  3.464102  7.522656  \n",
+       "2  3.464102  7.067635  \n",
+       "3  3.464102  6.389435  \n",
+       "4  3.464102  5.347495  "
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dataset.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "id": "91382296",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "id": "86c877ca",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from scipy.stats import skew"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "id": "704e12fe",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dataset.drop(dataset.columns[[6]], axis = 1,inplace = True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "id": "37fe7dc0",
+   "metadata": {
+    "scrolled": false
+   },
+   "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>BALANCE</th>\n",
+       "      <th>BALANCE_FREQUENCY</th>\n",
+       "      <th>PURCHASES</th>\n",
+       "      <th>PAYMENTS</th>\n",
+       "      <th>MINIMUM_PAYMENTS</th>\n",
+       "      <th>TENURE</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>6.395369</td>\n",
+       "      <td>0.904534</td>\n",
+       "      <td>9.767292</td>\n",
+       "      <td>14.205706</td>\n",
+       "      <td>11.811426</td>\n",
+       "      <td>3.464102</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>56.590347</td>\n",
+       "      <td>0.953463</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>64.054919</td>\n",
+       "      <td>32.746606</td>\n",
+       "      <td>3.464102</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>49.951465</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>27.805935</td>\n",
+       "      <td>24.941266</td>\n",
+       "      <td>25.045654</td>\n",
+       "      <td>3.464102</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>40.824877</td>\n",
+       "      <td>0.797724</td>\n",
+       "      <td>38.716921</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3.464102</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>28.595705</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>4.000000</td>\n",
+       "      <td>26.044861</td>\n",
+       "      <td>15.645806</td>\n",
+       "      <td>3.464102</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",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8945</th>\n",
+       "      <td>5.337932</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>17.062239</td>\n",
+       "      <td>18.044236</td>\n",
+       "      <td>6.991879</td>\n",
+       "      <td>2.449490</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8946</th>\n",
+       "      <td>4.379865</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>17.320508</td>\n",
+       "      <td>16.609073</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2.449490</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8947</th>\n",
+       "      <td>4.837217</td>\n",
+       "      <td>0.912871</td>\n",
+       "      <td>12.016655</td>\n",
+       "      <td>9.015031</td>\n",
+       "      <td>9.078456</td>\n",
+       "      <td>2.449490</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8948</th>\n",
+       "      <td>3.668455</td>\n",
+       "      <td>0.912871</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>7.249135</td>\n",
+       "      <td>7.466969</td>\n",
+       "      <td>2.449490</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8949</th>\n",
+       "      <td>19.305649</td>\n",
+       "      <td>0.816497</td>\n",
+       "      <td>33.064331</td>\n",
+       "      <td>7.947667</td>\n",
+       "      <td>9.396220</td>\n",
+       "      <td>2.449490</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>8950 rows × 6 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        BALANCE  BALANCE_FREQUENCY  PURCHASES   PAYMENTS  MINIMUM_PAYMENTS  \\\n",
+       "0      6.395369           0.904534   9.767292  14.205706         11.811426   \n",
+       "1     56.590347           0.953463   0.000000  64.054919         32.746606   \n",
+       "2     49.951465           1.000000  27.805935  24.941266         25.045654   \n",
+       "3     40.824877           0.797724  38.716921   0.000000               NaN   \n",
+       "4     28.595705           1.000000   4.000000  26.044861         15.645806   \n",
+       "...         ...                ...        ...        ...               ...   \n",
+       "8945   5.337932           1.000000  17.062239  18.044236          6.991879   \n",
+       "8946   4.379865           1.000000  17.320508  16.609073               NaN   \n",
+       "8947   4.837217           0.912871  12.016655   9.015031          9.078456   \n",
+       "8948   3.668455           0.912871   0.000000   7.249135          7.466969   \n",
+       "8949  19.305649           0.816497  33.064331   7.947667          9.396220   \n",
+       "\n",
+       "        TENURE  \n",
+       "0     3.464102  \n",
+       "1     3.464102  \n",
+       "2     3.464102  \n",
+       "3     3.464102  \n",
+       "4     3.464102  \n",
+       "...        ...  \n",
+       "8945  2.449490  \n",
+       "8946  2.449490  \n",
+       "8947  2.449490  \n",
+       "8948  2.449490  \n",
+       "8949  2.449490  \n",
+       "\n",
+       "[8950 rows x 6 columns]"
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "id": "00f67cf1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "BALANCE\n",
+      "0.8293588286126723\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Dell\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
+      "  warnings.warn(msg, FutureWarning)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "BALANCE_FREQUENCY\n",
+      "-2.819022293703172\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Dell\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
+      "  warnings.warn(msg, FutureWarning)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PURCHASES\n",
+      "1.7304621724372427\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Dell\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
+      "  warnings.warn(msg, FutureWarning)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PAYMENTS\n",
+      "1.9512080718799651\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Dell\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
+      "  warnings.warn(msg, FutureWarning)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MINIMUM_PAYMENTS\n",
+      "nan\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Dell\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
+      "  warnings.warn(msg, FutureWarning)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "TENURE\n",
+      "-3.063818528631033\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Dell\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
+      "  warnings.warn(msg, FutureWarning)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for col in dataset:\n",
+    "    print(col)\n",
+    "    print(skew(dataset[col]))\n",
+    "    \n",
+    "    \n",
+    "    plt.figure()\n",
+    "    sns.distplot(dataset[col])\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "id": "4ed8a2c1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.heatmap(dataset.corr(),annot=True)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "id": "a5c0fc71",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dataset[\"BALANCE\"]= np.sqrt(dataset[\"BALANCE\"])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "id": "72e9c544",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "-0.009702708808538752"
+      ]
+     },
+     "execution_count": 57,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "skew(dataset[\"BALANCE\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "id": "e95cd694",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dataset[\"BALANCE_FREQUENCY\"]= np.sqrt(dataset[\"BALANCE_FREQUENCY\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "id": "dbd30661",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "-4.738224327211256"
+      ]
+     },
+     "execution_count": 59,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "skew(dataset[\"BALANCE_FREQUENCY\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 94,
+   "id": "614411b9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "StandardScaler()"
+      ]
+     },
+     "execution_count": 94,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\n",
+    "    from sklearn.preprocessing import StandardScaler\n",
+    "    sc = StandardScaler()\n",
+    "    sc.fit(dataset)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "id": "51b3cabe",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "StandardScaler()"
+      ]
+     },
+     "execution_count": 76,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sc.fit(dataset[['BALANCE']]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "15f2f577",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#for col in [BALANCE, BALANCE_FREQUENCY, PURCHASES, PAYMENTS, MINIMUM_PAYMENTS, TENURE]:#"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "id": "a07e4248",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sc_data= sc.transform(dataset[['BALANCE']])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "id": "e0099815",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sc_data= sc_data.reshape(-1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 84,
+   "id": "cc05ba7c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Dell\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
+      "  warnings.warn(msg, FutureWarning)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='BALANCE', ylabel='Density'>"
+      ]
+     },
+     "execution_count": 84,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.distplot(dataset['BALANCE'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "id": "993dafb3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Dell\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
+      "  warnings.warn(msg, FutureWarning)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:ylabel='Density'>"
+      ]
+     },
+     "execution_count": 85,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.distplot(sc_data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 95,
+   "id": "3852fe62",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "scaled_data = sc.transform(dataset)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 96,
+   "id": "e6600a3b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[-1.20346242e+00, -4.02048767e-03, -5.96901316e-01,\n",
+       "        -8.85440415e-01, -6.45587718e-01,  3.53480557e-01],\n",
+       "       [ 1.05764674e+00,  1.99439740e-01, -1.04233710e+00,\n",
+       "         1.27404552e+00,  5.26200771e-01,  3.53480557e-01],\n",
+       "       [ 8.51618926e-01,  3.88166053e-01,  2.25748034e-01,\n",
+       "        -4.20372078e-01,  9.51613896e-02,  3.53480557e-01],\n",
+       "       ...,\n",
+       "       [-1.35267381e+00,  3.10278422e-02, -4.94319493e-01,\n",
+       "        -1.11030236e+00, -7.98558103e-01, -4.27612743e+00],\n",
+       "       [-1.48128655e+00,  3.10278422e-02, -1.04233710e+00,\n",
+       "        -1.18680159e+00, -8.88756617e-01, -4.27612743e+00],\n",
+       "       [-3.59052507e-01, -3.84487700e-01,  4.65556357e-01,\n",
+       "        -1.15654095e+00, -7.80772148e-01, -4.27612743e+00]])"
+      ]
+     },
+     "execution_count": 96,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "scaled_data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 97,
+   "id": "408612f7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.decomposition import PCA"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 104,
+   "id": "5b38a4fb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pca=PCA(n_components=2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 114,
+   "id": "33f143f2",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "ValueError",
+     "evalue": "Input contains NaN, infinity or a value too large for dtype('float64').",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "Input \u001b[1;32mIn [114]\u001b[0m, in \u001b[0;36m<cell line: 1>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[43mpca\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit_transform\u001b[49m\u001b[43m(\u001b[49m\u001b[43mscaled_data\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\decomposition\\_pca.py:407\u001b[0m, in \u001b[0;36mPCA.fit_transform\u001b[1;34m(self, X, y)\u001b[0m\n\u001b[0;32m    385\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mfit_transform\u001b[39m(\u001b[38;5;28mself\u001b[39m, X, y\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[0;32m    386\u001b[0m     \u001b[38;5;124;03m\"\"\"Fit the model with X and apply the dimensionality reduction on X.\u001b[39;00m\n\u001b[0;32m    387\u001b[0m \n\u001b[0;32m    388\u001b[0m \u001b[38;5;124;03m    Parameters\u001b[39;00m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    405\u001b[0m \u001b[38;5;124;03m    C-ordered array, use 'np.ascontiguousarray'.\u001b[39;00m\n\u001b[0;32m    406\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[1;32m--> 407\u001b[0m     U, S, Vt \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_fit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    408\u001b[0m     U \u001b[38;5;241m=\u001b[39m U[:, : \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mn_components_]\n\u001b[0;32m    410\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mwhiten:\n\u001b[0;32m    411\u001b[0m         \u001b[38;5;66;03m# X_new = X * V / S * sqrt(n_samples) = U * sqrt(n_samples)\u001b[39;00m\n",
+      "File \u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\decomposition\\_pca.py:430\u001b[0m, in \u001b[0;36mPCA._fit\u001b[1;34m(self, X)\u001b[0m\n\u001b[0;32m    424\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m issparse(X):\n\u001b[0;32m    425\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\n\u001b[0;32m    426\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mPCA does not support sparse input. See \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    427\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mTruncatedSVD for a possible alternative.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    428\u001b[0m     )\n\u001b[1;32m--> 430\u001b[0m X \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_validate_data\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    431\u001b[0m \u001b[43m    \u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m[\u001b[49m\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfloat64\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfloat32\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mensure_2d\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcopy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcopy\u001b[49m\n\u001b[0;32m    432\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    434\u001b[0m \u001b[38;5;66;03m# Handle n_components==None\u001b[39;00m\n\u001b[0;32m    435\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mn_components \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
+      "File \u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\base.py:566\u001b[0m, in \u001b[0;36mBaseEstimator._validate_data\u001b[1;34m(self, X, y, reset, validate_separately, **check_params)\u001b[0m\n\u001b[0;32m    564\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mValidation should be done on X, y or both.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m    565\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m no_val_X \u001b[38;5;129;01mand\u001b[39;00m no_val_y:\n\u001b[1;32m--> 566\u001b[0m     X \u001b[38;5;241m=\u001b[39m check_array(X, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mcheck_params)\n\u001b[0;32m    567\u001b[0m     out \u001b[38;5;241m=\u001b[39m X\n\u001b[0;32m    568\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m no_val_X \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m no_val_y:\n",
