stage z - pre-processing Loan Data.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>credit.policy</th>\n",
       "      <th>purpose</th>\n",
       "      <th>int.rate</th>\n",
       "      <th>installment</th>\n",
       "      <th>log.annual.inc</th>\n",
       "      <th>dti</th>\n",
       "      <th>fico</th>\n",
       "      <th>days.with.cr.line</th>\n",
       "      <th>revol.bal</th>\n",
       "      <th>revol.util</th>\n",
       "      <th>inq.last.6mths</th>\n",
       "      <th>delinq.2yrs</th>\n",
       "      <th>pub.rec</th>\n",
       "      <th>not.fully.paid</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>0.1189</td>\n",
       "      <td>829.10</td>\n",
       "      <td>11.350407</td>\n",
       "      <td>19.48</td>\n",
       "      <td>737</td>\n",
       "      <td>5639.958333</td>\n",
       "      <td>28854</td>\n",
       "      <td>52.1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>0.1071</td>\n",
       "      <td>228.22</td>\n",
       "      <td>11.082143</td>\n",
       "      <td>14.29</td>\n",
       "      <td>707</td>\n",
       "      <td>2760.000000</td>\n",
       "      <td>33623</td>\n",
       "      <td>76.7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>0.1357</td>\n",
       "      <td>366.86</td>\n",
       "      <td>10.373491</td>\n",
       "      <td>11.63</td>\n",
       "      <td>682</td>\n",
       "      <td>4710.000000</td>\n",
       "      <td>3511</td>\n",
       "      <td>25.6</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>0.1008</td>\n",
       "      <td>162.34</td>\n",
       "      <td>11.350407</td>\n",
       "      <td>8.10</td>\n",
       "      <td>712</td>\n",
       "      <td>2699.958333</td>\n",
       "      <td>33667</td>\n",
       "      <td>73.2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>0.1426</td>\n",
       "      <td>102.92</td>\n",
       "      <td>11.299732</td>\n",
       "      <td>14.97</td>\n",
       "      <td>667</td>\n",
       "      <td>4066.000000</td>\n",
       "      <td>4740</td>\n",
       "      <td>39.5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   credit.policy             purpose  int.rate  installment  log.annual.inc  \\\n",
       "0              1  debt_consolidation    0.1189       829.10       11.350407   \n",
       "1              1         credit_card    0.1071       228.22       11.082143   \n",
       "2              1  debt_consolidation    0.1357       366.86       10.373491   \n",
       "3              1  debt_consolidation    0.1008       162.34       11.350407   \n",
       "4              1         credit_card    0.1426       102.92       11.299732   \n",
       "\n",
       "     dti  fico  days.with.cr.line  revol.bal  revol.util  inq.last.6mths  \\\n",
       "0  19.48   737        5639.958333      28854        52.1               0   \n",
       "1  14.29   707        2760.000000      33623        76.7               0   \n",
       "2  11.63   682        4710.000000       3511        25.6               1   \n",
       "3   8.10   712        2699.958333      33667        73.2               1   \n",
       "4  14.97   667        4066.000000       4740        39.5               0   \n",
       "\n",
       "   delinq.2yrs  pub.rec  not.fully.paid  \n",
       "0            0        0               0  \n",
       "1            0        0               0  \n",
       "2            0        0               0  \n",
       "3            0        0               0  \n",
       "4            1        0               0  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('loan_data.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# - Missing Data\n",
    "   **I checked for the existance of Nan values and other values as well that could be used to express missing data but I \n",
    "    found nothing**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 9578 entries, 0 to 9577\n",
      "Data columns (total 14 columns):\n",
      " #   Column             Non-Null Count  Dtype  \n",
      "---  ------             --------------  -----  \n",
      " 0   credit.policy      9578 non-null   int64  \n",
      " 1   purpose            9578 non-null   object \n",
      " 2   int.rate           9578 non-null   float64\n",
      " 3   installment        9578 non-null   float64\n",
      " 4   log.annual.inc     9578 non-null   float64\n",
      " 5   dti                9578 non-null   float64\n",
      " 6   fico               9578 non-null   int64  \n",
      " 7   days.with.cr.line  9578 non-null   float64\n",
      " 8   revol.bal          9578 non-null   int64  \n",
      " 9   revol.util         9578 non-null   float64\n",
      " 10  inq.last.6mths     9578 non-null   int64  \n",
      " 11  delinq.2yrs        9578 non-null   int64  \n",
      " 12  pub.rec            9578 non-null   int64  \n",
      " 13  not.fully.paid     9578 non-null   int64  \n",
      "dtypes: float64(6), int64(7), object(1)\n",
      "memory usage: 1.0+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info() # no null values - still checking if other missing value expressions exist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Category:purpose -  0 missing data\n",
      "\n"
     ]
    }
   ],
   "source": [
    "notFound=0\n",
    "for ex in df['purpose']:\n",
    "    if ('x'or'xx'or'unknown'or'not found' or 'missing') in ex.lower():\n",
    "        notFound +=1\n",
    "print(f'Category:purpose -  {notFound} missing data\\n') #nope ther is nothing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# - Categorical Data\n",
    "   **The only categorical data was for the purpose and I used one hot encoder since this data is of no sequence/ pattern**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['debt_consolidation', 'credit_card', 'all_other',\n",
       "       'home_improvement', 'small_business', 'major_purchase',\n",
       "       'educational'], dtype=object)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['purpose'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "purpose\n",
       "debt_consolidation    3957\n",
       "all_other             2331\n",
       "credit_card           1262\n",
       "home_improvement       629\n",
       "small_business         619\n",
       "major_purchase         437\n",
       "educational            343\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = df.groupby('purpose').size().sort_values(ascending=False)\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7480829988>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(y = df['purpose'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>credit.policy</th>\n",
       "      <th>int.rate</th>\n",
       "      <th>installment</th>\n",
       "      <th>log.annual.inc</th>\n",
       "      <th>dti</th>\n",
       "      <th>fico</th>\n",
       "      <th>days.with.cr.line</th>\n",
       "      <th>revol.bal</th>\n",
       "      <th>revol.util</th>\n",
       "      <th>inq.last.6mths</th>\n",
       "      <th>delinq.2yrs</th>\n",
       "      <th>pub.rec</th>\n",
