Hypothesis Testing - Lending Club - 2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from scipy.stats import kurtosis, skew, stats\n",
    "from math import sqrt\n",
    "from numpy import mean, var\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv ( '/Volumes/DataScience/data/LendingClub/loan_small.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1303638 entries, 0 to 1303637\n",
      "Data columns (total 9 columns):\n",
      " #   Column          Non-Null Count    Dtype  \n",
      "---  ------          --------------    -----  \n",
      " 0   loan_amnt       1303638 non-null  int64  \n",
      " 1   int_rate        1303638 non-null  float64\n",
      " 2   emp_length      1228181 non-null  float64\n",
      " 3   home_ownership  1303638 non-null  object \n",
      " 4   annual_inc      1303638 non-null  float64\n",
      " 5   purpose         1303638 non-null  object \n",
      " 6   dti             1303326 non-null  float64\n",
      " 7   delinq_2yrs     1303638 non-null  int64  \n",
      " 8   loan_status     1303638 non-null  int64  \n",
      "dtypes: float64(4), int64(3), object(2)\n",
      "memory usage: 89.5+ MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>loan_amnt</th>\n",
       "      <th>int_rate</th>\n",
       "      <th>emp_length</th>\n",
       "      <th>annual_inc</th>\n",
       "      <th>dti</th>\n",
       "      <th>delinq_2yrs</th>\n",
       "      <th>loan_status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.303638e+06</td>\n",
       "      <td>1.303638e+06</td>\n",
       "      <td>1.228181e+06</td>\n",
       "      <td>1.303638e+06</td>\n",
       "      <td>1.303326e+06</td>\n",
       "      <td>1.303638e+06</td>\n",
       "      <td>1.303638e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.441684e+04</td>\n",
       "      <td>1.325730e+01</td>\n",
       "      <td>6.037018e+00</td>\n",
       "      <td>7.615875e+04</td>\n",
       "      <td>1.825884e+01</td>\n",
       "      <td>3.176027e-01</td>\n",
       "      <td>7.992648e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>8.699574e+03</td>\n",
       "      <td>4.760614e+00</td>\n",
       "      <td>3.584584e+00</td>\n",
       "      <td>7.004827e+04</td>\n",
       "      <td>1.093846e+01</td>\n",
       "      <td>8.766965e-01</td>\n",
       "      <td>4.005505e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>5.000000e+02</td>\n",
       "      <td>5.310000e+00</td>\n",
       "      <td>8.000000e-01</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>-1.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>8.000000e+03</td>\n",
       "      <td>9.750000e+00</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>4.584000e+04</td>\n",
       "      <td>1.179000e+01</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.200000e+04</td>\n",
       "      <td>1.274000e+01</td>\n",
       "      <td>6.000000e+00</td>\n",
       "      <td>6.500000e+04</td>\n",
       "      <td>1.761000e+01</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.000000e+04</td>\n",
       "      <td>1.599000e+01</td>\n",
       "      <td>1.000000e+01</td>\n",
       "      <td>9.000000e+04</td>\n",
       "      <td>2.404000e+01</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>4.000000e+04</td>\n",
       "      <td>3.099000e+01</td>\n",
       "      <td>1.000000e+01</td>\n",
       "      <td>1.099920e+07</td>\n",
       "      <td>9.990000e+02</td>\n",
       "      <td>2.900000e+01</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          loan_amnt      int_rate    emp_length    annual_inc           dti  \\\n",
       "count  1.303638e+06  1.303638e+06  1.228181e+06  1.303638e+06  1.303326e+06   \n",
       "mean   1.441684e+04  1.325730e+01  6.037018e+00  7.615875e+04  1.825884e+01   \n",
       "std    8.699574e+03  4.760614e+00  3.584584e+00  7.004827e+04  1.093846e+01   \n",
       "min    5.000000e+02  5.310000e+00  8.000000e-01  0.000000e+00 -1.000000e+00   \n",
       "25%    8.000000e+03  9.750000e+00  2.000000e+00  4.584000e+04  1.179000e+01   \n",
