hackathon participation.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",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import math\n",
    "import scipy.stats as stats\n",
    "from sklearn import metrics\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.metrics import confusion_matrix, classification_report, roc_auc_score, roc_curve\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from scipy.stats import zscore\n",
    "from warnings import filterwarnings\n",
    "filterwarnings(\"ignore\")\n",
    "\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
    "from imblearn.over_sampling import SMOTE\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "from xgboost.sklearn import XGBClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.feature_selection import RFE\n",
    "from imblearn.over_sampling import SMOTE\n",
    "\n",
    "import statsmodels.api as sm\n",
    "from pylab import rcParams"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of Train : (87500, 30)\n",
      "Shape of Test : (37500, 29)\n"
     ]
    }
   ],
   "source": [
    "train = pd.read_csv('Data_Train.csv')\n",
    "test = pd.read_csv('Data_Test.csv')\n",
    "\n",
    "# getting the shapes of the datasets\n",
    "print(\"Shape of Train :\", train.shape)\n",
    "print(\"Shape of Test :\", test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Asst_Reg</th>\n",
       "      <th>GGGrade</th>\n",
       "      <th>Experience</th>\n",
       "      <th>Validation</th>\n",
       "      <th>Yearly Income</th>\n",
       "      <th>Home Status</th>\n",
       "      <th>Unpaid 2 years</th>\n",
       "      <th>Already Defaulted</th>\n",
       "      <th>Designation</th>\n",
       "      <th>...</th>\n",
       "      <th>File Status</th>\n",
       "      <th>State</th>\n",
       "      <th>Account Open</th>\n",
       "      <th>Total Unpaid CL</th>\n",
       "      <th>Duration</th>\n",
       "      <th>Unpaid Amount</th>\n",
       "      <th>Reason</th>\n",
       "      <th>Claim Type</th>\n",
       "      <th>Due Fee</th>\n",
       "      <th>Loan/No Loan</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>95074</td>\n",
       "      <td>421802</td>\n",
       "      <td>II</td>\n",
       "      <td>&gt;10yrs</td>\n",
       "      <td>Vfied</td>\n",
       "      <td>633600.00</td>\n",
       "      <td>MOR??AGE</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>GLENDALE NISSAN</td>\n",
       "      <td>...</td>\n",
       "      <td>fully paid</td>\n",
       "      <td>California</td>\n",
       "      <td>17</td>\n",
       "      <td>58598.0</td>\n",
       "      <td>3 years</td>\n",
       "      <td>31216.05</td>\n",
       "      <td>debt  consolidation</td>\n",
       "      <td>I</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>134131</td>\n",
       "      <td>3964312</td>\n",
       "      <td>IV</td>\n",
       "      <td>7yrs</td>\n",
       "      <td>Source Verified</td>\n",
       "      <td>85483.20</td>\n",
       "      <td>REN?</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Business Teacher</td>\n",
       "      <td>...</td>\n",
       "      <td>whole</td>\n",
       "      <td>NC</td>\n",
       "      <td>15</td>\n",
       "      <td>18924.0</td>\n",
       "      <td>5 years</td>\n",
       "      <td>11660.49</td>\n",
       "      <td>debt  consolidation</td>\n",
       "      <td>I</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>92602</td>\n",
       "      <td>4247560</td>\n",
       "      <td>III</td>\n",
       "      <td>&lt;1yr</td>\n",
       "      <td>Source Verified</td>\n",
       "      <td>79200.00</td>\n",
       "      <td>REN?</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>driver</td>\n",
       "      <td>...</td>\n",
       "      <td>whole</td>\n",
       "      <td>Florida</td>\n",
       "      <td>7</td>\n",
       "      <td>15604.0</td>\n",
       "      <td>5 years</td>\n",
       "      <td>5637.87</td>\n",
       "      <td>major  purchase</td>\n",
       "      <td>I</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>22591</td>\n",
       "      <td>197179</td>\n",
       "      <td>III</td>\n",
       "      <td>&lt;1yr</td>\n",
       "      <td>Vfied</td>\n",
       "      <td>61600.00</td>\n",
       "      <td>REN?</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>AvePoint</td>\n",
       "      <td>...</td>\n",
       "      <td>fully paid</td>\n",
       "      <td>NewJersey</td>\n",
       "      <td>9</td>\n",
       "      <td>22410.0</td>\n",
       "      <td>3 years</td>\n",
       "      <td>15607.17</td>\n",
       "      <td>major  purchase</td>\n",
       "      <td>I</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>125060</td>\n",
       "      <td>4646684</td>\n",
       "      <td>V</td>\n",
       "      <td>2yrs</td>\n",
       "      <td>Source Verified</td>\n",
       "      <td>68053.92</td>\n",
       "      <td>REN?</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Lead Tester</td>\n",
       "      <td>...</td>\n",
       "      <td>fully paid</td>\n",
       "      <td>LA</td>\n",
       "      <td>10</td>\n",
       "      <td>36022.0</td>\n",
       "      <td>5 years</td>\n",
       "      <td>27472.86</td>\n",
       "      <td>debt  consolidation</td>\n",
       "      <td>I</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       ID  Asst_Reg GGGrade Experience       Validation  Yearly Income  \\\n",
       "0   95074    421802      II     >10yrs            Vfied      633600.00   \n",
       "1  134131   3964312      IV       7yrs  Source Verified       85483.20   \n",
       "2   92602   4247560     III       <1yr  Source Verified       79200.00   \n",
       "3   22591    197179     III       <1yr            Vfied       61600.00   \n",
       "4  125060   4646684       V       2yrs  Source Verified       68053.92   \n",
       "\n",
       "  Home Status  Unpaid 2 years  Already Defaulted       Designation  ...  \\\n",
       "0    MOR??AGE               0                  0   GLENDALE NISSAN  ...   \n",
       "1        REN?               0                  0  Business Teacher  ...   \n",
       "2        REN?               0                  0            driver  ...   \n",
       "3        REN?               0                  0          AvePoint  ...   \n",
       "4        REN?               0                  0       Lead Tester  ...   \n",
       "\n",
       "   File Status       State  Account Open  Total Unpaid CL  Duration  \\\n",
       "0   fully paid  California            17          58598.0   3 years   \n",
       "1        whole          NC            15          18924.0   5 years   \n",
       "2        whole     Florida             7          15604.0   5 years   \n",
       "3   fully paid   NewJersey             9          22410.0   3 years   \n",
       "4   fully paid          LA            10          36022.0   5 years   \n",
       "\n",
       "   Unpaid Amount               Reason  Claim Type  Due Fee  Loan/No Loan  \n",
       "0       31216.05  debt  consolidation           I      0.0             0  \n",
       "1       11660.49  debt  consolidation           I      0.0             0  \n",
       "2        5637.87      major  purchase           I      0.0             0  \n",
       "3       15607.17      major  purchase           I      0.0             1  \n",
       "4       27472.86  debt  consolidation           I      0.0             0  \n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Asst_Reg</th>\n",
       "      <th>GGGrade</th>\n",
       "      <th>Experience</th>\n",
       "      <th>Validation</th>\n",
       "      <th>Yearly Income</th>\n",
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       "      <th>0</th>\n",
       "      <td>198980</td>\n",
       "      <td>1773284</td>\n",
       "      <td>II</td>\n",
       "      <td>2yrs</td>\n",
       "      <td>Source Verified</td>\n",
       "      <td>140800.00</td>\n",
       "      <td>OWN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>processor</td>\n",
       "      <td>...</td>\n",
       "      <td>17</td>\n",
       "      <td>fully paid</td>\n",
       "      <td>California</td>\n",
       "      <td>23</td>\n",
       "      <td>33532.0</td>\n",
       "      <td>3 years</td>\n",
       "      <td>15755.94</td>\n",
       "      <td>major  purchase</td>\n",
       "      <td>I</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>167688</td>\n",
       "      <td>5298273</td>\n",
       "      <td>II</td>\n",
       "      <td>&gt;10yrs</td>\n",
       "      <td>Vfied</td>\n",
       "      <td>146080.00</td>\n",
       "      <td>MOR??AGE</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Speech Language Pathologist</td>\n",
       "      <td>...</td>\n",
       "      <td>19</td>\n",
       "      <td>whole</td>\n",
       "      <td>Florida</td>\n",
       "      <td>17</td>\n",
       "      <td>149234.0</td>\n",
       "      <td>5 years</td>\n",
       "      <td>68237.55</td>\n",
       "      <td>debt  consolidation</td>\n",
       "      <td>I</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>198768</td>\n",
       "      <td>189781</td>\n",
       "      <td>VI</td>\n",
       "      <td>&gt;10yrs</td>\n",
       "      <td>Vfied</td>\n",
       "      <td>202222.24</td>\n",
       "      <td>MOR??AGE</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Raytheon</td>\n",
       "      <td>...</td>\n",
       "      <td>37</td>\n",
       "      <td>whole</td>\n",
       "      <td>NM</td>\n",
       "      <td>15</td>\n",
       "      <td>70218.0</td>\n",
       "      <td>5 years</td>\n",
       "      <td>66272.76</td>\n",
       "      <td>debt  consolidation</td>\n",
       "      <td>I</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>192564</td>\n",
       "      <td>1530906</td>\n",
       "      <td>II</td>\n",
       "      <td>&gt;10yrs</td>\n",
       "      <td>Vfied</td>\n",
       "      <td>105600.00</td>\n",
       "      <td>MOR??AGE</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Computer/Network Technician</td>\n",
       "      <td>...</td>\n",
       "      <td>19</td>\n",
       "      <td>fully paid</td>\n",
       "      <td>DE</td>\n",
       "      <td>18</td>\n",
       "      <td>79182.0</td>\n",
       "      <td>3 years</td>\n",
       "      <td>15595.20</td>\n",
       "      <td>credit  card</td>\n",
       "      <td>I</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>151199</td>\n",
       "      <td>4410316</td>\n",
       "      <td>III</td>\n",
       "      <td>4yrs</td>\n",
       "      <td>Not Vfied</td>\n",
       "      <td>151360.00</td>\n",
       "      <td>MOR??AGE</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NDE Tech</td>\n",
       "      <td>...</td>\n",
       "      <td>25</td>\n",
       "      <td>fully paid</td>\n",
       "      <td>Florida</td>\n",
       "      <td>7</td>\n",
       "      <td>13612.0</td>\n",
       "      <td>3 years</td>\n",
       "      <td>8850.96</td>\n",
       "      <td>debt  consolidation</td>\n",
       "      <td>I</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       ID  Asst_Reg GGGrade Experience       Validation  Yearly Income  \\\n",
       "0  198980   1773284      II       2yrs  Source Verified      140800.00   \n",
       "1  167688   5298273      II     >10yrs            Vfied      146080.00   \n",
       "2  198768    189781      VI     >10yrs            Vfied      202222.24   \n",
       "3  192564   1530906      II     >10yrs            Vfied      105600.00   \n",
       "4  151199   4410316     III       4yrs        Not Vfied      151360.00   \n",
       "\n",
       "  Home Status  Unpaid 2 years  Already Defaulted                  Designation  \\\n",
       "0         OWN               0                  0                    processor   \n",
       "1    MOR??AGE               0                  0  Speech Language Pathologist   \n",
       "2    MOR??AGE               0                  0                     Raytheon   \n",
       "3    MOR??AGE               1                  0  Computer/Network Technician   \n",
       "4    MOR??AGE               0                  0                     NDE Tech   \n",
       "\n",
       "   ...  Sub GGGrade  File Status       State  Account Open  Total Unpaid CL  \\\n",
       "0  ...           17   fully paid  California            23          33532.0   \n",
       "1  ...           19        whole     Florida            17         149234.0   \n",
       "2  ...           37        whole          NM            15          70218.0   \n",
       "3  ...           19   fully paid          DE            18          79182.0   \n",
       "4  ...           25   fully paid     Florida             7          13612.0   \n",