+      "File \u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:800\u001b[0m, in \u001b[0;36mcheck_array\u001b[1;34m(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, estimator)\u001b[0m\n\u001b[0;32m    794\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[0;32m    795\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFound array with dim \u001b[39m\u001b[38;5;132;01m%d\u001b[39;00m\u001b[38;5;124m. \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m expected <= 2.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    796\u001b[0m             \u001b[38;5;241m%\u001b[39m (array\u001b[38;5;241m.\u001b[39mndim, estimator_name)\n\u001b[0;32m    797\u001b[0m         )\n\u001b[0;32m    799\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m force_all_finite:\n\u001b[1;32m--> 800\u001b[0m         \u001b[43m_assert_all_finite\u001b[49m\u001b[43m(\u001b[49m\u001b[43marray\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mallow_nan\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mforce_all_finite\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m==\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mallow-nan\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[0;32m    802\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m ensure_min_samples \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[0;32m    803\u001b[0m     n_samples \u001b[38;5;241m=\u001b[39m _num_samples(array)\n",
+      "File \u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:114\u001b[0m, in \u001b[0;36m_assert_all_finite\u001b[1;34m(X, allow_nan, msg_dtype)\u001b[0m\n\u001b[0;32m    107\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m (\n\u001b[0;32m    108\u001b[0m         allow_nan\n\u001b[0;32m    109\u001b[0m         \u001b[38;5;129;01mand\u001b[39;00m np\u001b[38;5;241m.\u001b[39misinf(X)\u001b[38;5;241m.\u001b[39many()\n\u001b[0;32m    110\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m allow_nan\n\u001b[0;32m    111\u001b[0m         \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m np\u001b[38;5;241m.\u001b[39misfinite(X)\u001b[38;5;241m.\u001b[39mall()\n\u001b[0;32m    112\u001b[0m     ):\n\u001b[0;32m    113\u001b[0m         type_err \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124minfinity\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m allow_nan \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mNaN, infinity\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m--> 114\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[0;32m    115\u001b[0m             msg_err\u001b[38;5;241m.\u001b[39mformat(\n\u001b[0;32m    116\u001b[0m                 type_err, msg_dtype \u001b[38;5;28;01mif\u001b[39;00m msg_dtype \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m X\u001b[38;5;241m.\u001b[39mdtype\n\u001b[0;32m    117\u001b[0m             )\n\u001b[0;32m    118\u001b[0m         )\n\u001b[0;32m    119\u001b[0m \u001b[38;5;66;03m# for object dtype data, we only check for NaNs (GH-13254)\u001b[39;00m\n\u001b[0;32m    120\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m X\u001b[38;5;241m.\u001b[39mdtype \u001b[38;5;241m==\u001b[39m np\u001b[38;5;241m.\u001b[39mdtype(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mobject\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m allow_nan:\n",
+      "\u001b[1;31mValueError\u001b[0m: Input contains NaN, infinity or a value too large for dtype('float64')."
+     ]
+    }
+   ],
+   "source": [
+    "pca.fit_transform(scaled_data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 112,
+   "id": "a86ed4ba",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 113,
+   "id": "2a6e809a",
+   "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>BALANCE</th>\n",
+       "      <th>BALANCE_FREQUENCY</th>\n",
+       "      <th>PURCHASES</th>\n",
+       "      <th>PAYMENTS</th>\n",
+       "      <th>MINIMUM_PAYMENTS</th>\n",
+       "      <th>TENURE</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2.528907</td>\n",
+       "      <td>0.951070</td>\n",
+       "      <td>9.767292</td>\n",
+       "      <td>14.205706</td>\n",
+       "      <td>11.811426</td>\n",
+       "      <td>3.464102</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>7.522656</td>\n",
+       "      <td>0.976454</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>64.054919</td>\n",
+       "      <td>32.746606</td>\n",
+       "      <td>3.464102</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>7.067635</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>27.805935</td>\n",
+       "      <td>24.941266</td>\n",
+       "      <td>25.045654</td>\n",
+       "      <td>3.464102</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>6.389435</td>\n",
+       "      <td>0.893154</td>\n",
+       "      <td>38.716921</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3.464102</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5.347495</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>4.000000</td>\n",
+       "      <td>26.044861</td>\n",
+       "      <td>15.645806</td>\n",
+       "      <td>3.464102</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",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8945</th>\n",
+       "      <td>2.310396</td>\n",
+       "      <td>1.000000</td>\n",
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+       "      <td>18.044236</td>\n",
+       "      <td>6.991879</td>\n",
+       "      <td>2.449490</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8946</th>\n",
+       "      <td>2.092813</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>17.320508</td>\n",
+       "      <td>16.609073</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2.449490</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8947</th>\n",
+       "      <td>2.199368</td>\n",
+       "      <td>0.955443</td>\n",
+       "      <td>12.016655</td>\n",
+       "      <td>9.015031</td>\n",
+       "      <td>9.078456</td>\n",
+       "      <td>2.449490</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8948</th>\n",
+       "      <td>1.915321</td>\n",
+       "      <td>0.955443</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>7.249135</td>\n",
+       "      <td>7.466969</td>\n",
+       "      <td>2.449490</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8949</th>\n",
+       "      <td>4.393819</td>\n",
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+       "      <td>33.064331</td>\n",
+       "      <td>7.947667</td>\n",
+       "      <td>9.396220</td>\n",
+       "      <td>2.449490</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>8950 rows × 6 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       BALANCE  BALANCE_FREQUENCY  PURCHASES   PAYMENTS  MINIMUM_PAYMENTS  \\\n",
+       "0     2.528907           0.951070   9.767292  14.205706         11.811426   \n",
+       "1     7.522656           0.976454   0.000000  64.054919         32.746606   \n",
+       "2     7.067635           1.000000  27.805935  24.941266         25.045654   \n",
+       "3     6.389435           0.893154  38.716921   0.000000               NaN   \n",
+       "4     5.347495           1.000000   4.000000  26.044861         15.645806   \n",
+       "...        ...                ...        ...        ...               ...   \n",
+       "8945  2.310396           1.000000  17.062239  18.044236          6.991879   \n",
+       "8946  2.092813           1.000000  17.320508  16.609073               NaN   \n",
+       "8947  2.199368           0.955443  12.016655   9.015031          9.078456   \n",
+       "8948  1.915321           0.955443   0.000000   7.249135          7.466969   \n",
+       "8949  4.393819           0.903602  33.064331   7.947667          9.396220   \n",
+       "\n",
+       "        TENURE  \n",
+       "0     3.464102  \n",
+       "1     3.464102  \n",
+       "2     3.464102  \n",
+       "3     3.464102  \n",
+       "4     3.464102  \n",
+       "...        ...  \n",
+       "8945  2.449490  \n",
+       "8946  2.449490  \n",
+       "8947  2.449490  \n",
+       "8948  2.449490  \n",
+       "8949  2.449490  \n",
+       "\n",
+       "[8950 rows x 6 columns]"
+      ]
+     },
+     "execution_count": 113,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 115,
+   "id": "bfb8ab8d",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "ValueError",
+     "evalue": "Input contains NaN, infinity or a value too large for dtype('float64').",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "Input \u001b[1;32mIn [115]\u001b[0m, in \u001b[0;36m<cell line: 1>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[43mpca\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit_transform\u001b[49m\u001b[43m(\u001b[49m\u001b[43mscaled_data\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\decomposition\\_pca.py:407\u001b[0m, in \u001b[0;36mPCA.fit_transform\u001b[1;34m(self, X, y)\u001b[0m\n\u001b[0;32m    385\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mfit_transform\u001b[39m(\u001b[38;5;28mself\u001b[39m, X, y\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[0;32m    386\u001b[0m     \u001b[38;5;124;03m\"\"\"Fit the model with X and apply the dimensionality reduction on X.\u001b[39;00m\n\u001b[0;32m    387\u001b[0m \n\u001b[0;32m    388\u001b[0m \u001b[38;5;124;03m    Parameters\u001b[39;00m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    405\u001b[0m \u001b[38;5;124;03m    C-ordered array, use 'np.ascontiguousarray'.\u001b[39;00m\n\u001b[0;32m    406\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[1;32m--> 407\u001b[0m     U, S, Vt \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_fit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    408\u001b[0m     U \u001b[38;5;241m=\u001b[39m U[:, : \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mn_components_]\n\u001b[0;32m    410\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mwhiten:\n\u001b[0;32m    411\u001b[0m         \u001b[38;5;66;03m# X_new = X * V / S * sqrt(n_samples) = U * sqrt(n_samples)\u001b[39;00m\n",
+      "File \u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\decomposition\\_pca.py:430\u001b[0m, in \u001b[0;36mPCA._fit\u001b[1;34m(self, X)\u001b[0m\n\u001b[0;32m    424\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m issparse(X):\n\u001b[0;32m    425\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\n\u001b[0;32m    426\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mPCA does not support sparse input. See \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    427\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mTruncatedSVD for a possible alternative.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    428\u001b[0m     )\n\u001b[1;32m--> 430\u001b[0m X \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_validate_data\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    431\u001b[0m \u001b[43m    \u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m[\u001b[49m\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfloat64\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfloat32\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mensure_2d\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcopy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcopy\u001b[49m\n\u001b[0;32m    432\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    434\u001b[0m \u001b[38;5;66;03m# Handle n_components==None\u001b[39;00m\n\u001b[0;32m    435\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mn_components \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
+      "File \u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\base.py:566\u001b[0m, in \u001b[0;36mBaseEstimator._validate_data\u001b[1;34m(self, X, y, reset, validate_separately, **check_params)\u001b[0m\n\u001b[0;32m    564\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mValidation should be done on X, y or both.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m    565\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m no_val_X \u001b[38;5;129;01mand\u001b[39;00m no_val_y:\n\u001b[1;32m--> 566\u001b[0m     X \u001b[38;5;241m=\u001b[39m check_array(X, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mcheck_params)\n\u001b[0;32m    567\u001b[0m     out \u001b[38;5;241m=\u001b[39m X\n\u001b[0;32m    568\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m no_val_X \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m no_val_y:\n",
+      "File \u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:800\u001b[0m, in \u001b[0;36mcheck_array\u001b[1;34m(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, estimator)\u001b[0m\n\u001b[0;32m    794\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[0;32m    795\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFound array with dim \u001b[39m\u001b[38;5;132;01m%d\u001b[39;00m\u001b[38;5;124m. \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m expected <= 2.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    796\u001b[0m             \u001b[38;5;241m%\u001b[39m (array\u001b[38;5;241m.\u001b[39mndim, estimator_name)\n\u001b[0;32m    797\u001b[0m         )\n\u001b[0;32m    799\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m force_all_finite:\n\u001b[1;32m--> 800\u001b[0m         \u001b[43m_assert_all_finite\u001b[49m\u001b[43m(\u001b[49m\u001b[43marray\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mallow_nan\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mforce_all_finite\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m==\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mallow-nan\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[0;32m    802\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m ensure_min_samples \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[0;32m    803\u001b[0m     n_samples \u001b[38;5;241m=\u001b[39m _num_samples(array)\n",
+      "File \u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:114\u001b[0m, in \u001b[0;36m_assert_all_finite\u001b[1;34m(X, allow_nan, msg_dtype)\u001b[0m\n\u001b[0;32m    107\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m (\n\u001b[0;32m    108\u001b[0m         allow_nan\n\u001b[0;32m    109\u001b[0m         \u001b[38;5;129;01mand\u001b[39;00m np\u001b[38;5;241m.\u001b[39misinf(X)\u001b[38;5;241m.\u001b[39many()\n\u001b[0;32m    110\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m allow_nan\n\u001b[0;32m    111\u001b[0m         \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m np\u001b[38;5;241m.\u001b[39misfinite(X)\u001b[38;5;241m.\u001b[39mall()\n\u001b[0;32m    112\u001b[0m     ):\n\u001b[0;32m    113\u001b[0m         type_err \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124minfinity\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m allow_nan \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mNaN, infinity\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m--> 114\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[0;32m    115\u001b[0m             msg_err\u001b[38;5;241m.\u001b[39mformat(\n\u001b[0;32m    116\u001b[0m                 type_err, msg_dtype \u001b[38;5;28;01mif\u001b[39;00m msg_dtype \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m X\u001b[38;5;241m.\u001b[39mdtype\n\u001b[0;32m    117\u001b[0m             )\n\u001b[0;32m    118\u001b[0m         )\n\u001b[0;32m    119\u001b[0m \u001b[38;5;66;03m# for object dtype data, we only check for NaNs (GH-13254)\u001b[39;00m\n\u001b[0;32m    120\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m X\u001b[38;5;241m.\u001b[39mdtype \u001b[38;5;241m==\u001b[39m np\u001b[38;5;241m.\u001b[39mdtype(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mobject\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m allow_nan:\n",
+      "\u001b[1;31mValueError\u001b[0m: Input contains NaN, infinity or a value too large for dtype('float64')."