       "50%    1.200000e+04  1.274000e+01  6.000000e+00  6.500000e+04  1.761000e+01   \n",
       "75%    2.000000e+04  1.599000e+01  1.000000e+01  9.000000e+04  2.404000e+01   \n",
       "max    4.000000e+04  3.099000e+01  1.000000e+01  1.099920e+07  9.990000e+02   \n",
       "\n",
       "        delinq_2yrs   loan_status  \n",
       "count  1.303638e+06  1.303638e+06  \n",
       "mean   3.176027e-01  7.992648e-01  \n",
       "std    8.766965e-01  4.005505e-01  \n",
       "min    0.000000e+00  0.000000e+00  \n",
       "25%    0.000000e+00  1.000000e+00  \n",
       "50%    0.000000e+00  1.000000e+00  \n",
       "75%    0.000000e+00  1.000000e+00  \n",
       "max    2.900000e+01  1.000000e+00  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>loan_amnt</th>\n",
       "      <th>int_rate</th>\n",
       "      <th>emp_length</th>\n",
       "      <th>home_ownership</th>\n",
       "      <th>annual_inc</th>\n",
       "      <th>purpose</th>\n",
       "      <th>dti</th>\n",
       "      <th>delinq_2yrs</th>\n",
       "      <th>loan_status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>30000</td>\n",
       "      <td>22.35</td>\n",
       "      <td>5.0</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>100000.0</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>30.46</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>40000</td>\n",
       "      <td>16.14</td>\n",
       "      <td>0.8</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>45000.0</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>50.53</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20000</td>\n",
       "      <td>7.56</td>\n",
       "      <td>10.0</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>100000.0</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>18.92</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4500</td>\n",
       "      <td>11.31</td>\n",
       "      <td>10.0</td>\n",
       "      <td>RENT</td>\n",
       "      <td>38500.0</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>4.64</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8425</td>\n",
       "      <td>27.27</td>\n",
       "      <td>3.0</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>450000.0</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>12.37</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   loan_amnt  int_rate  emp_length home_ownership  annual_inc  \\\n",
       "0      30000     22.35         5.0       MORTGAGE    100000.0   \n",
       "1      40000     16.14         0.8       MORTGAGE     45000.0   \n",
       "2      20000      7.56        10.0       MORTGAGE    100000.0   \n",
       "3       4500     11.31        10.0           RENT     38500.0   \n",
       "4       8425     27.27         3.0       MORTGAGE    450000.0   \n",
       "\n",
       "              purpose    dti  delinq_2yrs  loan_status  \n",
       "0  debt_consolidation  30.46            0            1  \n",
       "1         credit_card  50.53            0            1  \n",
       "2         credit_card  18.92            0            1  \n",
       "3         credit_card   4.64            0            1  \n",
       "4         credit_card  12.37            0            1  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "paid  = data.query ( 'loan_status == 1')\n",
    "chargeoff = data.query ( 'loan_status == 0 ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1041952 entries, 0 to 1303637\n",
      "Data columns (total 9 columns):\n",
      " #   Column          Non-Null Count    Dtype  \n",
      "---  ------          --------------    -----  \n",
      " 0   loan_amnt       1041952 non-null  int64  \n",
      " 1   int_rate        1041952 non-null  float64\n",
      " 2   emp_length      986892 non-null   float64\n",
      " 3   home_ownership  1041952 non-null  object \n",
      " 4   annual_inc      1041952 non-null  float64\n",
      " 5   purpose         1041952 non-null  object \n",
      " 6   dti             1041702 non-null  float64\n",
      " 7   delinq_2yrs     1041952 non-null  int64  \n",