       "      <th>not.fully.paid</th>\n",
       "      <th>purpose_credit_card</th>\n",
       "      <th>purpose_debt_consolidation</th>\n",
       "      <th>purpose_educational</th>\n",
       "      <th>purpose_home_improvement</th>\n",
       "      <th>purpose_major_purchase</th>\n",
       "      <th>purpose_small_business</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.1189</td>\n",
       "      <td>829.10</td>\n",
       "      <td>11.350407</td>\n",
       "      <td>19.48</td>\n",
       "      <td>737</td>\n",
       "      <td>5639.958333</td>\n",
       "      <td>28854</td>\n",
       "      <td>52.1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.1071</td>\n",
       "      <td>228.22</td>\n",
       "      <td>11.082143</td>\n",
       "      <td>14.29</td>\n",
       "      <td>707</td>\n",
       "      <td>2760.000000</td>\n",
       "      <td>33623</td>\n",
       "      <td>76.7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0.1357</td>\n",
       "      <td>366.86</td>\n",
       "      <td>10.373491</td>\n",
       "      <td>11.63</td>\n",
       "      <td>682</td>\n",
       "      <td>4710.000000</td>\n",
       "      <td>3511</td>\n",
       "      <td>25.6</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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0.1008</td>\n",
       "      <td>162.34</td>\n",
       "      <td>11.350407</td>\n",
       "      <td>8.10</td>\n",
       "      <td>712</td>\n",
       "      <td>2699.958333</td>\n",
       "      <td>33667</td>\n",
       "      <td>73.2</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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0.1426</td>\n",
       "      <td>102.92</td>\n",
       "      <td>11.299732</td>\n",
       "      <td>14.97</td>\n",
       "      <td>667</td>\n",
       "      <td>4066.000000</td>\n",
       "      <td>4740</td>\n",
       "      <td>39.5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   credit.policy  int.rate  installment  log.annual.inc    dti  fico  \\\n",
       "0              1    0.1189       829.10       11.350407  19.48   737   \n",
       "1              1    0.1071       228.22       11.082143  14.29   707   \n",
       "2              1    0.1357       366.86       10.373491  11.63   682   \n",
       "3              1    0.1008       162.34       11.350407   8.10   712   \n",
       "4              1    0.1426       102.92       11.299732  14.97   667   \n",
       "\n",
       "   days.with.cr.line  revol.bal  revol.util  inq.last.6mths  delinq.2yrs  \\\n",
       "0        5639.958333      28854        52.1               0            0   \n",
       "1        2760.000000      33623        76.7               0            0   \n",
       "2        4710.000000       3511        25.6               1            0   \n",
       "3        2699.958333      33667        73.2               1            0   \n",
       "4        4066.000000       4740        39.5               0            1   \n",
       "\n",
       "   pub.rec  not.fully.paid  purpose_credit_card  purpose_debt_consolidation  \\\n",
       "0        0               0                    0                           1   \n",
       "1        0               0                    1                           0   \n",
       "2        0               0                    0                           1   \n",
       "3        0               0                    0                           1   \n",
       "4        0               0                    1                           0   \n",
       "\n",
       "   purpose_educational  purpose_home_improvement  purpose_major_purchase  \\\n",
       "0                    0                         0                       0   \n",
       "1                    0                         0                       0   \n",
       "2                    0                         0                       0   \n",
       "3                    0                         0                       0   \n",
       "4                    0                         0                       0   \n",
       "\n",
       "   purpose_small_business  \n",
       "0                       0  \n",
       "1                       0  \n",
       "2                       0  \n",
       "3                       0  \n",
       "4                       0  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.get_dummies(df, columns=['purpose'], drop_first=True)\n",
    "df.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# - Data Cleaning:\n",
    "   **This requires knowledge with the field and more information about the data but I think there is nothing unordinary \n",
    "    about the data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>credit.policy</th>\n",
       "      <th>int.rate</th>\n",
       "      <th>installment</th>\n",
       "      <th>log.annual.inc</th>\n",
       "      <th>dti</th>\n",
       "      <th>fico</th>\n",
       "      <th>days.with.cr.line</th>\n",
       "      <th>revol.bal</th>\n",
       "      <th>revol.util</th>\n",
       "      <th>inq.last.6mths</th>\n",
       "      <th>delinq.2yrs</th>\n",
       "      <th>pub.rec</th>\n",
       "      <th>not.fully.paid</th>\n",
       "      <th>purpose_credit_card</th>\n",
       "      <th>purpose_debt_consolidation</th>\n",
       "      <th>purpose_educational</th>\n",
       "      <th>purpose_home_improvement</th>\n",
       "      <th>purpose_major_purchase</th>\n",
       "      <th>purpose_small_business</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>9578.000000</td>\n",
       "      <td>9578.000000</td>\n",
       "      <td>9578.000000</td>\n",
       "      <td>9578.000000</td>\n",
       "      <td>9578.000000</td>\n",
       "      <td>9578.000000</td>\n",
       "      <td>9578.000000</td>\n",
       "      <td>9.578000e+03</td>\n",
       "      <td>9578.000000</td>\n",
       "      <td>9578.000000</td>\n",
       "      <td>9578.000000</td>\n",
       "      <td>9578.000000</td>\n",
       "      <td>9578.000000</td>\n",
       "      <td>9578.000000</td>\n",
       "      <td>9578.000000</td>\n",
       "      <td>9578.000000</td>\n",
       "      <td>9578.000000</td>\n",
       "      <td>9578.000000</td>\n",
       "      <td>9578.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.804970</td>\n",
       "      <td>0.122640</td>\n",
       "      <td>319.089413</td>\n",
       "      <td>10.932117</td>\n",
       "      <td>12.606679</td>\n",
       "      <td>710.846314</td>\n",
       "      <td>4560.767197</td>\n",
       "      <td>1.691396e+04</td>\n",
       "      <td>46.799236</td>\n",
       "      <td>1.577469</td>\n",
       "      <td>0.163708</td>\n",
       "      <td>0.062122</td>\n",
       "      <td>0.160054</td>\n",
       "      <td>0.131760</td>\n",
       "      <td>0.413134</td>\n",
       "      <td>0.035811</td>\n",
       "      <td>0.065671</td>\n",
       "      <td>0.045625</td>\n",
       "      <td>0.064627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.396245</td>\n",