       "\n",
       "   Duration  Unpaid Amount               Reason  Claim Type  Due Fee  \n",
       "0   3 years       15755.94      major  purchase           I      0.0  \n",
       "1   5 years       68237.55  debt  consolidation           I      0.0  \n",
       "2   5 years       66272.76  debt  consolidation           I      0.0  \n",
       "3   3 years       15595.20         credit  card           I      0.0  \n",
       "4   3 years        8850.96  debt  consolidation           I      0.0  \n",
       "\n",
       "[5 rows x 29 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "train.columns = train.columns.str.strip().str.replace(' ', '_')\n",
    "test.columns = test.columns.str.strip().str.replace(' ', '_')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ID</th>\n",
       "      <td>87500.0</td>\n",
       "      <td>8.394625e+04</td>\n",
       "      <td>3.610010e+04</td>\n",
       "      <td>21560.0</td>\n",
       "      <td>5.261675e+04</td>\n",
       "      <td>8.390000e+04</td>\n",
       "      <td>1.152612e+05</td>\n",
       "      <td>1.465590e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Asst_Reg</th>\n",
       "      <td>87500.0</td>\n",
       "      <td>3.798914e+06</td>\n",
       "      <td>2.289038e+06</td>\n",
       "      <td>24847.0</td>\n",
       "      <td>1.483874e+06</td>\n",
       "      <td>4.132010e+06</td>\n",
       "      <td>5.952909e+06</td>\n",
       "      <td>7.351847e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yearly_Income</th>\n",
       "      <td>81925.0</td>\n",
       "      <td>1.348596e+05</td>\n",
       "      <td>9.882473e+04</td>\n",
       "      <td>8800.0</td>\n",
       "      <td>8.324659e+04</td>\n",
       "      <td>1.144000e+05</td>\n",
       "      <td>1.601600e+05</td>\n",
       "      <td>8.264031e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Unpaid_2_years</th>\n",
       "      <td>87500.0</td>\n",
       "      <td>3.329371e-01</td>\n",
       "      <td>8.760799e-01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.800000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Already_Defaulted</th>\n",
       "      <td>87500.0</td>\n",
       "      <td>5.725714e-03</td>\n",
       "      <td>8.350455e-02</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>6.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Debt_to_Income</th>\n",
       "      <td>84011.0</td>\n",
       "      <td>3.094266e+01</td>\n",
       "      <td>1.407981e+01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.042880e+01</td>\n",
       "      <td>3.013920e+01</td>\n",
       "      <td>4.090800e+01</td>\n",
       "      <td>6.392904e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Postal_Code</th>\n",
       "      <td>86111.0</td>\n",
       "      <td>5.115539e+04</td>\n",
       "      <td>3.121123e+04</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>2.290000e+04</td>\n",
       "      <td>4.770000e+04</td>\n",
       "      <td>8.020000e+04</td>\n",
       "      <td>9.990000e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lend_Amount</th>\n",
       "      <td>87500.0</td>\n",
       "      <td>2.592054e+04</td>\n",
       "      <td>1.443384e+04</td>\n",
       "      <td>1710.0</td>\n",
       "      <td>1.504800e+04</td>\n",
       "      <td>2.394000e+04</td>\n",
       "      <td>3.420000e+04</td>\n",
       "      <td>5.985000e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Deprecatory_Records</th>\n",
       "      <td>87500.0</td>\n",
       "      <td>2.024000e-01</td>\n",
       "      <td>6.464135e-01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>8.600000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Interest_Charged</th>\n",
       "      <td>87500.0</td>\n",
       "      <td>1.651837e+01</td>\n",
       "      <td>4.902011e+00</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.231000e+01</td>\n",
       "      <td>1.649000e+01</td>\n",
       "      <td>2.077000e+01</td>\n",
       "      <td>2.500000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Usage_Rate</th>\n",
       "      <td>87500.0</td>\n",
       "      <td>9.551735e+01</td>\n",
       "      <td>4.055404e+01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.604800e+01</td>\n",
       "      <td>9.700800e+01</td>\n",
       "      <td>1.265920e+02</td>\n",
       "      <td>1.534756e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Inquiries</th>\n",
       "      <td>87500.0</td>\n",
       "      <td>6.706400e-01</td>\n",
       "      <td>9.595719e-01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>8.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Present_Balance</th>\n",
       "      <td>87500.0</td>\n",
       "      <td>2.363895e+05</td>\n",
       "      <td>2.550568e+05</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.173493e+04</td>\n",
       "      <td>1.388062e+05</td>\n",
       "      <td>3.534755e+05</td>\n",
       "      <td>5.629234e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gross_Collection</th>\n",
       "      <td>87500.0</td>\n",
       "      <td>3.946170e+01</td>\n",
       "      <td>3.919196e+02</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>2.630847e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sub_GGGrade</th>\n",
       "      <td>87500.0</td>\n",
       "      <td>2.202489e+01</td>\n",
       "      <td>6.452697e+00</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1.700000e+01</td>\n",
       "      <td>2.100000e+01</td>\n",
       "      <td>2.600000e+01</td>\n",
       "      <td>4.500000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Account_Open</th>\n",
       "      <td>87500.0</td>\n",
       "      <td>1.277944e+01</td>\n",
       "      <td>5.371160e+00</td>\n",
       "      <td>2.0</td>\n",
       "      <td>9.000000e+00</td>\n",
       "      <td>1.200000e+01</td>\n",
       "      <td>1.600000e+01</td>\n",
       "      <td>8.300000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total_Unpaid_CL</th>\n",
       "      <td>83314.0</td>\n",
       "      <td>5.326864e+04</td>\n",
       "      <td>5.325551e+04</td>\n",
       "      <td>332.0</td>\n",
       "      <td>2.324000e+04</td>\n",
       "      <td>3.967400e+04</td>\n",
       "      <td>6.640000e+04</td>\n",
       "      <td>1.710630e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Unpaid_Amount</th>\n",
       "      <td>82648.0</td>\n",
       "      <td>2.955649e+04</td>\n",
       "      <td>3.745625e+04</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.144674e+04</td>\n",
       "      <td>2.089278e+04</td>\n",
       "      <td>3.644908e+04</td>\n",
       "      <td>1.338778e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Due_Fee</th>\n",
       "      <td>87500.0</td>\n",
       "      <td>3.164072e-01</td>\n",
       "      <td>3.546087e+00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.690500e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Loan/No_Loan</th>\n",
       "      <td>87500.0</td>\n",
       "      <td>1.887086e-01</td>\n",
       "      <td>3.912792e-01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       count          mean           std      min  \\\n",
       "ID                   87500.0  8.394625e+04  3.610010e+04  21560.0   \n",
       "Asst_Reg             87500.0  3.798914e+06  2.289038e+06  24847.0   \n",
       "Yearly_Income        81925.0  1.348596e+05  9.882473e+04   8800.0   \n",
       "Unpaid_2_years       87500.0  3.329371e-01  8.760799e-01      0.0   \n",
       "Already_Defaulted    87500.0  5.725714e-03  8.350455e-02      0.0   \n",
       "Debt_to_Income       84011.0  3.094266e+01  1.407981e+01      0.0   \n",
       "Postal_Code          86111.0  5.115539e+04  3.121123e+04   1000.0   \n",
       "Lend_Amount          87500.0  2.592054e+04  1.443384e+04   1710.0   \n",
       "Deprecatory_Records  87500.0  2.024000e-01  6.464135e-01      0.0   \n",
       "Interest_Charged     87500.0  1.651837e+01  4.902011e+00      8.0   \n",
       "Usage_Rate           87500.0  9.551735e+01  4.055404e+01      0.0   \n",
       "Inquiries            87500.0  6.706400e-01  9.595719e-01      0.0   \n",
       "Present_Balance      87500.0  2.363895e+05  2.550568e+05      0.0   \n",
       "Gross_Collection     87500.0  3.946170e+01  3.919196e+02      0.0   \n",
       "Sub_GGGrade          87500.0  2.202489e+01  6.452697e+00     11.0   \n",
       "Account_Open         87500.0  1.277944e+01  5.371160e+00      2.0   \n",
       "Total_Unpaid_CL      83314.0  5.326864e+04  5.325551e+04    332.0   \n",
       "Unpaid_Amount        82648.0  2.955649e+04  3.745625e+04      0.0   \n",
       "Due_Fee              87500.0  3.164072e-01  3.546087e+00      0.0   \n",
       "Loan/No_Loan         87500.0  1.887086e-01  3.912792e-01      0.0   \n",
       "\n",
       "                              25%           50%           75%           max  \n",
       "ID                   5.261675e+04  8.390000e+04  1.152612e+05  1.465590e+05  \n",
       "Asst_Reg             1.483874e+06  4.132010e+06  5.952909e+06  7.351847e+06  \n",
       "Yearly_Income        8.324659e+04  1.144000e+05  1.601600e+05  8.264031e+06  \n",
       "Unpaid_2_years       0.000000e+00  0.000000e+00  0.000000e+00  1.800000e+01  \n",
       "Already_Defaulted    0.000000e+00  0.000000e+00  0.000000e+00  6.000000e+00  \n",
       "Debt_to_Income       2.042880e+01  3.013920e+01  4.090800e+01  6.392904e+02  \n",
       "Postal_Code          2.290000e+04  4.770000e+04  8.020000e+04  9.990000e+04  \n",
       "Lend_Amount          1.504800e+04  2.394000e+04  3.420000e+04  5.985000e+04  \n",
       "Deprecatory_Records  0.000000e+00  0.000000e+00  0.000000e+00  8.600000e+01  \n",
       "Interest_Charged     1.231000e+01  1.649000e+01  2.077000e+01  2.500000e+01  \n",
       "Usage_Rate           6.604800e+01  9.700800e+01  1.265920e+02  1.534756e+03  \n",
       "Inquiries            0.000000e+00  0.000000e+00  1.000000e+00  8.000000e+00  \n",
       "Present_Balance      5.173493e+04  1.388062e+05  3.534755e+05  5.629234e+06  \n",
       "Gross_Collection     0.000000e+00  0.000000e+00  0.000000e+00  2.630847e+04  \n",
       "Sub_GGGrade          1.700000e+01  2.100000e+01  2.600000e+01  4.500000e+01  \n",
       "Account_Open         9.000000e+00  1.200000e+01  1.600000e+01  8.300000e+01  \n",
       "Total_Unpaid_CL      2.324000e+04  3.967400e+04  6.640000e+04  1.710630e+06  \n",
       "Unpaid_Amount        1.144674e+04  2.089278e+04  3.644908e+04  1.338778e+06  \n",
       "Due_Fee              0.000000e+00  0.000000e+00  0.000000e+00  1.690500e+02  \n",
       "Loan/No_Loan         0.000000e+00  0.000000e+00  0.000000e+00  1.000000e+00  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# let's decsribe the train set\n",
    "\n",
    "train.describe().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ID</th>\n",
       "      <td>37500.0</td>\n",
       "      <td>1.841439e+05</td>\n",
       "      <td>2.168322e+04</td>\n",
       "      <td>146560.0</td>\n",
       "      <td>1.653412e+05</td>\n",
       "      <td>1.842570e+05</td>\n",
       "      <td>2.028692e+05</td>\n",
       "      <td>221557.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Asst_Reg</th>\n",
       "      <td>37500.0</td>\n",
       "      <td>3.804858e+06</td>\n",
       "      <td>2.284115e+06</td>\n",
       "      <td>37522.0</td>\n",
       "      <td>1.495828e+06</td>\n",
       "      <td>4.134010e+06</td>\n",
       "      <td>5.934898e+06</td>\n",
       "      <td>7351969.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yearly_Income</th>\n",
       "      <td>35075.0</td>\n",
       "      <td>1.340308e+05</td>\n",
       "      <td>1.000845e+05</td>\n",
       "      <td>8800.0</td>\n",
       "      <td>8.419400e+04</td>\n",
       "      <td>1.144000e+05</td>\n",
       "      <td>1.584000e+05</td>\n",
       "      <td>8800000.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Unpaid_2_years</th>\n",
       "      <td>37500.0</td>\n",
       "      <td>3.364267e-01</td>\n",
       "      <td>8.876400e-01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>18.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Already_Defaulted</th>\n",
       "      <td>37500.0</td>\n",
       "      <td>5.306667e-03</td>\n",
       "      <td>7.796581e-02</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>3.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Debt_to_Income</th>\n",
       "      <td>35989.0</td>\n",
       "      <td>3.091903e+01</td>\n",
       "      <td>1.387167e+01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.047920e+01</td>\n",
       "      <td>3.018960e+01</td>\n",
       "      <td>4.072320e+01</td>\n",
       "      <td>116.508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Postal_Code</th>\n",