+     ]
+    }
+   ],
+   "source": [
+    "pca.fit_transform(scaled_data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 118,
+   "id": "1f5063de",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dataset= np.nan_to_num(dataset)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 119,
+   "id": "f92370d4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 2.52890675,  0.95106999,  9.76729236, 14.20570604, 11.81142612,\n",
+       "         3.46410162],\n",
+       "       [ 7.52265561,  0.97645411,  0.        , 64.0549186 , 32.74660619,\n",
+       "         3.46410162],\n",
+       "       [ 7.06763504,  1.        , 27.80593462, 24.94126585, 25.04565405,\n",
+       "         3.46410162],\n",
+       "       ...,\n",
+       "       [ 2.19936752,  0.9554427 , 12.01665511,  9.0150305 ,  9.07845631,\n",
+       "         2.44948974],\n",
+       "       [ 1.91532119,  0.9554427 ,  0.        ,  7.24913505,  7.46696913,\n",
+       "         2.44948974],\n",
+       "       [ 4.39381938,  0.90360212, 33.06433123,  7.94766658,  9.3962203 ,\n",
+       "         2.44948974]])"
+      ]
+     },
+     "execution_count": 119,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 120,
+   "id": "dbec905a",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "ValueError",
+     "evalue": "Input contains NaN, infinity or a value too large for dtype('float64').",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "Input \u001b[1;32mIn [120]\u001b[0m, in \u001b[0;36m<cell line: 1>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[43mpca\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit_transform\u001b[49m\u001b[43m(\u001b[49m\u001b[43mscaled_data\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\decomposition\\_pca.py:407\u001b[0m, in \u001b[0;36mPCA.fit_transform\u001b[1;34m(self, X, y)\u001b[0m\n\u001b[0;32m    385\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mfit_transform\u001b[39m(\u001b[38;5;28mself\u001b[39m, X, y\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[0;32m    386\u001b[0m     \u001b[38;5;124;03m\"\"\"Fit the model with X and apply the dimensionality reduction on X.\u001b[39;00m\n\u001b[0;32m    387\u001b[0m \n\u001b[0;32m    388\u001b[0m \u001b[38;5;124;03m    Parameters\u001b[39;00m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    405\u001b[0m \u001b[38;5;124;03m    C-ordered array, use 'np.ascontiguousarray'.\u001b[39;00m\n\u001b[0;32m    406\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[1;32m--> 407\u001b[0m     U, S, Vt \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_fit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    408\u001b[0m     U \u001b[38;5;241m=\u001b[39m U[:, : \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mn_components_]\n\u001b[0;32m    410\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mwhiten:\n\u001b[0;32m    411\u001b[0m         \u001b[38;5;66;03m# X_new = X * V / S * sqrt(n_samples) = U * sqrt(n_samples)\u001b[39;00m\n",
+      "File \u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\decomposition\\_pca.py:430\u001b[0m, in \u001b[0;36mPCA._fit\u001b[1;34m(self, X)\u001b[0m\n\u001b[0;32m    424\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m issparse(X):\n\u001b[0;32m    425\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\n\u001b[0;32m    426\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mPCA does not support sparse input. See \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    427\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mTruncatedSVD for a possible alternative.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    428\u001b[0m     )\n\u001b[1;32m--> 430\u001b[0m X \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_validate_data\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    431\u001b[0m \u001b[43m    \u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m[\u001b[49m\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfloat64\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfloat32\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mensure_2d\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcopy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcopy\u001b[49m\n\u001b[0;32m    432\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    434\u001b[0m \u001b[38;5;66;03m# Handle n_components==None\u001b[39;00m\n\u001b[0;32m    435\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mn_components \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
+      "File \u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\base.py:566\u001b[0m, in \u001b[0;36mBaseEstimator._validate_data\u001b[1;34m(self, X, y, reset, validate_separately, **check_params)\u001b[0m\n\u001b[0;32m    564\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mValidation should be done on X, y or both.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m    565\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m no_val_X \u001b[38;5;129;01mand\u001b[39;00m no_val_y:\n\u001b[1;32m--> 566\u001b[0m     X \u001b[38;5;241m=\u001b[39m check_array(X, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mcheck_params)\n\u001b[0;32m    567\u001b[0m     out \u001b[38;5;241m=\u001b[39m X\n\u001b[0;32m    568\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m no_val_X \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m no_val_y:\n",
+      "File \u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:800\u001b[0m, in \u001b[0;36mcheck_array\u001b[1;34m(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, estimator)\u001b[0m\n\u001b[0;32m    794\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[0;32m    795\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFound array with dim \u001b[39m\u001b[38;5;132;01m%d\u001b[39;00m\u001b[38;5;124m. \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m expected <= 2.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    796\u001b[0m             \u001b[38;5;241m%\u001b[39m (array\u001b[38;5;241m.\u001b[39mndim, estimator_name)\n\u001b[0;32m    797\u001b[0m         )\n\u001b[0;32m    799\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m force_all_finite:\n\u001b[1;32m--> 800\u001b[0m         \u001b[43m_assert_all_finite\u001b[49m\u001b[43m(\u001b[49m\u001b[43marray\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mallow_nan\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mforce_all_finite\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m==\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mallow-nan\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[0;32m    802\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m ensure_min_samples \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[0;32m    803\u001b[0m     n_samples \u001b[38;5;241m=\u001b[39m _num_samples(array)\n",
+      "File \u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:114\u001b[0m, in \u001b[0;36m_assert_all_finite\u001b[1;34m(X, allow_nan, msg_dtype)\u001b[0m\n\u001b[0;32m    107\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m (\n\u001b[0;32m    108\u001b[0m         allow_nan\n\u001b[0;32m    109\u001b[0m         \u001b[38;5;129;01mand\u001b[39;00m np\u001b[38;5;241m.\u001b[39misinf(X)\u001b[38;5;241m.\u001b[39many()\n\u001b[0;32m    110\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m allow_nan\n\u001b[0;32m    111\u001b[0m         \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m np\u001b[38;5;241m.\u001b[39misfinite(X)\u001b[38;5;241m.\u001b[39mall()\n\u001b[0;32m    112\u001b[0m     ):\n\u001b[0;32m    113\u001b[0m         type_err \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124minfinity\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m allow_nan \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mNaN, infinity\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m--> 114\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[0;32m    115\u001b[0m             msg_err\u001b[38;5;241m.\u001b[39mformat(\n\u001b[0;32m    116\u001b[0m                 type_err, msg_dtype \u001b[38;5;28;01mif\u001b[39;00m msg_dtype \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m X\u001b[38;5;241m.\u001b[39mdtype\n\u001b[0;32m    117\u001b[0m             )\n\u001b[0;32m    118\u001b[0m         )\n\u001b[0;32m    119\u001b[0m \u001b[38;5;66;03m# for object dtype data, we only check for NaNs (GH-13254)\u001b[39;00m\n\u001b[0;32m    120\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m X\u001b[38;5;241m.\u001b[39mdtype \u001b[38;5;241m==\u001b[39m np\u001b[38;5;241m.\u001b[39mdtype(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mobject\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m allow_nan:\n",
+      "\u001b[1;31mValueError\u001b[0m: Input contains NaN, infinity or a value too large for dtype('float64')."