       "      <td>0.026847</td>\n",
       "      <td>207.071301</td>\n",
       "      <td>0.614813</td>\n",
       "      <td>6.883970</td>\n",
       "      <td>37.970537</td>\n",
       "      <td>2496.930377</td>\n",
       "      <td>3.375619e+04</td>\n",
       "      <td>29.014417</td>\n",
       "      <td>2.200245</td>\n",
       "      <td>0.546215</td>\n",
       "      <td>0.262126</td>\n",
       "      <td>0.366676</td>\n",
       "      <td>0.338248</td>\n",
       "      <td>0.492422</td>\n",
       "      <td>0.185829</td>\n",
       "      <td>0.247720</td>\n",
       "      <td>0.208682</td>\n",
       "      <td>0.245880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.060000</td>\n",
       "      <td>15.670000</td>\n",
       "      <td>7.547502</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>612.000000</td>\n",
       "      <td>178.958333</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>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.103900</td>\n",
       "      <td>163.770000</td>\n",
       "      <td>10.558414</td>\n",
       "      <td>7.212500</td>\n",
       "      <td>682.000000</td>\n",
       "      <td>2820.000000</td>\n",
       "      <td>3.187000e+03</td>\n",
       "      <td>22.600000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.122100</td>\n",
       "      <td>268.950000</td>\n",
       "      <td>10.928884</td>\n",
       "      <td>12.665000</td>\n",
       "      <td>707.000000</td>\n",
       "      <td>4139.958333</td>\n",
       "      <td>8.596000e+03</td>\n",
       "      <td>46.300000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.140700</td>\n",
       "      <td>432.762500</td>\n",
       "      <td>11.291293</td>\n",
       "      <td>17.950000</td>\n",
       "      <td>737.000000</td>\n",
       "      <td>5730.000000</td>\n",
       "      <td>1.824950e+04</td>\n",
       "      <td>70.900000</td>\n",
       "      <td>2.000000</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",
       "      <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>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.216400</td>\n",
       "      <td>940.140000</td>\n",
       "      <td>14.528354</td>\n",
       "      <td>29.960000</td>\n",
       "      <td>827.000000</td>\n",
       "      <td>17639.958330</td>\n",
       "      <td>1.207359e+06</td>\n",
       "      <td>119.000000</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>5.000000</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",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       credit.policy     int.rate  installment  log.annual.inc          dti  \\\n",
       "count    9578.000000  9578.000000  9578.000000     9578.000000  9578.000000   \n",
       "mean        0.804970     0.122640   319.089413       10.932117    12.606679   \n",
       "std         0.396245     0.026847   207.071301        0.614813     6.883970   \n",
       "min         0.000000     0.060000    15.670000        7.547502     0.000000   \n",
       "25%         1.000000     0.103900   163.770000       10.558414     7.212500   \n",
       "50%         1.000000     0.122100   268.950000       10.928884    12.665000   \n",
       "75%         1.000000     0.140700   432.762500       11.291293    17.950000   \n",
       "max         1.000000     0.216400   940.140000       14.528354    29.960000   \n",
       "\n",
       "              fico  days.with.cr.line     revol.bal   revol.util  \\\n",
       "count  9578.000000        9578.000000  9.578000e+03  9578.000000   \n",
       "mean    710.846314        4560.767197  1.691396e+04    46.799236   \n",
       "std      37.970537        2496.930377  3.375619e+04    29.014417   \n",
       "min     612.000000         178.958333  0.000000e+00     0.000000   \n",
       "25%     682.000000        2820.000000  3.187000e+03    22.600000   \n",
       "50%     707.000000        4139.958333  8.596000e+03    46.300000   \n",
       "75%     737.000000        5730.000000  1.824950e+04    70.900000   \n",
       "max     827.000000       17639.958330  1.207359e+06   119.000000   \n",
       "\n",
       "       inq.last.6mths  delinq.2yrs      pub.rec  not.fully.paid  \\\n",
       "count     9578.000000  9578.000000  9578.000000     9578.000000   \n",
       "mean         1.577469     0.163708     0.062122        0.160054   \n",
       "std          2.200245     0.546215     0.262126        0.366676   \n",
       "min          0.000000     0.000000     0.000000        0.000000   \n",
       "25%          0.000000     0.000000     0.000000        0.000000   \n",
       "50%          1.000000     0.000000     0.000000        0.000000   \n",
       "75%          2.000000     0.000000     0.000000        0.000000   \n",
       "max         33.000000    13.000000     5.000000        1.000000   \n",
       "\n",
       "       purpose_credit_card  purpose_debt_consolidation  purpose_educational  \\\n",
       "count          9578.000000                 9578.000000          9578.000000   \n",
       "mean              0.131760                    0.413134             0.035811   \n",
       "std               0.338248                    0.492422             0.185829   \n",
       "min               0.000000                    0.000000             0.000000   \n",
       "25%               0.000000                    0.000000             0.000000   \n",
       "50%               0.000000                    0.000000             0.000000   \n",
       "75%               0.000000                    1.000000             0.000000   \n",
       "max               1.000000                    1.000000             1.000000   \n",
       "\n",
       "       purpose_home_improvement  purpose_major_purchase  \\\n",
       "count               9578.000000             9578.000000   \n",
       "mean                   0.065671                0.045625   \n",
       "std                    0.247720                0.208682   \n",
       "min                    0.000000                0.000000   \n",
       "25%                    0.000000                0.000000   \n",
       "50%                    0.000000                0.000000   \n",
       "75%                    0.000000                0.000000   \n",
       "max                    1.000000                1.000000   \n",
       "\n",
       "       purpose_small_business  \n",
       "count             9578.000000  \n",
       "mean                 0.064627  \n",
       "std                  0.245880  \n",
       "min                  0.000000  \n",
       "25%                  0.000000  \n",
       "50%                  0.000000  \n",
       "75%                  0.000000  \n",
       "max                  1.000000  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>credit.policy</th>\n",
       "      <th>int.rate</th>\n",
       "      <th>installment</th>\n",
       "      <th>log.annual.inc</th>\n",
       "      <th>dti</th>\n",
       "      <th>fico</th>\n",
       "      <th>days.with.cr.line</th>\n",