      " 8   loan_status     1041952 non-null  int64  \n",
      "dtypes: float64(4), int64(3), object(2)\n",
      "memory usage: 79.5+ MB\n"
     ]
    }
   ],
   "source": [
    "paid.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 261686 entries, 96 to 1303634\n",
      "Data columns (total 9 columns):\n",
      " #   Column          Non-Null Count   Dtype  \n",
      "---  ------          --------------   -----  \n",
      " 0   loan_amnt       261686 non-null  int64  \n",
      " 1   int_rate        261686 non-null  float64\n",
      " 2   emp_length      241289 non-null  float64\n",
      " 3   home_ownership  261686 non-null  object \n",
      " 4   annual_inc      261686 non-null  float64\n",
      " 5   purpose         261686 non-null  object \n",
      " 6   dti             261624 non-null  float64\n",
      " 7   delinq_2yrs     261686 non-null  int64  \n",
      " 8   loan_status     261686 non-null  int64  \n",
      "dtypes: float64(4), int64(3), object(2)\n",
      "memory usage: 20.0+ MB\n"
     ]
    }
   ],
   "source": [
    "chargeoff.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>loan_amnt</th>\n",
       "      <th>int_rate</th>\n",
       "      <th>emp_length</th>\n",
       "      <th>annual_inc</th>\n",
       "      <th>dti</th>\n",
       "      <th>delinq_2yrs</th>\n",
       "      <th>loan_status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>loan_amnt</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.143815</td>\n",
       "      <td>0.090323</td>\n",
       "      <td>0.310232</td>\n",
       "      <td>0.032081</td>\n",
       "      <td>-0.002967</td>\n",
       "      <td>-0.065220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>int_rate</th>\n",
       "      <td>0.143815</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.003822</td>\n",
       "      <td>-0.071268</td>\n",
       "      <td>0.148392</td>\n",
       "      <td>0.048250</td>\n",
       "      <td>-0.258427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>emp_length</th>\n",
       "      <td>0.090323</td>\n",
       "      <td>-0.003822</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.065584</td>\n",
       "      <td>0.027773</td>\n",
       "      <td>0.021730</td>\n",
       "      <td>0.014357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>annual_inc</th>\n",
       "      <td>0.310232</td>\n",
       "      <td>-0.071268</td>\n",
       "      <td>0.065584</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.141383</td>\n",
       "      <td>0.040632</td>\n",
       "      <td>0.041720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dti</th>\n",
       "      <td>0.032081</td>\n",
       "      <td>0.148392</td>\n",
       "      <td>0.027773</td>\n",
       "      <td>-0.141383</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.007472</td>\n",
       "      <td>-0.087184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>delinq_2yrs</th>\n",
       "      <td>-0.002967</td>\n",
       "      <td>0.048250</td>\n",
       "      <td>0.021730</td>\n",
       "      <td>0.040632</td>\n",
       "      <td>-0.007472</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.020074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>loan_status</th>\n",
       "      <td>-0.065220</td>\n",
       "      <td>-0.258427</td>\n",
       "      <td>0.014357</td>\n",
       "      <td>0.041720</td>\n",
       "      <td>-0.087184</td>\n",
       "      <td>-0.020074</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             loan_amnt  int_rate  emp_length  annual_inc       dti  \\\n",
       "loan_amnt     1.000000  0.143815    0.090323    0.310232  0.032081   \n",
       "int_rate      0.143815  1.000000   -0.003822   -0.071268  0.148392   \n",
       "emp_length    0.090323 -0.003822    1.000000    0.065584  0.027773   \n",
       "annual_inc    0.310232 -0.071268    0.065584    1.000000 -0.141383   \n",
       "dti           0.032081  0.148392    0.027773   -0.141383  1.000000   \n",
       "delinq_2yrs  -0.002967  0.048250    0.021730    0.040632 -0.007472   \n",
