Lots of Regression Tries-Some of it takes too long to run.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Import relevenat libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from pandas.plotting import scatter_matrix\n",
    "from sklearn import preprocessing\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.preprocessing import RobustScaler\n",
    "import statsmodels.api as sm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "credit = pd.read_csv('clean_data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>PAY_0</th>\n",
       "      <th>PAY_2</th>\n",
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       "      <th>BILL_AMT2</th>\n",
       "      <th>BILL_AMT3</th>\n",
       "      <th>...</th>\n",
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       "      <td>1000</td>\n",
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       "      <td>120000</td>\n",
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       "      <td>13559</td>\n",
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       "      <td>1000</td>\n",
       "      <td>1000</td>\n",
       "      <td>1000</td>\n",
       "      <td>5000</td>\n",
       "      <td>not default</td>\n",
       "      <td>90000</td>\n",
       "      <td>female</td>\n",
       "      <td>university</td>\n",
       "      <td>2</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>50000</td>\n",
       "      <td>female</td>\n",
       "      <td>university</td>\n",
       "      <td>1</td>\n",
       "      <td>37</td>\n",
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       "      <td>35835</td>\n",
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       "      <td>10000</td>\n",
       "      <td>9000</td>\n",
       "      <td>689</td>\n",
       "      <td>679</td>\n",
       "      <td>not default</td>\n",
       "      <td>50000</td>\n",
       "      <td>male</td>\n",
       "      <td>university</td>\n",
       "      <td>1</td>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>38000</td>\n",
       "      <td>20239</td>\n",
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       "      <td>29</td>\n",
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       "      <td>581</td>\n",
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       "      <td>1542</td>\n",
       "      <td>not default</td>\n",
       "      <td>100000</td>\n",
       "      <td>female</td>\n",
       "      <td>university</td>\n",
       "      <td>2</td>\n",
       "      <td>23</td>\n",
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       "      <td>not default</td>\n",
       "      <td>20000</td>\n",
       "      <td>male</td>\n",
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       "      <td>2</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
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       "      <td>0</td>\n",
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       "      <td>300</td>\n",
       "      <td>3738</td>\n",
       "      <td>66</td>\n",
       "      <td>not default</td>\n",
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       "      <td>female</td>\n",
       "      <td>high school</td>\n",
       "      <td>2</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>12261</td>\n",
       "      <td>21670</td>\n",
       "      <td>9966</td>\n",
       "      <td>...</td>\n",
       "      <td>8583</td>\n",
       "      <td>22301</td>\n",
       "      <td>0</td>\n",
       "      <td>3640</td>\n",
       "      <td>not default</td>\n",
       "      <td>260000</td>\n",
       "      <td>female</td>\n",
       "      <td>graduate school</td>\n",
       "      <td>2</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
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       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>65802</td>\n",
       "      <td>67369</td>\n",
       "      <td>65701</td>\n",
       "      <td>...</td>\n",
       "      <td>3000</td>\n",
       "      <td>3000</td>\n",
       "      <td>1500</td>\n",
       "      <td>0</td>\n",
       "      <td>default</td>\n",
       "      <td>70000</td>\n",
       "      <td>male</td>\n",
       "      <td>university</td>\n",
       "      <td>2</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>70887</td>\n",
       "      <td>67060</td>\n",
       "      <td>63561</td>\n",
       "      <td>...</td>\n",
       "      <td>3000</td>\n",
       "      <td>3000</td>\n",
       "      <td>3000</td>\n",
       "      <td>3000</td>\n",
       "      <td>not default</td>\n",
       "      <td>250000</td>\n",
       "      <td>male</td>\n",
       "      <td>graduate school</td>\n",
       "      <td>2</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>50614</td>\n",
       "      <td>29173</td>\n",
       "      <td>28116</td>\n",
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       "      <td>1100</td>\n",
       "      <td>1200</td>\n",
       "      <td>1300</td>\n",
       "      <td>1100</td>\n",
       "      <td>not default</td>\n",
       "      <td>50000</td>\n",
       "      <td>female</td>\n",
       "      <td>high school</td>\n",
       "      <td>3</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>15 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Unnamed: 0  PAY_0  PAY_2  PAY_3  PAY_4  PAY_5  PAY_6  BILL_AMT1  \\\n",
       "0            0      2      2      0      0      0      0       3913   \n",
       "1            1      0      2      0      0      0      2       2682   \n",
       "2            2      0      0      0      0      0      0      29239   \n",
       "3            3      0      0      0      0      0      0      46990   \n",
       "4            4      0      0      0      0      0      0       8617   \n",
       "5            5      0      0      0      0      0      0      64400   \n",
       "6            6      0      0      0      0      0      0     367965   \n",
       "7            7      0      0      0      0      0      0      11876   \n",
       "8            8      0      0      2      0      0      0      11285   \n",
       "9            9      0      0      0      0      0      0          0   \n",
       "10          10      0      0      2      0      0      0      11073   \n",
       "11          11      0      0      0      0      0      2      12261   \n",
       "12          13      1      2      2      0      0      2      65802   \n",
       "13          14      0      0      0      0      0      0      70887   \n",
       "14          15      1      2      0      0      0      0      50614   \n",
       "\n",
       "    BILL_AMT2  BILL_AMT3  ...  PAY_AMT3  PAY_AMT4  PAY_AMT5  PAY_AMT6  \\\n",
       "0        3102        689  ...         0         0         0         0   \n",
       "1        1725       2682  ...      1000      1000         0      2000   \n",
       "2       14027      13559  ...      1000      1000      1000      5000   \n",
       "3       48233      49291  ...      1200      1100      1069      1000   \n",
       "4        5670      35835  ...     10000      9000       689       679   \n",
       "5       57069      57608  ...       657      1000      1000       800   \n",
       "6      412023     445007  ...     38000     20239     13750     13770   \n",
       "7         380        601  ...         0       581      1687      1542   \n",
       "8       14096      12108  ...       432      1000      1000      1000   \n",
       "9           0          0  ...         0     13007      1122         0   \n",
       "10       9787       5535  ...        50       300      3738        66   \n",
       "11      21670       9966  ...      8583     22301         0      3640   \n",
       "12      67369      65701  ...      3000      3000      1500         0   \n",
       "13      67060      63561  ...      3000      3000      3000      3000   \n",
       "14      29173      28116  ...      1100      1200      1300      1100   \n",
       "\n",
       "        default   limit     sex        education  marriage age  \n",
       "0       default   20000  female       university         1  24  \n",
       "1       default  120000  female       university         2  26  \n",
       "2   not default   90000  female       university         2  34  \n",
       "3   not default   50000  female       university         1  37  \n",
       "4   not default   50000    male       university         1  57  \n",
       "5   not default   50000    male  graduate school         2  37  \n",
       "6   not default  500000    male  graduate school         2  29  \n",
       "7   not default  100000  female       university         2  23  \n",
       "8   not default  140000  female      high school         1  28  \n",
       "9   not default   20000    male      high school         2  35  \n",