       "      <td>36889.0</td>\n",
       "      <td>5.123395e+04</td>\n",
       "      <td>3.125043e+04</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>2.290000e+04</td>\n",
       "      <td>4.780000e+04</td>\n",
       "      <td>8.020000e+04</td>\n",
       "      <td>99900.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lend_Amount</th>\n",
       "      <td>37500.0</td>\n",
       "      <td>2.582800e+04</td>\n",
       "      <td>1.440206e+04</td>\n",
       "      <td>1710.0</td>\n",
       "      <td>1.491975e+04</td>\n",
       "      <td>2.381175e+04</td>\n",
       "      <td>3.420000e+04</td>\n",
       "      <td>59850.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Deprecatory_Records</th>\n",
       "      <td>37500.0</td>\n",
       "      <td>2.000800e-01</td>\n",
       "      <td>5.631369e-01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>21.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Interest_Charged</th>\n",
       "      <td>37500.0</td>\n",
       "      <td>1.651101e+01</td>\n",
       "      <td>4.912595e+00</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.225000e+01</td>\n",
       "      <td>1.651500e+01</td>\n",
       "      <td>2.077000e+01</td>\n",
       "      <td>25.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Usage_Rate</th>\n",
       "      <td>37500.0</td>\n",
       "      <td>9.525911e+01</td>\n",
       "      <td>4.049322e+01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.553200e+01</td>\n",
       "      <td>9.649200e+01</td>\n",
       "      <td>1.267640e+02</td>\n",
       "      <td>254.560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Inquiries</th>\n",
       "      <td>37500.0</td>\n",
       "      <td>6.766400e-01</td>\n",
       "      <td>9.645153e-01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>6.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Present_Balance</th>\n",
       "      <td>37500.0</td>\n",
       "      <td>2.353528e+05</td>\n",
       "      <td>2.543097e+05</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.129572e+04</td>\n",
       "      <td>1.383795e+05</td>\n",
       "      <td>3.514983e+05</td>\n",
       "      <td>4909890.180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gross_Collection</th>\n",
       "      <td>37500.0</td>\n",
       "      <td>3.948407e+01</td>\n",
       "      <td>3.641146e+02</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>13900.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sub_GGGrade</th>\n",
       "      <td>37500.0</td>\n",
       "      <td>2.203147e+01</td>\n",
       "      <td>6.481257e+00</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1.700000e+01</td>\n",
       "      <td>2.100000e+01</td>\n",
       "      <td>2.600000e+01</td>\n",
       "      <td>45.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Account_Open</th>\n",
       "      <td>37500.0</td>\n",
       "      <td>1.277779e+01</td>\n",
       "      <td>5.360527e+00</td>\n",
       "      <td>2.0</td>\n",
       "      <td>9.000000e+00</td>\n",
       "      <td>1.200000e+01</td>\n",
       "      <td>1.500000e+01</td>\n",
       "      <td>60.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total_Unpaid_CL</th>\n",
       "      <td>35686.0</td>\n",
       "      <td>5.344820e+04</td>\n",
       "      <td>5.215483e+04</td>\n",
       "      <td>332.0</td>\n",
       "      <td>2.324000e+04</td>\n",
       "      <td>3.950800e+04</td>\n",
       "      <td>6.640000e+04</td>\n",
       "      <td>1475408.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Unpaid_Amount</th>\n",
       "      <td>35431.0</td>\n",
       "      <td>2.928066e+04</td>\n",
       "      <td>3.560849e+04</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.141425e+04</td>\n",
       "      <td>2.068074e+04</td>\n",
       "      <td>3.618446e+04</td>\n",
       "      <td>1535829.660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Due_Fee</th>\n",
       "      <td>37500.0</td>\n",
       "      <td>3.097915e-01</td>\n",
       "      <td>3.396315e+00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>131.490</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       count          mean           std       min  \\\n",
       "ID                   37500.0  1.841439e+05  2.168322e+04  146560.0   \n",
       "Asst_Reg             37500.0  3.804858e+06  2.284115e+06   37522.0   \n",
       "Yearly_Income        35075.0  1.340308e+05  1.000845e+05    8800.0   \n",
       "Unpaid_2_years       37500.0  3.364267e-01  8.876400e-01       0.0   \n",
       "Already_Defaulted    37500.0  5.306667e-03  7.796581e-02       0.0   \n",
       "Debt_to_Income       35989.0  3.091903e+01  1.387167e+01       0.0   \n",
       "Postal_Code          36889.0  5.123395e+04  3.125043e+04    1000.0   \n",
       "Lend_Amount          37500.0  2.582800e+04  1.440206e+04    1710.0   \n",
       "Deprecatory_Records  37500.0  2.000800e-01  5.631369e-01       0.0   \n",
       "Interest_Charged     37500.0  1.651101e+01  4.912595e+00       8.0   \n",
       "Usage_Rate           37500.0  9.525911e+01  4.049322e+01       0.0   \n",
       "Inquiries            37500.0  6.766400e-01  9.645153e-01       0.0   \n",
       "Present_Balance      37500.0  2.353528e+05  2.543097e+05       0.0   \n",
       "Gross_Collection     37500.0  3.948407e+01  3.641146e+02       0.0   \n",
       "Sub_GGGrade          37500.0  2.203147e+01  6.481257e+00      11.0   \n",
       "Account_Open         37500.0  1.277779e+01  5.360527e+00       2.0   \n",
       "Total_Unpaid_CL      35686.0  5.344820e+04  5.215483e+04     332.0   \n",
       "Unpaid_Amount        35431.0  2.928066e+04  3.560849e+04       0.0   \n",
       "Due_Fee              37500.0  3.097915e-01  3.396315e+00       0.0   \n",
       "\n",
       "                              25%           50%           75%          max  \n",
       "ID                   1.653412e+05  1.842570e+05  2.028692e+05   221557.000  \n",
       "Asst_Reg             1.495828e+06  4.134010e+06  5.934898e+06  7351969.000  \n",
       "Yearly_Income        8.419400e+04  1.144000e+05  1.584000e+05  8800000.000  \n",
       "Unpaid_2_years       0.000000e+00  0.000000e+00  0.000000e+00       18.000  \n",
       "Already_Defaulted    0.000000e+00  0.000000e+00  0.000000e+00        3.000  \n",
       "Debt_to_Income       2.047920e+01  3.018960e+01  4.072320e+01      116.508  \n",
       "Postal_Code          2.290000e+04  4.780000e+04  8.020000e+04    99900.000  \n",
       "Lend_Amount          1.491975e+04  2.381175e+04  3.420000e+04    59850.000  \n",
       "Deprecatory_Records  0.000000e+00  0.000000e+00  0.000000e+00       21.000  \n",
       "Interest_Charged     1.225000e+01  1.651500e+01  2.077000e+01       25.000  \n",
       "Usage_Rate           6.553200e+01  9.649200e+01  1.267640e+02      254.560  \n",
       "Inquiries            0.000000e+00  0.000000e+00  1.000000e+00        6.000  \n",
       "Present_Balance      5.129572e+04  1.383795e+05  3.514983e+05  4909890.180  \n",
       "Gross_Collection     0.000000e+00  0.000000e+00  0.000000e+00    13900.000  \n",
       "Sub_GGGrade          1.700000e+01  2.100000e+01  2.600000e+01       45.000  \n",
       "Account_Open         9.000000e+00  1.200000e+01  1.500000e+01       60.000  \n",
       "Total_Unpaid_CL      2.324000e+04  3.950800e+04  6.640000e+04  1475408.000  \n",
       "Unpaid_Amount        1.141425e+04  2.068074e+04  3.618446e+04  1535829.660  \n",
       "Due_Fee              0.000000e+00  0.000000e+00  0.000000e+00      131.490  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# let's decsribe the train set\n",
    "\n",
    "test.describe().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 87500 entries, 0 to 87499\n",
      "Data columns (total 30 columns):\n",
      " #   Column               Non-Null Count  Dtype  \n",
      "---  ------               --------------  -----  \n",
      " 0   ID                   87500 non-null  int64  \n",
      " 1   Asst_Reg             87500 non-null  int64  \n",
      " 2   GGGrade              87500 non-null  object \n",
      " 3   Experience           87500 non-null  object \n",
      " 4   Validation           87500 non-null  object \n",
      " 5   Yearly_Income        81925 non-null  float64\n",
      " 6   Home_Status          87500 non-null  object \n",
      " 7   Unpaid_2_years       87500 non-null  int64  \n",
      " 8   Already_Defaulted    87500 non-null  int64  \n",
      " 9   Designation          86086 non-null  object \n",
      " 10  Debt_to_Income       84011 non-null  float64\n",
      " 11  Postal_Code          86111 non-null  float64\n",
      " 12  Lend_Amount          87500 non-null  float64\n",
      " 13  Deprecatory_Records  87500 non-null  int64  \n",
      " 14  Interest_Charged     87500 non-null  float64\n",
      " 15  Usage_Rate           87500 non-null  float64\n",
      " 16  Inquiries            87500 non-null  int64  \n",
      " 17  Present_Balance      87500 non-null  float64\n",
      " 18  Gross_Collection     87500 non-null  float64\n",
      " 19  Sub_GGGrade          87500 non-null  int64  \n",
      " 20  File_Status          87500 non-null  object \n",
      " 21  State                87500 non-null  object \n",
      " 22  Account_Open         87500 non-null  int64  \n",
      " 23  Total_Unpaid_CL      83314 non-null  float64\n",
      " 24  Duration             87500 non-null  object \n",
      " 25  Unpaid_Amount        82648 non-null  float64\n",
      " 26  Reason               87500 non-null  object \n",
      " 27  Claim_Type           87500 non-null  object \n",
      " 28  Due_Fee              87500 non-null  float64\n",
      " 29  Loan/No_Loan         87500 non-null  int64  \n",
      "dtypes: float64(11), int64(9), object(10)\n",
      "memory usage: 20.0+ MB\n"
     ]
    }
   ],
   "source": [
    "train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 37500 entries, 0 to 37499\n",
      "Data columns (total 29 columns):\n",
      " #   Column               Non-Null Count  Dtype  \n",
      "---  ------               --------------  -----  \n",
      " 0   ID                   37500 non-null  int64  \n",
      " 1   Asst_Reg             37500 non-null  int64  \n",
      " 2   GGGrade              37500 non-null  object \n",
      " 3   Experience           37500 non-null  object \n",
      " 4   Validation           37500 non-null  object \n",
      " 5   Yearly_Income        35075 non-null  float64\n",
      " 6   Home_Status          37500 non-null  object \n",
      " 7   Unpaid_2_years       37500 non-null  int64  \n",
      " 8   Already_Defaulted    37500 non-null  int64  \n",
      " 9   Designation          36914 non-null  object \n",
      " 10  Debt_to_Income       35989 non-null  float64\n",
      " 11  Postal_Code          36889 non-null  float64\n",
      " 12  Lend_Amount          37500 non-null  float64\n",
      " 13  Deprecatory_Records  37500 non-null  int64  \n",
      " 14  Interest_Charged     37500 non-null  float64\n",
      " 15  Usage_Rate           37500 non-null  float64\n",
      " 16  Inquiries            37500 non-null  int64  \n",
      " 17  Present_Balance      37500 non-null  float64\n",
      " 18  Gross_Collection     37500 non-null  float64\n",
      " 19  Sub_GGGrade          37500 non-null  int64  \n",
      " 20  File_Status          37500 non-null  object \n",
      " 21  State                37500 non-null  object \n",
      " 22  Account_Open         37500 non-null  int64  \n",
      " 23  Total_Unpaid_CL      35686 non-null  float64\n",
      " 24  Duration             37500 non-null  object \n",
      " 25  Unpaid_Amount        35431 non-null  float64\n",
      " 26  Reason               37500 non-null  object \n",
      " 27  Claim_Type           37500 non-null  object \n",
      " 28  Due_Fee              37500 non-null  float64\n",
      "dtypes: float64(11), int64(8), object(10)\n",
      "memory usage: 8.3+ MB\n"
     ]
    }
   ],
   "source": [
    "test.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ID                        0\n",
       "Asst_Reg                  0\n",
       "GGGrade                   0\n",
       "Experience                0\n",
       "Validation                0\n",
       "Yearly_Income          5575\n",
       "Home_Status               0\n",
       "Unpaid_2_years            0\n",
       "Already_Defaulted         0\n",
       "Designation            1414\n",
       "Debt_to_Income         3489\n",
       "Postal_Code            1389\n",
       "Lend_Amount               0\n",
       "Deprecatory_Records       0\n",
       "Interest_Charged          0\n",
       "Usage_Rate                0\n",
