{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "_F_-mF6xYvNl"
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "wmQtdBcrYvNw"
   },
   "outputs": [],
   "source": [
    "train = pd.read_csv('train_ltfs_finhack.csv')\n",
    "test = pd.read_csv('test_ltfs_finhack.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Z06UPxNKYvN4"
   },
   "outputs": [],
   "source": [
    "train_og = train.copy()\n",
    "test_og = test.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "hT8jlr_EhspD",
    "outputId": "c3881d57-2e34-43df-a8bf-ae2029343fb8"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['UniqueID', 'disbursed_amount', 'asset_cost', 'ltv', 'branch_id',\n",
       "       'supplier_id', 'manufacturer_id', 'Current_pincode_ID', 'Date.of.Birth',\n",
       "       'Employment.Type', 'DisbursalDate', 'State_ID', 'Employee_code_ID',\n",
       "       'MobileNo_Avl_Flag', 'Aadhar_flag', 'PAN_flag', 'VoterID_flag',\n",
       "       'Driving_flag', 'Passport_flag', 'PERFORM_CNS.SCORE',\n",
       "       'PERFORM_CNS.SCORE.DESCRIPTION', 'PRI.NO.OF.ACCTS', 'PRI.ACTIVE.ACCTS',\n",
       "       'PRI.OVERDUE.ACCTS', 'PRI.CURRENT.BALANCE', 'PRI.SANCTIONED.AMOUNT',\n",
       "       'PRI.DISBURSED.AMOUNT', 'SEC.NO.OF.ACCTS', 'SEC.ACTIVE.ACCTS',\n",
       "       'SEC.OVERDUE.ACCTS', 'SEC.CURRENT.BALANCE', 'SEC.SANCTIONED.AMOUNT',\n",
       "       'SEC.DISBURSED.AMOUNT', 'PRIMARY.INSTAL.AMT', 'SEC.INSTAL.AMT',\n",
       "       'NEW.ACCTS.IN.LAST.SIX.MONTHS', 'DELINQUENT.ACCTS.IN.LAST.SIX.MONTHS',\n",
       "       'AVERAGE.ACCT.AGE', 'CREDIT.HISTORY.LENGTH', 'NO.OF_INQUIRIES',\n",
       "       'loan_default'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to calculate missing values by column# Funct \n",
    "def missing_values_table(df):\n",
    "        # Total missing values\n",
    "        mis_val = df.isnull().sum()\n",
    "        \n",
    "        # Percentage of missing values\n",
    "        mis_val_percent = 100 * df.isnull().sum() / len(df)\n",
    "        \n",
    "        # Make a table with the results\n",
    "        mis_val_table = pd.concat([mis_val, mis_val_percent], axis=1)\n",
    "        \n",
    "        # Rename the columns\n",
    "        mis_val_table_ren_columns = mis_val_table.rename(\n",
    "        columns = {0 : 'Missing Values', 1 : '% of Total Values'})\n",
    "        \n",
    "        # Sort the table by percentage of missing descending\n",
    "        mis_val_table_ren_columns = mis_val_table_ren_columns[\n",
    "            mis_val_table_ren_columns.iloc[:,1] != 0].sort_values(\n",
    "        '% of Total Values', ascending=False).round(1)\n",
    "        \n",
    "        # Print some summary information\n",
    "        print (\"Your selected dataframe has \" + str(df.shape[1]) + \" columns.\\n\"      \n",
    "            \"There are \" + str(mis_val_table_ren_columns.shape[0]) +\n",
    "              \" columns that have missing values.\")\n",
    "        \n",
    "        # Return the dataframe with missing information\n",
    "        return mis_val_table_ren_columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Your selected dataframe has 41 columns.\n",
      "There are 1 columns that have missing values.\n"
     ]
    },
    {
     "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>Missing Values</th>\n",
       "      <th>% of Total Values</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Employment.Type</th>\n",
       "      <td>7661</td>\n",
       "      <td>3.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Missing Values  % of Total Values\n",
       "Employment.Type            7661                3.3"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Missing values statistics\n",
    "missing_values = missing_values_table(train)\n",
    "missing_values.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "m-xYaVA4YvOA"
   },
   "outputs": [],
   "source": [
    "train['source'] = 'train'\n",
    "test['source'] = 'test'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Ce40iyzhYvOH"
   },
   "outputs": [],
   "source": [
    "alldata = pd.concat([train, test], axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x14c9684ec18>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(alldata.disbursed_amount)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x14c96b805f8>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(alldata.ltv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x14c9690beb8>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(alldata.asset_cost)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Yw7pxO26YvOP"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Self employed    198533\n",
       "Salaried         147013\n",
       "Name: Employment.Type, dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alldata['Employment.Type'].fillna(alldata['Employment.Type'].mode()[0],inplace=True)\n",
    "alldata['Employment.Type'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 119
    },
    "colab_type": "code",
    "id": "L1Z0M8evYvOX",