       "10  not default  200000  female      high school         2  34  \n",
       "11  not default  260000  female  graduate school         2  51  \n",
       "12      default   70000    male       university         2  30  \n",
       "13  not default  250000    male  graduate school         2  29  \n",
       "14  not default   50000  female      high school         3  23  \n",
       "\n",
       "[15 rows x 25 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "credit.head(15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 29886 entries, 0 to 29885\n",
      "Data columns (total 25 columns):\n",
      " #   Column      Non-Null Count  Dtype \n",
      "---  ------      --------------  ----- \n",
      " 0   Unnamed: 0  29886 non-null  int64 \n",
      " 1   PAY_0       29886 non-null  int64 \n",
      " 2   PAY_2       29886 non-null  int64 \n",
      " 3   PAY_3       29886 non-null  int64 \n",
      " 4   PAY_4       29886 non-null  int64 \n",
      " 5   PAY_5       29886 non-null  int64 \n",
      " 6   PAY_6       29886 non-null  int64 \n",
      " 7   BILL_AMT1   29886 non-null  int64 \n",
      " 8   BILL_AMT2   29886 non-null  int64 \n",
      " 9   BILL_AMT3   29886 non-null  int64 \n",
      " 10  BILL_AMT4   29886 non-null  int64 \n",
      " 11  BILL_AMT5   29886 non-null  int64 \n",
      " 12  BILL_AMT6   29886 non-null  int64 \n",
      " 13  PAY_AMT1    29886 non-null  int64 \n",
      " 14  PAY_AMT2    29886 non-null  int64 \n",
      " 15  PAY_AMT3    29886 non-null  int64 \n",
      " 16  PAY_AMT4    29886 non-null  int64 \n",
      " 17  PAY_AMT5    29886 non-null  int64 \n",
      " 18  PAY_AMT6    29886 non-null  int64 \n",
      " 19  default     29886 non-null  object\n",
      " 20  limit       29886 non-null  int64 \n",
      " 21  sex         29886 non-null  object\n",
      " 22  education   29886 non-null  object\n",
      " 23  marriage    29886 non-null  int64 \n",
      " 24  age         29886 non-null  int64 \n",
      "dtypes: int64(22), object(3)\n",
      "memory usage: 5.7+ MB\n"
     ]
    }
   ],
   "source": [
    "credit.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>PAY_0</th>\n",
       "      <th>BILL_AMT1</th>\n",
       "      <th>BILL_AMT2</th>\n",
       "      <th>BILL_AMT3</th>\n",
       "      <th>BILL_AMT4</th>\n",
       "      <th>BILL_AMT5</th>\n",
       "      <th>BILL_AMT6</th>\n",
       "      <th>PAY_AMT1</th>\n",
       "      <th>PAY_AMT2</th>\n",
       "      <th>PAY_AMT3</th>\n",
       "      <th>PAY_AMT4</th>\n",
       "      <th>PAY_AMT5</th>\n",
       "      <th>PAY_AMT6</th>\n",
       "      <th>default</th>\n",
       "      <th>limit</th>\n",
       "      <th>education</th>\n",
       "      <th>marriage</th>\n",
       "      <th>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>3913</td>\n",
       "      <td>3102</td>\n",
       "      <td>689</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>689</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>default</td>\n",
       "      <td>20000</td>\n",
       "      <td>university</td>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2682</td>\n",
       "      <td>1725</td>\n",
       "      <td>2682</td>\n",
       "      <td>3272</td>\n",
       "      <td>3455</td>\n",
       "      <td>3261</td>\n",
       "      <td>0</td>\n",
       "      <td>1000</td>\n",
       "      <td>1000</td>\n",
       "      <td>1000</td>\n",
       "      <td>0</td>\n",
       "      <td>2000</td>\n",
       "      <td>default</td>\n",
       "      <td>120000</td>\n",
       "      <td>university</td>\n",
       "      <td>2</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>29239</td>\n",
       "      <td>14027</td>\n",
       "      <td>13559</td>\n",
       "      <td>14331</td>\n",
       "      <td>14948</td>\n",
       "      <td>15549</td>\n",
       "      <td>1518</td>\n",
       "      <td>1500</td>\n",
       "      <td>1000</td>\n",
       "      <td>1000</td>\n",
       "      <td>1000</td>\n",
       "      <td>5000</td>\n",
       "      <td>not default</td>\n",
       "      <td>90000</td>\n",
       "      <td>university</td>\n",
       "      <td>2</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>46990</td>\n",
       "      <td>48233</td>\n",
       "      <td>49291</td>\n",
       "      <td>28314</td>\n",
       "      <td>28959</td>\n",
       "      <td>29547</td>\n",
       "      <td>2000</td>\n",
       "      <td>2019</td>\n",
       "      <td>1200</td>\n",
       "      <td>1100</td>\n",
       "      <td>1069</td>\n",
       "      <td>1000</td>\n",
       "      <td>not default</td>\n",
       "      <td>50000</td>\n",
       "      <td>university</td>\n",
       "      <td>1</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>8617</td>\n",
       "      <td>5670</td>\n",
       "      <td>35835</td>\n",
       "      <td>20940</td>\n",
       "      <td>19146</td>\n",
       "      <td>19131</td>\n",
       "      <td>2000</td>\n",
       "      <td>36681</td>\n",
       "      <td>10000</td>\n",
       "      <td>9000</td>\n",
       "      <td>689</td>\n",
       "      <td>679</td>\n",
       "      <td>not default</td>\n",
       "      <td>50000</td>\n",
       "      <td>university</td>\n",
       "      <td>1</td>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  PAY_0  BILL_AMT1  BILL_AMT2  BILL_AMT3  BILL_AMT4  BILL_AMT5  \\\n",
       "0           0      2       3913       3102        689          0          0   \n",
       "1           1      0       2682       1725       2682       3272       3455   \n",
       "2           2      0      29239      14027      13559      14331      14948   \n",
       "3           3      0      46990      48233      49291      28314      28959   \n",
       "4           4      0       8617       5670      35835      20940      19146   \n",
       "\n",
       "   BILL_AMT6  PAY_AMT1  PAY_AMT2  PAY_AMT3  PAY_AMT4  PAY_AMT5  PAY_AMT6  \\\n",
       "0          0         0       689         0         0         0         0   \n",
       "1       3261         0      1000      1000      1000         0      2000   \n",
       "2      15549      1518      1500      1000      1000      1000      5000   \n",
       "3      29547      2000      2019      1200      1100      1069      1000   \n",
       "4      19131      2000     36681     10000      9000       689       679   \n",
       "\n",
       "       default   limit   education  marriage  age  \n",
       "0      default   20000  university         1   24  \n",
       "1      default  120000  university         2   26  \n",
       "2  not default   90000  university         2   34  \n",
       "3  not default   50000  university         1   37  \n",
       "4  not default   50000  university         1   57  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "credit = credit.drop(['PAY_2','PAY_3','PAY_4','PAY_5', 'PAY_6', 'sex'], axis=1)\n",
    "\n",
    "credit.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>PAY_0</th>\n",
       "      <th>BILL_AMT1</th>\n",
       "      <th>BILL_AMT2</th>\n",
       "      <th>BILL_AMT3</th>\n",
       "      <th>BILL_AMT4</th>\n",
       "      <th>BILL_AMT5</th>\n",
       "      <th>BILL_AMT6</th>\n",
       "      <th>PAY_AMT1</th>\n",
       "      <th>PAY_AMT2</th>\n",
       "      <th>PAY_AMT3</th>\n",
       "      <th>PAY_AMT4</th>\n",
       "      <th>PAY_AMT5</th>\n",
       "      <th>PAY_AMT6</th>\n",
       "      <th>limit</th>\n",
       "      <th>marriage</th>\n",
       "      <th>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>29886.000000</td>\n",
       "      <td>29886.000000</td>\n",
       "      <td>29886.000000</td>\n",
       "      <td>29886.000000</td>\n",
       "      <td>2.988600e+04</td>\n",
       "      <td>29886.000000</td>\n",
       "      <td>29886.000000</td>\n",
       "      <td>29886.000000</td>\n",
       "      <td>29886.000000</td>\n",
       "      <td>2.988600e+04</td>\n",
       "      <td>29886.000000</td>\n",
       "      <td>29886.000000</td>\n",
       "      <td>29886.000000</td>\n",
       "      <td>29886.000000</td>\n",
       "      <td>29886.000000</td>\n",
       "      <td>29886.000000</td>\n",
       "      <td>29886.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>14993.269625</td>\n",
       "      <td>0.357090</td>\n",
       "      <td>50958.613230</td>\n",
       "      <td>48945.006659</td>\n",
       "      <td>4.679070e+04</td>\n",
       "      <td>43044.455431</td>\n",
       "      <td>40092.583350</td>\n",
       "      <td>38640.885699</td>\n",
       "      <td>5632.388075</td>\n",
       "      <td>5.895663e+03</td>\n",
       "      <td>5171.638459</td>\n",
       "      <td>4780.227832</td>\n",
       "      <td>4740.723115</td>\n",