       "Inquiries                 0\n",
       "Present_Balance           0\n",
       "Gross_Collection          0\n",
       "Sub_GGGrade               0\n",
       "File_Status               0\n",
       "State                     0\n",
       "Account_Open              0\n",
       "Total_Unpaid_CL        4186\n",
       "Duration                  0\n",
       "Unpaid_Amount          4852\n",
       "Reason                    0\n",
       "Claim_Type                0\n",
       "Due_Fee                   0\n",
       "Loan/No_Loan              0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking if there exists any NULL values in the train set\n",
    "\n",
    "train.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ID                        0\n",
       "Asst_Reg                  0\n",
       "GGGrade                   0\n",
       "Experience                0\n",
       "Validation                0\n",
       "Yearly_Income          2425\n",
       "Home_Status               0\n",
       "Unpaid_2_years            0\n",
       "Already_Defaulted         0\n",
       "Designation             586\n",
       "Debt_to_Income         1511\n",
       "Postal_Code             611\n",
       "Lend_Amount               0\n",
       "Deprecatory_Records       0\n",
       "Interest_Charged          0\n",
       "Usage_Rate                0\n",
       "Inquiries                 0\n",
       "Present_Balance           0\n",
       "Gross_Collection          0\n",
       "Sub_GGGrade               0\n",
       "File_Status               0\n",
       "State                     0\n",
       "Account_Open              0\n",
       "Total_Unpaid_CL        1814\n",
       "Duration                  0\n",
       "Unpaid_Amount          2069\n",
       "Reason                    0\n",
       "Claim_Type                0\n",
       "Due_Fee                   0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking if there exists any NULL values in the test set\n",
    "\n",
    "test.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# filling the missing values in train as well as test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Null values left in the train set: 0\n",
      "Null values left in the test set: 0\n"
     ]
    }
   ],
   "source": [
    "# Replacing missing values from the selected columns with mean\n",
    "\n",
    "train['Postal_Code'].fillna(train['Postal_Code'].mean(), inplace=True)\n",
    "train['Yearly_Income'].fillna(train['Yearly_Income'].median(), inplace=True)\n",
    "train['Debt_to_Income'].fillna(train['Debt_to_Income'].median(), inplace=True)\n",
    "train['Total_Unpaid_CL'].fillna(train['Total_Unpaid_CL'].median(), inplace=True)\n",
    "train['Unpaid_Amount'].fillna(train['Unpaid_Amount'].median(), inplace=True)\n",
    "train['Designation'].fillna(train['Designation'].mode()[0], inplace=True)\n",
    "\n",
    "test['Postal_Code'].fillna(test['Postal_Code'].mean(), inplace=True)\n",
    "test['Yearly_Income'].fillna(test['Yearly_Income'].median(), inplace=True)\n",
    "test['Debt_to_Income'].fillna(test['Debt_to_Income'].median(), inplace=True)\n",
    "test['Total_Unpaid_CL'].fillna(test['Total_Unpaid_CL'].median(), inplace=True)\n",
    "test['Unpaid_Amount'].fillna(test['Unpaid_Amount'].median(), inplace=True)\n",
    "test['Designation'].fillna(test['Designation'].mode()[0], inplace=True)\n",
    "\n",
    "\n",
    "# let's check if there is any null values still left or not\n",
    "print(\"Null values left in the train set:\", train.isnull().sum().sum())\n",
    "print(\"Null values left in the test set:\", test.isnull().sum().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(87500, 29)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# let's save the unique id of the test set and labels set\n",
    "\n",
    "unique_id = test['ID']\n",
    "y_train_t = train.iloc[:, -1]\n",
    "\n",
    "# let's delete the last column from the dataset to  concat train and test\n",
    "train = train.drop(['Loan/No_Loan'], axis = 1)\n",
    "\n",
    "# shape of train\n",
    "train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(125000, 29)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# lets concat the train and test sets for preprocessing and visualizations\n",
    "\n",
    "data = pd.concat([train, test], axis = 0)\n",
    "\n",
    "# let's check the shape\n",
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['ID', 'Asst_Reg', 'GGGrade', 'Experience', 'Validation',\n",
       "       'Yearly_Income', 'Home_Status', 'Unpaid_2_years', 'Already_Defaulted',\n",
       "       'Designation', 'Debt_to_Income', 'Postal_Code', 'Lend_Amount',\n",
       "       'Deprecatory_Records', 'Interest_Charged', 'Usage_Rate', 'Inquiries',\n",
       "       'Present_Balance', 'Gross_Collection', 'Sub_GGGrade', 'File_Status',\n",
       "       'State', 'Account_Open', 'Total_Unpaid_CL', 'Duration', 'Unpaid_Amount',\n",
       "       'Reason', 'Claim_Type', 'Due_Fee'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking the columns names of the data\n",
    "\n",
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>ID</th>\n",
       "      <th>Asst_Reg</th>\n",
       "      <th>Yearly_Income</th>\n",
       "      <th>Unpaid_2_years</th>\n",
       "      <th>Already_Defaulted</th>\n",
       "      <th>Debt_to_Income</th>\n",
       "      <th>Postal_Code</th>\n",
       "      <th>Lend_Amount</th>\n",
       "      <th>Deprecatory_Records</th>\n",
       "      <th>Interest_Charged</th>\n",
       "      <th>Usage_Rate</th>\n",
       "      <th>Inquiries</th>\n",
       "      <th>Present_Balance</th>\n",
       "      <th>Gross_Collection</th>\n",
       "      <th>Sub_GGGrade</th>\n",
       "      <th>Account_Open</th>\n",
       "      <th>Total_Unpaid_CL</th>\n",
       "      <th>Unpaid_Amount</th>\n",
       "      <th>Due_Fee</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>95074</td>\n",
       "      <td>421802</td>\n",
       "      <td>633600.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>12.5496</td>\n",
       "      <td>91300.0</td>\n",
       "      <td>42023.25</td>\n",
       "      <td>0</td>\n",
       "      <td>15.39</td>\n",
       "      <td>88.924</td>\n",
       "      <td>3</td>\n",
       "      <td>607161.90</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19</td>\n",
       "      <td>17</td>\n",
       "      <td>58598.0</td>\n",
       "      <td>31216.05</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>134131</td>\n",
       "      <td>3964312</td>\n",
       "      <td>85483.20</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9.0048</td>\n",
       "      <td>27400.0</td>\n",
       "      <td>38133.00</td>\n",
       "      <td>0</td>\n",
       "      <td>9.94</td>\n",
       "      <td>102.856</td>\n",
       "      <td>0</td>\n",
       "      <td>269234.06</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30</td>\n",
       "      <td>15</td>\n",
       "      <td>18924.0</td>\n",
       "      <td>11660.49</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>92602</td>\n",
       "      <td>4247560</td>\n",
       "      <td>79200.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>24.9144</td>\n",
       "      <td>32800.0</td>\n",
       "      <td>17100.00</td>\n",
       "      <td>0</td>\n",
       "      <td>22.35</td>\n",
       "      <td>60.372</td>\n",
       "      <td>0</td>\n",
       "      <td>22476.53</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25</td>\n",
       "      <td>7</td>\n",
       "      <td>15604.0</td>\n",
       "      <td>5637.87</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>22591</td>\n",
       "      <td>197179</td>\n",
       "      <td>61600.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>16.5816</td>\n",
       "      <td>7000.0</td>\n",
       "      <td>5130.00</td>\n",
       "      <td>0</td>\n",
       "      <td>10.36</td>\n",
       "      <td>116.272</td>\n",
       "      <td>1</td>\n",
       "      <td>15242.09</td>\n",
       "      <td>0.0</td>\n",
       "      <td>22</td>\n",
       "      <td>9</td>\n",
       "      <td>22410.0</td>\n",
       "      <td>15607.17</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>125060</td>\n",
       "      <td>4646684</td>\n",
       "      <td>68053.92</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40.0512</td>\n",
       "      <td>70800.0</td>\n",
       "      <td>19665.00</td>\n",
       "      <td>0</td>\n",
       "      <td>13.68</td>\n",
       "      <td>127.280</td>\n",
       "      <td>1</td>\n",
       "      <td>65433.94</td>\n",
       "      <td>0.0</td>\n",
       "      <td>32</td>\n",
       "      <td>10</td>\n",
       "      <td>36022.0</td>\n",
       "      <td>27472.86</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       ID  Asst_Reg  Yearly_Income  Unpaid_2_years  Already_Defaulted  \\\n",
       "0   95074    421802      633600.00               0                  0   \n",
       "1  134131   3964312       85483.20               0                  0   \n",
       "2   92602   4247560       79200.00               0                  0   \n",
       "3   22591    197179       61600.00               0                  0   \n",
       "4  125060   4646684       68053.92               0                  0   \n",
       "\n",
       "   Debt_to_Income  Postal_Code  Lend_Amount  Deprecatory_Records  \\\n",
       "0         12.5496      91300.0     42023.25                    0   \n",
       "1          9.0048      27400.0     38133.00                    0   \n",
       "2         24.9144      32800.0     17100.00                    0   \n",
       "3         16.5816       7000.0      5130.00                    0   \n",
       "4         40.0512      70800.0     19665.00                    0   \n",
       "\n",
       "   Interest_Charged  Usage_Rate  Inquiries  Present_Balance  Gross_Collection  \\\n",
       "0             15.39      88.924          3        607161.90               0.0   \n",
       "1              9.94     102.856          0        269234.06               0.0   \n",
       "2             22.35      60.372          0         22476.53               0.0   \n",
       "3             10.36     116.272          1         15242.09               0.0   \n",
       "4             13.68     127.280          1         65433.94               0.0   \n",
       "\n",
       "   Sub_GGGrade  Account_Open  Total_Unpaid_CL  Unpaid_Amount  Due_Fee  \n",
       "0           19            17          58598.0       31216.05      0.0  \n",
       "1           30            15          18924.0       11660.49      0.0  \n",
       "2           25             7          15604.0        5637.87      0.0  \n",
       "3           22             9          22410.0       15607.17      0.0  \n",
       "4           32            10          36022.0       27472.86      0.0  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numerics = ['int64','float64']\n",
    "data.select_dtypes(include=numerics).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "column_list = ['Asst_Reg','Yearly_Income','Unpaid_2_years','Already_Defaulted','Debt_to_Income',\\\n",
    "                  'Lend_Amount','Deprecatory_Records','Interest_Charged','Usage_Rate','Inquiries',\\\n",
    "                  'Present_Balance','Gross_Collection','Sub_GGGrade','Account_Open','Total_Unpaid_CL',\\\n",
    "                  'Unpaid_Amount','Due_Fee']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in column_list:\n",
    "    data[i] = data[i]+1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
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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>ID</th>\n",
       "      <th>Asst_Reg</th>\n",
       "      <th>Yearly_Income</th>\n",
       "      <th>Unpaid_2_years</th>\n",
       "      <th>Already_Defaulted</th>\n",
       "      <th>Debt_to_Income</th>\n",
       "      <th>Postal_Code</th>\n",
       "      <th>Lend_Amount</th>\n",
       "      <th>Deprecatory_Records</th>\n",
       "      <th>Interest_Charged</th>\n",
       "      <th>Usage_Rate</th>\n",
       "      <th>Inquiries</th>\n",
       "      <th>Present_Balance</th>\n",
       "      <th>Gross_Collection</th>\n",
       "      <th>Sub_GGGrade</th>\n",
       "      <th>Account_Open</th>\n",
       "      <th>Total_Unpaid_CL</th>\n",
       "      <th>Unpaid_Amount</th>\n",
       "      <th>Due_Fee</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>95074</td>\n",
       "      <td>421803</td>\n",
       "      <td>633601.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>13.5496</td>\n",
       "      <td>91300.0</td>\n",
       "      <td>42024.25</td>\n",
       "      <td>1</td>\n",
       "      <td>16.39</td>\n",