+     ]
+    }
+   ],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 121,
+   "id": "c0b9c21b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.decomposition import PCA"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 122,
+   "id": "9399207c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pca=PCA(n_components=2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 124,
+   "id": "eaac9225",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[-26.06252146,  -5.43420482],\n",
+       "       [ 10.00313908,  27.45949235],\n",
+       "       [ -3.32994799,  -2.74696636],\n",
+       "       ...,\n",
+       "       [-29.18292311,  -9.98933498],\n",
+       "       [-38.43174903,  -4.77585865],\n",
+       "       [-16.63008953, -21.85651216]])"
+      ]
+     },
+     "execution_count": 124,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pca.fit_transform(dataset)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 126,
+   "id": "f3ea56e4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(8950, 2)"
+      ]
+     },
+     "execution_count": 126,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pca.fit_transform(dataset).shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 127,
+   "id": "26e4b1bf",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np \n",
+    "import pandas as pd\n",
+    "import random as rd\n",
+    "import matplotlib.pyplot as plt\n",
+    "from math import sqrt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 128,
+   "id": "80e3d86b",
+   "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>CUST_ID</th>\n",
+       "      <th>BALANCE</th>\n",
+       "      <th>BALANCE_FREQUENCY</th>\n",
+       "      <th>PURCHASES</th>\n",
+       "      <th>ONEOFF_PURCHASES</th>\n",
+       "      <th>INSTALLMENTS_PURCHASES</th>\n",
+       "      <th>CASH_ADVANCE</th>\n",
+       "      <th>PURCHASES_FREQUENCY</th>\n",
+       "      <th>ONEOFF_PURCHASES_FREQUENCY</th>\n",
+       "      <th>PURCHASES_INSTALLMENTS_FREQUENCY</th>\n",
+       "      <th>CASH_ADVANCE_FREQUENCY</th>\n",
+       "      <th>CASH_ADVANCE_TRX</th>\n",
+       "      <th>PURCHASES_TRX</th>\n",
+       "      <th>CREDIT_LIMIT</th>\n",
+       "      <th>PAYMENTS</th>\n",
+       "      <th>MINIMUM_PAYMENTS</th>\n",
+       "      <th>PRC_FULL_PAYMENT</th>\n",
+       "      <th>TENURE</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>C10001</td>\n",
+       "      <td>40.900749</td>\n",
+       "      <td>0.818182</td>\n",
+       "      <td>95.40</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>95.4</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.166667</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.083333</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1000.0</td>\n",
+       "      <td>201.802084</td>\n",
+       "      <td>139.509787</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>C10002</td>\n",
+       "      <td>3202.467416</td>\n",
+       "      <td>0.909091</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>6442.945483</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.250000</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7000.0</td>\n",
+       "      <td>4103.032597</td>\n",
+       "      <td>1072.340217</td>\n",
+       "      <td>0.222222</td>\n",
+       "      <td>12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>C10003</td>\n",
+       "      <td>2495.148862</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>773.17</td>\n",
+       "      <td>773.17</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>12</td>\n",
+       "      <td>7500.0</td>\n",
+       "      <td>622.066742</td>\n",
+       "      <td>627.284787</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>C10004</td>\n",
+       "      <td>1666.670542</td>\n",
+       "      <td>0.636364</td>\n",
+       "      <td>1499.00</td>\n",
+       "      <td>1499.00</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>205.788017</td>\n",
+       "      <td>0.083333</td>\n",
+       "      <td>0.083333</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.083333</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>7500.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>C10005</td>\n",
+       "      <td>817.714335</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>16.00</td>\n",
+       "      <td>16.00</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.083333</td>\n",
+       "      <td>0.083333</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1200.0</td>\n",
+       "      <td>678.334763</td>\n",
+       "      <td>244.791237</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>12</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  CUST_ID      BALANCE  BALANCE_FREQUENCY  PURCHASES  ONEOFF_PURCHASES  \\\n",
+       "0  C10001    40.900749           0.818182      95.40              0.00   \n",
+       "1  C10002  3202.467416           0.909091       0.00              0.00   \n",
+       "2  C10003  2495.148862           1.000000     773.17            773.17   \n",
+       "3  C10004  1666.670542           0.636364    1499.00           1499.00   \n",
+       "4  C10005   817.714335           1.000000      16.00             16.00   \n",
+       "\n",
+       "   INSTALLMENTS_PURCHASES  CASH_ADVANCE  PURCHASES_FREQUENCY  \\\n",
+       "0                    95.4      0.000000             0.166667   \n",
+       "1                     0.0   6442.945483             0.000000   \n",
+       "2                     0.0      0.000000             1.000000   \n",
+       "3                     0.0    205.788017             0.083333   \n",
+       "4                     0.0      0.000000             0.083333   \n",
+       "\n",
+       "   ONEOFF_PURCHASES_FREQUENCY  PURCHASES_INSTALLMENTS_FREQUENCY  \\\n",
+       "0                    0.000000                          0.083333   \n",
+       "1                    0.000000                          0.000000   \n",
+       "2                    1.000000                          0.000000   \n",
+       "3                    0.083333                          0.000000   \n",
+       "4                    0.083333                          0.000000   \n",
+       "\n",
+       "   CASH_ADVANCE_FREQUENCY  CASH_ADVANCE_TRX  PURCHASES_TRX  CREDIT_LIMIT  \\\n",
+       "0                0.000000                 0              2        1000.0   \n",
+       "1                0.250000                 4              0        7000.0   \n",
+       "2                0.000000                 0             12        7500.0   \n",
+       "3                0.083333                 1              1        7500.0   \n",
+       "4                0.000000                 0              1        1200.0   \n",
+       "\n",
+       "      PAYMENTS  MINIMUM_PAYMENTS  PRC_FULL_PAYMENT  TENURE  \n",
+       "0   201.802084        139.509787          0.000000      12  \n",
+       "1  4103.032597       1072.340217          0.222222      12  \n",
+       "2   622.066742        627.284787          0.000000      12  \n",
+       "3     0.000000               NaN          0.000000      12  \n",
+       "4   678.334763        244.791237          0.000000      12  "
+      ]
+     },
+     "execution_count": 128,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data = pd.read_csv(\"dataset.csv\")\n",
+    "data.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 129,
+   "id": "eabc52f1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 8950 entries, 0 to 8949\n",
+      "Data columns (total 18 columns):\n",
+      " #   Column                            Non-Null Count  Dtype  \n",
+      "---  ------                            --------------  -----  \n",
+      " 0   CUST_ID                           8950 non-null   object \n",
+      " 1   BALANCE                           8950 non-null   float64\n",
+      " 2   BALANCE_FREQUENCY                 8950 non-null   float64\n",
+      " 3   PURCHASES                         8950 non-null   float64\n",
+      " 4   ONEOFF_PURCHASES                  8950 non-null   float64\n",
+      " 5   INSTALLMENTS_PURCHASES            8950 non-null   float64\n",
+      " 6   CASH_ADVANCE                      8950 non-null   float64\n",
+      " 7   PURCHASES_FREQUENCY               8950 non-null   float64\n",
+      " 8   ONEOFF_PURCHASES_FREQUENCY        8950 non-null   float64\n",
+      " 9   PURCHASES_INSTALLMENTS_FREQUENCY  8950 non-null   float64\n",
+      " 10  CASH_ADVANCE_FREQUENCY            8950 non-null   float64\n",
+      " 11  CASH_ADVANCE_TRX                  8950 non-null   int64  \n",
+      " 12  PURCHASES_TRX                     8950 non-null   int64  \n",
+      " 13  CREDIT_LIMIT                      8949 non-null   float64\n",
+      " 14  PAYMENTS                          8950 non-null   float64\n",
+      " 15  MINIMUM_PAYMENTS                  8637 non-null   float64\n",
+      " 16  PRC_FULL_PAYMENT                  8950 non-null   float64\n",
+      " 17  TENURE                            8950 non-null   int64  \n",