       "      <td>5173.336612</td>\n",
       "      <td>166097.493141</td>\n",
       "      <td>1.551830</td>\n",
       "      <td>35.478318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>8656.647124</td>\n",
       "      <td>0.761398</td>\n",
       "      <td>72710.704177</td>\n",
       "      <td>70340.400764</td>\n",
       "      <td>6.866374e+04</td>\n",
       "      <td>63559.944993</td>\n",
       "      <td>59994.312029</td>\n",
       "      <td>58715.661504</td>\n",
       "      <td>16407.807257</td>\n",
       "      <td>2.294675e+04</td>\n",
       "      <td>16714.267008</td>\n",
       "      <td>15284.011660</td>\n",
       "      <td>14891.316340</td>\n",
       "      <td>17630.015209</td>\n",
       "      <td>127223.308653</td>\n",
       "      <td>0.522065</td>\n",
       "      <td>9.220866</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-165580.000000</td>\n",
       "      <td>-69777.000000</td>\n",
       "      <td>-1.572640e+05</td>\n",
       "      <td>-170000.000000</td>\n",
       "      <td>-81334.000000</td>\n",
       "      <td>-339603.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>21.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>7500.250000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3580.000000</td>\n",
       "      <td>2999.250000</td>\n",
       "      <td>2.700000e+03</td>\n",
       "      <td>2340.250000</td>\n",
       "      <td>1776.000000</td>\n",
       "      <td>1261.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>8.360000e+02</td>\n",
       "      <td>390.000000</td>\n",
       "      <td>299.000000</td>\n",
       "      <td>257.000000</td>\n",
       "      <td>120.250000</td>\n",
       "      <td>50000.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>28.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>14993.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>22398.500000</td>\n",
       "      <td>21228.500000</td>\n",
       "      <td>2.009350e+04</td>\n",
       "      <td>19058.000000</td>\n",
       "      <td>18107.500000</td>\n",
       "      <td>17075.500000</td>\n",
       "      <td>2100.000000</td>\n",
       "      <td>2.008000e+03</td>\n",
       "      <td>1800.000000</td>\n",
       "      <td>1500.000000</td>\n",
       "      <td>1500.000000</td>\n",
       "      <td>1500.000000</td>\n",
       "      <td>140000.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>34.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>22485.750000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>66978.750000</td>\n",
       "      <td>63827.750000</td>\n",
       "      <td>5.994800e+04</td>\n",
       "      <td>54353.500000</td>\n",
       "      <td>50072.000000</td>\n",
       "      <td>49056.250000</td>\n",
       "      <td>5005.000000</td>\n",
       "      <td>5.000000e+03</td>\n",
       "      <td>4500.000000</td>\n",
       "      <td>4003.750000</td>\n",
       "      <td>4012.000000</td>\n",
       "      <td>4000.000000</td>\n",
       "      <td>240000.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>41.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>29999.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>653062.000000</td>\n",
       "      <td>743970.000000</td>\n",
       "      <td>1.664089e+06</td>\n",
       "      <td>706864.000000</td>\n",
       "      <td>823540.000000</td>\n",
       "      <td>699944.000000</td>\n",
       "      <td>873552.000000</td>\n",
       "      <td>1.684259e+06</td>\n",
       "      <td>889043.000000</td>\n",
       "      <td>621000.000000</td>\n",
       "      <td>426529.000000</td>\n",
       "      <td>528666.000000</td>\n",
       "      <td>600000.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>79.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Unnamed: 0         PAY_0      BILL_AMT1      BILL_AMT2     BILL_AMT3  \\\n",
       "count  29886.000000  29886.000000   29886.000000   29886.000000  2.988600e+04   \n",
       "mean   14993.269625      0.357090   50958.613230   48945.006659  4.679070e+04   \n",
       "std     8656.647124      0.761398   72710.704177   70340.400764  6.866374e+04   \n",
       "min        0.000000      0.000000 -165580.000000  -69777.000000 -1.572640e+05   \n",
       "25%     7500.250000      0.000000    3580.000000    2999.250000  2.700000e+03   \n",
       "50%    14993.500000      0.000000   22398.500000   21228.500000  2.009350e+04   \n",
       "75%    22485.750000      0.000000   66978.750000   63827.750000  5.994800e+04   \n",
       "max    29999.000000      8.000000  653062.000000  743970.000000  1.664089e+06   \n",
       "\n",
       "           BILL_AMT4      BILL_AMT5      BILL_AMT6       PAY_AMT1  \\\n",
       "count   29886.000000   29886.000000   29886.000000   29886.000000   \n",
       "mean    43044.455431   40092.583350   38640.885699    5632.388075   \n",
       "std     63559.944993   59994.312029   58715.661504   16407.807257   \n",
       "min   -170000.000000  -81334.000000 -339603.000000       0.000000   \n",
       "25%      2340.250000    1776.000000    1261.000000    1000.000000   \n",
       "50%     19058.000000   18107.500000   17075.500000    2100.000000   \n",
       "75%     54353.500000   50072.000000   49056.250000    5005.000000   \n",
       "max    706864.000000  823540.000000  699944.000000  873552.000000   \n",
       "\n",
       "           PAY_AMT2       PAY_AMT3       PAY_AMT4       PAY_AMT5  \\\n",
       "count  2.988600e+04   29886.000000   29886.000000   29886.000000   \n",
       "mean   5.895663e+03    5171.638459    4780.227832    4740.723115   \n",
       "std    2.294675e+04   16714.267008   15284.011660   14891.316340   \n",
       "min    0.000000e+00       0.000000       0.000000       0.000000   \n",
       "25%    8.360000e+02     390.000000     299.000000     257.000000   \n",
       "50%    2.008000e+03    1800.000000    1500.000000    1500.000000   \n",
       "75%    5.000000e+03    4500.000000    4003.750000    4012.000000   \n",
       "max    1.684259e+06  889043.000000  621000.000000  426529.000000   \n",
       "\n",
       "            PAY_AMT6          limit      marriage           age  \n",
       "count   29886.000000   29886.000000  29886.000000  29886.000000  \n",
       "mean     5173.336612  166097.493141      1.551830     35.478318  \n",
       "std     17630.015209  127223.308653      0.522065      9.220866  \n",
       "min         0.000000   10000.000000      0.000000     21.000000  \n",
       "25%       120.250000   50000.000000      1.000000     28.000000  \n",
       "50%      1500.000000  140000.000000      2.000000     34.000000  \n",
       "75%      4000.000000  240000.000000      2.000000     41.000000  \n",
       "max    528666.000000  600000.000000      3.000000     79.000000  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "credit.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Box plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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": [
    "ax = sns.boxplot(data=credit, orient ='h', palette='Set2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "credit = credit[credit.BILL_AMT3< 400000]\n",
    "\n",
    "credit = credit[credit.BILL_AMT4< 400000]\n",
    "credit = credit[credit.BILL_AMT5< 400000]\n",
    "credit = credit[credit.BILL_AMT2< 400000]\n",
    "credit = credit[credit.BILL_AMT1< 400000]\n",
    "credit = credit[credit.BILL_AMT6< 400000]\n",
    "credit = credit[credit.PAY_AMT2 < 300000]\n",
    "credit = credit[credit.limit< 575000]\n",
    "credit = credit[credit.PAY_AMT1< 300000]\n",
    "credit = credit[credit.PAY_AMT3< 300000]\n",
    "credit = credit[credit.PAY_AMT4< 300000]\n",
    "credit = credit[credit.PAY_AMT5< 300000]\n",
    "credit = credit[credit.PAY_AMT6< 300000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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": [
    "ax = sns.boxplot(data=credit, orient ='h', palette='Set2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>Unnamed: 0</th>\n",
       "      <th>PAY_0</th>\n",
       "      <th>BILL_AMT1</th>\n",
       "      <th>BILL_AMT2</th>\n",
       "      <th>BILL_AMT3</th>\n",
       "      <th>BILL_AMT4</th>\n",
       "      <th>BILL_AMT5</th>\n",
       "      <th>BILL_AMT6</th>\n",
       "      <th>PAY_AMT1</th>\n",
       "      <th>PAY_AMT2</th>\n",
       "      <th>...</th>\n",
       "      <th>PAY_AMT6</th>\n",
       "      <th>limit</th>\n",
       "      <th>marriage</th>\n",
       "      <th>age</th>\n",
       "      <th>default_default</th>\n",
       "      <th>default_not default</th>\n",
       "      <th>education_graduate school</th>\n",
       "      <th>education_high school</th>\n",
       "      <th>education_other</th>\n",
       "      <th>education_university</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>3913</td>\n",