       "      <td>89.924</td>\n",
       "      <td>4</td>\n",
       "      <td>607162.90</td>\n",
       "      <td>1.0</td>\n",
       "      <td>20</td>\n",
       "      <td>18</td>\n",
       "      <td>58599.0</td>\n",
       "      <td>31217.05</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>134131</td>\n",
       "      <td>3964313</td>\n",
       "      <td>85484.20</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>10.0048</td>\n",
       "      <td>27400.0</td>\n",
       "      <td>38134.00</td>\n",
       "      <td>1</td>\n",
       "      <td>10.94</td>\n",
       "      <td>103.856</td>\n",
       "      <td>1</td>\n",
       "      <td>269235.06</td>\n",
       "      <td>1.0</td>\n",
       "      <td>31</td>\n",
       "      <td>16</td>\n",
       "      <td>18925.0</td>\n",
       "      <td>11661.49</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>92602</td>\n",
       "      <td>4247561</td>\n",
       "      <td>79201.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>25.9144</td>\n",
       "      <td>32800.0</td>\n",
       "      <td>17101.00</td>\n",
       "      <td>1</td>\n",
       "      <td>23.35</td>\n",
       "      <td>61.372</td>\n",
       "      <td>1</td>\n",
       "      <td>22477.53</td>\n",
       "      <td>1.0</td>\n",
       "      <td>26</td>\n",
       "      <td>8</td>\n",
       "      <td>15605.0</td>\n",
       "      <td>5638.87</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>22591</td>\n",
       "      <td>197180</td>\n",
       "      <td>61601.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>17.5816</td>\n",
       "      <td>7000.0</td>\n",
       "      <td>5131.00</td>\n",
       "      <td>1</td>\n",
       "      <td>11.36</td>\n",
       "      <td>117.272</td>\n",
       "      <td>2</td>\n",
       "      <td>15243.09</td>\n",
       "      <td>1.0</td>\n",
       "      <td>23</td>\n",
       "      <td>10</td>\n",
       "      <td>22411.0</td>\n",
       "      <td>15608.17</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>125060</td>\n",
       "      <td>4646685</td>\n",
       "      <td>68054.92</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>41.0512</td>\n",
       "      <td>70800.0</td>\n",
       "      <td>19666.00</td>\n",
       "      <td>1</td>\n",
       "      <td>14.68</td>\n",
       "      <td>128.280</td>\n",
       "      <td>2</td>\n",
       "      <td>65434.94</td>\n",
       "      <td>1.0</td>\n",
       "      <td>33</td>\n",
       "      <td>11</td>\n",
       "      <td>36023.0</td>\n",
       "      <td>27473.86</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       ID  Asst_Reg  Yearly_Income  Unpaid_2_years  Already_Defaulted  \\\n",
       "0   95074    421803      633601.00               1                  1   \n",
       "1  134131   3964313       85484.20               1                  1   \n",
       "2   92602   4247561       79201.00               1                  1   \n",
       "3   22591    197180       61601.00               1                  1   \n",
       "4  125060   4646685       68054.92               1                  1   \n",
       "\n",
       "   Debt_to_Income  Postal_Code  Lend_Amount  Deprecatory_Records  \\\n",
       "0         13.5496      91300.0     42024.25                    1   \n",
       "1         10.0048      27400.0     38134.00                    1   \n",
       "2         25.9144      32800.0     17101.00                    1   \n",
       "3         17.5816       7000.0      5131.00                    1   \n",
       "4         41.0512      70800.0     19666.00                    1   \n",
       "\n",
       "   Interest_Charged  Usage_Rate  Inquiries  Present_Balance  Gross_Collection  \\\n",
       "0             16.39      89.924          4        607162.90               1.0   \n",
       "1             10.94     103.856          1        269235.06               1.0   \n",
       "2             23.35      61.372          1         22477.53               1.0   \n",
       "3             11.36     117.272          2         15243.09               1.0   \n",
       "4             14.68     128.280          2         65434.94               1.0   \n",
       "\n",
       "   Sub_GGGrade  Account_Open  Total_Unpaid_CL  Unpaid_Amount  Due_Fee  \n",
       "0           20            18          58599.0       31217.05      1.0  \n",
       "1           31            16          18925.0       11661.49      1.0  \n",
       "2           26             8          15605.0        5638.87      1.0  \n",
       "3           23            10          22411.0       15608.17      1.0  \n",
       "4           33            11          36023.0       27473.86      1.0  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.select_dtypes(include=numerics).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1440 with 17 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# check the distribution of disbursed amount\n",
    "# Before log transformation\n",
    "\n",
    "plt.figure(figsize=(15, 20))\n",
    "\n",
    "plt.subplot(6, 3, 1)\n",
    "sns.distplot(data['Asst_Reg'])\n",
    "\n",
    "plt.subplot(6, 3, 2)\n",
    "sns.distplot(data['Yearly_Income'])\n",
    "\n",
    "plt.subplot(6, 3, 3)\n",
    "sns.distplot(data['Unpaid_2_years'])\n",
    "\n",
    "plt.subplot(6, 3, 4)\n",
    "sns.distplot(data['Already_Defaulted'])\n",
    "\n",
    "plt.subplot(6, 3, 5)\n",
    "sns.distplot(data['Debt_to_Income'])\n",
    "\n",
    "plt.subplot(6, 3, 6)\n",
    "sns.distplot(data['Lend_Amount'])\n",
    "\n",
    "plt.subplot(6, 3, 7)\n",
    "sns.distplot(data['Deprecatory_Records'])\n",
    "\n",
    "plt.subplot(6, 3, 8)\n",
    "sns.distplot(data['Interest_Charged'])\n",
    "\n",
    "plt.subplot(6, 3, 9)\n",
    "sns.distplot(data['Usage_Rate'])\n",
    "\n",
    "plt.subplot(6, 3, 10)\n",
    "sns.distplot(data['Inquiries'])\n",
    "\n",
    "plt.subplot(6, 3, 11)\n",
    "sns.distplot(data['Present_Balance'])\n",
    "\n",
    "plt.subplot(6, 3, 12)\n",
    "sns.distplot(data['Gross_Collection'])\n",
    "\n",
    "plt.subplot(6, 3, 13)\n",
    "sns.distplot(data['Sub_GGGrade'])\n",
    "\n",
    "plt.subplot(6, 3, 14)\n",
    "sns.distplot(data['Account_Open'])\n",
    "\n",
    "plt.subplot(6, 3, 15)\n",
    "sns.distplot(data['Total_Unpaid_CL'])\n",
    "\n",
    "plt.subplot(6, 3, 16)\n",
    "sns.distplot(data['Unpaid_Amount'])\n",
    "\n",
    "plt.subplot(6, 3, 17)\n",
    "sns.distplot(data['Due_Fee'])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# performing log transformations of variables\n",
    "\n",
    "data['Asst_Reg'] = np.log(data['Asst_Reg'])\n",
    "data['Yearly_Income'] = np.log(data['Yearly_Income'])\n",
    "data['Unpaid_2_years'] = np.log(data['Unpaid_2_years'])\n",
    "data['Already_Defaulted'] = np.log(data['Already_Defaulted'])\n",
    "data['Debt_to_Income'] = np.log(data['Debt_to_Income'])\n",
    "data['Lend_Amount'] = np.log(data['Lend_Amount'])\n",
    "data['Deprecatory_Records'] = np.log(data['Deprecatory_Records'])\n",
    "data['Interest_Charged'] = np.log(data['Interest_Charged'])\n",
    "data['Usage_Rate'] = np.log(data['Usage_Rate'])\n",
    "data['Inquiries'] = np.log(data['Inquiries'])\n",
    "data['Present_Balance'] = np.log(data['Present_Balance'])\n",
    "data['Gross_Collection'] = np.log(data['Gross_Collection'])\n",
    "data['Sub_GGGrade'] = np.log(data['Sub_GGGrade'])\n",
    "data['Account_Open'] = np.log(data['Account_Open'])\n",
    "data['Total_Unpaid_CL'] = np.log(data['Total_Unpaid_CL'])\n",
    "data['Unpaid_Amount'] = np.log(data['Unpaid_Amount'])\n",
    "data['Due_Fee'] = np.log(data['Due_Fee'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1440 with 17 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# check the distribution of disbursed amount\n",
    "# After log transformation\n",
    "\n",
    "plt.figure(figsize=(15, 20))\n",
    "\n",
    "plt.subplot(6, 3, 1)\n",
    "sns.distplot(data['Asst_Reg'])\n",
    "\n",
    "plt.subplot(6, 3, 2)\n",
    "sns.distplot(data['Yearly_Income'])\n",
    "\n",
    "plt.subplot(6, 3, 3)\n",
    "sns.distplot(data['Unpaid_2_years'])\n",
    "\n",
    "plt.subplot(6, 3, 4)\n",
    "sns.distplot(data['Already_Defaulted'])\n",
    "\n",
    "plt.subplot(6, 3, 5)\n",
    "sns.distplot(data['Debt_to_Income'])\n",
    "\n",
    "plt.subplot(6, 3, 6)\n",
    "sns.distplot(data['Lend_Amount'])\n",
    "\n",
    "plt.subplot(6, 3, 7)\n",
    "sns.distplot(data['Deprecatory_Records'])\n",
    "\n",
    "plt.subplot(6, 3, 8)\n",
    "sns.distplot(data['Interest_Charged'])\n",
    "\n",
    "plt.subplot(6, 3, 9)\n",
    "sns.distplot(data['Usage_Rate'])\n",
    "\n",
    "plt.subplot(6, 3, 10)\n",
    "sns.distplot(data['Inquiries'])\n",
    "\n",
    "plt.subplot(6, 3, 11)\n",
    "sns.distplot(data['Present_Balance'])\n",
    "\n",
    "plt.subplot(6, 3, 12)\n",
    "sns.distplot(data['Gross_Collection'])\n",
    "\n",
    "plt.subplot(6, 3, 13)\n",
    "sns.distplot(data['Sub_GGGrade'])\n",
    "\n",
    "plt.subplot(6, 3, 14)\n",
    "sns.distplot(data['Account_Open'])\n",
    "\n",
    "plt.subplot(6, 3, 15)\n",
    "sns.distplot(data['Total_Unpaid_CL'])\n",
    "\n",
    "plt.subplot(6, 3, 16)\n",
    "sns.distplot(data['Unpaid_Amount'])\n",
    "\n",
    "plt.subplot(6, 3, 17)\n",
    "sns.distplot(data['Due_Fee'])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "II     35505\n",
       "III    35334\n",
       "I      20300\n",
       "IV     19725\n",
       "V      10252\n",
       "VI      3155\n",
       "VII      729\n",
       "Name: GGGrade, dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['GGGrade'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['GGGrade'] = data['GGGrade'].replace({\"I\":1,\"II\":2,\"III\":3,\"IV\":4,\"V\":5,\"VI\":6,\"VII\":7})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    35505\n",
       "3    35334\n",
       "1    20300\n",
       "4    19725\n",
       "5    10252\n",
       "6     3155\n",
       "7      729\n",
       "Name: GGGrade, dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['GGGrade'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9yrs       5242\n",
       "6yrs       6112\n",
       "7yrs       6731\n",
       "8yrs       6748\n",
       "4yrs       7563\n",
       "5yrs       8035\n",
       "1yrs       8296\n",
       "<1yr      10311\n",
       "3yrs      10478\n",
       "2yrs      11476\n",
       ">10yrs    44008\n",
       "Name: Experience, dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Experience'].value_counts(ascending=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['Experience'] = data['Experience'].replace({\"<1yr\":0,\"1yrs\":1,\"2yrs\":2,\"3yrs\":3,\"4yrs\":4,\"5yrs\":5,\"6yrs\":6,\"7yrs\":7,\"8yrs\":8,\"9yrs\":9,\">10yrs\":10})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9      5242\n",
       "6      6112\n",
       "7      6731\n",
       "8      6748\n",
       "4      7563\n",
       "5      8035\n",
       "1      8296\n",
       "0     10311\n",
       "3     10478\n",
       "2     11476\n",
       "10    44008\n",
       "Name: Experience, dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Experience'].value_counts(ascending=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Not Vfied          37842\n",
       "Vfied              38008\n",
       "Source Verified    49150\n",
       "Name: Validation, dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Validation'].value_counts(ascending=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['Validation'] = data['Validation'].replace({\"Not Vfied\":1,\"Vfied\":2,\"Source Verified\":3})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    37842\n",
       "2    38008\n",
       "3    49150\n",
       "Name: Validation, dtype: int64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Validation'].value_counts(ascending=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NONE            6\n",
       "OTHER           8\n",
       "OWN         12008\n",
       "REN?        49906\n",
       "MOR??AGE    63072\n",
       "Name: Home_Status, dtype: int64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Home_Status'].value_counts(ascending=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['Home_Status'] = data['Home_Status'].replace({\"NONE\":1,\"OTHER\":2,\"OWN\":3,\"REN?\":2,\"MOR??AGE\":3})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1        6\n",
       "2    49914\n",
       "3    75080\n",
       "Name: Home_Status, dtype: int64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Home_Status'].value_counts(ascending=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Asst. store manager                   1\n",
       "Operation Mgnt                        1\n",
       "Automotive Equipment Technician       1\n",