       "      <th>revol.bal</th>\n",
       "      <th>revol.util</th>\n",
       "      <th>inq.last.6mths</th>\n",
       "      <th>delinq.2yrs</th>\n",
       "      <th>pub.rec</th>\n",
       "      <th>not.fully.paid</th>\n",
       "      <th>purpose_credit_card</th>\n",
       "      <th>purpose_debt_consolidation</th>\n",
       "      <th>purpose_educational</th>\n",
       "      <th>purpose_home_improvement</th>\n",
       "      <th>purpose_major_purchase</th>\n",
       "      <th>purpose_small_business</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0600</td>\n",
       "      <td>15.67</td>\n",
       "      <td>7.547502</td>\n",
       "      <td>0.00</td>\n",
       "      <td>612.0</td>\n",
       "      <td>178.958333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.2164</td>\n",
       "      <td>940.14</td>\n",
       "      <td>14.528354</td>\n",
       "      <td>29.96</td>\n",
       "      <td>827.0</td>\n",
       "      <td>17639.958330</td>\n",
       "      <td>1207359.0</td>\n",
       "      <td>119.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     credit.policy  int.rate  installment  log.annual.inc    dti   fico  \\\n",
       "min            0.0    0.0600        15.67        7.547502   0.00  612.0   \n",
       "max            1.0    0.2164       940.14       14.528354  29.96  827.0   \n",
       "\n",
       "     days.with.cr.line  revol.bal  revol.util  inq.last.6mths  delinq.2yrs  \\\n",
       "min         178.958333        0.0         0.0             0.0          0.0   \n",
       "max       17639.958330  1207359.0       119.0            33.0         13.0   \n",
       "\n",
       "     pub.rec  not.fully.paid  purpose_credit_card  purpose_debt_consolidation  \\\n",
       "min      0.0             0.0                  0.0                         0.0   \n",
       "max      5.0             1.0                  1.0                         1.0   \n",
       "\n",
       "     purpose_educational  purpose_home_improvement  purpose_major_purchase  \\\n",
       "min                  0.0                       0.0                     0.0   \n",
       "max                  1.0                       1.0                     1.0   \n",
       "\n",
       "     purpose_small_business  \n",
       "min                     0.0  \n",
       "max                     1.0  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe().iloc[[3,7]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "credit.policy\n",
       "1    7710\n",
       "0    1868\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('credit.policy').size().sort_values(ascending=False) #boolian"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7485de9a88>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(y = df['credit.policy']) # boolian - categorical"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['credit.policy'] = df['credit.policy'].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.1189, 0.1071, 0.1357, 0.1008, 0.1426, 0.0788, 0.1496, 0.1114,\n",
       "       0.1134, 0.1221, 0.1347, 0.1324, 0.0859, 0.0714, 0.0863, 0.1103,\n",
       "       0.1317, 0.0894, 0.1039, 0.1513, 0.08  , 0.1355, 0.1229, 0.0901,\n",
       "       0.0743, 0.1375, 0.0807, 0.1028, 0.087 , 0.1122, 0.0996, 0.0933,\n",
       "       0.0838, 0.0775, 0.1059, 0.1596, 0.1154, 0.1343, 0.1249, 0.0964,\n",
       "       0.1186, 0.1501, 0.128 , 0.1091, 0.1217, 0.1533, 0.0712, 0.1438,\n",
       "       0.1565, 0.1467, 0.1312, 0.147 , 0.1407, 0.1014, 0.1046, 0.133 ,\n",
       "       0.0983, 0.1393, 0.092 , 0.1236, 0.1362, 0.1078, 0.1583, 0.1109,\n",
       "       0.1141, 0.1267, 0.1204, 0.0951, 0.1172, 0.1299, 0.1488, 0.152 ,\n",
       "       0.1425, 0.1836, 0.1615, 0.06  , 0.0832, 0.1261, 0.0945, 0.1197,\n",
       "       0.1387, 0.0976, 0.1292, 0.0737, 0.0768, 0.1166, 0.1418, 0.1545,\n",
       "       0.1482, 0.1703, 0.145 , 0.1671, 0.1576, 0.1608, 0.164 , 0.1734,\n",
       "       0.1051, 0.157 , 0.1222, 0.1273, 0.1379, 0.1253, 0.1128, 0.1286,\n",
       "       0.1287, 0.097 , 0.1001, 0.1538, 0.1191, 0.1254, 0.1159, 0.138 ,\n",
       "       0.1096, 0.1064, 0.1349, 0.1033, 0.1475, 0.1601, 0.1507, 0.1412,\n",
       "       0.1633, 0.1696, 0.1146, 0.1304, 0.1272, 0.1209, 0.1083, 0.1178,\n",
       "       0.1241, 0.1588, 0.0907, 0.102 , 0.1336, 0.1557, 0.0938, 0.1493,\n",
       "       0.1462, 0.1367, 0.0963, 0.1126, 0.1442, 0.1148, 0.1399, 0.1525,\n",
       "       0.143 , 0.1392, 0.1904, 0.1872, 0.162 , 0.1715, 0.1568, 0.0988,\n",
       "       0.1062, 0.1746, 0.0932, 0.1411, 0.1505, 0.1316, 0.16  , 0.1158,\n",
       "       0.1284, 0.1095, 0.1695, 0.1474, 0.1537, 0.1632, 0.0751, 0.1422,\n",
       "       0.1218, 0.1663, 0.1726, 0.1853, 0.1348, 0.1531, 0.1635, 0.179 ,\n",
       "       0.1758, 0.1843, 0.1821, 0.1183, 0.074 , 0.1682, 0.0774, 0.1322,\n",
       "       0.2086, 0.1461, 0.1311, 0.1916, 0.1884, 0.1607, 0.2011, 0.167 ,\n",
       "       0.1979, 0.1739, 0.1704, 0.1913, 0.1774, 0.0705, 0.1878, 0.1809,\n",
       "       0.2017, 0.1982, 0.1947, 0.2121, 0.1459, 0.1385, 0.1025, 0.1099,\n",
       "       0.1136, 0.2052, 0.1719, 0.0639, 0.1645, 0.0676, 0.1793, 0.209 ,\n",
       "       0.2016, 0.183 , 0.1941, 0.1756, 0.1691, 0.1754, 0.1722, 0.1628,\n",
       "       0.1786, 0.1659, 0.1741, 0.1709, 0.1457, 0.1804, 0.1646, 0.1551,\n",
       "       0.1772, 0.1829, 0.1861, 0.1797, 0.1766, 0.1854, 0.1665, 0.1791,\n",
       "       0.1886, 0.1759, 0.1443, 0.1728, 0.1936, 0.1683, 0.1778, 0.2164,\n",
       "       0.1867])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['int.rate'].unique() #length: 249, dtype: int64 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7485e59a88>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(df['int.rate'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4, 5], dtype=int64)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['pub.rec'].unique() # considered categorical too"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7485e32ec8>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(y = df['pub.rec']) # 2,3,4,5 could be considered as rare phenomena in the data but could be considered necessary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>Count</th>\n",
       "      <th>Percentage</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pub.rec</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9019</td>\n",
       "      <td>94.16%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>533</td>\n",
       "      <td>5.56%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>19</td>\n",
       "      <td>0.2%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5</td>\n",
       "      <td>0.05%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>0.01%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0.01%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Count Percentage\n",
       "pub.rec                  \n",
       "0         9019     94.16%\n",
       "1          533      5.56%\n",
       "2           19       0.2%\n",
       "3            5      0.05%\n",