       "      <td>3102</td>\n",
       "      <td>689</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>689</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>20000</td>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2682</td>\n",
       "      <td>1725</td>\n",
       "      <td>2682</td>\n",
       "      <td>3272</td>\n",
       "      <td>3455</td>\n",
       "      <td>3261</td>\n",
       "      <td>0</td>\n",
       "      <td>1000</td>\n",
       "      <td>...</td>\n",
       "      <td>2000</td>\n",
       "      <td>120000</td>\n",
       "      <td>2</td>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>29239</td>\n",
       "      <td>14027</td>\n",
       "      <td>13559</td>\n",
       "      <td>14331</td>\n",
       "      <td>14948</td>\n",
       "      <td>15549</td>\n",
       "      <td>1518</td>\n",
       "      <td>1500</td>\n",
       "      <td>...</td>\n",
       "      <td>5000</td>\n",
       "      <td>90000</td>\n",
       "      <td>2</td>\n",
       "      <td>34</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>46990</td>\n",
       "      <td>48233</td>\n",
       "      <td>49291</td>\n",
       "      <td>28314</td>\n",
       "      <td>28959</td>\n",
       "      <td>29547</td>\n",
       "      <td>2000</td>\n",
       "      <td>2019</td>\n",
       "      <td>...</td>\n",
       "      <td>1000</td>\n",
       "      <td>50000</td>\n",
       "      <td>1</td>\n",
       "      <td>37</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>8617</td>\n",
       "      <td>5670</td>\n",
       "      <td>35835</td>\n",
       "      <td>20940</td>\n",
       "      <td>19146</td>\n",
       "      <td>19131</td>\n",
       "      <td>2000</td>\n",
       "      <td>36681</td>\n",
       "      <td>...</td>\n",
       "      <td>679</td>\n",
       "      <td>50000</td>\n",
       "      <td>1</td>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  PAY_0  BILL_AMT1  BILL_AMT2  BILL_AMT3  BILL_AMT4  BILL_AMT5  \\\n",
       "0           0      2       3913       3102        689          0          0   \n",
       "1           1      0       2682       1725       2682       3272       3455   \n",
       "2           2      0      29239      14027      13559      14331      14948   \n",
       "3           3      0      46990      48233      49291      28314      28959   \n",
       "4           4      0       8617       5670      35835      20940      19146   \n",
       "\n",
       "   BILL_AMT6  PAY_AMT1  PAY_AMT2  ...  PAY_AMT6   limit  marriage  age  \\\n",
       "0          0         0       689  ...         0   20000         1   24   \n",
       "1       3261         0      1000  ...      2000  120000         2   26   \n",
       "2      15549      1518      1500  ...      5000   90000         2   34   \n",
       "3      29547      2000      2019  ...      1000   50000         1   37   \n",
       "4      19131      2000     36681  ...       679   50000         1   57   \n",
       "\n",
       "   default_default  default_not default  education_graduate school  \\\n",
       "0                1                    0                          0   \n",
       "1                1                    0                          0   \n",
       "2                0                    1                          0   \n",
       "3                0                    1                          0   \n",
       "4                0                    1                          0   \n",
       "\n",
       "   education_high school  education_other  education_university  \n",
       "0                      0                0                     1  \n",
       "1                      0                0                     1  \n",
       "2                      0                0                     1  \n",
       "3                      0                0                     1  \n",
       "4                      0                0                     1  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "credit = pd.get_dummies(credit)\n",
    "credit.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "credit['grad_school']=credit['education_graduate school']\n",
    "credit['high_school']=credit['education_high school']\n",
    "credit['other_school']=credit['education_other']\n",
    "credit['university']=credit['education_university']\n",
    "credit['default']=credit['default_default']\n",
    "credit['not_default']=credit['default_not default']\n",
    "credit = credit.drop(['education_graduate school',\n",
    "                      'education_high school',\n",
    "                      'education_other',\n",
    "                      'education_university',\n",
    "                      'default_default',\n",
    "                      'default_not default'], \n",
    "                     axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>Unnamed: 0</th>\n",
       "      <th>PAY_0</th>\n",
       "      <th>BILL_AMT1</th>\n",
       "      <th>BILL_AMT2</th>\n",
       "      <th>BILL_AMT3</th>\n",
       "      <th>BILL_AMT4</th>\n",
       "      <th>BILL_AMT5</th>\n",
       "      <th>BILL_AMT6</th>\n",
       "      <th>PAY_AMT1</th>\n",
       "      <th>PAY_AMT2</th>\n",
       "      <th>...</th>\n",
       "      <th>PAY_AMT6</th>\n",
       "      <th>limit</th>\n",
       "      <th>marriage</th>\n",
       "      <th>age</th>\n",
       "      <th>grad_school</th>\n",
       "      <th>high_school</th>\n",
       "      <th>other_school</th>\n",
       "      <th>university</th>\n",
       "      <th>default</th>\n",
       "      <th>not_default</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>29617.000000</td>\n",
       "      <td>29617.000000</td>\n",
       "      <td>29617.000000</td>\n",
       "      <td>29617.000000</td>\n",
       "      <td>29617.000000</td>\n",
       "      <td>29617.000000</td>\n",
       "      <td>29617.000000</td>\n",
       "      <td>29617.000000</td>\n",
       "      <td>29617.000000</td>\n",
       "      <td>29617.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>29617.000000</td>\n",
       "      <td>29617.000000</td>\n",
       "      <td>29617.000000</td>\n",
       "      <td>29617.000000</td>\n",
       "      <td>29617.000000</td>\n",
       "      <td>29617.000000</td>\n",
       "      <td>29617.000000</td>\n",
       "      <td>29617.000000</td>\n",
       "      <td>29617.000000</td>\n",
       "      <td>29617.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>14987.826350</td>\n",
       "      <td>0.356788</td>\n",
       "      <td>48340.815410</td>\n",
       "      <td>46450.710842</td>\n",
       "      <td>44220.488976</td>\n",
       "      <td>40728.386940</td>\n",
       "      <td>38036.125570</td>\n",
       "      <td>36695.241550</td>\n",
       "      <td>5384.746294</td>\n",
       "      <td>5390.962994</td>\n",
       "      <td>...</td>\n",
       "      <td>4856.833136</td>\n",
       "      <td>163503.044873</td>\n",
       "      <td>1.552284</td>\n",
       "      <td>35.440153</td>\n",
       "      <td>0.350812</td>\n",
       "      <td>0.164703</td>\n",
       "      <td>0.015599</td>\n",
       "      <td>0.468886</td>\n",
       "      <td>0.221629</td>\n",
       "      <td>0.778371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>8650.633579</td>\n",
       "      <td>0.758858</td>\n",
       "      <td>65173.620319</td>\n",
       "      <td>63085.288806</td>\n",
       "      <td>60668.713942</td>\n",
       "      <td>56791.527182</td>\n",
       "      <td>54019.789661</td>\n",
       "      <td>53141.350441</td>\n",
       "      <td>14056.775195</td>\n",
       "      <td>14643.093560</td>\n",
       "      <td>...</td>\n",
       "      <td>15269.488789</td>\n",
       "      <td>124504.101265</td>\n",
       "      <td>0.521981</td>\n",
       "      <td>9.210740</td>\n",
       "      <td>0.477232</td>\n",
       "      <td>0.370918</td>\n",
       "      <td>0.123921</td>\n",
       "      <td>0.499039</td>\n",
       "      <td>0.415350</td>\n",
       "      <td>0.415350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-154973.000000</td>\n",
       "      <td>-69777.000000</td>\n",
       "      <td>-61506.000000</td>\n",
       "      <td>-65167.000000</td>\n",
       "      <td>-61372.000000</td>\n",
       "      <td>-209051.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>7503.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3492.000000</td>\n",
       "      <td>2947.000000</td>\n",
       "      <td>2629.000000</td>\n",
       "      <td>2298.000000</td>\n",
       "      <td>1738.000000</td>\n",