    "outputId": "7a5bee5f-1525-44f7-a290-9a34b9008708"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    34\n",
       "1    33\n",
       "2    33\n",
       "3    25\n",
       "4    41\n",
       "Name: Age, dtype: int64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alldata['Age'] = alldata['Date.of.Birth'].apply(lambda x: 118 - int(x[-2:]))\n",
    "alldata['Age'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.style.use('fivethirtyeight')\n",
    "\n",
    "# Plot the distribution of ages in years\n",
    "plt.hist(alldata['Age'] , edgecolor = 'k', bins = 25)\n",
    "plt.title('Age of Client'); plt.xlabel('Age (years)'); plt.ylabel('Count');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (10, 8))\n",
    "\n",
    "# KDE plot of loans that were repaid on time\n",
    "sns.kdeplot(alldata.loc[alldata['loan_default'] == 0, 'Age'] , label = 'target == 0')\n",
    "\n",
    "# KDE plot of loans which were not repaid on time\n",
    "sns.kdeplot(alldata.loc[alldata['loan_default'] == 1, 'Age'] , label = 'target == 1')\n",
    "\n",
    "# Labeling of plot\n",
    "plt.xlabel('Age (years)'); plt.ylabel('Density'); plt.title('Distribution of Ages');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>loan_default</th>\n",
       "      <th>Age</th>\n",
       "      <th>YEARS_BINNED</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>34</td>\n",
       "      <td>(30.0, 35.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>33</td>\n",
       "      <td>(30.0, 35.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>33</td>\n",
       "      <td>(30.0, 35.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>25</td>\n",
       "      <td>(20.0, 25.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>41</td>\n",
       "      <td>(40.0, 45.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.0</td>\n",
       "      <td>28</td>\n",
       "      <td>(25.0, 30.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.0</td>\n",
       "      <td>30</td>\n",
       "      <td>(25.0, 30.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.0</td>\n",
       "      <td>29</td>\n",
       "      <td>(25.0, 30.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.0</td>\n",
       "      <td>27</td>\n",
       "      <td>(25.0, 30.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.0</td>\n",
       "      <td>50</td>\n",
       "      <td>(45.0, 50.0]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   loan_default  Age  YEARS_BINNED\n",
       "0           0.0   34  (30.0, 35.0]\n",
       "1           1.0   33  (30.0, 35.0]\n",
       "2           0.0   33  (30.0, 35.0]\n",
       "3           1.0   25  (20.0, 25.0]\n",
       "4           1.0   41  (40.0, 45.0]\n",
       "5           0.0   28  (25.0, 30.0]\n",
       "6           0.0   30  (25.0, 30.0]\n",
       "7           0.0   29  (25.0, 30.0]\n",
       "8           0.0   27  (25.0, 30.0]\n",
       "9           0.0   50  (45.0, 50.0]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Age information into a separate dataframe\n",
    "age_data = alldata[['loan_default', 'Age']]\n",
    "age_data['Age'] = age_data['Age']\n",
    "\n",
    "# Bin the age data\n",
    "age_data['YEARS_BINNED'] = pd.cut(age_data['Age'], bins = np.linspace(20, 70, num = 11))\n",
    "age_data.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>loan_default</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YEARS_BINNED</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>(20.0, 25.0]</th>\n",
       "      <td>0.239303</td>\n",
       "      <td>23.098352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(25.0, 30.0]</th>\n",
       "      <td>0.223462</td>\n",
       "      <td>27.945300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(30.0, 35.0]</th>\n",
       "      <td>0.214453</td>\n",
       "      <td>32.912339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(35.0, 40.0]</th>\n",
       "      <td>0.206950</td>\n",
       "      <td>37.913832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(40.0, 45.0]</th>\n",
       "      <td>0.201858</td>\n",
       "      <td>42.900977</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(45.0, 50.0]</th>\n",
       "      <td>0.202634</td>\n",
       "      <td>47.821586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(50.0, 55.0]</th>\n",
       "      <td>0.195987</td>\n",
       "      <td>52.773412</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(55.0, 60.0]</th>\n",
       "      <td>0.186339</td>\n",
       "      <td>57.605324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(60.0, 65.0]</th>\n",
       "      <td>0.145900</td>\n",
       "      <td>61.785561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(65.0, 70.0]</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>69.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              loan_default        Age\n",
       "YEARS_BINNED                         \n",
       "(20.0, 25.0]      0.239303  23.098352\n",
       "(25.0, 30.0]      0.223462  27.945300\n",