       "loan_status  -0.065220 -0.258427    0.014357    0.041720 -0.087184   \n",
       "\n",
       "             delinq_2yrs  loan_status  \n",
       "loan_amnt      -0.002967    -0.065220  \n",
       "int_rate        0.048250    -0.258427  \n",
       "emp_length      0.021730     0.014357  \n",
       "annual_inc      0.040632     0.041720  \n",
       "dti            -0.007472    -0.087184  \n",
       "delinq_2yrs     1.000000    -0.020074  \n",
       "loan_status    -0.020074     1.000000  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.corr()"
   ]
  },
  {
   "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": [
    "plt.title('Distribution of loan amounts for good ( paid ) vs bad ( chargeoff ) loans')\n",
    "ax = sns.kdeplot(paid['loan_amnt'], bw=10000, label='paid')\n",
    "ax = sns.kdeplot(chargeoff['loan_amnt'], bw=10000, label='chargeoff')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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mbZF0UmrrPGBhJbfJzMz2VOmrto4E5kmqI0taCyLidkl3SaoHBKwEPpbqLwZOB1qAl4ALACJio6QvAstTvSsiYmOa/jgwFxhOdrWWr9gyM6uiiiaSiHgI+MMS5Sd3Uj+ACzuZNweYU6K8GTimWKRmZtZb/mW7mZkV4kRiZmaFOJGYmVkhTiRmZlaIE4mZmRXiRGJmZoU4kZiZWSFOJGZmVogTiZmZFeJEYmZmhTiRmJlZIU4kZmZWiBOJmZkV4kRiZmaFOJGYmVkhTiRmZlZIpYfaPUDS/ZJ+KWmVpC+k8vGSlklqkXSrpGGpfP/0vCXNH5dr69Op/FeSTs2VT0llLZIureT2mJnZ3irdI9kOnBwRbwEmAVPSWOxfAa6OiEZgEzA91Z8ObErlV6d6SJoInAO8CZgCfENSXRrC9+vAacBE4NxU18zMqqSiiSQy29LToekRwMnAbal8HnBmmp6anpPmv1uSUvn8iNgeEU+Sjel+Qnq0RMQTEfEKMD/VNTOzKqn4OZLUc1gJrAeWAr8GNkfEjlRlDdCQphuAZwHS/BeAUfnyDst0Vl4qjhmSmiU1t7W19cWmmZkZVUgkEbEzIiYBY8h6EG+o9Do7iWN2RDRFRFN9fX0tQjAzG5CqdtVWRGwG7gb+CBgpaUiaNQZoTdOtwFiANP9QYEO+vMMynZWbmVmVVPqqrXpJI9P0cODPgNVkCeWsVG0asDBNL0rPSfPviohI5eekq7rGAxOA+4HlwIR0FdgwshPyiyq5TWZmtqch3Vcp5EhgXrq6aj9gQUTcLulRYL6kK4EHgetT/euBGyW1ABvJEgMRsUrSAuBRYAdwYUTsBJB0EbAEqAPmRMSqCm+TmZnlVDSRRMRDwB+WKH+C7HxJx/LfAh/opK2rgKtKlC8GFhcO1szMesW/bDczs0KcSMzMrBAnEjMzK8SJxMzMCnEiMTOzQpxIzMysECcSMzMrxInEzMwKcSIxM7NCnEjMzKwQJxIzMyvEicTMzApxIjEzs0KcSMzMrBAnEjMzK8SJxMzMCqn0ULtjJd0t6VFJqyTNSuWXS2qVtDI9Ts8t82lJLZJ+JenUXPmUVNYi6dJc+XhJy1L5rWnIXTMzq5JK90h2ABdHxETgJOBCSRPTvKsjYlJ6LAZI884B3gRMAb4hqS4N1ft14DRgInBurp2vpLYagU3A9Apvk5mZ5VQ0kUTE2oh4IE1vBVYDDV0sMhWYHxHbI+JJoIVsSN4TgJaIeCIiXgHmA1MlCTgZuC0tPw84szJbY2ZmpfQ4kUga35OyLpYfRzZ++7JUdJGkhyTNkXRYKmsAns0ttiaVdVY+CtgcETs6lJda/wxJzZKa29raehq2mZl1o5weyfdLlN1Womwvkkak5T8ZEVuAbwKvBSYBa4F/LSOOXomI2RHRFBFN9fX1lV6dmdmgMaS7CpLeQHbO4lBJf56bdQhwQA+WH0qWRG6KiP8AiIh1ufnfBm5PT1uBsbnFx6QyOinfAIyUNCT1SvL1zcysCnrSI3k9cAYwEnhv7nEc8JGuFkznMK4HVkfEV3PlR+aqvR94JE0vAs6RtH86bDYBuB9YDkxIV2gNIzshvygiArgbOCstPw1Y2INtMjOzPtJtjyQiFgILJf1RRPyizPbfDnwYeFjSylT2GbKrriYBATwFfDSta5WkBcCjZFd8XRgROwEkXQQsAeqAORGxKrV3CTBf0pXAg2SJy8zMqqTbRJLTIukzwLj8chHx150tEBE/B1Ri1uIulrkKuKpE+eJSy0XEE2RXdZmZWQ2Uk0gWAj8DfgLsrEw4ZmbW35STSA6MiEsqFomZmfVL5Vz+e3v+ViZmZmZQXiKZRZZMXpa0RdJWSVsqFZiZmfUPPT60FREHVzIQMzPrn8o5R4KkY9n7qq3/6OOYzMysH+lxIpE0BzgWWAX8PhUH4ERiZjaIldMjOSndDt7MzGyXck62/yI3BoiZmRlQXo/kBrJk8hywnewX6xERx1YkMjMz6xfKSSTXk+6bxe5zJGZmNsiVk0jaImJRxSIxM7N+qZxE8qCkm4Efkh3aAnz5r5nZYFdOIhlOlkBOyZX58l8zs0GunF+2X1DJQMzMrH8q5weJBwDTyYbd3TXEblfjkZiZ2cBXzqGtG4HHgFOBK4C/BFZ3tYCksWSXDY8mOww2OyKukXQ4cCvZ7VaeAj4YEZvS0LzXAKcDLwHnR8QDqa1pwOdS01dGxLxU/lZgLtmht8XArDQEb0Vce+21tLS0VKr5fqX9dZg1a1aNI9k3NDY2MnPmzFqHYVZ15SSSxoj4gKSpETEvnXj/WTfL7AAujogHJB0MrJC0FDgfuDMivizpUuBSsiFzTyMbp30CcCLwTeDElHguA5rIEtIKSYsiYlOq8xFgGVkimQLcUcZ2laWlpYWVj6xm54GHV2oV/cZ+r2T5esUT62ocSe3VvbSx1iGY1Uw5ieR36e9mSccAzwGv6mqBiFgLrE3TWyWtBhqAqcDkVG0ecA9ZIpkK3JB6FPdJGinpyFR3aURsBEjJaIqke4BDIuK+VH4DcCYVTCQAOw88nJff4KFZbLfhj3U6enRVuce8m3vMe6pkj7mcRDJb0mFkh5cWASOAf+zpwpLGAX9I1nMYnZIMZAlpdJpuAJ7NLbYmlXVVvqZEean1zwBmABx99NE9DdusX2lpaeHxVQ9y9AiPhj3sd9kdoLY/3VzjSGrvmW11FW2/nKu2rkuT9wKv6Thf0rT28xYl5o0Avg98MiK2ZKdCdrUbkip2TiO3ntnAbICmpqaKr8+sVo4esZPPHOcx52y3Lz1wSEXbL+emjd0p2X+UNJQsidyU+/HiunTIivR3fSpvBcbmFh+TyroqH1Oi3MzMqqQvE4n2Ksi6HtcDqyPiq7lZi4BpaXoasDBXfp4yJwEvpENgS4BTJB2WDq+dAixJ87ZIOimt67xcW2ZmVgVljZDYjVKHi95OutGjpJWp7DPAl4EFkqYDTwMfTPMWk13620J2+e8FABGxUdIXgeWp3hXtJ96Bj7P78t87qPCJdjMz21NfJpK9eiQR8fNS5cm7S9QP4MJSlSNiDjCnRHkzcExZkZqZWZ/py0Nb/9WHbZmZWT9Rzi1S9gf+guzX6LuWi4