       "5            1      0.01%\n",
       "4            1      0.01%"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = df.groupby('pub.rec').size().sort_values(ascending=False)\n",
    "perc = []\n",
    "for i in x.values:\n",
    "    perc.append(str(round(100*i/len(df),2))+'%')\n",
    "\n",
    "y = pd.Series(data=perc, index=list(x.index))\n",
    "new_df = pd.DataFrame(columns = ['Count', 'Percentage'])\n",
    "new_df['Count'] = x\n",
    "new_df['Percentage'] = y\n",
    "\n",
    "new_df      # I think that 2,3,4,and 5 could be outliers except if these values are simply rare appearances but possible"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3], dtype=int64)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# we could remove them though using data cleaning\n",
    "df = df.drop(df[df['pub.rec']==5].index)\n",
    "df = df.drop(df[df['pub.rec']==4].index)\n",
    "df['pub.rec'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  1,  2,  4,  3,  5,  6, 13,  7,  8, 11], dtype=int64)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['delinq.2yrs'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 1, 2, 3]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y=  df.groupby('pub.rec').size().sort_values(ascending=False)\n",
    "list(y.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x74863fc6c8>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(rc={'figure.figsize': [15, 15]}, font_scale=1.3, style='dark')\n",
    "plt.subplot(1,2,1)\n",
    "f = df.groupby('delinq.2yrs').size().sort_values(ascending=False)\n",
    "\n",
    "sns.set(rc={'figure.figsize': [5, 5]}, font_scale=1.3, style='dark')\n",
    "plt.pie(list(f.values), labels=list(f.index) , autopct='%1.1f%%',startangle=90)\n",
    "plt.title('delinq.2yrs')\n",
    "\n",
    "plt.subplot(1,2,2)\n",
    "sns.countplot(y = df['delinq.2yrs']) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x74863b7a08>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x=df['delinq.2yrs'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>Count</th>\n",
       "      <th>Percentage</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>delinq.2yrs</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8456</td>\n",
       "      <td>88.3%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>832</td>\n",
       "      <td>8.69%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>192</td>\n",
       "      <td>2.01%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>65</td>\n",
       "      <td>0.68%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>19</td>\n",
       "      <td>0.2%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>0.06%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2</td>\n",
       "      <td>0.02%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1</td>\n",
       "      <td>0.01%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1</td>\n",
       "      <td>0.01%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>0.01%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1</td>\n",
       "      <td>0.01%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Count Percentage\n",
       "delinq.2yrs                  \n",
       "0             8456      88.3%\n",
       "1              832      8.69%\n",
       "2              192      2.01%\n",
       "3               65      0.68%\n",
       "4               19       0.2%\n",
       "5                6      0.06%\n",
       "6                2      0.02%\n",
       "13               1      0.01%\n",
       "11               1      0.01%\n",
       "8                1      0.01%\n",
       "7                1      0.01%"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = df.groupby('delinq.2yrs').size().sort_values(ascending=False)\n",
    "perc = []\n",
    "for i in x.values:\n",
    "    perc.append(str(round(100*i/len(df),2))+'%')\n",
    "\n",
    "y = pd.Series(data=perc, index=list(x.index))\n",
    "new_df = pd.DataFrame(columns = ['Count', 'Percentage'])\n",
    "new_df['Count'] = x\n",
    "new_df['Percentage'] = y\n",
    "\n",
    "new_df # I think we could remove all those greater than 3   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x748654b588>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = df.drop(df[df['delinq.2yrs']>3].index)\n",
    "sns.countplot(y = df['delinq.2yrs']) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# - Detect and Handle Outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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>int.rate</th>\n",
       "      <th>installment</th>\n",
       "      <th>log.annual.inc</th>\n",
       "      <th>dti</th>\n",
       "      <th>fico</th>\n",
       "      <th>days.with.cr.line</th>\n",
       "      <th>revol.bal</th>\n",
       "      <th>revol.util</th>\n",
       "      <th>inq.last.6mths</th>\n",
       "      <th>delinq.2yrs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.0600</td>\n",
       "      <td>15.67</td>\n",
       "      <td>7.547502</td>\n",
       "      <td>0.00</td>\n",
       "      <td>612.0</td>\n",
       "      <td>178.958333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.2164</td>\n",
       "      <td>940.14</td>\n",
       "      <td>14.528354</td>\n",
       "      <td>29.96</td>\n",
       "      <td>827.0</td>\n",
       "      <td>17639.958330</td>\n",
       "      <td>1207359.0</td>\n",
       "      <td>119.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     int.rate  installment  log.annual.inc    dti   fico  days.with.cr.line  \\\n",
       "min    0.0600        15.67        7.547502   0.00  612.0         178.958333   \n",
       "max    0.2164       940.14       14.528354  29.96  827.0       17639.958330   \n",
       "\n",
       "     revol.bal  revol.util  inq.last.6mths  delinq.2yrs  \n",
       "min        0.0         0.0             0.0          0.0  \n",
       "max  1207359.0       119.0            33.0          3.0  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe().iloc[[3,7]].drop(['credit.policy','not.fully.paid','pub.rec','purpose_credit_card',\n",
    "                                'purpose_debt_consolidation','purpose_educational','purpose_home_improvement'\n",
    "                                ,'purpose_major_purchase','purpose_small_business'], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "according to values:\n",
    "- days.with.cr.line / revol.bal\n",
    "- installment / fico / revol.util\n",
    "- log.annual.inc / inq.last.6mths / dti / int.rate / delinq.2yrs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x748472b888>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(rc={'figure.figsize': [15, 10]}, font_scale=1.3)\n",
    "sns.boxplot(data=df[['int.rate','dti','log.annual.inc','inq.last.6mths','delinq.2yrs']], orient=\"h\", palette=\"Set2\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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>credit.policy</th>\n",
       "      <th>int.rate</th>\n",
       "      <th>installment</th>\n",