       "(30.0, 35.0]      0.214453  32.912339\n",
       "(35.0, 40.0]      0.206950  37.913832\n",
       "(40.0, 45.0]      0.201858  42.900977\n",
       "(45.0, 50.0]      0.202634  47.821586\n",
       "(50.0, 55.0]      0.195987  52.773412\n",
       "(55.0, 60.0]      0.186339  57.605324\n",
       "(60.0, 65.0]      0.145900  61.785561\n",
       "(65.0, 70.0]      0.000000  69.000000"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Group by the bin and calculate averages\n",
    "age_groups  = age_data.groupby('YEARS_BINNED').mean()\n",
    "age_groups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (8, 8))\n",
    "\n",
    "# Graph the age bins and the average of the target as a bar plot\n",
    "plt.bar(age_groups.index.astype(str), 100 * age_groups['loan_default'])\n",
    "\n",
    "# Plot labeling\n",
    "plt.xticks(rotation = 75); plt.xlabel('Age Group (years)'); plt.ylabel('Failure to Repay (%)')\n",
    "plt.title('Failure to Repay by Age Group');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    345546.000000\n",
       "mean         34.454324\n",
       "std          10.765448\n",
       "min          19.000000\n",
       "25%          26.000000\n",
       "50%          32.000000\n",
       "75%          41.000000\n",
       "max         118.000000\n",
       "Name: Age, dtype: float64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Statistical Info for Age\n",
    "#alldata['Age'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "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>AVERAGE.ACCT.AGE</th>\n",
       "      <th>Aadhar_flag</th>\n",
       "      <th>CREDIT.HISTORY.LENGTH</th>\n",
       "      <th>Current_pincode_ID</th>\n",
       "      <th>DELINQUENT.ACCTS.IN.LAST.SIX.MONTHS</th>\n",
       "      <th>Date.of.Birth</th>\n",
       "      <th>DisbursalDate</th>\n",
       "      <th>Driving_flag</th>\n",
       "      <th>Employee_code_ID</th>\n",
       "      <th>Employment.Type</th>\n",
       "      <th>...</th>\n",
       "      <th>VoterID_flag</th>\n",
       "      <th>asset_cost</th>\n",
       "      <th>branch_id</th>\n",
       "      <th>disbursed_amount</th>\n",
       "      <th>loan_default</th>\n",
       "      <th>ltv</th>\n",
       "      <th>manufacturer_id</th>\n",
       "      <th>source</th>\n",
       "      <th>supplier_id</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>874</th>\n",
       "      <td>0yrs 0mon</td>\n",
       "      <td>1</td>\n",
       "      <td>0yrs 0mon</td>\n",
       "      <td>5940</td>\n",
       "      <td>0</td>\n",
       "      <td>16-07-00</td>\n",
       "      <td>08-10-18</td>\n",
       "      <td>0</td>\n",
       "      <td>1464</td>\n",
       "      <td>Self employed</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>80906</td>\n",
       "      <td>11</td>\n",
       "      <td>56013</td>\n",
       "      <td>0.0</td>\n",
       "      <td>71.69</td>\n",
       "      <td>49</td>\n",
       "      <td>train</td>\n",
       "      <td>24654</td>\n",
       "      <td>118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1300</th>\n",
       "      <td>0yrs 0mon</td>\n",
       "      <td>1</td>\n",
       "      <td>0yrs 0mon</td>\n",
       "      <td>6207</td>\n",
       "      <td>0</td>\n",
       "      <td>05-03-00</td>\n",
       "      <td>27-10-18</td>\n",
       "      <td>0</td>\n",
       "      <td>785</td>\n",
       "      <td>Self employed</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>63872</td>\n",
       "      <td>20</td>\n",
       "      <td>46952</td>\n",
       "      <td>1.0</td>\n",
       "      <td>75.15</td>\n",
       "      <td>45</td>\n",
       "      <td>train</td>\n",
       "      <td>14158</td>\n",
       "      <td>118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1864</th>\n",
       "      <td>0yrs 0mon</td>\n",
       "      <td>1</td>\n",
       "      <td>0yrs 0mon</td>\n",
       "      <td>7124</td>\n",
       "      <td>0</td>\n",
       "      <td>01-01-00</td>\n",
       "      <td>28-09-18</td>\n",
       "      <td>0</td>\n",
       "      <td>413</td>\n",
       "      <td>Self employed</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>80109</td>\n",
       "      <td>63</td>\n",
       "      <td>65282</td>\n",
       "      <td>0.0</td>\n",
       "      <td>83.64</td>\n",
       "      <td>86</td>\n",
       "      <td>train</td>\n",
       "      <td>17066</td>\n",
       "      <td>118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1872</th>\n",
       "      <td>0yrs 0mon</td>\n",
       "      <td>1</td>\n",
       "      <td>0yrs 0mon</td>\n",
       "      <td>7094</td>\n",
       "      <td>0</td>\n",
       "      <td>18-08-00</td>\n",
       "      <td>16-09-18</td>\n",
       "      <td>0</td>\n",
       "      <td>413</td>\n",
       "      <td>Self employed</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>106503</td>\n",
       "      <td>63</td>\n",
       "      <td>73217</td>\n",
       "      <td>0.0</td>\n",