       "Accoun Specialist                     1\n",
       "ALH Building Systems                  1\n",
       "                                   ... \n",
       "Owner                               891\n",
       "RN                                  926\n",
       "Nurse                              1081\n",
       "Super Lead                         1917\n",
       "School Teacher                     4251\n",
       "Name: Designation, Length: 54539, dtype: int64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Designation'].value_counts(ascending=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "54539"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Designation'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "labelencoder = LabelEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['Designation'] = labelencoder.fit_transform(data['Designation'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "fully paid    58746\n",
       "whole         66254\n",
       "Name: File_Status, dtype: int64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['File_Status'].value_counts(ascending=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['File_Status'] = data['File_Status'].replace({\"fully paid\":1,\"whole\":2})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    58746\n",
       "2    66254\n",
       "Name: File_Status, dtype: int64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['File_Status'].value_counts(ascending=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ND               70\n",
       "ME               79\n",
       "NE              209\n",
       "SD              255\n",
       "VT              258\n",
       "WY              280\n",
       "AK              323\n",
       "DC              339\n",
       "DE              348\n",
       "MT              374\n",
       "RI              549\n",
       "WV              574\n",
       "MS              580\n",
       "NH              629\n",
       "HI              659\n",
       "NM              688\n",
       "AR              918\n",
       "UT              921\n",
       "OK             1104\n",
       "KS             1160\n",
       "KY             1201\n",
       "SC             1424\n",
       "LA             1481\n",
       "OR             1500\n",
       "WI             1593\n",
       "AL             1647\n",
       "NV             1692\n",
       "CT             1928\n",
       "TN             1962\n",
       "MO             2030\n",
       "IN             2082\n",
       "MN             2342\n",
       "WA             2648\n",
       "CO             2651\n",
       "MA             2868\n",
       "AZ             2922\n",
       "Maryland       2941\n",
       "MI             3213\n",
       "NC             3587\n",
       "VA             3626\n",
       "GA             4092\n",
       "Ohio           4229\n",
       "PA             4504\n",
       "NewJersey      4642\n",
       "IL             5009\n",
       "Florida        8307\n",
       "TX            10141\n",
       "Newyork       10282\n",
       "California    18139\n",
       "Name: State, dtype: int64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['State'].value_counts(ascending=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "49"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['State'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['State'] = labelencoder.fit_transform(data['State'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "26       70\n",
       "18       79\n",
       "27      209\n",
       "39      255\n",
       "44      258\n",
       "48      280\n",
       "0       323\n",
       "7       339\n",
       "8       348\n",
       "23      374\n",
       "37      549\n",
       "47      574\n",
       "22      580\n",
       "28      629\n",
       "11      659\n",
       "29      688\n",
       "2       918\n",
       "42      921\n",
       "33     1104\n",
       "14     1160\n",
       "15     1201\n",
       "38     1424\n",
       "16     1481\n",
       "34     1500\n",
       "46     1593\n",
       "1      1647\n",
       "30     1692\n",
       "5      1928\n",
       "40     1962\n",
       "21     2030\n",
       "13     2082\n",
       "20     2342\n",
       "45     2648\n",
       "4      2651\n",
       "17     2868\n",
       "3      2922\n",
       "24     2941\n",
       "19     3213\n",
       "25     3587\n",
       "43     3626\n",
       "10     4092\n",
       "35     4229\n",
       "36     4504\n",
       "31     4642\n",
       "12     5009\n",
       "9      8307\n",
       "41    10141\n",
       "32    10282\n",
       "6     18139\n",
       "Name: State, dtype: int64"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['State'].value_counts(ascending=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5 years    39130\n",
       "3 years    85870\n",
       "Name: Duration, dtype: int64"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Duration'].value_counts(ascending=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['Duration'] = data['Duration'].replace({\"5 years\":1,\"3 years\":2})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "renewable  energy         58\n",
       "wedding                  151\n",
       "house                    447\n",
       "vacation                 593\n",
       "moving                   722\n",
       "car                     1011\n",
       "small  business         1055\n",
       "medical                 1155\n",
       "major  purchase         2119\n",
       "other                   5468\n",
       "home  improvement       7043\n",
       "credit  card           30156\n",
       "debt  consolidation    75022\n",
       "Name: Reason, dtype: int64"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Reason'].value_counts(ascending=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Reason'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['Reason'] = labelencoder.fit_transform(data['Reason'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9        58\n",
       "12      151\n",
       "4       447\n",
       "11      593\n",
       "7       722\n",
       "0      1011\n",
       "10     1055\n",
       "6      1155\n",
       "5      2119\n",
       "8      5468\n",
       "3      7043\n",
       "1     30156\n",
       "2     75022\n",
       "Name: Reason, dtype: int64"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Reason'].value_counts(ascending=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "J        66\n",
       "I    124934\n",
       "Name: Claim_Type, dtype: int64"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Claim_Type'].value_counts(ascending=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['Claim_Type'] = data['Claim_Type'].replace({\"J\":1,\"I\":2})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1        66\n",
       "2    124934\n",
       "Name: Claim_Type, dtype: int64"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Claim_Type'].value_counts(ascending=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 125000 entries, 0 to 37499\n",
      "Data columns (total 29 columns):\n",
      " #   Column               Non-Null Count   Dtype  \n",
      "---  ------               --------------   -----  \n",
      " 0   ID                   125000 non-null  int64  \n",
      " 1   Asst_Reg             125000 non-null  float64\n",
      " 2   GGGrade              125000 non-null  int64  \n",
      " 3   Experience           125000 non-null  int64  \n",
      " 4   Validation           125000 non-null  int64  \n",
      " 5   Yearly_Income        125000 non-null  float64\n",
      " 6   Home_Status          125000 non-null  int64  \n",
      " 7   Unpaid_2_years       125000 non-null  float64\n",
      " 8   Already_Defaulted    125000 non-null  float64\n",
      " 9   Designation          125000 non-null  int32  \n",
      " 10  Debt_to_Income       125000 non-null  float64\n",
      " 11  Postal_Code          125000 non-null  float64\n",
      " 12  Lend_Amount          125000 non-null  float64\n",
      " 13  Deprecatory_Records  125000 non-null  float64\n",
      " 14  Interest_Charged     125000 non-null  float64\n",
      " 15  Usage_Rate           125000 non-null  float64\n",
      " 16  Inquiries            125000 non-null  float64\n",
      " 17  Present_Balance      125000 non-null  float64\n",
      " 18  Gross_Collection     125000 non-null  float64\n",
      " 19  Sub_GGGrade          125000 non-null  float64\n",
      " 20  File_Status          125000 non-null  int64  \n",
      " 21  State                125000 non-null  int32  \n",
      " 22  Account_Open         125000 non-null  float64\n",
      " 23  Total_Unpaid_CL      125000 non-null  float64\n",
      " 24  Duration             125000 non-null  int64  \n",
      " 25  Unpaid_Amount        125000 non-null  float64\n",
      " 26  Reason               125000 non-null  int32  \n",
      " 27  Claim_Type           125000 non-null  int64  \n",
      " 28  Due_Fee              125000 non-null  float64\n",
      "dtypes: float64(18), int32(3), int64(8)\n",
      "memory usage: 27.2 MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Asst_Reg', 'GGGrade', 'Experience', 'Validation', 'Yearly_Income',\n",
       "       'Home_Status', 'Unpaid_2_years', 'Already_Defaulted', 'Designation',\n",
       "       'Debt_to_Income', 'Lend_Amount', 'Deprecatory_Records',\n",
       "       'Interest_Charged', 'Usage_Rate', 'Inquiries', 'Present_Balance',\n",
       "       'Gross_Collection', 'Sub_GGGrade', 'File_Status', 'State',\n",
       "       'Account_Open', 'Total_Unpaid_CL', 'Duration', 'Unpaid_Amount',\n",
       "       'Reason', 'Claim_Type', 'Due_Fee'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# removing unnecassary columns\n",
    "\n",
    "data = data.drop(['ID', 'Postal_Code'], axis = 1)\n",
    "\n",
    "# checking the new columns of data\n",
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
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       "    .dataframe thead th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Asst_Reg</th>\n",
       "      <th>GGGrade</th>\n",
       "      <th>Experience</th>\n",
       "      <th>Validation</th>\n",
       "      <th>Yearly_Income</th>\n",
       "      <th>Home_Status</th>\n",
       "      <th>Unpaid_2_years</th>\n",
       "      <th>Already_Defaulted</th>\n",
       "      <th>Designation</th>\n",
       "      <th>Debt_to_Income</th>\n",
       "      <th>...</th>\n",
       "      <th>Sub_GGGrade</th>\n",
       "      <th>File_Status</th>\n",
       "      <th>State</th>\n",
       "      <th>Account_Open</th>\n",
       "      <th>Total_Unpaid_CL</th>\n",
       "      <th>Duration</th>\n",
       "      <th>Unpaid_Amount</th>\n",
       "      <th>Reason</th>\n",
       "      <th>Claim_Type</th>\n",
       "      <th>Due_Fee</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>56438</th>\n",
       "      <td>15.283503</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>7647</td>\n",
       "      <td>3.141666</td>\n",
       "      <td>...</td>\n",
       "      <td>2.564949</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>2.944439</td>\n",
       "      <td>11.893052</td>\n",
       "      <td>2</td>\n",
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       "      <td>2</td>\n",
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       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
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       "      <td>15.026837</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
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       "      <td>11.692585</td>\n",
       "      <td>3</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37681</td>\n",
       "      <td>3.438467</td>\n",
       "      <td>...</td>\n",
       "      <td>3.044522</td>\n",
       "      <td>2</td>\n",
       "      <td>39</td>\n",
       "      <td>2.708050</td>\n",
       "      <td>11.316558</td>\n",
       "      <td>1</td>\n",
       "      <td>10.731403</td>\n",
       "      <td>2</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>39001</th>\n",
       "      <td>15.694979</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>11.903893</td>\n",
       "      <td>3</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15236</td>\n",
       "      <td>3.057655</td>\n",
       "      <td>...</td>\n",
       "      <td>2.833213</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>2.564949</td>\n",
       "      <td>10.955550</td>\n",
       "      <td>2</td>\n",
       "      <td>10.329361</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6047</th>\n",
       "      <td>13.745066</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>11.547221</td>\n",