       "      <th>log.annual.inc</th>\n",
       "      <th>dti</th>\n",
       "      <th>fico</th>\n",
       "      <th>days.with.cr.line</th>\n",
       "      <th>revol.bal</th>\n",
       "      <th>revol.util</th>\n",
       "      <th>inq.last.6mths</th>\n",
       "      <th>delinq.2yrs</th>\n",
       "      <th>pub.rec</th>\n",
       "      <th>not.fully.paid</th>\n",
       "      <th>purpose_credit_card</th>\n",
       "      <th>purpose_debt_consolidation</th>\n",
       "      <th>purpose_educational</th>\n",
       "      <th>purpose_home_improvement</th>\n",
       "      <th>purpose_major_purchase</th>\n",
       "      <th>purpose_small_business</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.1189</td>\n",
       "      <td>829.10</td>\n",
       "      <td>11.350407</td>\n",
       "      <td>19.48</td>\n",
       "      <td>737</td>\n",
       "      <td>5639.958333</td>\n",
       "      <td>28854</td>\n",
       "      <td>52.1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.1071</td>\n",
       "      <td>228.22</td>\n",
       "      <td>11.082143</td>\n",
       "      <td>14.29</td>\n",
       "      <td>707</td>\n",
       "      <td>2760.000000</td>\n",
       "      <td>33623</td>\n",
       "      <td>76.7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0.1357</td>\n",
       "      <td>366.86</td>\n",
       "      <td>10.373491</td>\n",
       "      <td>11.63</td>\n",
       "      <td>682</td>\n",
       "      <td>4710.000000</td>\n",
       "      <td>3511</td>\n",
       "      <td>25.6</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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0.1008</td>\n",
       "      <td>162.34</td>\n",
       "      <td>11.350407</td>\n",
       "      <td>8.10</td>\n",
       "      <td>712</td>\n",
       "      <td>2699.958333</td>\n",
       "      <td>33667</td>\n",
       "      <td>73.2</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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0.1426</td>\n",
       "      <td>102.92</td>\n",
       "      <td>11.299732</td>\n",
       "      <td>14.97</td>\n",
       "      <td>667</td>\n",
       "      <td>4066.000000</td>\n",
       "      <td>4740</td>\n",
       "      <td>39.5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   credit.policy  int.rate  installment  log.annual.inc    dti  fico  \\\n",
       "0              1    0.1189       829.10       11.350407  19.48   737   \n",
       "1              1    0.1071       228.22       11.082143  14.29   707   \n",
       "2              1    0.1357       366.86       10.373491  11.63   682   \n",
       "3              1    0.1008       162.34       11.350407   8.10   712   \n",
       "4              1    0.1426       102.92       11.299732  14.97   667   \n",
       "\n",
       "   days.with.cr.line  revol.bal  revol.util  inq.last.6mths  delinq.2yrs  \\\n",
       "0        5639.958333      28854        52.1               0            0   \n",
       "1        2760.000000      33623        76.7               0            0   \n",
       "2        4710.000000       3511        25.6               1            0   \n",
       "3        2699.958333      33667        73.2               1            0   \n",
       "4        4066.000000       4740        39.5               0            1   \n",
       "\n",
       "   pub.rec  not.fully.paid  purpose_credit_card  purpose_debt_consolidation  \\\n",
       "0        0               0                    0                           1   \n",
       "1        0               0                    1                           0   \n",
       "2        0               0                    0                           1   \n",
       "3        0               0                    0                           1   \n",
       "4        0               0                    1                           0   \n",
       "\n",
       "   purpose_educational  purpose_home_improvement  purpose_major_purchase  \\\n",
       "0                    0                         0                       0   \n",
       "1                    0                         0                       0   \n",
       "2                    0                         0                       0   \n",
       "3                    0                         0                       0   \n",
       "4                    0                         0                       0   \n",
       "\n",
       "   purpose_small_business  \n",
       "0                       0  \n",
       "1                       0  \n",
       "2                       0  \n",
       "3                       0  \n",
       "4                       0  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7487ae12c8>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(rc={'figure.figsize': [10, 5]}, font_scale=1.3)\n",
    "sns.boxplot(x=df['int.rate'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7487bbb788>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(rc={'figure.figsize': [10, 7]}, font_scale=1.3)\n",
    "sns.boxplot(data=df[['installment','fico','revol.util']], orient=\"h\", palette=\"Set2\")\n",
    "\n",
    "# revol.util is the only attribute having no outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7488dcc8c8>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(rc={'figure.figsize': [10, 5]}, font_scale=1.3)\n",
    "sns.boxplot(data=df['revol.bal'], orient=\"h\", palette=\"Set2\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7488e76e48>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(data=df['days.with.cr.line'], orient=\"h\", palette=\"Set2\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "attributes = ['int.rate','installment','log.annual.inc','dti','fico','days.with.cr.line',\n",
    "              'revol.bal','revol.util','inq.last.6mths','delinq.2yrs']\n",
    "for att in attributes:\n",
    "    lower_limit = df[att].mean()-3*df[att].std()\n",
    "    upper_limit = df[att].mean()+3*df[att].std()\n",
    "    df = df[(df[att]>upper_limit)|(df[att]<lower_limit)] \n",
    "    \n",
    "    # it appears that simply dropping the outliers resulted in removing all the\n",
    "    #examples since outliers of an attribute differ than the other so removing outiers was not the best idea"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "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>int.rate</th>\n",
       "      <th>installment</th>\n",
       "      <th>log.annual.inc</th>\n",
       "      <th>dti</th>\n",
       "      <th>fico</th>\n",
       "      <th>days.with.cr.line</th>\n",
       "      <th>revol.bal</th>\n",
       "      <th>revol.util</th>\n",
       "      <th>inq.last.6mths</th>\n",
       "      <th>delinq.2yrs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     int.rate  installment  log.annual.inc  dti  fico  days.with.cr.line  \\\n",
       "min       NaN          NaN             NaN  NaN   NaN                NaN   \n",
       "max       NaN          NaN             NaN  NaN   NaN                NaN   \n",