       "      <td>1221.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>820.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>50000.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>28.000000</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>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>14989.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>21936.000000</td>\n",
       "      <td>20818.000000</td>\n",
       "      <td>19888.000000</td>\n",
       "      <td>18887.000000</td>\n",
       "      <td>17950.000000</td>\n",
       "      <td>16791.000000</td>\n",
       "      <td>2100.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1500.000000</td>\n",
       "      <td>140000.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>34.000000</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>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>22465.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>65226.000000</td>\n",
       "      <td>62061.000000</td>\n",
       "      <td>58743.000000</td>\n",
       "      <td>52402.000000</td>\n",
       "      <td>49339.000000</td>\n",
       "      <td>48433.000000</td>\n",
       "      <td>5000.000000</td>\n",
       "      <td>5000.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>4000.000000</td>\n",
       "      <td>230000.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>29999.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>399099.000000</td>\n",
       "      <td>397332.000000</td>\n",
       "      <td>398634.000000</td>\n",
       "      <td>387706.000000</td>\n",
       "      <td>395430.000000</td>\n",
       "      <td>394932.000000</td>\n",
       "      <td>298887.000000</td>\n",
       "      <td>294318.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>290000.000000</td>\n",
       "      <td>570000.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>75.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Unnamed: 0         PAY_0      BILL_AMT1      BILL_AMT2  \\\n",
       "count  29617.000000  29617.000000   29617.000000   29617.000000   \n",
       "mean   14987.826350      0.356788   48340.815410   46450.710842   \n",
       "std     8650.633579      0.758858   65173.620319   63085.288806   \n",
       "min        0.000000      0.000000 -154973.000000  -69777.000000   \n",
       "25%     7503.000000      0.000000    3492.000000    2947.000000   \n",
       "50%    14989.000000      0.000000   21936.000000   20818.000000   \n",
       "75%    22465.000000      0.000000   65226.000000   62061.000000   \n",
       "max    29999.000000      8.000000  399099.000000  397332.000000   \n",
       "\n",
       "           BILL_AMT3      BILL_AMT4      BILL_AMT5      BILL_AMT6  \\\n",
       "count   29617.000000   29617.000000   29617.000000   29617.000000   \n",
       "mean    44220.488976   40728.386940   38036.125570   36695.241550   \n",
       "std     60668.713942   56791.527182   54019.789661   53141.350441   \n",
       "min    -61506.000000  -65167.000000  -61372.000000 -209051.000000   \n",
       "25%      2629.000000    2298.000000    1738.000000    1221.000000   \n",
       "50%     19888.000000   18887.000000   17950.000000   16791.000000   \n",
       "75%     58743.000000   52402.000000   49339.000000   48433.000000   \n",
       "max    398634.000000  387706.000000  395430.000000  394932.000000   \n",
       "\n",
       "            PAY_AMT1       PAY_AMT2  ...       PAY_AMT6          limit  \\\n",
       "count   29617.000000   29617.000000  ...   29617.000000   29617.000000   \n",
       "mean     5384.746294    5390.962994  ...    4856.833136  163503.044873   \n",
       "std     14056.775195   14643.093560  ...   15269.488789  124504.101265   \n",
       "min         0.000000       0.000000  ...       0.000000   10000.000000   \n",
       "25%      1000.000000     820.000000  ...     100.000000   50000.000000   \n",
       "50%      2100.000000    2000.000000  ...    1500.000000  140000.000000   \n",
       "75%      5000.000000    5000.000000  ...    4000.000000  230000.000000   \n",
       "max    298887.000000  294318.000000  ...  290000.000000  570000.000000   \n",
       "\n",
       "           marriage           age   grad_school   high_school  other_school  \\\n",
       "count  29617.000000  29617.000000  29617.000000  29617.000000  29617.000000   \n",
       "mean       1.552284     35.440153      0.350812      0.164703      0.015599   \n",
       "std        0.521981      9.210740      0.477232      0.370918      0.123921   \n",
       "min        0.000000     21.000000      0.000000      0.000000      0.000000   \n",
       "25%        1.000000     28.000000      0.000000      0.000000      0.000000   \n",
       "50%        2.000000     34.000000      0.000000      0.000000      0.000000   \n",
       "75%        2.000000     41.000000      1.000000      0.000000      0.000000   \n",
       "max        3.000000     75.000000      1.000000      1.000000      1.000000   \n",
       "\n",
       "         university       default   not_default  \n",
       "count  29617.000000  29617.000000  29617.000000  \n",
       "mean       0.468886      0.221629      0.778371  \n",
       "std        0.499039      0.415350      0.415350  \n",
       "min        0.000000      0.000000      0.000000  \n",
       "25%        0.000000      0.000000      1.000000  \n",
       "50%        0.000000      0.000000      1.000000  \n",
       "75%        1.000000      0.000000      1.000000  \n",
       "max        1.000000      1.000000      1.000000  \n",
       "\n",
       "[8 rows x 23 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "credit.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 0, 'Unnamed: 0'),\n",
       " Text(1.5, 0, 'PAY_0'),\n",
       " Text(2.5, 0, 'BILL_AMT1'),\n",
       " Text(3.5, 0, 'BILL_AMT2'),\n",
       " Text(4.5, 0, 'BILL_AMT3'),\n",
       " Text(5.5, 0, 'BILL_AMT4'),\n",
       " Text(6.5, 0, 'BILL_AMT5'),\n",
       " Text(7.5, 0, 'BILL_AMT6'),\n",
       " Text(8.5, 0, 'PAY_AMT1'),\n",
       " Text(9.5, 0, 'PAY_AMT2'),\n",
       " Text(10.5, 0, 'PAY_AMT3'),\n",
       " Text(11.5, 0, 'PAY_AMT4'),\n",
       " Text(12.5, 0, 'PAY_AMT5'),\n",
       " Text(13.5, 0, 'PAY_AMT6'),\n",
       " Text(14.5, 0, 'limit'),\n",
       " Text(15.5, 0, 'marriage'),\n",
       " Text(16.5, 0, 'age'),\n",
       " Text(17.5, 0, 'grad_school'),\n",
       " Text(18.5, 0, 'high_school'),\n",
       " Text(19.5, 0, 'other_school'),\n",
       " Text(20.5, 0, 'university'),\n",
       " Text(21.5, 0, 'default'),\n",
       " Text(22.5, 0, 'not_default')]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "corr = credit.corr()\n",
    "ax = sns.heatmap(corr, \n",
    "                 vmin =-1, \n",
    "                 vmax=1, \n",
    "                 center=0, \n",
    "                 cmap=sns.diverging_palette(20, 220, n=200),\n",
    "                 square=True\n",
    "                )\n",
    "ax.set_xticklabels(ax.get_xticklabels(),\n",
    "                  rotation=45,\n",
    "                  horizontalalignment='right')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "limit           1.000000\n",
       "grad_school     0.261932\n",
       "BILL_AMT5       0.239969\n",
       "BILL_AMT4       0.235326\n",
       "BILL_AMT6       0.233848\n",
       "BILL_AMT1       0.226036\n",
       "BILL_AMT3       0.224876\n",
       "BILL_AMT2       0.219942\n",
       "PAY_AMT3        0.213908\n",
       "PAY_AMT2        0.213210\n",
       "PAY_AMT6        0.210038\n",
       "PAY_AMT5        0.204703\n",
       "PAY_AMT4        0.201226\n",
       "PAY_AMT1        0.195396\n",
       "not_default     0.158402\n",
       "age             0.138477\n",
       "Unnamed: 0      0.025904\n",
       "other_school    0.014308\n",
       "marriage       -0.111020\n",
       "high_school    -0.141925\n",
       "university     -0.148551\n",
       "default        -0.158402\n",
       "PAY_0          -0.177787\n",
       "Name: limit, dtype: float64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_matrix = credit.corr()\n",
    "corr_matrix['limit'].sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "credit = credit.drop(['other_school'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "#  try different scales to find the most suitable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = pd.DataFrame({\n",
    "    \"Bill6\": credit.BILL_AMT6,\n",
    "    \"Bill5\": credit.BILL_AMT5,\n",
    "    'Bill4': credit.BILL_AMT4,\n",
    "    'Bill3': credit.BILL_AMT3,\n",
    "    'Bill2': credit.BILL_AMT2,\n",
    "    'Bill1': credit.BILL_AMT1,\n",
    "    'Pay6': credit.PAY_AMT6,\n",
    "    'Pay5': credit.PAY_AMT5,\n",
    "    'Pay4': credit.PAY_AMT4,\n",
    "    'Pay3': credit.PAY_AMT3,\n",
    "    'Pay2': credit.PAY_AMT2,\n",
    "    'Pay1': credit.PAY_AMT1,\n",
    "    'not default': credit.not_default,\n",
    "    'default': credit.default,\n",