+      "dtypes: float64(14), int64(3), object(1)\n",
+      "memory usage: 1.2+ MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "data.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 148,
+   "id": "1f681528",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "X = data[[\"PAYMENTS\", \"BALANCE\"]]\n",
+    "plt.scatter(X[\"PAYMENTS\"], X[\"BALANCE\"], c=\"blue\")\n",
+    "plt.xlabel(\"PAYMENTS\")\n",
+    "plt.ylabel(\" BALANCE\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 149,
+   "id": "2aea8d51",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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q/i9V/Q+uXKeqP0tqJyLXichTIrI9ULdcRO4QkUfc50sD5zaJyE4R2SEi5wTqTxeRh9y5K93e7YjIESJyk6u/R0QmMo++ArLYRoKIzC1DHTjglSJLUq3P3WMYRmtIyv67S0QeiyiPJtz7euDcvrrLgbtU9WTgLvcdETkFuAAvPuVc4GoR8fPfXgNsAE52xb/nJXjR9ScBVwAfSB5u9eR9449ZYcxljDfDo2EYdZGkkUwCvxwovwJ8GBBgW1xDVf0acLCv+jzgBnd8A/C2QP2NTvvZBewEXi8iq4BjVPVu58f88b42/r0+A5ztayuDpKo3/qwCygyPhmHURZKN5ICqHgCeBt4KfBnP0P5vVPXf5XjeL6rqk+7eTwIvc/XHAo8Hrtvn6o51x/3189qo6ovAj4Be2ENFZIOIzIjIzP79+3N0Oz1btpSTRr6fPAKq310X6k2LbmnYDWM4SFraWiwivw98BzgTOE9VL1TV75Tcj7CpV2Pq49osrFTdqqqTqjq5cuXKnF0Mp3+yBLj00nKFib8kVWRiLsMdOMvzh9392DCGiqiQd+cWvA/YDbwHeHt/iWvr2k8A2wPfdwCr3PEqYIc73gRsClz3BTzNZxXwcKB+LfCx4DXueBHwA5w7c1wpM0VKXNqQqank9CjBsmzZ3B4kIt73YGqOQSchzPp8y3tkGN2CAilSrifiLd+TQXpxnJBynlS3qkulIiIfAg6o6n8TkcuB5ar6ZyJyKvBJ4PXAL+EZ4k9W1VkRuQ/4Y+Ae4Dbgr1T1NhH5Q+DVqnqpiFzgBNs74voD5caRRMVqLF0KP/tZehfgJUu8afaFF+bq+tOmFI0LKbr3edbn217rhtEt4uJIcic/xLN3xJ3/FPAk8AKeZnMJng3jLuAR97k8cP1m4FE8reUtgfpJYLs7dxVzQZRHAp/GM8zfC5yYpt9laiRpN7GKK6OjXhbgpLf3pMSPSYkFi2gIcdpVVEI800gMo1uQN/vvgovhJcDFwJ3A97K0bUopU5Bk2cQqqiRlCE56Rr8wi1puyrs0lrS/SpRgaFu2YMMw4ikkSICjgN8DbsbzkvohcBYwktS2iSWPIIlKJ55lE6uoMjZWjmbTr+VECZOsadHjBFmSYGhDGvY29NEwmkBuQQJMO+FxLfBbwCiwK65N00tWQRIlLHq9OSN4cCIqUyAUFVBlTIpxQi4oULNMxk2ZvE1rMoz0FBEkDwAPAn8CHO/qHotr0/SSVZBkfSOvU1j4Xl5RpQx7RJKtI+tk3KTJ2+w4hpGeOEGSFJD4r4B3AMcAd4rIPwBHi8i/iGvXJeIiysNSlyxZUm1/fETgrLMWpkEJUkaCxqRUK2G5xeJSumS9vkqamNgyb6yQBX8aAyVKwoQVPA+qDwN7gW9kaduUUqZGAgu9lpYty6ZVFLGPjI15xvoozaSMN+upqfleZf6Snk/W7U2btB1q0zSSMh0ibInOKBvK8tr6eSMvIv7yPG0HXcqykYRNOlmDEKPcfsMm2ShhMTrqCZMqJpKpKdXFi+ffd2TE67dv34hzXQ6zhTRp8m7aBJz3Z9Okn6nRXUoXJN492Zu37SBLXq+tsAmzf9JJKxiCgiRJI/Eng7jrfM2kbAN2mvEsWbJQ2Pj9CZukqxJ6eWmK4V81v7bWJC3P6C5VCZLH87YdZCkSRxI36WTVRtKU4ASbtMRWxdtnFoGYRfNo0uTdJEwjMZpMnCCJTZESh4jsVdXWbZNU1Va7USlE8rJ0KRx5JBw86GX+XbMGbrghetOsKlKPpE08GfZsS5GSHT/RZfB33J8qp8x2hpGF3FvtisjnReSWkPJ5IlK2Dytle/r89Kfzd0m84QZvH/jR0fDrRYpvz9tPL+VvOCzFve3QmJ28e8jY3jPZMA+3CohSVZym8mtxJa5tU0uZKVKC1BGM6C8DpYmmL8vYvmRJvueEtV2ypNplLFsyM5JomoNFm6ACr63jgT/N03bQpSpBkpQzq0xhkuXaokxNxXuMxQUe9hvhFy+u7h/WJggjDWZPyk+cIEnaavfniMgKEdkoIl8DvgL8YomKUasIU41vu62eZ2exw2RZbotS99et85bVwoISb7ghevlk8+b5afHB+15V4GGTAh2N5tLEINROECVhPAHE0cBFwP8FHsMLRtwX16bppahGEvXmW4c2kkd7yTsmEU/LCl7jv835Gkrc8lHdLql5n2fLYcOFaST5oUCurWeBr+Jts+t7eA1Vrq1+6rCFjI6qnn12ci6tpLJ0afzSkz+BRj1HZKGLc9LyUZzrb5X/sHkmCFsOGz7sd56fIoLkMrydCbcDfw68fNgFSdkp38Mm77CgvTihE3c+7J8kS/r74EScJ4FjXf+weSYIezsdTkwLzUduQfLzi+BEvB0MHwJ+BrwPeEWatk0rTddIzj47W+qUqan00fF5xhBcGkpaPkrSRKr+h806QVhEuGGkJ06QpDK2q+pjqrpFVV8N/DLwC8DteWwyIvJKEdkWKM+IyHtE5P0i8r1A/ZpAm00islNEdojIOYH600XkIXfuSpG0IXT5CcuGu3hxefffts2LH0lCBC691DN2J8Vm9BsSsxgWg/dOig2Juq+It6971XEN69Z5zzl8ON3zLNbFMMohtddWgCeBP1fVl+d5oKruUNXTVPU04HTgEPA5d/oK/5yq3gYgIqcAFwCnAucCV4uIH5Z3DbABONmVc/P0KQv9wV+9XvoI8DQkCRE/4OwTn4Crr/bqwoRbENX5nlhpJ8pguvio54h4Ufdx9+2vb0pAWFKKfMMwUhKlqniaDGfgufr+PfBaPFvJPwNPAefGtU1TgDcDX3fH7wf+JOSaTcCmwPcvAG8AVgEPB+rXAh9LembZcSR174oYRVRiyTCbQZQ9IU3ix40bo/eJT2uMb5Kx09bLDSMdFDC2z7jJ/neBp4EzXP2/BL4V1zZNAa4D/kjnBMluvB0ZrwNe6uqvAi4MtLkWOB9vb5Q7A/VnArdGPGeDG8vM6tWrS/mhJnknVVHSGIHTek3lnUDTGNzj7msGbsNoJ3GCJGlpa5GqflFVPw38s6r+o9NiHk5UdRIQkSXAbwOfdlXX4HmFnYa3fPZh/9KQ5hpTv7BSdauqTqrq5MqVK4t0G5hLkldmksYkFi9Ot+Ti2wmiltuKBl4lBXQl2SmqDghLWjaLOt+U5TbDaCVREsYTQHwz7Djse9YCnAd8MeLcBLDdHQ98aav/LTvrLohlaiT+ElKSNhH15h+3EVaapa2iGkWVGknSslnckl6TltsMo4lQYGlrFngG+DHwojv2v78Q1zapADcC7wp8XxU4vgy40R2fCjwAHAGcgBdhP+rO3YdnxxE8L7I1Sc8te4fEusvixQuTIWaNFYlze81z3yyTbhk2kihBmiSk4oRrHuFm9hVjmMgtSKoqwBhwAHhJoO4TeHEqDwK39AmWzcCjwA7gLYH6STwHgEfxbCmS9Oyy92xvSlm6dG5C9LWOuISLaUuY7aPXm7/dbtYJtMgEHCeIkuJC8gST5umHYXSRxgmSQZasgqTqSPYqS5iHVdbiBz02ZdKM0zryaiRRZXR07rn9wi9ur3rD6CJxgiRPHMlQsXz5oHuQn61b08eMRBnnVb0NtZqSWTfKKL9nD/zkJwuDQ4NxIVFxI1HMznqfQecKVe8zKt7Hssgaw4gJkg4zO5scrAhegOOll0Zf50+o/ezZU793U5xgPHBgfpBo/06BUTsJjo+H38+vD0tRn6d/htFVFg26A03n4MFB9yA/o6Nzk+j69eECYXzcc9P12bo1WnCEsWGD91nXtq5btizcnzzI88/DsmXwgx+En1+3LryvYXue+5pMWi3DouKNYcU0kgSi3jDHx2Fqqt6+ZCU4yUdtTuVPfNPT3jVZhAjUv8QV1Cqi2Ls3W1xI0p7nUX8DvZ7tk95ULC6oZqKMJ10tZbj/Bg3NdRrPFy1Kf+2yZQvjQuLiRIp4p/kG+bpdYaP63OuV6xzQJGcDIxn7fVUD5rWVX5Coxk+SdQqStOnlo0pc0GER766yJ+4sv5ew51bhUWUxI+3B0vBUQ5wgsaWtHHz963Nqc1HSZg7u9dKll4/j0CG45pr53kcbNnhqf5yROG4ZKXjv/u9VL3lFLUlF2bWKeFRlTVFvDA7bl71+TJAkEOb6GZyMi5L2Hk8/XfxZYfgTflSK+I0bvYkzSpj0etVM3GkJm+Dz7jNi6+rdwPaZqR8TJAlkcf2sksOHq7v33r3eBLx+/XwNSdUzwE9PR8dgfPSjzfvHzbPPSNgLg6+tGe3C9pkZAFFrXl0tWW0kddpA8pSlS4vfI00uqqjUKFH7oAzauJnVpmHr6t3CbFrlQ4yNRLzzw8Pk5KTOzMykvn7RouwusXXRHwMyPQ0XXpjtHiLelDk+ni4tvn99rwfPPedFk/fT63maSpvsCCMj4cuMItVqg4bRFkTkflWdDDtnS1sJNFWIjIx4k3hwPX/dunSG8SD+5LlnTzrDv3/9gQPhQsQ/d+GF7bIzNG15zjDahAmSBLJOzHVx+LA3YWvfev6WLckCYXzc0xr6US13//l+O0OTjdm2rm4Y+TFBksCaNYPuQToOHYKLLvKOL700+rqREc+4HuVK7C9bldmvzZvDjdkXXwwrVsQLlrqET1J0u2EY0ZiNJIEVK4rHb9SNb8fIg6+BlbmNsIi3RJR0z7Gx+ZO3L3z6c2DZBG8Y9WM2kgK0TYhAfiHiL+WUHf+xenW6e/YHMYa5Xg8qfb1hGNGYIDF+zlFHeZ9Re7AsXTqnsaSN6veFU1qjdVDgWISyR5NtS4YBAxIkIrJbRB4SkW0iMuPqlovIHSLyiPt8aeD6TSKyU0R2iMg5gfrT3X12isiVImWaij3KtBc0nQMHvKWk554LP3/kkXNG6TQusUE7Q1qjdVDgmCeVBUoaLSEqwKTKAuwGVvTVfRC43B1fDnzAHZ8CPAAcAZyAtz/7qDt3L/AGQIDbCeznHlXyZP8dGRl84GETih/clSXIMUhS0sn+IMa4LK51BpwNMrjNAiWNpkDTsv9GCJIdwCp3vArY4Y43AZsC133BCY9VwMOB+rXAx5KenSf7b9Gsu10p/mSadN3ixeGTbZhg8O8XNUGHTeJ1pgkfdEryqJ+3SD3PNwyfJgqSXcA3gfuBDa7uh33XPO0+rwIuDNRfC5wPTAJ3BurPBG6NeN4GYAaYWb16deYfYJEU620sUWlX/BT0Se17veifZRlv93W+pQ9aIxj08w3DJ06QDMrY/kZVfR3wFuAPReRNMdeG2T00pn5hpepWVZ1U1cmVK1dm7myU8XnYuO22dHvAx21PXEY69jqN8IM2+FugpNEGBiJIVPUJ9/kU8Dng9cD3RWQVgPt8yl2+Dzg+0Pw44AlXf1xIfalMT8Mzz5R912bz05+G1/txIElb3aYxhhfxRKrTCF/Hs+J+Fm0JlDTPsiEnSlWpqgBLgaMDx98AzgU+xHxj+wfd8anMN7Y/xpyx/T7gDOaM7WuSnp/VRlJkC9oulqCxe8mSheej7CNBitodumQjGbQNJok0S5FNH4NRDjTJRgKc6ATDA8C3gc2uvgfcBTziPpcH2mzG89baQcAzC89Ost2duwoXqR9XsgqSYbOPpCnj4/H7pSdRxrp/V7y2mmwDSSsgmjwGozziBImlSElgYqLcdCFpGB1tbtZhmEvsGPanE5d2fXrai0qP+nkOImW736e9e73lqi1b6l02anL6+qi//f7tC5o8BqM8LEVKAU46qf5nNlmIgDfhZrUdBAPr4u5bJ00I9mty0GVaR4Mmj8GoBxMkCXzlK/U/c3S0/mdmYcuW7N5ESVsWD8ITqQm5vJrslZVWQISNQcQTzGZ4HxKi1ry6Wtqy1W6TbTM+WWwHceMZ1FaoTQn2a+q2sFmM6P4Ywv52zfDeDWiSsX3QpS2CpKkljTE9bGIsYpCNm2iLTMJmJE4m68/XfqbdxQRJoJggyV+WLMnv2rtxYz4X0aR8W21xIx4WmqLlGeVjgiRQTJCkLyKeBuK/jfopUuLeTuPeSPNoD3H3a5sb8TBgGkl3iRMk5v6bQPmJ6duD7745PQ3vfvfCTb7Cdiss2xU07n5Q7rOM4tiult3F3H+NXKh6Ww2/613hO0WGeTiV7Qoad7+muJ1aepA52pLSxSgXEyRDQBF34gMH4IUXos/3xxSU7c4ad78muM42IRalaWRJzGlCuCNErXl1tQybjWR0NDotfBklbO27bLtDVV5bZWA2gfyYs0O7wGwkc5iNpDy6uPadNWWKpQfJT9oULEYzMBuJUTpLl8JRR8E739mdJYk8y1SDTjPfZga914tRHiZIKmIt0+xigllG2MUEa2n3f3+vN2c83bjRs5scODA34V58cfsnuDwpU6q20yQJt6CQWbHCK77A+YM/CBdATRFMTXGWMEogas2rq6UOG8lapvQnzF/8/QljupapgdtM8pagLWJkJPyaNFHvVVCWnSRvMN2g0syH2RjiSpHA0CowG0m7wAIS8wuSPDmvdjEeemIX4wMXCHlKr5d+wspK0Um4zMkoq+G8DkN/nHDLs+na6Gi2MVbNoJ0ljPSYIAmUrILkyCOz/7POEv7fP4sMXChkLSMjniBJe30WyhACZXpNZU1SmDflS1m5q8pM7GkpTIwkTJAESh1LW13TSNKWrEtbZSRyLHtiTDvR5+l7HuET16YLGonRHholSIDjgS8D/4S31e67Xf37ge8B21xZE2izCdiJt9XuOYH604GH3LkrqWCr3TwTatdsJFGTT3/JuixRxCaRtNRW9cSYp+95BWe/cPNznvnPS/t7bJqNxGgXTRMkq4DXueOjge8CpzhB8ich15+Ct7/7EcAJePuzj7pz9wJvAAS4ncB+7lGlroDEtUzpLsZ1FtFdjLdWiAQnm6RrNm7M9KPNPbEmvYnXMTHm6XsZmXHDhKh/314vXZJNs0sYeWiUIFnQAbgZ+K0YQbIJ2BT4/gUnPFYBDwfq1wIfS3resEW2l1H8ySbr9WEEJ7FeT3Xx4vlt0wiBJmySlWeZqgx7jkXSG4OisYIEmAD2Asc4QbIbeBC4Dnipu+Yq4MJAm2uB84FJ4M5A/ZnArRHP2QDMADOrV6/O9MNr8k6FdZTgHiRZ1uTDJtWwyXfJkvlv0UVTy9dJ1jf7MpwLbL8PY1DECZKBBSSKyDLgs8B7VPUZ4Brg5cBpwJPAh/1LQ5prTP3CStWtqjqpqpMrV67M1E8NveNwMDIC1103lyIkLPguikOH4MIL5we8hQX8Pf88LFs2l+APkoPlyggCLCMoL0tyQv/6oplxLYjPaCRREqbKAizGW6J6b8T5CWC7O7alrQGVsLfcqals7sDBt+6kt+ms7rfBfvR6w7EzYpv7brQbmrS0hadJfBz4SF/9qsDxZcCN7vhU5hvbH2PO2H4fcAZzxvY1Sc83QZK+9C8V5XU59e+VtCSVZckqq9AJLkHFCcK8QZF1Gq/NWG4MgqYJkl8FFM8Wss2VNcAn8Fx5HwRu6RMsm/G8tXYQ8MzCs5Nsd+euoiHuv10ovqto0DC+ZEn++4l49ws753t7JRnRgxNnWqGTNY1InEAKwzQEY1iIEySWRj6BqDThXWfJEu/z+efLud/4uPcZlzY8Kq24yPzfwdjYQltL8Npg+vaoe6bpb5pU5pYK3RgWLI18AYZRiIAnQLIKEREvM3C/IVzEm2yjJnQ/bXiYEb1fiIAnRKJ2few3OudNSZ62naVCNwwTJEaJqMJtt815JkG4IOhnZMQrmzfD+vXzvZqi2s7OpvPcivJm6vXm+hhGWi8o86IyDBMkRsns3TvnFhsnCILMznrX7dkD117rCQPfpTZqsvddZ3u9ubqjjlp4XZSr8Ec/6t1/aipcC1qzJrnfcfevc994wxg4UcaTrhYzts8vWYzoixcnu/6OjuZPce6XZcvSRb+nNXQneTlt3LjQ0J/V4G5eVEbXwYztc9ie7XMsXerZG5Yv93Y7TKLXg4MHvWWo2dnk69Msa2Wl1/O0iXXryjN0m8Hc6DrT097S8d693rLrli3ZAmHBjO1GBD/9qTfRpxEiMLe1bhohAtU4Kjz77NxxWYZuM5gn05TteY3sJG3XXAYmSIzciMwZxesiuId6WYbuqOt9J4BhnzjrmIiM6ghLTRT8PyoDEyRGbnyLAsw3eldNnLtwHkN3VA6xoBPAME+cdUxERnXUoXGbIDEKs2cPPPPMXBBjWsbGvLiToLtvWBxKP74GUUYSxLD7hMWopJk4u7r8Y0t/7aYWF/UoK3xXi3ltVVd6veTdFINeXUl7lkAxb6q85EnV3uVUKU1J22/ko6y/TZqUa2vQxQRJdSVp75Y8f7yDcK3NM3F2ebLtspAcFsr4P4oTJOb+m0CX3X/LZmRkfp6rIKOjcMMN2ZedBoFvXA7aBcbG4pfNonKy9ef+aitluI8a7cbcf0tiLdPsYoJZRtjFBGvpyCJ4SURNmCLphUgT7Ax5bC9R683Ll1fTx7rJuomXMWREqSpdLXmXttYypT9hvn7/E8Z0LVMDX1JqQ0lD3UsoZS6bTU2FZwhYvNiWgIxugNlIiguSXYyHzpC7GB/4JF118Q3kefckSWsnyLqxVREhUIXQikof0wU7SVYsbUz3MEESKHkFyWzELDmLDHyir7oEvZWCE0SSh1aayTl4vzTP99sUFQJVGMfzeHt1ETPOdxMTJIGSR5CsZUpnCZ8lhkEj6fXm/0zSbrm7dKnXNuqtNO3uhf2TexlCoIpJv8ueW1mwn0M3iRMkrTe2i8i5IrJDRHaKyOVVPOMv2MwIuqD+MMKfMzz5wqenYcUKuPDCdLsOqs7l5+qPDp+ehne+M3qnQ5+wSPUyAuSKBmmFOQU0LaW830cRWLTI+6zDgcECGIeQKAnThgKM4u3XfiKwBHgAOCWuTR6NJEobmYWBawt1lax7n4+MhNf7mkl/avgwzSBqbb2MN94iyy9xbZtiG4j7fVW9zGQaSTehq0tbwBuALwS+bwI2xbXJI0iGwdA+Ph49+Y+OZttfJM4gn2avkqQJp6w1+LyTfhsmyqI/4yKYjaSbdFmQnA/8beD7O4GrQq7bAMwAM6tXr874w+u262/wHzzuuqSo9aCQiLOhJBnWIb1WMKg3/zYY1ZN+xlX3tSmamVEeXRYkvxsiSP4qrk2ROJJdjOssorsYb6UQGR/3dgOM+gePm/yT3nDDDPJRb6Vx9+q/TxMxjcQYRrosSGpZ2hp0SaMN+JP02WfPrz/yyGxbxsat/UetuUcF3UW9lUbZSJYsacebaxuWbgZpIzG6SZcFySLgMeCEgLH91Lg2WQWJavmC4cgjk4XDKafMPX9qamGwW5JrbV7iliSC2oQfQ5L32f1j6vXaNbm1YemmzN+XYcQJktYnbRSRNcBH8Dy4rlPVWGfLrEkbDcMwjPikjYvq7kzZqOptwG2D7odhGMaw0vqARMMwDGOwmCAxDMMwCmGCxDAMwyiECRLDMAyjEK332sqKiOwHUqQcDGUF8IMSu9N0bLzdxsbbbcoe77iqrgw7MXSCpAgiMhPl/tZFbLzdxsbbbeocry1tGYZhGIUwQWIYhmEUwgRJNrYOugM1Y+PtNjbeblPbeM1GYhiGYRTCNBLDMAyjECZIDMMwjEKYIEmJiJwrIjtEZKeIXD7o/qRFRK4TkadEZHugbrmI3CEij7jPlwbObXJj3CEi5wTqTxeRh9y5K0VEXP0RInKTq79HRCZqHWAfInK8iHxZRP5JRL4tIu929Z0cs4gcKSL3isgDbrz/xdV3crw+IjIqIt8SkVvd986OV0R2u35uE5EZV9es8Ubll7cyb9+TUeBR4ETm9j05ZdD9Stn3NwGvA7YH6j4IXO6OLwc+4I5PcWM7Am+Pl0eBUXfuXryNxAS4HXiLq/8D4K/d8QXATQMe7yrgde74aOC7blydHLPr2zJ3vBi4Bzijq+MNjPu9wCeBW4fgb3o3sKKvrlHjHegfQ1sKOXZibFIBJpgvSHYAq9zxKmBH2LiAL7ixrwIeDtSvBT4WvMYdL8KLpJVBjznQ15uB3xqGMQNjwDeBX+nyeIHjgLuA32BOkHR5vLtZKEgaNV5b2krHscDjge/7XF1b+UVVfRLAfb7M1UeN81h33F8/r42qvgj8COhV1vMMOBX9tXhv6Z0ds1vm2QY8Bdyhqp0eL95Gdn8GHA7UdXm8CnxRRO4XkQ2urlHjbf3GVjUhIXVd9JuOGmfc+Bv5sxGRZcBngfeo6jNuOTj00pC6Vo1ZVWeB00TkF4DPicirYi5v9XhF5K3AU6p6v4iclaZJSF1rxut4o6o+ISIvA+4QkYdjrh3IeE0jScc+4PjA9+OAJwbUlzL4voisAnCfT7n6qHHuc8f99fPaiMgi4CXAwcp6ngIRWYwnRKZV9e9ddafHDKCqPwS+ApxLd8f7RuC3RWQ3cCPwGyIyRXfHi6o+4T6fAj4HvJ6GjdcESTruA04WkRNEZAmeQeqWAfepCLcA693xejw7gl9/gfPiOAE4GbjXqc4/FpEznKfHRX1t/HudD3xJ3WLrIHD9uxb4J1X9H4FTnRyziKx0mggichTwm8DDdHS8qrpJVY9T1Qm8/8MvqeqFdHS8IrJURI72j4E3A9tp2ngHZUBqWwHW4HkAPQpsHnR/MvT7U8CTwAt4bx6X4K1/3gU84j6XB67f7Ma4A+fV4eon3R/wo8BVzGVFOBL4NLATzyvkxAGP91fx1PIHgW2urOnqmIHXAN9y490O/CdX38nx9o39LOaM7Z0cL56n6AOufNufe5o2XkuRYhiGYRTClrYMwzCMQpggMQzDMAphgsQwDMMohAkSwzAMoxAmSAzDMIxCmCAxjBSIyKzLvrpdRD4tImOufpGI/EBE/tJ9f7OI3B3IrDrq2v1rEXm/iKiInBS472WubtJ9D2Z63SYiV7r660XkeyJyhPu+wl376sC1B0Vklzu+U0RGXJbX7e6e97nYAsMoFRMkhpGOZ1X1NFV9FfA8cKmrfzOev/47RERU9YvAHrx4HYA/Bu5T1W+47w/hBdL5nA98p+9Zv+6edZqq/vtA/SxwcfBCVX3IvxYvsOxP3fffBH4P+CXgNar6auB3gB/mHL9hRGKCxDCy8w+Ar1WsBT4K7MVL3w5wGbBJRE4F/gh4X6Dt/wbOAxCRE/ES5O1P+dyPAJe5NBZpWAU8qaqHAVR1n6o+nbKtYaTGBIlhZMBN4m8BHnIpSc4GbsXLILAWfp6N9SPA3cB/VdVg3qJngMddYsW1wE0hj/lyYLnqskD9XuD/Ae9M2d2/A/6tu8+HReS1acdpGFkwQWIY6TjKpWqfwZvQrwXeCnxZVQ/hJYn8HREZddf/T7wNha4PudeNeMtbb8NLwtdPcGnrir5zfwH8KSn+d1V1H/BKvD0qDgN3icjZSe0MIyuWRt4w0vGss0P8HBFZC7zRZaIFL//RrwN3quphEYnKP/R54EPAjManuF+Aqu50Au0dKa9/Dm83vNtF5Pt4wuuu1A80jBSYIDGMHIjIMXgJIo93kzUi8i685ao749qq6rMi8j68JKB52AL8nxR9fB3wz+rtZTGCl+DxwZzPNIxITJAYRj7ejpdu+7lA3c3AB0XkiL76BajqjTGnvywis+74QVW9qK/tt0Xkm8DrEvr4MuBvfJdhvMyuVyW0MYzMWPZfwzAMoxBmbDcMwzAKYYLEMAzDKIQJEsMwDKMQJkgMwzCMQpggMQzDMAphgsQwDMMohAkSwzAMoxD/H3egGE8rYPFlAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "K=3\n",
+    "\n",
+    "Centroids = (X.sample(n=K))\n",
+    "plt.scatter(X[\"PAYMENTS\"], X[\"BALANCE\"], c=\"blue\")\n",
+    "plt.scatter(Centroids[\"PAYMENTS\"], Centroids[\"BALANCE\"], c=\"red\")\n",
+    "plt.xlabel(\"PAYMENTS\")\n",
+    "plt.ylabel(\"BALANCE\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 150,
+   "id": "1b087e0c",
+   "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>PAYMENTS</th>\n",
+       "      <th>BALANCE</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>227</th>\n",
+       "      <td>2187.930651</td>\n",
+       "      <td>3019.019487</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8879</th>\n",
+       "      <td>169.249683</td>\n",
+       "      <td>137.744895</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7213</th>\n",
+       "      <td>239.928287</td>\n",
+       "      <td>470.353862</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         PAYMENTS      BALANCE\n",
+       "227   2187.930651  3019.019487\n",
+       "8879   169.249683   137.744895\n",
+       "7213   239.928287   470.353862"
+      ]
+     },
+     "execution_count": 150,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Centroids"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 151,
+   "id": "a3e86a4b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Dell\\AppData\\Local\\Temp\\ipykernel_20704\\1517157340.py:14: 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",
+      "  X[i] = ED\n",
+      "C:\\Users\\Dell\\AppData\\Local\\Temp\\ipykernel_20704\\1517157340.py:14: 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",
+      "  X[i] = ED\n",
+      "C:\\Users\\Dell\\AppData\\Local\\Temp\\ipykernel_20704\\1517157340.py:14: 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",
+      "  X[i] = ED\n",
+      "C:\\Users\\Dell\\AppData\\Local\\Temp\\ipykernel_20704\\1517157340.py:26: 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",
+      "  X[\"Cluster\"]=C\n",
+      "C:\\Users\\Dell\\AppData\\Local\\Temp\\ipykernel_20704\\1517157340.py:14: 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",
+      "  X[i] = ED\n",
+      "C:\\Users\\Dell\\AppData\\Local\\Temp\\ipykernel_20704\\1517157340.py:26: 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",
+      "  X[\"Cluster\"]=C\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2380.310520589781\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Dell\\AppData\\Local\\Temp\\ipykernel_20704\\1517157340.py:14: 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",
+      "  X[i] = ED\n",
+      "C:\\Users\\Dell\\AppData\\Local\\Temp\\ipykernel_20704\\1517157340.py:26: 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",
+      "  X[\"Cluster\"]=C\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2026.704717912281\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Dell\\AppData\\Local\\Temp\\ipykernel_20704\\1517157340.py:14: 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",
+      "  X[i] = ED\n",
+      "C:\\Users\\Dell\\AppData\\Local\\Temp\\ipykernel_20704\\1517157340.py:26: 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",
+      "  X[\"Cluster\"]=C\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1861.0627538435633\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Dell\\AppData\\Local\\Temp\\ipykernel_20704\\1517157340.py:14: 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",
+      "  X[i] = ED\n",
+      "C:\\Users\\Dell\\AppData\\Local\\Temp\\ipykernel_20704\\1517157340.py:26: 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",
+      "  X[\"Cluster\"]=C\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1946.5644432619774\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Dell\\AppData\\Local\\Temp\\ipykernel_20704\\1517157340.py:14: 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",
+      "  X[i] = ED\n",
+      "C:\\Users\\Dell\\AppData\\Local\\Temp\\ipykernel_20704\\1517157340.py:26: 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",
+      "  X[\"Cluster\"]=C\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2042.8586900620694\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Dell\\AppData\\Local\\Temp\\ipykernel_20704\\1517157340.py:14: 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",
+      "  X[i] = ED\n",
+      "C:\\Users\\Dell\\AppData\\Local\\Temp\\ipykernel_20704\\1517157340.py:26: 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",
+      "  X[\"Cluster\"]=C\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1856.1445079406496\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Dell\\AppData\\Local\\Temp\\ipykernel_20704\\1517157340.py:14: 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",
+      "  X[i] = ED\n",
+      "C:\\Users\\Dell\\AppData\\Local\\Temp\\ipykernel_20704\\1517157340.py:26: 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",
+      "  X[\"Cluster\"]=C\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1598.5474025389212\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Dell\\AppData\\Local\\Temp\\ipykernel_20704\\1517157340.py:14: 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",
+      "  X[i] = ED\n",
+      "C:\\Users\\Dell\\AppData\\Local\\Temp\\ipykernel_20704\\1517157340.py:26: 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",
+      "  X[\"Cluster\"]=C\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1425.093190904552\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Dell\\AppData\\Local\\Temp\\ipykernel_20704\\1517157340.py:14: 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",
+      "  X[i] = ED\n",
+      "C:\\Users\\Dell\\AppData\\Local\\Temp\\ipykernel_20704\\1517157340.py:26: 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",