       "\n",
       "     revol.bal  revol.util  inq.last.6mths  delinq.2yrs  \n",
       "min        NaN         NaN             NaN          NaN  \n",
       "max        NaN         NaN             NaN          NaN  "
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe().iloc[[3,7]].drop(['credit.policy','not.fully.paid','pub.rec','purpose_credit_card',\n",
    "                                'purpose_debt_consolidation','purpose_educational','purpose_home_improvement'\n",
    "                                ,'purpose_major_purchase','purpose_small_business'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 0 entries\n",
      "Data columns (total 19 columns):\n",
      " #   Column                      Non-Null Count  Dtype  \n",
      "---  ------                      --------------  -----  \n",
      " 0   credit.policy               0 non-null      int32  \n",
      " 1   int.rate                    0 non-null      float64\n",
      " 2   installment                 0 non-null      float64\n",
      " 3   log.annual.inc              0 non-null      float64\n",
      " 4   dti                         0 non-null      float64\n",
      " 5   fico                        0 non-null      int64  \n",
      " 6   days.with.cr.line           0 non-null      float64\n",
      " 7   revol.bal                   0 non-null      int64  \n",
      " 8   revol.util                  0 non-null      float64\n",
      " 9   inq.last.6mths              0 non-null      int64  \n",
      " 10  delinq.2yrs                 0 non-null      int64  \n",
      " 11  pub.rec                     0 non-null      int64  \n",
      " 12  not.fully.paid              0 non-null      int64  \n",
      " 13  purpose_credit_card         0 non-null      uint8  \n",
      " 14  purpose_debt_consolidation  0 non-null      uint8  \n",
      " 15  purpose_educational         0 non-null      uint8  \n",
      " 16  purpose_home_improvement    0 non-null      uint8  \n",
      " 17  purpose_major_purchase      0 non-null      uint8  \n",
      " 18  purpose_small_business      0 non-null      uint8  \n",
      "dtypes: float64(6), int32(1), int64(6), uint8(6)\n",
      "memory usage: 0.0 bytes\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# - Dealing with unbalanced data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.utils import resample\n",
    "paid = df[df['not.fully.paid']==1]\n",
    "not_paid = df[df['not.fully.paid']==0]\n",
    "paid_upsampled = resample(paid, replace=True, n_samples=len(not_paid))\n",
    "upsampled_df = pd.concat([not_paid, paid_upsampled])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "not.fully.paid\n",
       "0    8016\n",
       "1    1529\n",
       "dtype: int64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('not.fully.paid').size().sort_values(ascending=False) # boolian - categorical"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "not.fully.paid\n",
       "1    8016\n",
       "0    8016\n",
       "dtype: int64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "upsampled_df.groupby('not.fully.paid').size().sort_values(ascending=False) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7488efae88>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAmMAAAFHCAYAAADtHUvAAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAAdMUlEQVR4nO3de3BU9d3H8c/GJCRitAQD1FG5KUS8kJBoBCIWKsrIpURBitg6KkI7FqxEQ6AlzSAUHopSYLBcngolisOMzaQFq6UKxSLSMYSLYIDiBUspBBADIZcN5Pf84bAtDxpO2D37O7v7fs0wkz1nL98fh/nwydmbzxhjBAAAACvibA8AAAAQyyhjAAAAFlHGAAAALKKMAQAAWEQZAwAAsIgyBgAAYBFlDAAAwKJ42wO01IkTp9XUFBsfjda27RU6frzG9hhhE2vrlWJvzS1Zb1ycT23atHZ5ovAiv6JbrK2Z9TavJRkWcWWsqcnETJhJiqm1SrG3Xin21hxr6/1v5Ff0i7U1s97Q4GlKAAAAiyhjAAAAFlHGAAAALKKMAQAAWEQZAwAAsIgyBgAAYBFlDAAAwCLKGAAAgEWUMQAAAIsoYwAAABb5jDGx9V0GAKzxN55V9Ze1jq4bF+dT27ZXuDwRADhX39CoUyfrHV23JRkWcd9NOXFWmY6dOG17DACXYNWcMbZHsIr8AiLbqjljdErOylhL8DQlAACARZQxAAAAiyhjAAAAFlHGAAAALKKMAQAAWEQZAwAAsIgyBgAAYBFlDAAAwCLKGAAAgEWUMQAAAIsoYwAAABZRxgAAACyijAEAAFhEGQMAALCIMgYAAGARZQwAAMAiyhgAAIBFlDEAAACLKGMAAAAWUcYAAAAsoowBAABYRBkDAACwiDIGAABgEWUMAADAIsoYAACARZQxAAAAiyhjAAAAFlHGAAAALKKMAQAAWEQZAwAAsIgyBgAAYBFlDAAAwCLKGAAAgEWUMQAAAIsoYwAAABZRxgAAACyijAEAAFhEGQMAALCIMgYAAGARZQwAAMAiyhgAAIBFlDEAAACLKGMAAAAWUcYAAAAsoowBAABYRBkDAACwiDIGAABgEWUMAADAorCWscbGRk2fPl05OTnKycnR3Llz1dTUFM4RAOCSkF8A3BIfzgd78cUX9d5772np0qWqqanR5MmTdeWVV2rcuHHhHAMAWoz8AuCWsJ0Za2ho0GuvvabCwkL17NlTffv2VX5+vn73u9/x2yUATyO/ALgpbGWssrJSdXV1ys7ODmzLzs7WsWPH9Pnnn4drDABoMfILgJvCVsaOHDmiyy+/XCkpKYFtaWlpkqTDhw+HawwAaDHyC4CbwlbG6urqlJiYeN62c5f9fn+4xgCAFiO/ALgpbGUsKSnpgtA6dzk5OTlcYwBAi5FfANwUtjLWoUMH1dbW6vTp04FtR48elSS1b98+XGMAQIuRXwDcFLYylp6eruTkZG3dujWwrby8XFdffbWuv/76cI0BAC1GfgFwU1ifphwxYoRmzJihiooKvf/++3rhhRf06KOPhmsEALgk5BcAN4X1Q1+fe+45NTQ0aOzYsWrVqpVGjBihJ598MpwjAMAlIb8AuMVnjDG2h2iJibPKdOzE6YtfEYDnrJozRkePnnJ03bg4n9q2vcLlicKL/AIim1sZxheFAwAAWEQZAwAAsIgyBgAAYBFlDAAAwCLKGAAAgEWUMQAAAIsoYwAAABZRxgAAACyijAEAAFhEGQMAALCIMgYAAGARZQwAAMAiyhgAAIBFlDEAAACLKGMAAAAWUcYAAAAsoowBAABYRBkDAACwiDIGAABgEWUMAADAovjmdr7//vuO76h3795BDwMAoUSGAYgEzZaxxx57TD6fT8YYSZLP55OkCy5LUmVlpVszAsAlIcMARIJmy9i6desCP3/wwQdasGCBCgsL1atXLyUkJGjHjh2aPXu2fvzjH7s+KAC0FBkGIBI0W8auv/76wM8/+tGP9Pzzz6tfv36Bbf3791erVq1UVFSk4cOHuzclAFwCMgxAJHD8Av5Dhw7p2muvvWB7Wlqajh8/HtKhACDUyDAAXuW4jKWnp+u3v/2tzp49G9jm9/u1aNEi3Xbbba4MBwChQoYB8Kpmn6b8b88++6yeeOIJbd68WTfddJOMMdq1a5caGhpUUlLi5owAEDQyDIBXOT4zlp2drTVr1mjQoEHy+/1qbGxUXl6e1qxZo+7du7s5IwAEjQwD4FWOz4xJX70YdvLkyW7NAgCuIsMAeFGzZaygoEBFRUW64oorVFBQ0OwdzZkzJ6SDAUCwyDAAkaDZMnbw4EE1NTUFfgaASEKGAYgEzZaxVatWfe3PABAJyDAAkaBFrxlrbGxUVVVV4DdNY4z8fr927NihBx980JUBASBUyDAAXuS4jG3atEkFBQU6ceLEBfsSExMJMgCeRoYB8CrHH20xb9483XTTTVq8eLGSkpK0YMECFRQUKDk5WS+88IKbMwJA0MgwAF7l+MzY/v379dprr6lHjx7q3r27rrrqKg0cOFDJyclasWKF7rnnHjfnBICgkGEAvMrxmTGfz6dvfetbkqSOHTtq3759kqTc3Fz94x//cGc6AAgRMgyAVzkuY507d9aWLVskSZ06ddKHH34oSTpx4oTOnDnjznQAECJkGACvcvw05ZgxY1RUVKS4uDgNGjRIQ4cOVWJiorZt26bMzEw3ZwSAoJFhALzKcRkbMWKErrrqKrVp00adO3fWL3/5Sy1ZskTf/va3VVxc7OKIABA8MgyAV7Xoc8YGDhwY+HnYsGEaNmxYyAcCALeQYQC8yPFrxiTpzTff1KhRo9SzZ09lZmbq0Ucf1ebNm92aDQBCigwD4EWOy1hpaakmTZqkdu3a6ac//ameeuopXX755Ro7dqw2bNjg5owAEDQyDIBXOX6acunSpZo0aZKefPLJwLaxY8fqpZde0oIFC9S/f39XBgSAUCDDAHiV4zNjhw4dOu/1FucMHjxYn3zySUiHAoBQI8MAeJXjMnbrrbfq3XffvWD7tm3bdNNNN4V0KAAINTIMgFc5fppy2LBhmj17tj755BPdeeedio+P186dO1VSUqLRo0fr9ddfD1x3xIgRrgwLAJeKDAPgVT5jjHFyxfT0dGd36POpsrIyqKEARCd/41lVf1nr6LpxcT61bXtFyB6bDAMQrPqGRp06We/oui3JMMdl7OzZs47uUJIuu+wyx9dtqePHa9TU5GjkiJeWlqKjR0/ZHiNsYm29UuytuSXrDXUZ80KGkV/RLdbWzHqb15IMc/w0pZsFCwDcRoYB8KoWfejr16mpqdG0adNCMQsAhB0ZBsC2oMuY3+//2ncoAUAkIMMA2BZ0GUtNTdXGjRtDMQsAhB0ZBsA2x2Xs5Zdf1hdffOHmLADgGjIMgFc5LmMlJSXq16+fJkyYoI0bN8rhmzABwBPIMABe5biMrV+/XosXL1ZiYqImTpyou+++W/PmzdM///lPN+cDgJAgwwB4leOPtvD5fMrNzVVubq5OnjypP/3pT/rzn/+sl19+WZmZmXrooYd03333KSEhwc15AeCSkGEAvOqSXsAfFxenhIQExcXFyRij6upqzZ49W/fee68qKipCPSMAhBQZBsBLHJ8ZM8Zo06ZNKisr0zvvvKOEhATdf//9mjRpkm6++WY1NjZq2rRpKigo0Ntvv+3mzADQYmQYAK9yXMbuuusuHT9+XBkZGSoqKtL999+vpKSkwP6EhAQNGDBA69evd2VQAAgGGQbAqxyXsSFDhmjkyJHq2rXrN16nd+/e+stf/hKSwQAglMgwAF7luIwVFhZe9DopKSlBDQMAbiHDAHhVs2UsNzfX8R1t2rQp6GEAIJTIMACRoNkyNmrUKPl8vnDNAgAhRYYBiATNlrEJEyaEaw4ACDkyDEAkaLaMrV692