    "    'age': credit.age,\n",
    "    'ed_high school': credit.high_school,\n",
    "    'ed_university': credit.university,\n",
    "    'ed_graduate school': credit.grad_school,\n",
    "    'ontime': credit.PAY_0,\n",
    "    \n",
    "    \n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Bill6', 'Bill5', 'Bill4', 'Bill3', 'Bill2', 'Bill1', 'Pay6', 'Pay5',\n",
       "       'Pay4', 'Pay3', 'Pay2', 'Pay1', 'not default', 'default', 'age',\n",
       "       'ed_high school', 'ed_university', 'ed_graduate school', 'ontime'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.columns\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = preprocessing.MinMaxScaler()\n",
    "minmax_scaled_df = scaler.fit_transform(x)\n",
    "minmax_scaled_df = pd.DataFrame(minmax_scaled_df, columns=['Bill6','Bill5','Bill4','Bill3','Bill2','Bill1', \n",
    "                                                          'Pay6', 'Pay5', 'Pay4', 'Pay3', 'Pay2','Pay1',\n",
    "                                                          'not default', 'default', 'age','ed_high school','ed_university', 'ed_graduate school',\n",
    "                                                          'ontime'])\n",
    "scaler = preprocessing.StandardScaler()\n",
    "scaled_df = scaler.fit_transform(x)\n",
    "scaled_df = pd.DataFrame(scaled_df, columns=['Bill6','Bill5','Bill4','Bill3','Bill2','Bill1', \n",
    "                                                          'Pay6', 'Pay5', 'Pay4', 'Pay3', 'Pay2','Pay1',\n",
    "                                                          'not default', 'default', 'age','ed_high school','ed_university', 'ed_graduate school',\n",
    "                                                        'ontime'])\n",
    "\n",
    "scaler = preprocessing.RobustScaler()\n",
    "robust_scaled_df = scaler.fit_transform(x)\n",
    "robust_scaled_df = pd.DataFrame(robust_scaled_df, columns=['Bill6','Bill5','Bill4','Bill3','Bill2','Bill1', \n",
    "                                                          'Pay6', 'Pay5', 'Pay4', 'Pay3', 'Pay2','Pay1',\n",
    "                                                          'not default', 'default', 'age','ed_high school','ed_university', 'ed_graduate school',\n",
    "                                                          'ontime'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\conda\\envs\\sherri\\lib\\site-packages\\seaborn\\distributions.py:369: UserWarning: Default bandwidth for data is 0; skipping density estimation.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\conda\\envs\\sherri\\lib\\site-packages\\seaborn\\distributions.py:369: UserWarning: Default bandwidth for data is 0; skipping density estimation.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\conda\\envs\\sherri\\lib\\site-packages\\seaborn\\distributions.py:369: UserWarning: Default bandwidth for data is 0; skipping density estimation.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\conda\\envs\\sherri\\lib\\site-packages\\seaborn\\distributions.py:369: UserWarning: Default bandwidth for data is 0; skipping density estimation.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\conda\\envs\\sherri\\lib\\site-packages\\seaborn\\distributions.py:369: UserWarning: Default bandwidth for data is 0; skipping density estimation.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\conda\\envs\\sherri\\lib\\site-packages\\seaborn\\distributions.py:369: UserWarning: Default bandwidth for data is 0; skipping density estimation.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\conda\\envs\\sherri\\lib\\site-packages\\seaborn\\distributions.py:369: UserWarning: Default bandwidth for data is 0; skipping density estimation.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\conda\\envs\\sherri\\lib\\site-packages\\seaborn\\distributions.py:369: UserWarning: Default bandwidth for data is 0; skipping density estimation.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\conda\\envs\\sherri\\lib\\site-packages\\seaborn\\distributions.py:369: UserWarning: Default bandwidth for data is 0; skipping density estimation.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\conda\\envs\\sherri\\lib\\site-packages\\seaborn\\distributions.py:369: UserWarning: Default bandwidth for data is 0; skipping density estimation.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\conda\\envs\\sherri\\lib\\site-packages\\seaborn\\distributions.py:369: UserWarning: Default bandwidth for data is 0; skipping density estimation.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\conda\\envs\\sherri\\lib\\site-packages\\seaborn\\distributions.py:369: UserWarning: Default bandwidth for data is 0; skipping density estimation.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\conda\\envs\\sherri\\lib\\site-packages\\seaborn\\distributions.py:369: UserWarning: Default bandwidth for data is 0; skipping density estimation.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\conda\\envs\\sherri\\lib\\site-packages\\seaborn\\distributions.py:369: UserWarning: Default bandwidth for data is 0; skipping density estimation.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\conda\\envs\\sherri\\lib\\site-packages\\seaborn\\distributions.py:369: UserWarning: Default bandwidth for data is 0; skipping density estimation.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\conda\\envs\\sherri\\lib\\site-packages\\seaborn\\distributions.py:369: UserWarning: Default bandwidth for data is 0; skipping density estimation.\n",
      "  warnings.warn(msg, UserWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1440 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "fig, [[ax1, ax2], [ax3, ax4]] = plt.subplots(ncols=2, nrows=2,figsize=(15,20))\n",
    "ax1.set_title('Before Scaling')\n",
    "sns.kdeplot(x['Bill6'], ax=ax1)\n",
    "sns.kdeplot(x['Bill5'], ax=ax1)\n",
    "sns.kdeplot(x['Bill4'], ax=ax1)\n",
    "sns.kdeplot(x['Bill3'], ax=ax1)\n",
    "sns.kdeplot(x['Bill2'], ax=ax1)\n",
    "sns.kdeplot(x['Bill1'], ax=ax1)\n",
    "sns.kdeplot(x['Pay6'], ax=ax1)\n",
    "sns.kdeplot(x['Pay5'], ax=ax1)\n",
    "sns.kdeplot(x['Pay4'], ax=ax1)\n",
    "sns.kdeplot(x['Pay3'], ax=ax1)\n",
    "sns.kdeplot(x['Pay2'], ax=ax1)              \n",
    "sns.kdeplot(x['Pay1'], ax=ax1)    \n",
    "sns.kdeplot(x['not default'], ax=ax1)             \n",
    "sns.kdeplot(x['default'], ax=ax1)              \n",
    "sns.kdeplot(x['age'], ax=ax1)\n",
    "sns.kdeplot(x['ed_high school'], ax=ax1)\n",
    "sns.kdeplot(x['ed_university'], ax=ax1)\n",
    "sns.kdeplot(x['ed_graduate school'], ax=ax1)\n",
    "sns.kdeplot(x['ontime'], ax=ax1)\n",
    "\n",
    "ax2.set_title('After Min-Max Scaling')\n",
    "sns.kdeplot(minmax_scaled_df['Bill6'], ax=ax2)\n",
    "sns.kdeplot(minmax_scaled_df['Bill5'], ax=ax2)\n",
    "sns.kdeplot(minmax_scaled_df['Bill4'], ax=ax2)\n",
    "sns.kdeplot(minmax_scaled_df['Bill3'], ax=ax2)\n",
    "sns.kdeplot(minmax_scaled_df['Bill2'], ax=ax2)\n",
    "sns.kdeplot(minmax_scaled_df['Bill1'], ax=ax2)\n",
    "sns.kdeplot(minmax_scaled_df['Pay6'], ax=ax2)\n",
    "sns.kdeplot(minmax_scaled_df['Pay5'], ax=ax2)\n",
    "sns.kdeplot(minmax_scaled_df['Pay4'], ax=ax2)\n",
    "sns.kdeplot(minmax_scaled_df['Pay3'], ax=ax2)\n",
    "sns.kdeplot(minmax_scaled_df['Pay2'], ax=ax2)\n",
    "sns.kdeplot(minmax_scaled_df['Pay1'], ax=ax2)\n",
    "sns.kdeplot(minmax_scaled_df['not default'], ax=ax2)\n",
    "sns.kdeplot(minmax_scaled_df['default'], ax=ax2)\n",
    "sns.kdeplot(minmax_scaled_df['age'], ax=ax2)\n",
    "sns.kdeplot(minmax_scaled_df['ed_high school'], ax=ax2)\n",
    "sns.kdeplot(minmax_scaled_df['ed_university'], ax=ax2)\n",
    "sns.kdeplot(minmax_scaled_df['ed_graduate school'], ax=ax2)\n",
    "sns.kdeplot(minmax_scaled_df['ontime'], ax=ax2)\n",
    "\n",
    "ax3.set_title('After Standard Scaler')\n",
    "sns.kdeplot(scaled_df['Bill6'], ax=ax3)\n",
    "sns.kdeplot(scaled_df['Bill5'], ax=ax3)\n",
    "sns.kdeplot(scaled_df['Bill4'], ax=ax3)\n",
    "sns.kdeplot(scaled_df['Bill3'], ax=ax3)\n",
    "sns.kdeplot(scaled_df['Bill2'], ax=ax3)\n",
    "sns.kdeplot(scaled_df['Bill1'], ax=ax3)\n",
    "sns.kdeplot(scaled_df['Pay6'], ax=ax3)\n",
    "sns.kdeplot(scaled_df['Pay5'], ax=ax3)\n",
    "sns.kdeplot(scaled_df['Pay4'], ax=ax3)\n",
    "sns.kdeplot(scaled_df['Pay3'], ax=ax3)\n",
    "sns.kdeplot(scaled_df['Pay2'], ax=ax3)\n",
    "sns.kdeplot(scaled_df['Pay1'], ax=ax3)\n",
    "sns.kdeplot(scaled_df['not default'], ax=ax3)\n",
    "sns.kdeplot(scaled_df['default'], ax=ax3)\n",
    "sns.kdeplot(scaled_df['ed_high school'], ax=ax3)  \n",
    "sns.kdeplot(scaled_df['ed_university'], ax=ax3) \n",
    "sns.kdeplot(scaled_df['ed_graduate school'], ax=ax3) \n",
    "sns.kdeplot(scaled_df['ontime'], ax=ax3) \n",
    "    \n",
    "ax4.set_title('After Robust Scaling')\n",