gr0t+L+jo4MzPb95VzaGsh8AKwgtxt5M3MbHArJ5GMiYgpFYvEzMz6pXLOkfy3pDdXLBIzM+uXyumRvAM4X9KTZIe2RHah1bEViczMzPqFchLJaRWLwszM+q1y7rX1NICkV5Eb2MrMzAa3Hp8jkfQ+SY8DTwI/JRuQyr8iNzMb5Mo52f5F4CTgfyJiPNkv0++rSFRmZtZvlJNIfhcRG4D9JO0XEXeTjVhoZmaDWDkn2zencUV+BtwkaT3wYmXCMjOz/qKcHslU4GXgk8CPgF8D761EUGZm1n+Uc9XWi5JGA8cDG4A70qEuMzMbxMq5auuDwP3AB8jGD1km6axKBWZmZv1DOedIPgscHxHrASTVAz8BbqtEYGZm1j+Uc45kv/YkkmzobnlJcyStl/RIruxySa2SVqbH6bl5n5bUIulXkk7NlU9JZS2SLs2Vj5e0LJXfKmlYGdtjZmZ9oJxE8iNJSySdL+l84P+TDY3blblAqTsGXx0Rk9JjMYCkicA5wJvSMt+QVCepDvg62S1aJgLnproAX0ltNQKbgOllbI+ZmfWBHieSiPgHYDZwbHrMjohLulnmXmBjV3VypgLzI2J7RDxJNm77CenREhFPRMQrwHxgqiQBJ7P70No84Myebo+ZmfWNssZsj4jvA9/vg/VeJOk8oBm4OCI2AQ3s+Uv5NakM4NkO5ScCo4DNEbGjRP29SJoBzAA4+uij+2ATzMwMetAjkbRV0pYSj62StvRind8EXgtMAtYC/9qLNsoWEbMjoikimurr66uxSjOzQaHbHklEHNyXK4yIde3Tkr4N3J6etgJjc1XHpDI6Kd8AjJQ0JPVK8vXNzKxKyjq01RckHRkRa9PT9wPtV3QtAm6W9FXgKGAC2e9WBEyQNJ4sUZwDfCgiQtLdwFlk502mkY0rX1Gtra3UvfQCwx/r7joDG0zqXtpAa+uO7iuaDUAVTSSSbgEmA0dIWgNcBkyWNAkIslvRfxQgIlZJWgA8CuwALoyInamdi4AlQB0wJyJWpVVcAsyXdCXwIHB9JbfHzMz2VtFEEhHnliju9MM+Iq4CripRvpgSlxpHxBNkV3VVTUNDA89tH8LLbzi9+8o2aAx/bDENDaNrHYZZTZTzOxIzM7O9OJGYmVkhTiRmZlaIE4mZmRXiRGJmZoU4kZiZWSFOJGZmVogTiZmZFVL1W6SYWeW0trby4tY6vvTAIbUOxfYhT2+t46DWyt2K0D0SMzMrxD0SswGkoaGB7TvW8pnjejPCgw1UX3rgEPZv6HS4psLcIzEzs0KcSMzMrBAnEjMzK8SJxMzMCnEiMTOzQiqaSCTNkbRe0iO5ssMlLZX0ePp7WCqXpK9JapH0kKTjcstMS/UflzQtV/5WSQ+nZb4mSZXcHjMz21uleyRzgSkdyi4F7oyICcCd6TnAaWTjtE8AZgDfhCzxkA3ReyLZaIiXtSefVOcjueU6rsvMzCqsookkIu4FNnYongrMS9PzgDNz5TdE5j5gpKQjgVOBpRGxMSI2AUuBKWneIRFxX0QEcEOuLTMzq5JanCMZHRFr0/RzQPtA1w3As7l6a1JZV+VrSpSXJGmGpGZJzW1tbcW2wMzMdqnpyfbUk4gqrWt2RDRFRFN9fX01VmlmNijUIpGsS4elSH/Xp/JWYGyu3phU1lX5mBLlZmZWRbVIJIuA9iuvpgELc+Xnpau3TgJeSIfAlgCnSDosnWQ/BViS5m2RdFK6Wuu8XFtmZlYlFb1po6RbgMnAEZLWkF199WVggaTpwNPAB1P1xcDpQAvwEnABQERslPRFYHmqd0VEtJ/A/zjZlWHDgTvSo+LqXtrI8McWV2NV+7T9fpvdGPD3B/iW5XUvbWT36T6zwaWiiSQizu1k1rtL1A3gwk7amQPMKVHeDBxTJMZyNTY2VnN1+7SWlq0ANL7GH6Aw2vuGDVq+jXyZZs6cWesQ9hmzZs0C4JprrqlxJGZWS75FipmZFeJEYmZmhTiRmJlZIU4kZmZWiBOJmZkV4qu2zAaYZ7bV8aUH/NuedS9l35NHH/j7GkdSe89sq2NCBdt3IjEbQPxblt1eaWkBYP9X+zWZQGX3DScSswHEv3Pazb9zqh6fIzEzs0KcSMzMrBAnEjMzK8SJxMzMCnEiMTOzQpxIzMysECcSMzMrpGaJRNJTkh6WtFJScyo7XNJSSY+nv4elckn6mqQWSQ9JOi7XzrRU/3FJ0zpbn5mZVUateyR/EhGTIqIpPb8UuDMiJgB3pucAp5H9OHMCMAP4JmSJh2z43hOBE4DL2pOPmZlVR60TSUdTgXlpeh5wZq78hsjcB4yUdCRwKrA0IjZGxCZgKTCl2kGbmQ1mtUwkAfxY0gpJM1LZ6IhYm6afA9oHA28Ans0tuyaVdVa+F0kzJDVLam5ra+urbTAzG/Rqea+td0REq6RXAUslPZafGREhKfpqZRExG5gN0NTU1GftmpkNdjXrkUREa/q7HvhPsnMc69IhK9Lf9al6KzA2t/iYVNZZuZmZVUlNEomkgyQd3D4NnAI8AiwC2q+8mgYsTNOLgPPS1VsnAS+kQ2BLgFMkHZZOsp+SyszMrEpqdWhrNPCfktpjuDkifiRpObBA0nTgaeCDqf5i4HSgBXgJuAAgIjZK+iKwPNW7IiI2Vm8zzMysJokkIp4A3lKifAPw7hLlAVzYSVtzgDl9HaOZmfXMvnb5r5mZ9TNOJGZmVogTiZmZFeJEYmZmhTiRmJlZIU4kZmZWiBOJmZkV4kRiZmaFOJGYmVkhTiRmZlaIE4mZmRXiRGJmZoU4kZiZWSFOJGZmVkgth9q1Aq699lpaWlpqGkP7+mfNmlXTOAAaGxuZOXNmrcMwG5ScSKzXhg8fXusQzGwfoGzMqP5N0hTgGqAOuC4ivtxV/aampmhubq5KbGaDzb7QW4bdPebGxsaaxjFQesuSVkREU6l5/f4ciaQ64OvAacBE4FxJE2sblZnV2vDhw91rrpKBcGjrBKAlDd+LpPnAVBoiYDsAAAQpSURBVODRmkZlNkgNhG/fVp5+3yMBGoBnc8/XpDIzM6uCgZBIekTSDEnNkprb2tpqHY6Z2YAxEBJJKzA293xMKttDRMyOiKaIaKqvr69acGZmA91ASCTLgQmSxksaBpwDLKpxTGZmg0a/P9keETskXQQsIbv8d05ErKpxWGZmg0a/TyQAEbEYWFzrOMzMBqOBcGjLzMxqyInEzMwKGRC3SCmXpDbg6VrHMUAcATxf6yDMOuH9s++8OiJKXvI6KBOJ9R1JzZ3df8es1rx/VocPbZmZWSFOJGZmVogTiRU1u9YBmHXB+2cV+ByJmZkV4h6JmZkV4kRiZmaFOJFYr0maIulXklokXVrreMzaSZojab2kR2ody2DgRGK94iGObR83F5hS6yAGCycS661dQxxHxCtA+xDHZjUXEfcCG2sdx2DhRGK95SGOzQxwIjEzs4KcSKy3ejTEsZkNfE4k1lse4tjMACcS66WI2AG0D3G8GljgIY5tXyHpFuAXwOslrZE0vdYxDWS+RYqZmRXiHomZmRXiRGJmZoU4kZiZWSFOJGZmVogTiZmZFeJEYmZmhTiRmHVB0rYarnuypLf1VT2zSnEiMdt3TQZ6kiB6Ws+sIpxIzHpAmX+R9IikhyWdncpHSLpT0gOpfGoqHydptaRvS1ol6ceShnfR/ickPSrpIUnzJY0DPgb8naSVkt4p6b2Slkl6UNJPJI3upN5cSWfl2t6W/h4p6d5U7xFJ76zU62WDi3/ZbtYFSdsiYoSkvyD7wJ4CHEF2r7ETgTbgwIjYIukI4D5gAvBqoAVoioiVkhYAiyLiu52s5zfA+IjYLmlkRGyWdDmwLSL+b6pzGLA5IkLS3wBvjIiLS9SbC9weEbd12IaLgQMi4qo0MNmBEbG17181G2yG1DoAs37iHcAtEbETWCfpp8DxwB3AlyS9C/g92Zgso9MyT0bEyjS9AhjXRfsPATdJ+gHwg07qjAFulXQkMAx4ssxtWA7MkTQU+EEuNrNCfGjLrJi/BOqBt0bEJGAdcECatz1Xbyddf3F7D9nQxccByyWVqnst8O8R8Wbgo7n1dLSD9L8taT+ypNM+auC7yG73P1fSed1unVkPOJGY9czPgLMl1UmqJ/tAvh84FFgfEb+T9Cdkh7TKkj7sx0bE3cAlqc0RwFbg4FzVQ9k95su0XHnHek8Bb03T7wOGpvW8GlgXEd8GriNLWmaFOZGY9cx/kh1++iVwF/CpiHgOuAlokvQwcB7wWC/argO+m9p4EPhaRGwGfgi8v/0kOnA58D1JK4Dnc8t3rPdt4I8l/RL4I+DFVG8y8EtJDwJnA9f0Ilazvfhku5mZFeIeiZmZFeKrtsyqSNLXgbd3KL4mIr5Ti3jM+oIPbZmZWSE+tGVmZoU4kZiZWSFOJGZmVogTiZmZFfK/GVGwtTTJG90AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.title('Distribution of loan amount for good vs bad loans ')\n",