       "      <td>70.33</td>\n",
       "      <td>86</td>\n",
       "      <td>train</td>\n",
       "      <td>17066</td>\n",
       "      <td>118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1874</th>\n",
       "      <td>0yrs 0mon</td>\n",
       "      <td>1</td>\n",
       "      <td>0yrs 0mon</td>\n",
       "      <td>7106</td>\n",
       "      <td>0</td>\n",
       "      <td>01-01-00</td>\n",
       "      <td>26-08-18</td>\n",
       "      <td>0</td>\n",
       "      <td>413</td>\n",
       "      <td>Self employed</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>65223</td>\n",
       "      <td>63</td>\n",
       "      <td>56713</td>\n",
       "      <td>1.0</td>\n",
       "      <td>89.23</td>\n",
       "      <td>86</td>\n",
       "      <td>train</td>\n",
       "      <td>17066</td>\n",
       "      <td>118</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 43 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     AVERAGE.ACCT.AGE  Aadhar_flag CREDIT.HISTORY.LENGTH  Current_pincode_ID  \\\n",
       "874         0yrs 0mon            1             0yrs 0mon                5940   \n",
       "1300        0yrs 0mon            1             0yrs 0mon                6207   \n",
       "1864        0yrs 0mon            1             0yrs 0mon                7124   \n",
       "1872        0yrs 0mon            1             0yrs 0mon                7094   \n",
       "1874        0yrs 0mon            1             0yrs 0mon                7106   \n",
       "\n",
       "      DELINQUENT.ACCTS.IN.LAST.SIX.MONTHS Date.of.Birth DisbursalDate  \\\n",
       "874                                     0      16-07-00      08-10-18   \n",
       "1300                                    0      05-03-00      27-10-18   \n",
       "1864                                    0      01-01-00      28-09-18   \n",
       "1872                                    0      18-08-00      16-09-18   \n",
       "1874                                    0      01-01-00      26-08-18   \n",
       "\n",
       "      Driving_flag  Employee_code_ID Employment.Type  ...  VoterID_flag  \\\n",
       "874              0              1464   Self employed  ...             0   \n",
       "1300             0               785   Self employed  ...             0   \n",
       "1864             0               413   Self employed  ...             0   \n",
       "1872             0               413   Self employed  ...             0   \n",
       "1874             0               413   Self employed  ...             0   \n",
       "\n",
       "      asset_cost  branch_id  disbursed_amount  loan_default    ltv  \\\n",
       "874        80906         11             56013           0.0  71.69   \n",
       "1300       63872         20             46952           1.0  75.15   \n",
       "1864       80109         63             65282           0.0  83.64   \n",
       "1872      106503         63             73217           0.0  70.33   \n",
       "1874       65223         63             56713           1.0  89.23   \n",
       "\n",
       "      manufacturer_id  source  supplier_id  Age  \n",
       "874                49   train        24654  118  \n",
       "1300               45   train        14158  118  \n",
       "1864               86   train        17066  118  \n",
       "1872               86   train        17066  118  \n",
       "1874               86   train        17066  118  \n",
       "\n",
       "[5 rows x 43 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Seems to be a zero in age\n",
    "#alldata[alldata['Age']==118].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "23     15491\n",
       "24     15408\n",
       "22     15217\n",
       "26     15062\n",
       "25     14844\n",
       "28     14721\n",
       "27     13754\n",
       "30     13548\n",
       "29     12951\n",
       "31     12667\n",
       "32     12267\n",
       "33     11672\n",
       "21     11382\n",
       "34     11073\n",
       "35     10705\n",
       "36     10303\n",
       "38     10072\n",
       "37      9240\n",
       "40      8750\n",
       "43      8463\n",
       "42      8358\n",
       "39      8323\n",
       "41      7950\n",
       "44      6962\n",
       "45      6743\n",
       "46      6704\n",
       "48      6236\n",
       "47      5638\n",
       "49      4713\n",
       "50      4672\n",
       "51      3767\n",
       "53      3669\n",
       "52      3422\n",
       "20      3282\n",
       "54      2635\n",
       "55      2364\n",
       "19      2358\n",
       "56      2148\n",
       "58      1787\n",
       "57      1761\n",
       "59      1172\n",
       "118      984\n",
       "60       908\n",
       "61       610\n",
       "62       510\n",
       "63       248\n",
       "64        31\n",
       "69         1\n",
       "Name: Age, dtype: int64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 984 count for Age=0\n",