    "sns.kdeplot(robust_scaled_df['Bill6'], ax=ax4)\n",
    "sns.kdeplot(robust_scaled_df['Bill5'], ax=ax4)\n",
    "sns.kdeplot(robust_scaled_df['Bill4'], ax=ax4)\n",
    "sns.kdeplot(robust_scaled_df['Bill3'], ax=ax4)\n",
    "sns.kdeplot(robust_scaled_df['Bill2'], ax=ax4)\n",
    "sns.kdeplot(robust_scaled_df['Bill1'], ax=ax4)\n",
    "sns.kdeplot(robust_scaled_df['Pay6'], ax=ax4)\n",
    "sns.kdeplot(robust_scaled_df['Pay5'], ax=ax4)\n",
    "sns.kdeplot(robust_scaled_df['Pay4'], ax=ax4)\n",
    "sns.kdeplot(robust_scaled_df['Pay3'], ax=ax4)\n",
    "sns.kdeplot(robust_scaled_df['Pay2'], ax=ax4)\n",
    "sns.kdeplot(robust_scaled_df['Pay1'], ax=ax4)\n",
    "sns.kdeplot(robust_scaled_df['not default'], ax=ax4)\n",
    "sns.kdeplot(robust_scaled_df['default'], ax=ax4)\n",
    "sns.kdeplot(robust_scaled_df['ed_high school'], ax=ax4)\n",
    "sns.kdeplot(robust_scaled_df['ed_university'], ax=ax4)\n",
    "sns.kdeplot(robust_scaled_df['ed_graduate school'], ax=ax4)\n",
    "sns.kdeplot(robust_scaled_df['ontime'], ax=ax4)\n",
    "\n",
    "plt.show()    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# not real sure which schaler is the best for this dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# predictor and target split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = credit.drop(['limit', 'Unnamed: 0'], axis=1)\n",
    "y = credit['limit']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X,y,random_state=0, test_size=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# Import the relevant packages\n",
    "\n",
    "from sklearn.linear_model import LinearRegression\n",
    "#import xgboost as xgb\n",
    "from sklearn.ensemble import AdaBoostRegressor\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "from sklearn.svm import SVR\n",
    "\n",
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The selected features are : ['PAY_0', 'BILL_AMT1', 'BILL_AMT2', 'BILL_AMT3', 'BILL_AMT5', 'PAY_AMT1', 'PAY_AMT2', 'PAY_AMT3', 'PAY_AMT4', 'PAY_AMT5', 'PAY_AMT6', 'marriage', 'age', 'grad_school', 'high_school', 'university', 'default', 'not_default']\n"
     ]
    }
   ],
   "source": [
    "# Linear Regression\n",
    "X1 = sm.add_constant(X_train)\n",
    "ols = sm.OLS(y_train,X1)\n",
    "lr = ols.fit()\n",
    "\n",
    "selected_features = list(X.columns)\n",
    "pmax = 1\n",
    "while (len(selected_features)>0):\n",
    "    p= []\n",
    "    X_1 = X[selected_features]\n",
    "    X_1 = sm.add_constant(X_1)\n",
    "    model = sm.OLS(y,X_1).fit()\n",
    "    p = pd.Series(model.pvalues.values[1:],index = selected_features)      \n",
    "    pmax = max(p)\n",
    "    feature_with_p_max = p.idxmax()\n",
    "    if(pmax>0.05):\n",
    "        selected_features.remove(feature_with_p_max)\n",
    "    else:\n",
    "        break  \n",
    "        \n",
    "print('The selected features are :', selected_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "const           4.736045e-50\n",
      "PAY_0           9.726965e-68\n",
      "BILL_AMT1       3.800063e-30\n",
      "BILL_AMT2       1.793538e-02\n",
      "BILL_AMT3       4.108650e-04\n",
      "BILL_AMT5       4.442352e-11\n",
      "PAY_AMT1        4.581173e-38\n",
      "PAY_AMT2        4.633089e-49\n",
      "PAY_AMT3        5.951283e-39\n",
      "PAY_AMT4        2.086677e-33\n",
      "PAY_AMT5        4.708025e-63\n",
      "PAY_AMT6        3.160888e-70\n",
      "marriage        1.912713e-66\n",
      "age            2.065728e-112\n",
      "grad_school     3.693191e-22\n",
      "high_school     1.790395e-13\n",
      "university      2.613539e-02\n",
      "default         3.278210e-19\n",
      "not_default     1.816860e-82\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(model.pvalues)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# XGboost\n",
    "tuned_parameters = [{'max_depth': [5,10, 15, 20, 25, 30],'learning_rate':[0.001, 0.01, 0.1, 0.5], 'n_estimators': [100,150,200, 250, 300]}]\n",
    "MSE_xgb = ['mean_squared_error(y_test,y_pred2)']\n",
    "for value in MSE_xgb:\n",
    "    regr = GridSearchCV(xgb.XGBRegressor(silent = True), tuned_parameters, cv=4)\n",
    "    regr.fit(X_train, y_train)\n",
    "    y_true, y_pred2 = y_test, regr.predict(X_test)\n",
    "    \n",
    "print('The best hyper-parameters for XGBBoost are: ',regr.best_params_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# AdaBoost\n",
    "tuned_parameters = [{'learning_rate': [0.1,1,2,3,4,5], 'n_estimators': [100,200,300, 400, 500]}]\n",
    "MSE_ada = ['mean_squared_error(y_test,y_pred3)']\n",
    "for value in MSE_ada:\n",
    "    adaregr = GridSearchCV(AdaBoostRegressor(), tuned_parameters, cv=4)\n",
    "    adaregr.fit(X_train, y_train)\n",
    "    y_true, y_pred3 = y_test, adaregr.predict(X_test)\n",
    "    \n",
    "print('The best hyper-parameters for AdaBoost are: ', adaregr.best_params_ )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Decision Tree\n",
    "tuned_parameters = [{'max_depth': [1,2,3,4,5,10, 15, 20, 25, 50, 100,200]}]\n",
    "MSE_dt = ['mean_squared_error(y_test,y_pred4)']\n",
    "for value in MSE_dt:\n",
    "    regressor_dt = GridSearchCV(DecisionTreeRegressor(), tuned_parameters, cv=4)\n",
    "    regressor_dt.fit(X_train, y_train)\n",
    "    y_true, y_pred4 = y_test, regressor_dt.predict(X_test)\n",
    "    \n",
    "print('The optimum max_depth for Decision Tree is: ', regressor_dt.best_params_ )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Random Forests\n",
    "tuned_parameters = [{'max_depth': [5,10, 15, 20, 50, 70], 'n_estimators': [10, 25, 50, 100,150, 200, 250]}]\n",
    "MSE_rf = ['mean_squared_error(y_test, y_pred5)']\n",
    "for value in MSE_rf:\n",
    "    regr_rf = GridSearchCV(RandomForestRegressor(), tuned_parameters, cv=4)\n",
    "    regr_rf.fit(X_train, y_train)\n",
    "    y_true, y_pred5 = y_test, regr_rf.predict(X_test)\n",
    "    \n",
    "print('The best hyper-parameters for Random Forests are: ',regr_rf.best_params_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# KNN\n",
    "scaler = preprocessing.RobustScaler()                         # Features Scaling is required for distance-based algorithms\n",
    "X_train_scaled = scaler.fit_transform(X_train)\n",
    "X_test_scaled = scaler.fit_transform(X_test)\n",
    "\n",
    "tuned_parameters = [{'n_neighbors': [1,2,3,4,5,10,15,20], 'p': [1,2]}]\n",
    "MSE_knn = ['mean_squared_error(y_test,y_pred)']\n",
    "for i in MSE_knn:\n",
    "    model = GridSearchCV(KNeighborsRegressor(), tuned_parameters, cv=4)\n",
    "    model.fit(X_train_scaled, y_train)\n",
    "    y_true, y_pred6 = y_test, model.predict(X_test_scaled)\n",
    "    \n",
    "print('The best hyper-parameters for KNN are: ', model.best_params_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SVM\n",
    "#tuned_parameters = [{'kernel': ['linear', 'rbf', 'poly'], 'C':[1, 2, 3, 5, 6, 7, 10], 'gamma': [0.0001, 0.001, 0.01, 0.1, 1]}]\n",
    "#MSE_svm = ['mean_squared_error(y_test,y_pred7)']\n",
    "#for value in MSE_svm:\n",
    "#    svr_regr = GridSearchCV(SVR(), tuned_parameters, cv=4)\n",
    "#    svr_regr.fit(X_train_scaled, y_train)\n",
    "#    y_true, y_pred7 = y_test, svr_regr.predict(X_test_scaled)\n",
    "    \n",
    "#print('The best hyper-parameters for SVR are: ', svr_regr.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = preprocessing.RobustScaler()\n",
    "X_train_scaled = scaler.fit_transform(X_train)\n",
    "X_test_scaled = scaler.fit_transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Linear Regression\n",
    "regressor = LinearRegression()\n",
    "regressor.fit(X_train_scaled, y_train)\n",
    "\n",
    "# XGBoost\n",
    "#regr = xgb.XGBRegressor(learning_rate=0.1, max_depth=5, n_estimators=150, random_state = 0, silent = True)\n",
    "#regr.fit(X_train, y_train)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AdaBoostRegressor(base_estimator=None, learning_rate=0.1, loss='linear',\n",
       "                  n_estimators=100, random_state=123)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# AdaBoost\n",
    "adaregr = AdaBoostRegressor(random_state=123, learning_rate = 0.1, n_estimators=100)\n",
    "adaregr.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeRegressor(ccp_alpha=0.0, criterion='mse', max_depth=5,\n",
       "                      max_features=None, max_leaf_nodes=None,\n",
       "                      min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                      min_samples_leaf=1, min_samples_split=2,\n",
       "                      min_weight_fraction_leaf=0.0, presort='deprecated',\n",
       "                      random_state=123, splitter='best')"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Decision Tree\n",
    "regressor_dt = DecisionTreeRegressor(random_state=123, max_depth = 5)\n",