+      "  X[\"Cluster\"]=C\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1225.1703264911735\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Dell\\AppData\\Local\\Temp\\ipykernel_20704\\1517157340.py:14: 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",
+      "  X[i] = ED\n",
+      "C:\\Users\\Dell\\AppData\\Local\\Temp\\ipykernel_20704\\1517157340.py:26: 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",
+      "  X[\"Cluster\"]=C\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "696.214517713354\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Dell\\AppData\\Local\\Temp\\ipykernel_20704\\1517157340.py:14: 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",
+      "  X[i] = ED\n",
+      "C:\\Users\\Dell\\AppData\\Local\\Temp\\ipykernel_20704\\1517157340.py:26: 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",
+      "  X[\"Cluster\"]=C\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "490.59965331177284\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Dell\\AppData\\Local\\Temp\\ipykernel_20704\\1517157340.py:14: 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",
+      "  X[i] = ED\n",
+      "C:\\Users\\Dell\\AppData\\Local\\Temp\\ipykernel_20704\\1517157340.py:26: 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",
+      "  X[\"Cluster\"]=C\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "455.40075432225194\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Dell\\AppData\\Local\\Temp\\ipykernel_20704\\1517157340.py:14: 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",
+      "  X[i] = ED\n",
+      "C:\\Users\\Dell\\AppData\\Local\\Temp\\ipykernel_20704\\1517157340.py:26: 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",
+      "  X[\"Cluster\"]=C\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "254.4052570339701\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Dell\\AppData\\Local\\Temp\\ipykernel_20704\\1517157340.py:14: 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",
+      "  X[i] = ED\n",
+      "C:\\Users\\Dell\\AppData\\Local\\Temp\\ipykernel_20704\\1517157340.py:26: 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",
+      "  X[\"Cluster\"]=C\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "295.4726330774505\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Dell\\AppData\\Local\\Temp\\ipykernel_20704\\1517157340.py:14: 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",
+      "  X[i] = ED\n",
+      "C:\\Users\\Dell\\AppData\\Local\\Temp\\ipykernel_20704\\1517157340.py:26: 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",
+      "  X[\"Cluster\"]=C\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "271.5115123254775\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Dell\\AppData\\Local\\Temp\\ipykernel_20704\\1517157340.py:14: 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",
+      "  X[i] = ED\n",
+      "C:\\Users\\Dell\\AppData\\Local\\Temp\\ipykernel_20704\\1517157340.py:26: 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",
+      "  X[\"Cluster\"]=C\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "386.7985411507914\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Dell\\AppData\\Local\\Temp\\ipykernel_20704\\1517157340.py:14: 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",
+      "  X[i] = ED\n",
+      "C:\\Users\\Dell\\AppData\\Local\\Temp\\ipykernel_20704\\1517157340.py:26: 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",
+      "  X[\"Cluster\"]=C\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "236.0601390235887\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Dell\\AppData\\Local\\Temp\\ipykernel_20704\\1517157340.py:14: 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",
+      "  X[i] = ED\n",
+      "C:\\Users\\Dell\\AppData\\Local\\Temp\\ipykernel_20704\\1517157340.py:26: 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",
+      "  X[\"Cluster\"]=C\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "461.662050516107\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Dell\\AppData\\Local\\Temp\\ipykernel_20704\\1517157340.py:14: 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",
+      "  X[i] = ED\n",
+      "C:\\Users\\Dell\\AppData\\Local\\Temp\\ipykernel_20704\\1517157340.py:26: 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",
+      "  X[\"Cluster\"]=C\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "473.21412632830413\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Dell\\AppData\\Local\\Temp\\ipykernel_20704\\1517157340.py:14: 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",
+      "  X[i] = ED\n",
+      "C:\\Users\\Dell\\AppData\\Local\\Temp\\ipykernel_20704\\1517157340.py:26: 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",
+      "  X[\"Cluster\"]=C\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "308.6814302453015\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Dell\\AppData\\Local\\Temp\\ipykernel_20704\\1517157340.py:14: 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",
+      "  X[i] = ED\n",
+      "C:\\Users\\Dell\\AppData\\Local\\Temp\\ipykernel_20704\\1517157340.py:26: 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",
+      "  X[\"Cluster\"]=C\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "255.4978952241937\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Dell\\AppData\\Local\\Temp\\ipykernel_20704\\1517157340.py:14: 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",
+      "  X[i] = ED\n",
+      "C:\\Users\\Dell\\AppData\\Local\\Temp\\ipykernel_20704\\1517157340.py:26: 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",
+      "  X[\"Cluster\"]=C\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "260.50486627289524\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Dell\\AppData\\Local\\Temp\\ipykernel_20704\\1517157340.py:14: 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",
+      "  X[i] = ED\n",
+      "C:\\Users\\Dell\\AppData\\Local\\Temp\\ipykernel_20704\\1517157340.py:26: 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",
+      "  X[\"Cluster\"]=C\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "198.0036579029712\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Dell\\AppData\\Local\\Temp\\ipykernel_20704\\1517157340.py:14: 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",
+      "  X[i] = ED\n",
+      "C:\\Users\\Dell\\AppData\\Local\\Temp\\ipykernel_20704\\1517157340.py:26: 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",
+      "  X[\"Cluster\"]=C\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "216.9532031394632\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Dell\\AppData\\Local\\Temp\\ipykernel_20704\\1517157340.py:14: 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",
+      "  X[i] = ED\n",
+      "C:\\Users\\Dell\\AppData\\Local\\Temp\\ipykernel_20704\\1517157340.py:26: 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",
+      "  X[\"Cluster\"]=C\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "244.03063841406674\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Dell\\AppData\\Local\\Temp\\ipykernel_20704\\1517157340.py:14: 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",
+      "  X[i] = ED\n",
+      "C:\\Users\\Dell\\AppData\\Local\\Temp\\ipykernel_20704\\1517157340.py:26: 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",
+      "  X[\"Cluster\"]=C\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "333.1218397856759\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Dell\\AppData\\Local\\Temp\\ipykernel_20704\\1517157340.py:14: 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",
+      "  X[i] = ED\n",
+      "C:\\Users\\Dell\\AppData\\Local\\Temp\\ipykernel_20704\\1517157340.py:26: 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",
+      "  X[\"Cluster\"]=C\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "299.06069853505426\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Dell\\AppData\\Local\\Temp\\ipykernel_20704\\1517157340.py:14: 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",
+      "  X[i] = ED\n",
+      "C:\\Users\\Dell\\AppData\\Local\\Temp\\ipykernel_20704\\1517157340.py:26: 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",
+      "  X[\"Cluster\"]=C\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "121.15819933727346\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Dell\\AppData\\Local\\Temp\\ipykernel_20704\\1517157340.py:14: 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",
+      "  X[i] = ED\n",
+      "C:\\Users\\Dell\\AppData\\Local\\Temp\\ipykernel_20704\\1517157340.py:26: 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",
+      "  X[\"Cluster\"]=C\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "72.34143196024809\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Dell\\AppData\\Local\\Temp\\ipykernel_20704\\1517157340.py:14: 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",
+      "  X[i] = ED\n",
+      "C:\\Users\\Dell\\AppData\\Local\\Temp\\ipykernel_20704\\1517157340.py:26: 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",