    "ax = sns.boxplot(x=\"loan_status\", y=\"loan_amnt\", data=data)"
   ]
  },
  {
   "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": [
    "plt.title('Distribution of loan amount for good vs bad loans ')\n",
    "ax = sns.boxplot(x=\"loan_status\", y=\"annual_inc\", data=data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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": [
    "plt.title('Distribution of loan amount for good vs bad loans ')\n",
    "ax = sns.boxplot(x=\"loan_status\", y=\"emp_length\", data=data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2 sample, 2 sided t-test pvalue:  0.0 t-stat: 73.88055546669763\n"
     ]
    }
   ],
   "source": [
    "statistic, pvalue = stats.ttest_ind(chargeoff['loan_amnt'], paid['loan_amnt'], equal_var = False)\n",
    "print(\"2 sample, 2 sided t-test pvalue:  {} t-stat: {}\".format(pvalue,statistic))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2 sample, 2 sided t-test pvalue:  0.0 t-stat: -50.18848605958238\n"
     ]
    }
   ],
   "source": [
    "statistic, pvalue = stats.ttest_ind(chargeoff['annual_inc'], paid['annual_inc'], equal_var = False)\n",
    "print(\"2 sample, 2 sided t-test pvalue:  {} t-stat: {}\".format(pvalue,statistic))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2 sample, 2 sided t-test pvalue:  nan t-stat: nan\n"
     ]
    }
   ],
   "source": [
    "statistic, pvalue = stats.ttest_ind(chargeoff['dti'], paid['dti'], equal_var = False)\n",
    "print(\"2 sample, 2 sided t-test pvalue:  {} t-stat: {}\".format(pvalue,statistic))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2 sample, 2 sided t-test pvalue:  0.0 t-stat: 291.15495845193226\n"
     ]
    }
   ],
   "source": [
    "statistic, pvalue = stats.ttest_ind(chargeoff['int_rate'], paid['int_rate'], equal_var = False)\n",
    "print(\"2 sample, 2 sided t-test pvalue:  {} t-stat: {}\".format(pvalue,statistic))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cohen's d: 0.16317318655201116\n"
     ]
    }
   ],
   "source": [
    "# function to calculate Cohen's d for independent samples\n",
    "def cohend(d1, d2):\n",
    "    n1, n2 = len(d1), len(d2)\n",
    "    s1, s2 = var(d1, ddof=1), var(d2, ddof=1)\n",
    "    s = sqrt(((n1 - 1) * s1 + (n2 - 1) * s2) / (n1 + n2 - 2))\n",
    "    u1, u2 = mean(d1), mean(d2)\n",
    "    return (u1 - u2) / s\n",
    "d = cohend(chargeoff['loan_amnt'], paid['loan_amnt'])\n",
    "print(\"cohen's d: {}\".format(d))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "        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>loan_amnt</th>\n",
       "      <th>int_rate</th>\n",
       "      <th>emp_length</th>\n",
       "      <th>home_ownership</th>\n",
       "      <th>annual_inc</th>\n",
       "      <th>purpose</th>\n",
       "      <th>dti</th>\n",
       "      <th>delinq_2yrs</th>\n",
       "      <th>loan_status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>30000</td>\n",
       "      <td>22.35</td>\n",
       "      <td>5.0</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>100000.0</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>30.46</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>40000</td>\n",
       "      <td>16.14</td>\n",
       "      <td>0.8</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>45000.0</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>50.53</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20000</td>\n",
       "      <td>7.56</td>\n",
       "      <td>10.0</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>100000.0</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>18.92</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4500</td>\n",