    "regressor_dt.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, ccp_alpha=0.0, criterion='mse',\n",
       "                      max_depth=15, max_features='auto', max_leaf_nodes=None,\n",
       "                      max_samples=None, min_impurity_decrease=0.0,\n",
       "                      min_impurity_split=None, min_samples_leaf=1,\n",
       "                      min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                      n_estimators=250, n_jobs=None, oob_score=False,\n",
       "                      random_state=125, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Random Forests\n",
    "regr_rf = RandomForestRegressor(max_depth=15, random_state=125,\n",
    "                             n_estimators=250)\n",
    "regr_rf.fit(X_train, y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsRegressor(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "                    metric_params=None, n_jobs=None, n_neighbors=20, p=1,\n",
       "                    weights='uniform')"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "# KNN\n",
    "neigh = KNeighborsRegressor(n_neighbors = 20, metric = 'minkowski', p = 1)\n",
    "neigh.fit(X_train_scaled, y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVR(C=0.01, cache_size=200, coef0=0.0, degree=3, epsilon=150000, gamma=0.0001,\n",
       "    kernel='linear', max_iter=-1, shrinking=True, tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# SVR\n",
    "svr_regr = SVR(gamma=0.0001, kernel = 'linear', C =0.01, epsilon=150000)\n",
    "svr_regr.fit(X_train_scaled, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# Predict price using the models above"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred1 = regressor.predict(X_test_scaled)    # Linear regression\n",
    "#y_pred2 = regr.predict(X_test)                # XGBoost\n",
    "y_pred3 = adaregr.predict(X_test)             # AdaBoost\n",
    "y_pred4 = regressor_dt.predict(X_test)        # Decision Tree\n",
    "y_pred5 = regr_rf.predict(X_test)             # Random Forests\n",
    "y_pred6 = neigh.predict(X_test_scaled)        # KNN\n",
    "y_pred7 = svr_regr.predict(X_test_scaled)     # SVR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Find R Squared (R^2) Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The R^2 value for Linear Regression is        : 0.256\n",
      "The R^2 value for AdaBoost Regressor is       : 0.295\n",
      "The R^2 value for Decision Tree Regressor is  : 0.349\n",
      "The R^2 value for Random Forests Regressor is : 0.45\n",
      "The R^2 value for KNN Regressor is            : 0.4\n",
      "The R^2 value for SVM Regressor is            : -0.015\n"
     ]
    }
   ],
   "source": [
    "\n",
    "print('The R^2 value for Linear Regression is        :', round((r2_score(y_test,y_pred1)), 3))\n",
    "#print('The R^2 value for XGBoost Regressor is        :', round((r2_score(y_test,y_pred2)), 3))\n",
    "print('The R^2 value for AdaBoost Regressor is       :', round((r2_score(y_test,y_pred3)), 3))\n",
    "print('The R^2 value for Decision Tree Regressor is  :', round((r2_score(y_test,y_pred4)), 3))\n",
    "print('The R^2 value for Random Forests Regressor is :', round((r2_score(y_test,y_pred5)), 3))\n",
    "print('The R^2 value for KNN Regressor is            :', round((r2_score(y_test,y_pred6)), 3))\n",
    "print('The R^2 value for SVM Regressor is            :', round((r2_score(y_test,y_pred7)), 3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Find adjusted R Squared (Adj R2) Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Adj R2 value for Linear Regression is        : 0.255\n",
      "The Adj R2 value for AdaBoost Regressor is       : 0.294\n",
      "The Adj R2 value for Decision Tree Regressor is  : 0.348\n",
      "The Adj R2 value for Random Forests Regressor is : 0.45\n",
      "The Adj R2 value for KNN Regressor is            : 0.4\n",
      "The Adj R2 value for SVM Regressor is            : 0.137\n"
     ]
    }
   ],
   "source": [
    "\n",
    "print('The Adj R2 value for Linear Regression is        :', round((1 - (1-r2_score(y_test,y_pred1))*(len(y)-1)/(len(y)-X.shape[1]-1)),3))\n",
    "#print('The Adj R2 value for XGBoost Regressor is        :', round((1 - (1-r2_score(y_test,y_pred2))*(len(y)-1)/(len(y)-X.shape[1]-1)),3))\n",
    "print('The Adj R2 value for AdaBoost Regressor is       :', round((1 - (1-r2_score(y_test,y_pred3))*(len(y)-1)/(len(y)-X.shape[1]-1)),3))\n",
    "print('The Adj R2 value for Decision Tree Regressor is  :', round((1 - (1-r2_score(y_test,y_pred4))*(len(y)-1)/(len(y)-X.shape[1]-1)),3))\n",
    "print('The Adj R2 value for Random Forests Regressor is :', round((1 - (1-r2_score(y_test,y_pred5))*(len(y)-1)/(len(y)-X.shape[1]-1)),3))\n",
    "print('The Adj R2 value for KNN Regressor is            :', round((1 - (1-r2_score(y_test,y_pred6))*(len(y)-1)/(len(y)-X.shape[1]-1)),3))\n",
    "print('The Adj R2 value for SVM Regressor is            :', round((1 - (1-r2_score(y_test,y_pred7))*(len(y)-1)/(len(y)-X.shape[1]-1)),3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Find Mean Squared Errors (MSE) and Root Mean Squared Errors (RMSE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "MSE_lr  = mean_squared_error(y_test,y_pred1)\n",
    "#MSE_xgb = mean_squared_error(y_test,y_pred2)\n",
    "MSE_ada = mean_squared_error(y_test,y_pred3)\n",
    "MSE_dt  = mean_squared_error(y_test,y_pred4)\n",
    "MSE_rf  = mean_squared_error(y_test,y_pred5)\n",
    "MSE_knn = mean_squared_error(y_test,y_pred6)\n",
    "MSE_svr = mean_squared_error(y_test,y_pred7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The RMSE value for Linear Regression is        : 107143.0\n",
      "The RMSE value for AdaBoost Regressor is       : 104309.0\n",
      "The RMSE value for Decision Tree Regressor is  : 100233.0\n",
      "The RMSE value for Random Forests Regressor is : 92113.0\n",
      "The RMSE value for KNN Regressor is            : 96187.0\n",
      "The RMSE value for SVM Regressor is            : 115314.0\n"
     ]
    }
   ],
   "source": [
    "print('The RMSE value for Linear Regression is        :', round(np.sqrt(MSE_lr)));\n",
    "#print('The RMSE value for XGBoost Regressor is        :', round(np.sqrt(MSE_xgb)));\n",
    "print('The RMSE value for AdaBoost Regressor is       :', round(np.sqrt(MSE_ada)));\n",
    "print('The RMSE value for Decision Tree Regressor is  :', round(np.sqrt(MSE_dt)));\n",
    "print('The RMSE value for Random Forests Regressor is :', round(np.sqrt(MSE_rf)));\n",
    "print('The RMSE value for KNN Regressor is            :', round(np.sqrt(MSE_knn)));\n",
    "print('The RMSE value for SVM Regressor is            :', round(np.sqrt(MSE_svr)));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Plot the actual vs predicted prices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Linear Regression')"
      ]
     },
     "execution_count": 61,
     "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": [
    "plt.scatter(y_test, y_pred1)\n",
    "plt.xlabel(\"Actual limit\")\n",
    "plt.ylabel(\"Predicted limit\")\n",
    "plt.title(\"Linear Regression\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Adaboost')"
      ]
     },
     "execution_count": 62,
     "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": [
    "plt.scatter(y_test, y_pred3)\n",
    "plt.xlabel(\"Actual limit\")\n",
    "plt.ylabel(\"Predicted limit\")\n",
    "plt.title(\"Adaboost\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Decision Tree')"
      ]
     },
     "execution_count": 63,
     "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": [
    "plt.scatter(y_test, y_pred4)\n",
    "plt.xlabel(\"Actual limit\")\n",
    "plt.ylabel(\"Predicted limit\")\n",
    "plt.title(\"Decision Tree\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Random Forest')"
      ]
     },
     "execution_count": 64,
     "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": [
    "plt.scatter(y_test, y_pred5)\n",
    "plt.xlabel(\"Actual limit\")\n",
    "plt.ylabel(\"Predicted limit\")\n",
    "plt.title(\"Random Forest\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'KNN')"
      ]
     },
     "execution_count": 65,
     "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": [
    "plt.scatter(y_test, y_pred6)\n",
    "plt.xlabel(\"Actual limit\")\n",
    "plt.ylabel(\"Predicted limit\")\n",
    "plt.title(\"KNN\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'SVM')"
      ]
     },
     "execution_count": 60,
     "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": [
    "\n",
    "plt.scatter(y_test, y_pred7)\n",
    "plt.xlabel(\"Actual limit\")\n",
    "plt.ylabel(\"Predicted limit\")\n",
    "plt.title(\"SVM\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.8.3"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}