    "#alldata['Age'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "34.454324460419166"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#alldata['Age'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "xmPiYCbeYvOl"
   },
   "outputs": [],
   "source": [
    "alldata.drop('Date.of.Birth', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 119
    },
    "colab_type": "code",
    "id": "QMajD_8xYvOt",
    "outputId": "73057497-a248-4dd9-ea14-f3739bc9cee9"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0\n",
       "1    1\n",
       "2    0\n",
       "3    1\n",
       "4    0\n",
       "Name: CREDIT.HISTORY.LENGTH, dtype: object"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alldata['CREDIT.HISTORY.LENGTH'] = alldata['CREDIT.HISTORY.LENGTH'].apply(lambda x: x.split(' ')[0])\n",
    "alldata['CREDIT.HISTORY.LENGTH'] = alldata['CREDIT.HISTORY.LENGTH'].apply(lambda x: x.split('yrs')[0])\n",
    "alldata['CREDIT.HISTORY.LENGTH'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 289
    },
    "colab_type": "code",
    "id": "ISZ29iqrYvO4",
    "outputId": "15253530-92be-4590-8775-405e4ee3f3d2"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     0\n",
       "1     1\n",
       "2     0\n",
       "3     1\n",
       "4     0\n",
       "5     2\n",
       "6     0\n",
       "7     0\n",
       "8     4\n",
       "9     1\n",
       "10    0\n",
       "11    2\n",
       "12    2\n",
       "13    2\n",
       "14    7\n",
       "Name: Credit_History, dtype: int64"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alldata['Credit_History'] = alldata['CREDIT.HISTORY.LENGTH'].map(int)\n",
    "alldata['Credit_History'].head(15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1071
    },
    "colab_type": "code",
    "id": "UMToseENawe9",
    "outputId": "fbc44ab7-540f-4b60-9234-3a660b6f2f83"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         0\n",
       "1         1\n",
       "2         0\n",
       "3         1\n",
       "4         0\n",
       "5         1\n",
       "6         0\n",
       "7         0\n",
       "8         1\n",
       "9         1\n",
       "10        0\n",
       "11        1\n",
       "12        1\n",
       "13        1\n",
       "14        1\n",
       "15        0\n",
       "16        1\n",
       "17        1\n",
       "18        1\n",
       "19        0\n",
       "20        0\n",
       "21        1\n",
       "22        0\n",
       "23        1\n",
       "24        0\n",
       "25        0\n",
       "26        0\n",
       "27        0\n",
       "28        1\n",
       "29        1\n",
       "         ..\n",
       "112362    0\n",
       "112363    0\n",
       "112364    1\n",
       "112365    1\n",
       "112366    0\n",
       "112367    0\n",
       "112368    0\n",
       "112369    0\n",
       "112370    0\n",
       "112371    0\n",
       "112372    0\n",
       "112373    0\n",
       "112374    1\n",
       "112375    0\n",
       "112376    0\n",
       "112377    1\n",
       "112378    0\n",
       "112379    0\n",
       "112380    0\n",
       "112381    1\n",
       "112382    0\n",
       "112383    1\n",
       "112384    0\n",
       "112385    0\n",
       "112386    1\n",
       "112387    0\n",
       "112388    0\n",
       "112389    1\n",
       "112390    0\n",
       "112391    0\n",
       "Name: Credit_History, Length: 345546, dtype: int64"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alldata['Credit_History'].map(lambda c: 1 if c>= 1 else 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "MqbOZRPOYvPF"
   },
   "outputs": [],
   "source": [
    "alldata.drop('CREDIT.HISTORY.LENGTH', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 119
    },
    "colab_type": "code",
    "id": "R-cWOeCOYvPW",
    "outputId": "43b6d70c-b9a5-4c60-f8ef-982b02cb38de"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AVERAGE.ACCT.AGE                 200\n",
       "DisbursalDate                    111\n",
       "Employment.Type                    2\n",
       "PERFORM_CNS.SCORE.DESCRIPTION     20\n",
       "source                             2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alldata.select_dtypes('object').apply(pd.Series.nunique, axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 289
    },
    "colab_type": "code",
    "id": "0tOLVmJKYvPh",
    "outputId": "14950669-41b4-4dd1-e1c0-e5eaba418c35"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     0\n",
       "1     1\n",
       "2     0\n",
       "3     0\n",
       "4     0\n",
       "5     1\n",
       "6     0\n",
       "7     0\n",
       "8     4\n",
       "9     1\n",
       "10    0\n",
       "11    2\n",
       "12    1\n",
       "13    1\n",
       "14    3\n",
       "Name: Avg_acct_yrs, dtype: int64"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alldata['AVERAGE.ACCT.AGE'] = alldata['AVERAGE.ACCT.AGE'].apply(lambda x: x.split(' ')[0])\n",
    "alldata['AVERAGE.ACCT.AGE'] = alldata['AVERAGE.ACCT.AGE'].apply(lambda x: x.split('yrs')[0])\n",
    "\n",
    "alldata['Avg_acct_yrs'] = alldata['AVERAGE.ACCT.AGE'].map(int)\n",