       "      <td>11.31</td>\n",
       "      <td>10.0</td>\n",
       "      <td>RENT</td>\n",
       "      <td>38500.0</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>4.64</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8425</td>\n",
       "      <td>27.27</td>\n",
       "      <td>3.0</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>450000.0</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>12.37</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   loan_amnt  int_rate  emp_length home_ownership  annual_inc  \\\n",
       "0      30000     22.35         5.0       MORTGAGE    100000.0   \n",
       "1      40000     16.14         0.8       MORTGAGE     45000.0   \n",
       "2      20000      7.56        10.0       MORTGAGE    100000.0   \n",
       "3       4500     11.31        10.0           RENT     38500.0   \n",
       "4       8425     27.27         3.0       MORTGAGE    450000.0   \n",
       "\n",
       "              purpose    dti  delinq_2yrs  loan_status  \n",
       "0  debt_consolidation  30.46            0            1  \n",
       "1         credit_card  50.53            0            1  \n",
       "2         credit_card  18.92            0            1  \n",
       "3         credit_card   4.64            0            1  \n",
       "4         credit_card  12.37            0            1  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "paid.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cohen's d: -0.10424820883300534\n"
     ]
    }
   ],
   "source": [
    "# function to calculate Cohen's d for independent samples\n",
    "def cohend(d1, d2):\n",
    "    n1, n2 = len(d1), len(d2)\n",
    "    s1, s2 = var(d1, ddof=1), var(d2, ddof=1)\n",
    "    s = sqrt(((n1 - 1) * s1 + (n2 - 1) * s2) / (n1 + n2 - 2))\n",
    "    u1, u2 = mean(d1), mean(d2)\n",
    "    return (u1 - u2) / s\n",
    "d = cohend(chargeoff['annual_inc'], paid['annual_inc'])\n",
    "print(\"cohen's d: {}\".format(d))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cohen's d: -0.10424820883300534\n"
     ]
    }
   ],
   "source": [
    "# function to calculate Cohen's d for independent samples\n",
    "def cohend(d1, d2):\n",
    "    n1, n2 = len(d1), len(d2)\n",
    "    s1, s2 = var(d1, ddof=1), var(d2, ddof=1)\n",
    "    s = sqrt(((n1 - 1) * s1 + (n2 - 1) * s2) / (n1 + n2 - 2))\n",
    "    u1, u2 = mean(d1), mean(d2)\n",
    "    return (u1 - u2) / s\n",
    "d = cohend(chargeoff['annual_inc'], paid['annual_inc'])\n",
    "print(\"cohen's d: {}\".format(d))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# using Pearson understand co-relation variable effecting loan failures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>loan_amnt</th>\n",
       "      <th>int_rate</th>\n",
       "      <th>emp_length</th>\n",
       "      <th>home_ownership</th>\n",
       "      <th>annual_inc</th>\n",
       "      <th>purpose</th>\n",
       "      <th>dti</th>\n",
       "      <th>delinq_2yrs</th>\n",
       "      <th>loan_status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>30000</td>\n",
       "      <td>22.35</td>\n",
       "      <td>5.0</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>100000.0</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>30.46</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>40000</td>\n",
       "      <td>16.14</td>\n",
       "      <td>0.8</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>45000.0</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>50.53</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20000</td>\n",
       "      <td>7.56</td>\n",
       "      <td>10.0</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>100000.0</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>18.92</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4500</td>\n",
       "      <td>11.31</td>\n",
       "      <td>10.0</td>\n",
       "      <td>RENT</td>\n",
       "      <td>38500.0</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>4.64</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8425</td>\n",