tGd+Hw+PfTQQyEZCABChQwDEAmaLWO/+MUvHN0JQQbAi8gwAJGg2TK2Z8+ecM0BACFHhgGIBJf0dUgAAAAIDcefM3axt4jztnAAXkaGAfAqx2Xs/79FvLGxUQcOHNC7776r/Px8V4YDgFAhwwB4leMy9k1vEX/11VdVXl6uMWPGhGwoAAg1MgyAVwX9mrHvfOc72rhxYyhmAYCwI8MA2BZ0Gdu8ebNatWoVilkAIOzIMAC2XfJrxiSppqZGH3/8sR599NGQD/ZN2ra9ImyPFSpn/A06Ue23PQYQ07yQYZGYX8FIS4u9L16PtTXH2nrbXJXoyv/njstYly5dLtiWkJCgsWPHaujQoSEdqjkfLp4s/8njYXu8UMgq+F9JlDHAJi9kWCTmF4D/cOv/82bL2MSJE1VcXKzU1FQ98MADysjIUEJCQsiHAAA3kGEAIkGzrxnbsGGDqqurJUk//OEPderUqbAMBQChQIYBiATNnhm78cYb9YMf/ECdO3eWMUZPPfXUN/5WuXLlSlcGBIBLRYYBiATNlrF58+Zp+fLlOnXqlHw+n1JTU3nXEYCIQYYBiATNlrGOHTuquLhYkrRt2zbNmDFDbdq0CcdcABA0MgxAJHD8bsr169e7OQcAuIoMA+BVQX/oa01NjaZMmRKKWQAg7MgwALYFXcb8fr/+/ve/h2IWAAg7MgyAbUGXsdTUVE7/A4hYZBgA2xyXsbKyMvn9F37qbG1trZYvXx7SoQAg1MgwAF7VbBk7e/as/H6//H6/pkyZoi+++CJw+dyfyspKvfjii+GaFwAcI8MARIJm3025atUqzZw5Uz6fT8YY9e/f/2uvl5WV5cpwABAMMgxAJGi2jI0ePVopKSlqamrS1KlTNWXKFKWk/Ocb2n0+n1q3bq3evXu7PigAtBQZBiASNFvG4uPjNXz4cElfhdbgwYOVmJgYlsEAIFhkGIBI4PhDX/Py8nTw4EGtXLlSe/bsUWJiom688UY9/PDDuu6669ycEQCCRoYB8CrH76bcu3evhg8frjfffFMpKSlKSEjQmjVrNHz4cO3bt8/NGQEgaGQYAK9yfGZs7ty5uuOOOzRv3rzAF+3W19dr0qRJevHFF7V48WLXhgSAYJFhALzK8Zmx8vJyTZw4MRBikpSUlKQJEyaovLzcleEAIFTIMABe5biMJSUlfeO+pqamkAwDAG4hwwB4leMylpWVpfnz56uhoSGwrb6+XgsWLOAzegB4HhkGwKscv2bsmWee0fe//30NGDBAPXr0kCTt3r1bZ86c0SuvvOLagAAQCmQYAK9yfGasa9eu+sMf/hD4zB5JeuCBB1RWVqZu3bq5MhwAhAoZBsCrHJ8Zk6RrrrlGzz33nFuzAICryDAAXuS4jDU1NWnNmjWqqKhQY2OjjDHn7Z81a1bIhwOAUCHDAHiV4zI2Z84crVixQl26dDnvu92kr75mBAC8jAwD4FWOy9i6detUUFCgxx9/3M15AMAVZBgAr3L8Av4vvvhC99xzj5uzAIBryDAAXuW4jN1+++2qqKhwcxYAcA0ZBsCrHD9NOXDgQM2YMUM7d+5U586dlZiYGNjn8/n00EMPuTIgAIQCGQbAqxyXsaKiIknSqlWrLthHkAHwOjIMgFc5LmN79uxxcw4AcBUZBsCrHL9mDAAAAKFHGQMAALCIMgYAAGARZQwAAMAiyhgAAIBFlDEAAACLKGMAAAAWUcYAAAAsoowBAABYRBkDAACwiDIGAABgEWUMAADAIsoYAACARZQxAAAAiyhjAAAAFlHGAAAALKKMAQAAWGSljPn9fg0ePFgbNmyw8fAAcMnILwChFvYyVl9fr6efflr79+8P90MDQFDILwBuCGsZ2717t0aMGKFDhw6F82EBIGjkFwC3hLWMbdmyRd/97ne1evXqcD4sAASN/ALglvhwPtgTTzwRzocDgJAhvwC4hXdTAgAAWEQZAwAAsIgyBgAAYBFlDAAAwCLKGAAAgEWUMQAAAIvC+tEW/23v3r22HhoAgkJ+AQglzowBAABYRBkDAACwiDIGAABgEWUMAADAIsoYAACARZQxAAAAiyhjAAAAFlHGAAAALKKMAQAAWEQZAwAAsIgyBgAAYBFlDAAAwCLKGAAAgEWUMQAAAIsoYwAAABZRxgAAACyijAEAAFhEGQMAALCIMgYAAGARZQwAAMAiyhgAAIBFlDEAAACLKGMAAAAWUcYAAAAsoowBAABYRBkDAACwiDIGAABgEWUMAADAIsoYAACARZQxAAAAiyhjAAAAFlHGAAAALKKMAQAAWEQZAwAAsIgyBgAAYBFlDAAAwCLKGAAAgEWUMQAAAIsoYwAAABZRxgAAACyijAEAAFhEGQMAALCIMgYAAGARZQwAAMAiyhgAAIBFlDEAAACLKGMAAAAWxdseoKVu/dH/2B6hxc74G2yPAMADIjG/APyHW/+fR1wZO368Rk1NxvYYANBisZRfaWkpOnr0lO0xwirW1hyL6z1R7XflvnmaEgAAwCLKGAAAgEWUMQAAAIsoYwAAABZRxgAAACyijAEAAFhEGQMAALCIMgYAAGARZQwAAMAiyhgAAIBFEfd1SHFxPtsjhBXrjX6xtman643Gv5doXFNzYm29UuytmfWG5ro+Y0xsfFEaAACAB/E0JQAAgEWUMQAAAIsoYwAAABZRxgAAACyijAEAAFhEGQMAALCIMgYAAGARZQwAAMAiyhgAAIBFlDEAAACLPF/GGhsbNX36dOXk5CgnJ0dz585VU1OT7bEcO3z4sCZOnKicnBz17dtXU6ZMUXV1tSTp1KlTys/PV1ZWlu666y4tX778vNsGu9+2+fPna8CAAYHLFzuWwe635cyZM/rVr36lPn36KDs7W/n5+Tp16pSk6DzGJ0+eVGFhoXJyctSnTx8VFRXp9OnTkqL3GAcj0tdEhkV3hsVafkkezTDjcbNnzzb33nuv2b59u9m0aZPp27evWbJkie2xHDl79qzJy8szjz/+uKmsrDQ7duwweXl5Zvz48cYYYyZMmGBGjRplKisrzZtvvmkyMjLM2rVrA7cPdr9Nu3fvNj169DD9+/cPbLvYsQx2vy2zZs0yubm5ZsuWLWbXrl1myJAhZvLkycaY6DzGkyZNMiNHjjSVlZVm27Zt5r777jM/+9nPjDHRe4yDEclrIsOiP8NiLb+M8WaGebqM1dfXm549e5r169cHtpWWlpo+ffqYs2fPWpzMmV27dplu3bqZqqqqwLby8nLTrVs3c/DgQdO9e3ezd+/ewL6FCxeaBx980Bhjgt5vk9/vN0OHDjVjxowJBNnFjmWw+205efKkufnmm82GDRsC2/7617+aIUOGRO0x7tWr13mBunLlStO/f/+oPcbBiPQ1kWHRnWGxmF/GeDPDPP00ZWVlperq6pSdnR3Ylp2drWPHjunzzz+3OJkz11xzjZYtW6a0tLTANp/PJ0navn27rrzySnXr1i2wLzs7W7t371ZDQ0PQ+2166aWXdN1112nQoEGBbRc7lsHut6W8vFzx8fHKzc0NbLv77ru1Zs2aqD3Gbdq00dq1a1VTU6Pq6mqtW7dOt956a9Qe42BE+prIsOjOsFjML8mbGebpMnbkyBFdfvnlSklJCWw7FwqHDx+2NZZjbdq0Ub9+/c7btmLFCnXq1ElHjhxRu3btztuXlpampqYmVVVVBb3flo8++kirV69WcXHxedsvdiyD3W/LgQMH1KFDB73zzjsaNmyY+vXrp6KiItXU1ETtMZ4+fbp27typ22+/XTk5OaqurtaMGTOi9hgHI9LXRIb9RzT++47F/JK8mWGeLmN1dXVKTEw8b9u5y36/38ZIQVm6dKnWrVunqVOnXnRtwe63we/3q7CwUM8999x5v0lLFz+WkbheSaqtrVVVVZWWLFmiqVOnas6cOdq6daumTJkStWv+9NNP1aVLF61cuVLLly+XMUaFhYVRu95gRNuayLDoWm8s5pfkzQyLD2pFLktKSrpgAecuJycn2xjpki1atEgLFixQUVGR7r77bu3fv7/ZtV1s7V78u3nppZfUrl075eXlXbAv2PV4cb2SFB8fr9OnT2vWrFnq3r27JKm4uFiPPPKI0tPTo27Nn3/+uWbMmKG33npLHTt2lCTNmzdPgwcPVq9evaJuvcGKpjWRYdGXYbGWX5J3M8zTZaxDhw6qra3V6dOn1bp1a0nS0aNHJUnt27e3OVqLzJw5UyUlJSouLtbo0aMlfbW2c2s5p6qqSvHx8Wrbtm3Q+2344x//qKNHjyozM1PSV2/xPXPmjDIzM7Vs2bJmj2VCQkJQ+205dxq+a9eugW3nfjbGRN0x3rVrlxISEgIhJkk33HCDkpKSVFdXF5XHOBhkWGT9+461DIu1/JK8m2GefpoyPT1dycnJ2rp1a2BbeXm5rr76al1//fUWJ3Nu/vz5euWVVzRr1qxAiElSRkaGvvzyS3388ceBbVu3blWPHj3UqlWroPfbUFJSorVr16qsrExlZWUaP3682rVrp7KyMt1yyy3NHsuLHWuv/ls4F9offfRRYNvHH3+suLg45eXlRd0xbt++vRoaGvTZZ58Fth08eFD19fXq3bt3VB7jYETDmsiw6M2wWMsvycMZFqJ3irrm+eefNwMHDjRbt241mzdv9sTnsjhVWVlp0tPTzZw5c0xVVdV5fxobG8348ePNyJEjze7du81bb71lMjIyzBtvvBG4fbD7bSspKTnvM3oudiyD3W/LT37yEzN06FCzY8cOs2PHDjNkyBDz9NNPG2OCP4ZeO8aNjY0mLy/PPPzww2bXrl3mww8/NKNGjTKPPPKIMSZ6j3EwInlNZFj0Z1gs5Zcx3s0wz5ex+vp68/Of/9xkZmaaO++808ydO9c0NTXZHsuRX//616Zbt25f+2fv3r3mxIkTZsKECea2224zd911l1m+fPl5tw92v23/P8gudiyD3W/L6dOnzbRp00x2drbJysoyU6dONTU1NcaY4I+hF49xVVWVmTRpkrnzzjtN7969TWFhofnyyy+NMdF7jIMRyWsiw6I/w2Itv4zxZob5jDEmZOf/AAAA0CKefs0YAABAtKOMAQAAWEQZAwAAsIgyBgAAYBFlDAAAwCLKGAAAgEWUMUS9/fv36+2337Y9BgC0GPkVGyhjiHrjxo3T9u3bbY8BAC1GfsUGyhgAAIBFlDG4qq6uTrNnz1a/fv3Us2dPPfjgg3rvvfckSf/+9781efJk5ebm6rbbbtPDDz+sDz74IHDbhQsXqm/fvufdX2lpqbp3766GhgZJ0oABA7R06VI988wz6tWrl3Jzc1VYWKja2trA/n/9619atmyZBgwYEKZVA4gG5BfChTIGV+Xn5+utt95ScXGx1qxZo969e2v8+PHas2ePRo8erU8//VQLFizQ73//e3Xt2lWPPfZYi0/JL1y4ULfccotKS0uVn5+vtWvX6uWXX5Ykvf766+rQoYMeeeQRvf76624sEUCUIr8QLvG2B0D0+uyzz/TOO+9o0aJFgd/qnn32Wfl8Pm3cuFHHjh3T6tWr1b59e0nS9OnTtXv3bi1ZskS/+c1vHD9OTk6OnnjiCUlSp06d9MYbb2jbtm2SpNTUVF122WVKTk5WampqiFcIIFqRXwgnzozBNXv27JEkZWRknLc9Pz9fhw4d0rXXXhsIMkny+XzKysrS3r17W/Q4Xbp0Oe9ySkqK/H7/JU4NAOQXwosyBtckJCS0+DbGmMDtfD7fBfvPnDlzwbbExMSWDwcAzSC/EE6UMbjmhhtukCTt3LnzvO0jR45UWlqaDh48qMOHDwe2G2NUXl6uG2+8UdJXYVhbWytjTOA6Bw4cCMPkAGId+YVwoozBNR07dtSgQYM0c+ZM/e1vf9OBAwc0Z84c7du3T/fcc4+uvvpqPf3006qoqND+/ftVVFSkffv26bHHHpMkZWZmqra2VosWLdLBgwe1du1alZaWtniO1q1b68CBAzpy5EiolwggSpFfCCfKGFw1c+ZM9evXT5MnT9b3vvc9VVRUaNmyZUpPT9err76q9u3ba9y4cRoxYoQ+++wzrVixQllZWZKkO+64Q88884xWr16t+++/X6WlpZo6dWqLZ3j88ce1ZcsWDRs27GufJgCAr0N+IVx85r/PoQIAACCsODMGAABgEWUMAADAIsoYAACARZQxAAAAiyhjAAAAFlHGAAAALKKMAQAAWEQZAwAAsOj/AC+Ks7pBx5A0AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(1,2,1)\n",
    "sns.countplot(y = df['not.fully.paid'])\n",
    "plt.subplot(1,2,2)\n",
    "sns.countplot(y = upsampled_df['not.fully.paid'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# - Split Data to Train and Test Sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "x = upsampled_df.drop('not.fully.paid',axis=1)\n",
    "y = upsampled_df['not.fully.paid']\n",
    "x_train, x_test, y_train, y_test = train_test_split(x,y,test_size=0.25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x_train: 12024  -   x_test: 4008  -  y_train: 12024  -  y_test:4008\n"
     ]
    }
   ],
   "source": [
    "print(f'x_train: {len(x_train)}  -   x_test: {len(x_test)}  -  y_train: {len(y_train)}  -  y_test:{len(y_test)}')"
   ]
  },
  {
   "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}