    "alldata['Avg_acct_yrs'].head(15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "6qV6uMymYvPz"
   },
   "outputs": [],
   "source": [
    "alldata.drop('AVERAGE.ACCT.AGE', axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 119
    },
    "colab_type": "code",
    "id": "Q3cNbyMHYvRF",
    "outputId": "fe91e3e8-49fb-4a73-ac7e-d606b9891a41"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.000000\n",
       "1    0.765828\n",
       "2    0.000000\n",
       "3    0.000000\n",
       "4    0.000000\n",
       "Name: cltv, dtype: float64"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alldata['cltv']=(alldata['PRI.DISBURSED.AMOUNT'] + alldata['SEC.DISBURSED.AMOUNT']) / alldata['asset_cost']\n",
    "alldata['cltv'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "colab_type": "code",
    "id": "VJTZcpSmYvRS",
    "outputId": "6c3058f5-55fe-43e5-f54b-ac4b1900889a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.8955\n",
       "1    0.7323\n",
       "2    0.8963\n",
       "3    0.8848\n",
       "4    0.8839\n",
       "5    0.8966\n",
       "6    0.7642\n",
       "7    0.7189\n",
       "8    0.8956\n",
       "9    0.8695\n",
       "Name: ltv, dtype: float64"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alldata['ltv']=alldata['ltv']/100\n",
    "alldata['ltv'].head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 119
    },
    "colab_type": "code",
    "id": "npoXaflbYvSZ",
    "outputId": "42c09ac4-863b-4a94-f4d2-ad9e8c50aaba"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DisbursalDate                    111\n",
       "Employment.Type                    2\n",
       "PERFORM_CNS.SCORE.DESCRIPTION     20\n",
       "source                             2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alldata.select_dtypes('object').apply(pd.Series.nunique, axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "J7hZf03MYvTN",
    "outputId": "67829049-3230-4100-b4c1-3f802635ee77"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(345546, 43)"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alldata.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "aBWh8YcRYvTf"
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "var_mod = ['PERFORM_CNS.SCORE.DESCRIPTION','Employment.Type','DisbursalDate']\n",
    "le=LabelEncoder()\n",
    "for i in var_mod:\n",
    "        alldata[i]=le.fit_transform(alldata[i])\n",
    "        if(i!='loan_default'):\n",
    "          alldata[i]=le.fit_transform(alldata[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7096"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alldata['Current_pincode_ID'].nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### From the Data Dictionary provide, Current_pincode_ID is the current pincode of the customer. I decided to use this information to use in feature engineering. Using the assumption 7096 customers applied for numerous loans. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "gSwhz6Exhsuw"
   },
   "outputs": [],
   "source": [
    "alldata['pc_disbursed_sum']= alldata.groupby('Current_pincode_ID')['disbursed_amount'].transform('sum')\n",
    "alldata['pc_pri_no_accts_sum']= alldata.groupby('Current_pincode_ID')['PRI.NO.OF.ACCTS'].transform('sum')\n",
    "alldata['pc_pri_no_accts_count']= alldata.groupby('Current_pincode_ID')['PRI.NO.OF.ACCTS'].transform('count')\n",
    "alldata['pc_pri_overdue_accts_sum']= alldata.groupby('Current_pincode_ID')['PRI.OVERDUE.ACCTS'].transform('sum')\n",
    "alldata['pc_pri_overdue_accts_count']= alldata.groupby('Current_pincode_ID')['PRI.OVERDUE.ACCTS'].transform('count')\n",
    "alldata['pc_pri_active_accts_sum']= alldata.groupby('Current_pincode_ID')['PRI.ACTIVE.ACCTS'].transform('sum')\n",
    "alldata['pc_pri_active_accts_count']= alldata.groupby('Current_pincode_ID')['PRI.ACTIVE.ACCTS'].transform('count')\n",
    "alldata['pc_new_accts_six_sum']= alldata.groupby('Current_pincode_ID')['NEW.ACCTS.IN.LAST.SIX.MONTHS'].transform('sum')\n",
    "alldata['pc_new_accts_six_count']= alldata.groupby('Current_pincode_ID')['NEW.ACCTS.IN.LAST.SIX.MONTHS'].transform('count')\n",
    "alldata['pc_delinq_accts_six_sum']= alldata.groupby('Current_pincode_ID')['DELINQUENT.ACCTS.IN.LAST.SIX.MONTHS'].transform('sum')\n",
    "alldata['pc_delinq_accts_six_count']= alldata.groupby('Current_pincode_ID')['DELINQUENT.ACCTS.IN.LAST.SIX.MONTHS'].transform('count')\n",
    "alldata['pc_pri_current_bal_sum']= alldata.groupby('Current_pincode_ID')['PRI.CURRENT.BALANCE'].transform('sum')\n",
    "alldata['pc_pri_disbursed_sum']= alldata.groupby('Current_pincode_ID')['PRI.DISBURSED.AMOUNT'].transform('sum')\n",
    "alldata['pc_pri_sanctioned_sum']= alldata.groupby('Current_pincode_ID')['PRI.SANCTIONED.AMOUNT'].transform('sum')\n",
    "alldata['pc_pri_instal_sum']= alldata.groupby('Current_pincode_ID')['PRIMARY.INSTAL.AMT'].transform('sum')\n",
    "alldata['pc_cns_score']= alldata.groupby('Current_pincode_ID')['PERFORM_CNS.SCORE'].transform('max')\n",
    "alldata['pc_asset_cost_sum']= alldata.groupby('Current_pincode_ID')['asset_cost'].transform('sum')\n",