+      "  X[\"Cluster\"]=C\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "102.1227933372968\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Dell\\AppData\\Local\\Temp\\ipykernel_20704\\1517157340.py:14: 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",
+      "  X[i] = ED\n",
+      "C:\\Users\\Dell\\AppData\\Local\\Temp\\ipykernel_20704\\1517157340.py:26: 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",
+      "  X[\"Cluster\"]=C\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "80.56854477319405\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Dell\\AppData\\Local\\Temp\\ipykernel_20704\\1517157340.py:14: 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",
+      "  X[i] = ED\n",
+      "C:\\Users\\Dell\\AppData\\Local\\Temp\\ipykernel_20704\\1517157340.py:26: 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",
+      "  X[\"Cluster\"]=C\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "4.1952505433025635\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Dell\\AppData\\Local\\Temp\\ipykernel_20704\\1517157340.py:14: 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",
+      "  X[i] = ED\n",
+      "C:\\Users\\Dell\\AppData\\Local\\Temp\\ipykernel_20704\\1517157340.py:26: 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",
+      "  X[\"Cluster\"]=C\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-1.433251390004557\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Dell\\AppData\\Local\\Temp\\ipykernel_20704\\1517157340.py:14: 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",
+      "  X[i] = ED\n",
+      "C:\\Users\\Dell\\AppData\\Local\\Temp\\ipykernel_20704\\1517157340.py:26: 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",
+      "  X[\"Cluster\"]=C\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-1.9615739841195818\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Dell\\AppData\\Local\\Temp\\ipykernel_20704\\1517157340.py:14: 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",
+      "  X[i] = ED\n",
+      "C:\\Users\\Dell\\AppData\\Local\\Temp\\ipykernel_20704\\1517157340.py:26: 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",
+      "  X[\"Cluster\"]=C\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-2.539945687794443\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Dell\\AppData\\Local\\Temp\\ipykernel_20704\\1517157340.py:14: 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",
+      "  X[i] = ED\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Dell\\AppData\\Local\\Temp\\ipykernel_20704\\1517157340.py:26: 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",
+      "  X[\"Cluster\"]=C\n"
+     ]
+    }
+   ],
+   "source": [
+    "diff = 1\n",
+    "j=0\n",
+    "\n",
+    "while(diff!=0):\n",
+    "    XD=X\n",
+    "    i=1\n",
+    "    for index1, row_c in Centroids.iterrows():\n",
+    "        ED=[]\n",
+    "        for index2, row_d in XD.iterrows():\n",
+    "            d1 = (row_c[\"PAYMENTS\"]-row_d[\"PAYMENTS\"])**2\n",
+    "            d2 = (row_c[\"BALANCE\"]-row_d[\"BALANCE\"])**2\n",
+    "            d = sqrt(d1+d2)\n",
+    "            ED.append(d)\n",
+    "        X[i] = ED\n",
+    "        i = i+1\n",
+    "    \n",
+    "    C = []\n",
+    "    for index, row in X.iterrows():\n",
+    "        min_dist=row[1]\n",
+    "        pos=1\n",
+    "        for i in range(K):\n",
+    "            if row[i+1] < min_dist:\n",
+    "                min_dist = row[i+1]\n",
+    "                pos = i+1\n",
+    "        C.append(pos)\n",
+    "    X[\"Cluster\"]=C\n",
+    "    Centroids_new = X.groupby([\"Cluster\"]).mean()[[\"BALANCE\", \"PAYMENTS\"]]\n",
+    "    if j == 0:\n",
+    "        diff = 1\n",
+    "        j = j+1\n",
+    "    else:\n",
+    "        diff = (Centroids_new['BALANCE'] - Centroids['BALANCE']).sum() + (Centroids_new['PAYMENTS'] - Centroids['PAYMENTS']).sum()\n",
+    "        print(diff.sum())\n",
+    "    Centroids = X.groupby([\"Cluster\"]).mean()[[\"BALANCE\",\"PAYMENTS\"]]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 152,
+   "id": "aff8f387",
+   "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>PAYMENTS</th>\n",
+       "      <th>BALANCE</th>\n",
+       "      <th>1</th>\n",
+       "      <th>2</th>\n",
+       "      <th>3</th>\n",
+       "      <th>Cluster</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>201.802084</td>\n",
+       "      <td>40.900749</td>\n",
+       "      <td>17979.268721</td>\n",
+       "      <td>1111.534345</td>\n",
+       "      <td>5699.929990</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>4103.032597</td>\n",
+       "      <td>3202.467416</td>\n",
+       "      <td>13560.662669</td>\n",
+       "      <td>3915.737610</td>\n",
+       "      <td>1586.857924</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>622.066742</td>\n",
+       "      <td>2495.148862</td>\n",
+       "      <td>17099.712390</td>\n",
+       "      <td>1721.145775</td>\n",
+       "      <td>3625.546224</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1666.670542</td>\n",
+       "      <td>17835.136255</td>\n",
+       "      <td>1312.106761</td>\n",
+       "      <td>4629.881750</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>678.334763</td>\n",
+       "      <td>817.714335</td>\n",
+       "      <td>17332.344556</td>\n",
+       "      <td>320.433972</td>\n",
+       "      <td>4791.297788</td>\n",
+       "      <td>2</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",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8945</th>\n",
+       "      <td>325.594462</td>\n",
+       "      <td>28.493517</td>\n",
+       "      <td>17862.752073</td>\n",
+       "      <td>1035.808296</td>\n",
+       "      <td>5639.046431</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8946</th>\n",
+       "      <td>275.861322</td>\n",
+       "      <td>19.183215</td>\n",
+       "      <td>17913.209415</td>\n",
+       "      <td>1075.674332</td>\n",
+       "      <td>5674.983951</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8947</th>\n",
+       "      <td>81.270775</td>\n",
+       "      <td>23.398673</td>\n",
+       "      <td>18100.294205</td>\n",
+       "      <td>1212.263337</td>\n",
+       "      <td>5784.945112</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8948</th>\n",
+       "      <td>52.549959</td>\n",
+       "      <td>13.457564</td>\n",
+       "      <td>18130.597145</td>\n",
+       "      <td>1240.548938</td>\n",
+       "      <td>5810.027448</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8949</th>\n",
+       "      <td>63.165404</td>\n",
+       "      <td>372.708075</td>\n",
+       "      <td>18032.502679</td>\n",
+       "      <td>1035.186832</td>\n",
+       "      <td>5519.153691</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>8950 rows × 6 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         PAYMENTS      BALANCE             1            2            3  \\\n",
+       "0      201.802084    40.900749  17979.268721  1111.534345  5699.929990   \n",
+       "1     4103.032597  3202.467416  13560.662669  3915.737610  1586.857924   \n",
+       "2      622.066742  2495.148862  17099.712390  1721.145775  3625.546224   \n",
+       "3        0.000000  1666.670542  17835.136255  1312.106761  4629.881750   \n",
+       "4      678.334763   817.714335  17332.344556   320.433972  4791.297788   \n",
+       "...           ...          ...           ...          ...          ...   \n",
+       "8945   325.594462    28.493517  17862.752073  1035.808296  5639.046431   \n",
+       "8946   275.861322    19.183215  17913.209415  1075.674332  5674.983951   \n",
+       "8947    81.270775    23.398673  18100.294205  1212.263337  5784.945112   \n",
+       "8948    52.549959    13.457564  18130.597145  1240.548938  5810.027448   \n",
+       "8949    63.165404   372.708075  18032.502679  1035.186832  5519.153691   \n",
+       "\n",
+       "      Cluster  \n",
+       "0           2  \n",
+       "1           3  \n",
+       "2           2  \n",
+       "3           2  \n",
+       "4           2  \n",
+       "...       ...  \n",
+       "8945        2  \n",
+       "8946        2  \n",
+       "8947        2  \n",
+       "8948        2  \n",
+       "8949        2  \n",
+       "\n",
+       "[8950 rows x 6 columns]"
+      ]
+     },
+     "execution_count": 152,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 153,
+   "id": "5d2a00b4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "color=['blue','green','cyan']\n",
+    "for k in range(K):\n",
+    "    data=X[X[\"Cluster\"]==k+1]\n",
+    "    plt.scatter(data[\"PAYMENTS\"],data[\"BALANCE\"],c=color[k])\n",
+    "plt.scatter(Centroids[\"PAYMENTS\"],Centroids[\"BALANCE\"],c='red')\n",
+    "plt.xlabel('PAYMENTS')\n",
+    "plt.ylabel('BALANCE')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e69a8fbd",
+   "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": "3.9.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}