       "      <td>3</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9007</td>\n",
       "      <td>3.081066</td>\n",
       "      <td>...</td>\n",
       "      <td>3.555348</td>\n",
       "      <td>1</td>\n",
       "      <td>25</td>\n",
       "      <td>2.079442</td>\n",
       "      <td>10.563052</td>\n",
       "      <td>1</td>\n",
       "      <td>10.591611</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3.629395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42652</th>\n",
       "      <td>15.721350</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>12.078245</td>\n",
       "      <td>3</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>48284</td>\n",
       "      <td>2.905282</td>\n",
       "      <td>...</td>\n",
       "      <td>2.833213</td>\n",
       "      <td>2</td>\n",
       "      <td>30</td>\n",
       "      <td>2.484907</td>\n",
       "      <td>9.994835</td>\n",
       "      <td>2</td>\n",
       "      <td>9.341806</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Asst_Reg  GGGrade  Experience  Validation  Yearly_Income  Home_Status  \\\n",
       "56438  15.283503        1           2           3      11.647465            3   \n",
       "16585  15.026837        2          10           2      11.692585            3   \n",
       "39001  15.694979        2          10           3      11.903893            3   \n",
       "6047   13.745066        5           0           3      11.547221            3   \n",
       "42652  15.721350        2           5           1      12.078245            3   \n",
       "\n",
       "       Unpaid_2_years  Already_Defaulted  Designation  Debt_to_Income  ...  \\\n",
       "56438        0.000000                0.0         7647        3.141666  ...   \n",
       "16585        0.693147                0.0        37681        3.438467  ...   \n",
       "39001        0.693147                0.0        15236        3.057655  ...   \n",
       "6047         0.000000                0.0         9007        3.081066  ...   \n",
       "42652        0.000000                0.0        48284        2.905282  ...   \n",
       "\n",
       "       Sub_GGGrade  File_Status  State  Account_Open  Total_Unpaid_CL  \\\n",
       "56438     2.564949            2      9      2.944439        11.893052   \n",
       "16585     3.044522            2     39      2.708050        11.316558   \n",
       "39001     2.833213            1      6      2.564949        10.955550   \n",
       "6047      3.555348            1     25      2.079442        10.563052   \n",
       "42652     2.833213            2     30      2.484907         9.994835   \n",
       "\n",
       "       Duration  Unpaid_Amount  Reason  Claim_Type   Due_Fee  \n",
       "56438         2      10.604078       2           2  0.000000  \n",
       "16585         1      10.731403       2           2  0.000000  \n",
       "39001         2      10.329361       2           2  0.000000  \n",
       "6047          1      10.591611       2           2  3.629395  \n",
       "42652         2       9.341806       2           2  0.000000  \n",
       "\n",
       "[5 rows x 27 columns]"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# looking at the sample of the pre-processed data\n",
    "\n",
    "data.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    70988\n",
       "1    16512\n",
       "Name: Loan/No_Loan, dtype: int64"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking the target variable\n",
    "\n",
    "y_train_t.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of train : (87500, 27)\n",
      "Shape of test : (37500, 27)\n"
     ]
    }
   ],
   "source": [
    "# separating train and test datasets from data\n",
    "\n",
    "train_new = data.iloc[:87500,:]\n",
    "test_new = data.iloc[87500:,:]\n",
    "\n",
    "# checking the shape of train and test\n",
    "print(\"Shape of train :\", train_new.shape)\n",
    "print(\"Shape of test :\", test_new.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of x: (141976, 27)\n",
      "Shape of y: (141976,)\n"
     ]
    }
   ],
   "source": [
    "# applying SMOTE\n",
    "\n",
    "x_resample, y_resample = SMOTE().fit_resample(train_new, y_train_t.values.ravel()) \n",
    "\n",
    "# checking the shape of x_resample and y_resample\n",
    "print(\"Shape of x:\", x_resample.shape)\n",
    "print(\"Shape of y:\", y_resample.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(99383, 27)\n",
      "(99383,)\n",
      "(42593, 27)\n",
      "(42593,)\n"
     ]
    }
   ],
   "source": [
    "# train and valid sets from train\n",
    "\n",
    "x_train, x_valid, y_train, y_valid = train_test_split(x_resample, y_resample, test_size = 0.3, random_state = 0)\n",
    "\n",
    "# checking the shapes\n",
    "print(x_train.shape)\n",
    "print(y_train.shape)\n",
    "print(x_valid.shape)\n",
    "print(y_valid.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "# applying standardization\n",
    "\n",
    "sc = StandardScaler()\n",
    "x_train = sc.fit_transform(x_train)\n",
    "x_valid = sc.transform(x_valid)\n",
    "x_test = sc.transform(test_new)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Accuracy:  0.9999798758338951\n",
      "Testing Accuarcy:  0.8800741905946987\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.86      0.91      0.88     21138\n",
      "           1       0.90      0.85      0.88     21455\n",
      "\n",
      "    accuracy                           0.88     42593\n",
      "   macro avg       0.88      0.88      0.88     42593\n",
      "weighted avg       0.88      0.88      0.88     42593\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# RANDOM FOREST CLASSIFIER\n",
    "\n",
    "model_rf = RandomForestClassifier(random_state=123)\n",
    "model_rf.fit(x_train, y_train)\n",
    "\n",
    "y_pred = model_rf.predict(x_valid)\n",
    "\n",
    "print(\"Training Accuracy: \", model_rf.score(x_train, y_train))\n",
    "print('Testing Accuarcy: ', model_rf.score(x_valid, y_valid))\n",
    "\n",
    "# making a classification report\n",
    "cr = classification_report(y_valid,  y_pred)\n",
    "print(cr)\n",
    "\n",
    "# making a confusion matrix\n",
    "cm = confusion_matrix(y_valid, y_pred)\n",
    "sns.heatmap(cm, annot = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "# RANDOM FOREST CLASSIFIER using Grid Search\n",
    "\n",
    "grid_array_rfc = {'max_depth': [1,2,3,4,5],\n",
    "             'max_features': [1,2,3,4,5],\n",
    "             'min_samples_leaf': [10,15,20],\n",
    "             'min_samples_split': [90,100,110],\n",
    "             'n_estimators': [50,100,150]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "grid_search_rfc = GridSearchCV(estimator=model_rf,param_grid=grid_array_rfc,scoring='f1',cv = 5,n_jobs=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-71-4e3c8a8f5dcb>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mgrid_search_rfc\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx_train\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0my_train\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\model_selection\\_search.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, X, y, groups, **fit_params)\u001b[0m\n\u001b[0;32m    889\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mresults\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    890\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 891\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_run_search\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mevaluate_candidates\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    892\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    893\u001b[0m             \u001b[1;31m# multimetric is determined here because in the case of a callable\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\model_selection\\_search.py\u001b[0m in \u001b[0;36m_run_search\u001b[1;34m(self, evaluate_candidates)\u001b[0m\n\u001b[0;32m   1390\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_run_search\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mevaluate_candidates\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1391\u001b[0m         \u001b[1;34m\"\"\"Search all candidates in param_grid\"\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1392\u001b[1;33m         \u001b[0mevaluate_candidates\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mParameterGrid\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mparam_grid\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1393\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1394\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\model_selection\\_search.py\u001b[0m in \u001b[0;36mevaluate_candidates\u001b[1;34m(candidate_params, cv, more_results)\u001b[0m\n\u001b[0;32m    836\u001b[0m                     )\n\u001b[0;32m    837\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 838\u001b[1;33m                 out = parallel(\n\u001b[0m\u001b[0;32m    839\u001b[0m                     delayed(_fit_and_score)(\n\u001b[0;32m    840\u001b[0m                         \u001b[0mclone\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbase_estimator\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\joblib\\parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, iterable)\u001b[0m\n\u001b[0;32m   1059\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1060\u001b[0m             \u001b[1;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mretrieval_context\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1061\u001b[1;33m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mretrieve\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1062\u001b[0m             \u001b[1;31m# Make sure that we get a last message telling us we are done\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1063\u001b[0m             \u001b[0melapsed_time\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m-\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_start_time\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\joblib\\parallel.py\u001b[0m in \u001b[0;36mretrieve\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    938\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    939\u001b[0m                 \u001b[1;32mif\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'supports_timeout'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 940\u001b[1;33m                     \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_output\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mextend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mjob\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    941\u001b[0m                 \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    942\u001b[0m                     \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_output\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mextend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mjob\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\joblib\\_parallel_backends.py\u001b[0m in \u001b[0;36mwrap_future_result\u001b[1;34m(future, timeout)\u001b[0m\n\u001b[0;32m    540\u001b[0m         AsyncResults.get from multiprocessing.\"\"\"\n\u001b[0;32m    541\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 542\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mfuture\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mresult\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    543\u001b[0m         \u001b[1;32mexcept\u001b[0m \u001b[0mCfTimeoutError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    544\u001b[0m             \u001b[1;32mraise\u001b[0m \u001b[0mTimeoutError\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\concurrent\\futures\\_base.py\u001b[0m in \u001b[0;36mresult\u001b[1;34m(self, timeout)\u001b[0m\n\u001b[0;32m    432\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__get_result\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    433\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 434\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_condition\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwait\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    435\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    436\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_state\u001b[0m \u001b[1;32min\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mCANCELLED\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mCANCELLED_AND_NOTIFIED\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\threading.py\u001b[0m in \u001b[0;36mwait\u001b[1;34m(self, timeout)\u001b[0m\n\u001b[0;32m    300\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m    \u001b[1;31m# restore state no matter what (e.g., KeyboardInterrupt)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    301\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mtimeout\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 302\u001b[1;33m                 \u001b[0mwaiter\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0macquire\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    303\u001b[0m                 \u001b[0mgotit\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    304\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "grid_search_rfc.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "grid_search_rfc.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "rfc_gs = grid_search_rfc.best_estimator_\n",