    "#sec\n",
    "alldata['pc_sec_no_accts_sum']= alldata.groupby('Current_pincode_ID')['SEC.NO.OF.ACCTS'].transform('sum')\n",
    "alldata['pc_sec_no_accts_count']= alldata.groupby('Current_pincode_ID')['SEC.NO.OF.ACCTS'].transform('count')\n",
    "alldata['pc_sec_overdue_accts_sum']= alldata.groupby('Current_pincode_ID')['SEC.OVERDUE.ACCTS'].transform('sum')\n",
    "alldata['pc_sec_overdue_accts_count']= alldata.groupby('Current_pincode_ID')['SEC.OVERDUE.ACCTS'].transform('count')\n",
    "alldata['pc_sec_active_accts_sum']= alldata.groupby('Current_pincode_ID')['SEC.ACTIVE.ACCTS'].transform('sum')\n",
    "alldata['pc_sec_active_accts_count']= alldata.groupby('Current_pincode_ID')['SEC.ACTIVE.ACCTS'].transform('count')\n",
    "alldata['pc_sec_current_bal_sum']= alldata.groupby('Current_pincode_ID')['SEC.CURRENT.BALANCE'].transform('sum')\n",
    "alldata['pc_sec_disbursed_sum']= alldata.groupby('Current_pincode_ID')['SEC.DISBURSED.AMOUNT'].transform('sum')\n",
    "alldata['pc_sec_sanctioned_sum']= alldata.groupby('Current_pincode_ID')['SEC.SANCTIONED.AMOUNT'].transform('sum')\n",
    "alldata['pc_sec_instal_sum']= alldata.groupby('Current_pincode_ID')['SEC.INSTAL.AMT'].transform('sum')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ODSLHekOlFw-"
   },
   "outputs": [],
   "source": [
    "alldata['total_pc_pri_no_accts_count'] = alldata['pc_pri_no_accts_count'] +alldata['pc_sec_no_accts_count']\n",
    "alldata['total_pc_pri_no_accts_sum']=alldata['pc_pri_no_accts_sum'] +alldata['pc_sec_no_accts_sum']\n",
    "alldata['total_pc_disbursed']=alldata['pc_pri_disbursed_sum']+alldata['pc_sec_disbursed_sum']\n",
    "alldata['total_pc_pri_instal_sum']=alldata['pc_pri_instal_sum']+alldata['pc_sec_instal_sum']\n",
    "alldata['total_pc_cltv'] = alldata['total_pc_disbursed'] / alldata['pc_asset_cost_sum']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 119
    },
    "colab_type": "code",
    "id": "MCHx8m08Ku1x",
    "outputId": "ec3093f9-a6d0-43f6-8c79-41f45c47c9a8"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0\n",
       "1    1\n",
       "2    0\n",
       "3    0\n",
       "4    0\n",
       "Name: ltv_good, dtype: int64"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alldata['ltv_good']=alldata['ltv'].map(lambda c: 1 if c  <= 0.80  else 0)\n",
    "alldata['ltv_good'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "nrT-SJfYhsu2"
   },
   "outputs": [],
   "source": [
    "alldata['branch_disbursed_sum']= alldata.groupby('branch_id')['disbursed_amount'].transform('sum')\n",
    "alldata['branch_pri_no_accts_sum']= alldata.groupby('branch_id')['PRI.NO.OF.ACCTS'].transform('sum')\n",
    "alldata['branch_pri_no_accts_count']= alldata.groupby('branch_id')['PRI.NO.OF.ACCTS'].transform('count')\n",
    "alldata['branch_pri_overdue_accts_sum']= alldata.groupby('branch_id')['PRI.OVERDUE.ACCTS'].transform('sum')\n",
    "alldata['branch_pri_overdue_accts_count']= alldata.groupby('branch_id')['PRI.OVERDUE.ACCTS'].transform('count')\n",
    "alldata['branch_pri_active_accts_sum']= alldata.groupby('branch_id')['PRI.ACTIVE.ACCTS'].transform('sum')\n",
    "alldata['branch_pri_active_accts_count']= alldata.groupby('branch_id')['PRI.ACTIVE.ACCTS'].transform('count')\n",
    "alldata['branch_new_accts_six_sum']= alldata.groupby('branch_id')['NEW.ACCTS.IN.LAST.SIX.MONTHS'].transform('sum')\n",
    "alldata['branch_new_accts_six_count']= alldata.groupby('branch_id')['NEW.ACCTS.IN.LAST.SIX.MONTHS'].transform('count')\n",
    "alldata['branch_delinq_accts_six_sum']= alldata.groupby('branch_id')['DELINQUENT.ACCTS.IN.LAST.SIX.MONTHS'].transform('sum')\n",
    "alldata['branch_delinq_accts_six_count']= alldata.groupby('branch_id')['DELINQUENT.ACCTS.IN.LAST.SIX.MONTHS'].transform('count')\n",
    "alldata['branch_pri_current_bal_sum']= alldata.groupby('branch_id')['PRI.CURRENT.BALANCE'].transform('sum')\n",
    "alldata['branch_pri_disbursed_sum']= alldata.groupby('branch_id')['PRI.DISBURSED.AMOUNT'].transform('sum')\n",
    "alldata['branch_pri_sanctioned_sum']= alldata.groupby('branch_id')['PRI.SANCTIONED.AMOUNT'].transform('sum')\n",
    "alldata['branch_pri_instal_sum']= alldata.groupby('branch_id')['PRIMARY.INSTAL.AMT'].transform('sum')\n",
    "alldata['branch_cns_score']= alldata.groupby('branch_id')['PERFORM_CNS.SCORE'].transform('max')\n",
    "#sec\n",
    "alldata['branch_sec_no_accts_sum']= alldata.groupby('branch_id')['SEC.NO.OF.ACCTS'].transform('sum')\n",
    "alldata['branch_sec_no_accts_count']= alldata.groupby('branch_id')['SEC.NO.OF.ACCTS'].transform('count')\n",
    "alldata['branch_sec_overdue_accts_sum']= alldata.groupby('branch_id')['SEC.OVERDUE.ACCTS'].transform('sum')\n",
    "alldata['branch_sec_overdue_accts_count']= alldata.groupby('branch_id')['SEC.OVERDUE.ACCTS'].transform('count')\n",
    "alldata['branch_sec_active_accts_sum']= alldata.groupby('branch_id')['SEC.ACTIVE.ACCTS'].transform('sum')\n",
    "alldata['branch_sec_active_accts_count']= alldata.groupby('branch_id')['SEC.ACTIVE.ACCTS'].transform('count')\n",