       "      <td>27.27</td>\n",
       "      <td>3.0</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>450000.0</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>12.37</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   loan_amnt  int_rate  emp_length home_ownership  annual_inc  \\\n",
       "0      30000     22.35         5.0       MORTGAGE    100000.0   \n",
       "1      40000     16.14         0.8       MORTGAGE     45000.0   \n",
       "2      20000      7.56        10.0       MORTGAGE    100000.0   \n",
       "3       4500     11.31        10.0           RENT     38500.0   \n",
       "4       8425     27.27         3.0       MORTGAGE    450000.0   \n",
       "\n",
       "              purpose    dti  delinq_2yrs  loan_status  \n",
       "0  debt_consolidation  30.46            0            1  \n",
       "1         credit_card  50.53            0            1  \n",
       "2         credit_card  18.92            0            1  \n",
       "3         credit_card   4.64            0            1  \n",
       "4         credit_card  12.37            0            1  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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": [
    "plt.title(\"BMI Versus Charges\")\n",
    "ax = sns.scatterplot(x=\"loan_amnt\", y=\"delinq_2yrs\", data=data)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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>loan_amnt</th>\n",
       "      <th>int_rate</th>\n",
       "      <th>emp_length</th>\n",
       "      <th>home_ownership</th>\n",
       "      <th>annual_inc</th>\n",
       "      <th>purpose</th>\n",
       "      <th>dti</th>\n",
       "      <th>delinq_2yrs</th>\n",
       "      <th>loan_status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>30000</td>\n",
       "      <td>22.35</td>\n",
       "      <td>5.0</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>100000.0</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>30.46</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>40000</td>\n",
       "      <td>16.14</td>\n",
       "      <td>0.8</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>45000.0</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>50.53</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20000</td>\n",
       "      <td>7.56</td>\n",
       "      <td>10.0</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>100000.0</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>18.92</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4500</td>\n",
       "      <td>11.31</td>\n",
       "      <td>10.0</td>\n",
       "      <td>RENT</td>\n",
       "      <td>38500.0</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>4.64</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8425</td>\n",
       "      <td>27.27</td>\n",
       "      <td>3.0</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>450000.0</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>12.37</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   loan_amnt  int_rate  emp_length home_ownership  annual_inc  \\\n",
       "0      30000     22.35         5.0       MORTGAGE    100000.0   \n",
       "1      40000     16.14         0.8       MORTGAGE     45000.0   \n",
       "2      20000      7.56        10.0       MORTGAGE    100000.0   \n",
       "3       4500     11.31        10.0           RENT     38500.0   \n",
       "4       8425     27.27         3.0       MORTGAGE    450000.0   \n",
       "\n",
       "              purpose    dti  delinq_2yrs  loan_status  \n",
       "0  debt_consolidation  30.46            0            1  \n",
       "1         credit_card  50.53            0            1  \n",
       "2         credit_card  18.92            0            1  \n",
       "3         credit_card   4.64            0            1  \n",
       "4         credit_card  12.37            0            1  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
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