    "rfc_gs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = rf_gs.predict(x_valid)\n",
    "\n",
    "print(\"Training Accuracy: \", rf_gs.score(x_train, y_train))\n",
    "print('Testing Accuarcy: ', rf_gs.score(x_valid, y_valid))\n",
    "\n",
    "# making a classification report\n",
    "cr = classification_report(y_valid,  y_pred)\n",
    "print(cr)\n",
    "\n",
    "# making a confusion matrix\n",
    "cm = confusion_matrix(y_valid, y_pred)\n",
    "sns.heatmap(cm, annot = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Accuracy:  0.8486260225591902\n",
      "Testing Accuarcy:  0.8503509966426408\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.85      0.84      0.85     21138\n",
      "           1       0.85      0.86      0.85     21455\n",
      "\n",
      "    accuracy                           0.85     42593\n",
      "   macro avg       0.85      0.85      0.85     42593\n",
      "weighted avg       0.85      0.85      0.85     42593\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ADA BOOST CLASSIFIER\n",
    "\n",
    "model_ada = AdaBoostClassifier(random_state=123)\n",
    "model_ada.fit(x_train, y_train)\n",
    "\n",
    "y_pred = model_ada.predict(x_valid)\n",
    "\n",
    "print(\"Training Accuracy: \", model_ada.score(x_train, y_train))\n",
    "print('Testing Accuarcy: ', model_ada.score(x_valid, y_valid))\n",
    "\n",
    "# making a classification report\n",
    "cr = classification_report(y_valid,  y_pred)\n",
    "print(cr)\n",
    "\n",
    "# making a confusion matrix\n",
    "cm = confusion_matrix(y_valid, y_pred)\n",
    "sns.heatmap(cm, annot = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Accuracy:  0.9203787368060936\n",
      "Testing Accuarcy:  0.8964853379663325\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.86      0.95      0.90     21138\n",
      "           1       0.94      0.85      0.89     21455\n",
      "\n",
      "    accuracy                           0.90     42593\n",
      "   macro avg       0.90      0.90      0.90     42593\n",
      "weighted avg       0.90      0.90      0.90     42593\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Xg-Boost Classifier\n",
    "\n",
    "from xgboost.sklearn import XGBClassifier\n",
    "\n",
    "\n",
    "model_xgb = XGBClassifier(random_state=123)\n",
    "model_xgb.fit(x_train, y_train)\n",
    "\n",
    "y_pred = model_xgb.predict(x_valid)\n",
    "\n",
    "print(\"Training Accuracy: \", model_xgb.score(x_train, y_train))\n",
    "print('Testing Accuarcy: ', model_xgb.score(x_valid, y_valid))\n",
    "\n",
    "# making a classification report\n",
    "cr = classification_report(y_valid,  y_pred)\n",
    "print(cr)\n",
    "\n",
    "# making a confusion matrix\n",
    "cm = confusion_matrix(y_valid, y_pred)\n",
    "sns.heatmap(cm, annot = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Accuracy:  0.8652888320940201\n",
      "Testing Accuarcy:  0.8681003920832061\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.86      0.88      0.87     21138\n",
      "           1       0.88      0.85      0.87     21455\n",
      "\n",
      "    accuracy                           0.87     42593\n",
      "   macro avg       0.87      0.87      0.87     42593\n",
      "weighted avg       0.87      0.87      0.87     42593\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Gradient Boost Classifier\n",
    "\n",
    "model_gbc = GradientBoostingClassifier(random_state=123)\n",
    "model_gbc.fit(x_train, y_train)\n",
    "\n",
    "y_pred = model_gbc.predict(x_valid)\n",
    "\n",
    "print(\"Training Accuracy: \", model_gbc.score(x_train, y_train))\n",
    "print('Testing Accuarcy: ', model_gbc.score(x_valid, y_valid))\n",
    "\n",
    "# making a classification report\n",
    "cr = classification_report(y_valid,  y_pred)\n",
    "print(cr)\n",
    "\n",
    "# making a confusion matrix\n",
    "cm = confusion_matrix(y_valid, y_pred)\n",
    "sns.heatmap(cm, annot = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sel = RFE(GradientBoostingClassifier(n_estimators=100,random_state=123, n_jobs=-1),n_features_to_select=15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sel.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train_rfe = sel.transform(x_train)\n",
    "x_valid_rfe = sel.transform(x_valid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Gradient Boost Classifier using Feature Selection RFE\n",
    "\n",
    "model_gbc_rfe = GradientBoostingClassifier(random_state=123)\n",
    "model_gbc_rfe.fit(x_train_rfe, y_train)\n",
    "\n",
    "y_pred = model_gbc.predict(x_valid_rfe)\n",
    "\n",
    "print(\"Training Accuracy: \", model_gbc_rfe.score(x_train_rfe, y_train))\n",
    "print('Testing Accuarcy: ', model_gbc_rfe.score(x_valid_rfe, y_valid))\n",
    "\n",
    "# making a classification report\n",
    "cr = classification_report(y_valid,  y_pred)\n",
    "print(cr)\n",
    "\n",
    "# making a confusion matrix\n",
    "cm = confusion_matrix(y_valid, y_pred)\n",
    "sns.heatmap(cm, annot = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Gradient Boosting Classifier using Grid Search\n",
    "\n",
    "grid_array_gbc = {\"n_estimators\":[5,50,250,500],\n",
    "                  \"max_depth\":[1,3,5,7,9],\n",
    "                  \"learning_rate\":[0.01,0.1,1,10,100]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "grid_search_gbc = GridSearchCV(model_gbc, grid_array_gbc, scoring='f1', cv = 5, n_jobs=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "grid_search_gbc.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "grid_search_gbc.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "gbc_gs = grid_search_gbc.best_estimator_\n",
    "gbc_gs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = gbc_gs.predict(x_valid)\n",
    "\n",
    "print(\"Training Accuracy: \", gbc_gs.score(x_train, y_train))\n",
    "print('Testing Accuarcy: ', gbc_gs.score(x_valid, y_valid))\n",
    "\n",
    "# making a classification report\n",
    "cr = classification_report(y_valid,  y_pred)\n",
    "print(cr)\n",
    "\n",
    "# making a confusion matrix\n",
    "cm = confusion_matrix(y_valid, y_pred)\n",
    "sns.heatmap(cm, annot = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Accuracy:  0.8098065061429017\n",
      "Testing Accuarcy:  0.8123400558777264\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.81      0.82      0.81     21138\n",
      "           1       0.82      0.81      0.81     21455\n",
      "\n",
      "    accuracy                           0.81     42593\n",
      "   macro avg       0.81      0.81      0.81     42593\n",
      "weighted avg       0.81      0.81      0.81     42593\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Logistic Regression Classifier\n",
    "\n",
    "\n",
    "model_lr = LogisticRegression(random_state=123)\n",
    "model_lr.fit(x_train, y_train)\n",
    "\n",
    "y_pred = model_lr.predict(x_valid)\n",
    "\n",
    "print(\"Training Accuracy: \", model_lr.score(x_train, y_train))\n",
    "print('Testing Accuarcy: ', model_lr.score(x_valid, y_valid))\n",
    "\n",
    "# making a classification report\n",
    "cr = classification_report(y_valid,  y_pred)\n",
    "print(cr)\n",
    "\n",
    "# making a confusion matrix\n",
    "cm = confusion_matrix(y_valid, y_pred)\n",
    "sns.heatmap(cm, annot = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Logistic Regression using Grid Search\n",
    "\n",
    "grid_array_lr = {\"C\":np.logspace(-3,3,7), \"penalty\":[\"l1\",\"l2\"]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "grid_search_lr = GridSearchCV(model_lr, grid_array_lr, scoring='f1', cv = 5, n_jobs=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "grid_search_lr.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "grid_search_lr.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "lr_gs = grid_search_lr.best_estimator_\n",
    "lr_gs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = lr_gs.predict(x_valid)\n",
    "\n",
    "print(\"Training Accuracy: \", lr_gs.score(x_train, y_train))\n",
    "print('Testing Accuarcy: ', lr_gs.score(x_valid, y_valid))\n",
    "\n",
    "# making a classification report\n",
    "cr = classification_report(y_valid,  y_pred)\n",
    "print(cr)\n",
    "\n",
    "# making a confusion matrix\n",
    "cm = confusion_matrix(y_valid, y_pred)\n",
    "sns.heatmap(cm, annot = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Accuracy:  0.8804322670879325\n",
      "Testing Accuarcy:  0.8206747587631771\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.87      0.75      0.81     21138\n",
      "           1       0.78      0.89      0.83     21455\n",
      "\n",
      "    accuracy                           0.82     42593\n",
      "   macro avg       0.83      0.82      0.82     42593\n",
      "weighted avg       0.83      0.82      0.82     42593\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# KNeighbors Classifier \n",
    "\n",
    "\n",
    "model_knn = KNeighborsClassifier(random_state=123)\n",
    "model_knn.fit(x_train, y_train)\n",
    "\n",
    "y_pred = model_knn.predict(x_valid)\n",
    "\n",
    "print(\"Training Accuracy: \", model_knn.score(x_train, y_train))\n",
    "print('Testing Accuarcy: ', model_knn.score(x_valid, y_valid))\n",
    "\n",
    "# making a classification report\n",
    "cr = classification_report(y_valid,  y_pred)\n",
    "print(cr)\n",
    "\n",
    "# making a confusion matrix\n",
    "cm = confusion_matrix(y_valid, y_pred)\n",
    "sns.heatmap(cm, annot = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Support Vector Classifier\n",
    "\n",
    "model_svc = SVC(kernel=\"linear\")\n",
    "model_svc.fit(x_train, y_train)\n",
    "\n",
    "y_pred = model_svc.predict(x_valid)\n",
    "\n",
    "print(\"Training Accuracy: \", model_svc.score(x_train, y_train))\n",
    "print('Testing Accuarcy: ', model_svc.score(x_valid, y_valid))\n",
    "\n",
    "# making a classification report\n",
    "cr = classification_report(y_valid,  y_pred)\n",
    "print(cr)\n",
    "\n",
    "# making a confusion matrix\n",
    "cm = confusion_matrix(y_valid, y_pred)\n",
    "sns.heatmap(cm, annot = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "# let's predict for the tests set\n",
    "\n",
    "y_pred_rf = model_rf.predict(x_test)\n",
    "y_pred_ada = model_ada.predict(x_test)\n",
    "y_pred_xgb = model_xgb.predict(x_test)\n",
    "y_pred_gbc = model_gbc.predict(x_test)\n",
    "y_pred_lr = model_lr.predict(x_test)\n",
    "y_pred_knn = model_knn.predict(x_test)\n",
    "y_pred_svc = model_svc.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "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>ID</th>\n",
       "      <th>Loan/No Loan</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>198980</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>167688</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>198768</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>192564</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>151199</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       ID  Loan/No Loan\n",
       "0  198980             0\n",
       "1  167688             0\n",
       "2  198768             1\n",
       "3  192564             1\n",
       "4  151199             1"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  let's create a submission file\n",
    "\n",
    "#Create a  DataFrame with the passengers ids and our prediction regarding whether they survived or not\n",
    "submission = pd.DataFrame({'ID': unique_id,'Loan/No Loan': y_pred_lr})\n",
    "\n",
    "#Visualize the first 5 rows\n",
    "submission.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved file: submission1.csv\n"
     ]
    }
   ],
   "source": [
    "#Convert DataFrame to a csv file that can be uploaded\n",
    "\n",
    "piyush = 'submission1.csv'\n",
    "\n",
    "submission.to_csv(piyush,index=False)\n",
    "\n",
    "print('Saved file: ' + piyush)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
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 },
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