    "alldata['branch_sec_current_bal_sum']= alldata.groupby('branch_id')['SEC.CURRENT.BALANCE'].transform('sum')\n",
    "alldata['branch_sec_disbursed_sum']= alldata.groupby('branch_id')['SEC.DISBURSED.AMOUNT'].transform('sum')\n",
    "alldata['branch_sec_sanctioned_sum']= alldata.groupby('branch_id')['SEC.SANCTIONED.AMOUNT'].transform('sum')\n",
    "alldata['branch_sec_instal_sum']= alldata.groupby('branch_id')['SEC.INSTAL.AMT'].transform('sum')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "y03wxMsphsu7"
   },
   "outputs": [],
   "source": [
    "alldata['supp_disbursed_sum']= alldata.groupby('supplier_id')['disbursed_amount'].transform('sum')\n",
    "alldata['supp_pri_no_accts_sum']= alldata.groupby('supplier_id')['PRI.NO.OF.ACCTS'].transform('sum')\n",
    "alldata['supp_pri_no_accts_count']= alldata.groupby('supplier_id')['PRI.NO.OF.ACCTS'].transform('count')\n",
    "alldata['supp_pri_overdue_accts_sum']= alldata.groupby('supplier_id')['PRI.OVERDUE.ACCTS'].transform('sum')\n",
    "alldata['supp_pri_overdue_accts_count']= alldata.groupby('supplier_id')['PRI.OVERDUE.ACCTS'].transform('count')\n",
    "alldata['supp_pri_active_accts_sum']= alldata.groupby('supplier_id')['PRI.ACTIVE.ACCTS'].transform('sum')\n",
    "alldata['supp_pri_active_accts_count']= alldata.groupby('supplier_id')['PRI.ACTIVE.ACCTS'].transform('count')\n",
    "alldata['supp_new_accts_six_sum']= alldata.groupby('supplier_id')['NEW.ACCTS.IN.LAST.SIX.MONTHS'].transform('sum')\n",
    "alldata['supp_new_accts_six_count']= alldata.groupby('supplier_id')['NEW.ACCTS.IN.LAST.SIX.MONTHS'].transform('count')\n",
    "alldata['supp_delinq_accts_six_sum']= alldata.groupby('supplier_id')['DELINQUENT.ACCTS.IN.LAST.SIX.MONTHS'].transform('sum')\n",
    "alldata['supp_delinq_accts_six_count']= alldata.groupby('supplier_id')['DELINQUENT.ACCTS.IN.LAST.SIX.MONTHS'].transform('count')\n",
    "alldata['supp_pri_current_bal_sum']= alldata.groupby('supplier_id')['PRI.CURRENT.BALANCE'].transform('sum')\n",
    "alldata['supp_pri_disbursed_sum']= alldata.groupby('supplier_id')['PRI.DISBURSED.AMOUNT'].transform('sum')\n",
    "alldata['supp_pri_sanctioned_sum']= alldata.groupby('supplier_id')['PRI.SANCTIONED.AMOUNT'].transform('sum')\n",
    "alldata['supp_pri_instal_sum']= alldata.groupby('supplier_id')['PRIMARY.INSTAL.AMT'].transform('sum')\n",
    "alldata['supp_cns_score']= alldata.groupby('supplier_id')['PERFORM_CNS.SCORE'].transform('max')\n",
    "#sec\n",
    "alldata['supp_sec_no_accts_sum']= alldata.groupby('supplier_id')['SEC.NO.OF.ACCTS'].transform('sum')\n",
    "alldata['supp_sec_no_accts_count']= alldata.groupby('supplier_id')['SEC.NO.OF.ACCTS'].transform('count')\n",
    "alldata['supp_sec_overdue_accts_sum']= alldata.groupby('supplier_id')['SEC.OVERDUE.ACCTS'].transform('sum')\n",
    "alldata['supp_sec_overdue_accts_count']= alldata.groupby('supplier_id')['SEC.OVERDUE.ACCTS'].transform('count')\n",
    "alldata['supp_sec_active_accts_sum']= alldata.groupby('supplier_id')['SEC.ACTIVE.ACCTS'].transform('sum')\n",
    "alldata['supp_sec_active_accts_count']= alldata.groupby('supplier_id')['SEC.ACTIVE.ACCTS'].transform('count')\n",
    "alldata['supp_sec_current_bal_sum']= alldata.groupby('supplier_id')['SEC.CURRENT.BALANCE'].transform('sum')\n",
    "alldata['supp_sec_disbursed_sum']= alldata.groupby('supplier_id')['SEC.DISBURSED.AMOUNT'].transform('sum')\n",
    "alldata['supp_sec_sanctioned_sum']= alldata.groupby('supplier_id')['SEC.SANCTIONED.AMOUNT'].transform('sum')\n",
    "alldata['supp_sec_instal_sum']= alldata.groupby('supplier_id')['SEC.INSTAL.AMT'].transform('sum')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "9mxDa_d3hsvB"
   },
   "outputs": [],
   "source": [
    "alldata['emp_disbursed_sum']= alldata.groupby('Employee_code_ID')['disbursed_amount'].transform('sum')\n",
    "alldata['emp_pri_no_accts_sum']= alldata.groupby('Employee_code_ID')['PRI.NO.OF.ACCTS'].transform('sum')\n",
    "alldata['emp_pri_no_accts_count']= alldata.groupby('Employee_code_ID')['PRI.NO.OF.ACCTS'].transform('count')\n",
    "alldata['emp_pri_overdue_accts_sum']= alldata.groupby('Employee_code_ID')['PRI.OVERDUE.ACCTS'].transform('sum')\n",
    "alldata['emp_pri_overdue_accts_count']= alldata.groupby('Employee_code_ID')['PRI.OVERDUE.ACCTS'].transform('count')\n",
    "alldata['emp_pri_active_accts_sum']= alldata.groupby('Employee_code_ID')['PRI.ACTIVE.ACCTS'].transform('sum')\n",
    "alldata['emp_pri_active_accts_count']= alldata.groupby('Employee_code_ID')['PRI.ACTIVE.ACCTS'].transform('count')\n",
    "alldata['emp_new_accts_six_sum']= alldata.groupby('Employee_code_ID')['NEW.ACCTS.IN.LAST.SIX.MONTHS'].transform('sum')\n",
    "alldata['emp_new_accts_six_count']= alldata.groupby('Employee_code_ID')['NEW.ACCTS.IN.LAST.SIX.MONTHS'].transform('count')\n",
    "alldata['emp_delinq_accts_six_sum']= alldata.groupby('Employee_code_ID')['DELINQUENT.ACCTS.IN.LAST.SIX.MONTHS'].transform('sum')\n",
    "alldata['emp_delinq_accts_six_count']= alldata.groupby('Employee_code_ID')['DELINQUENT.ACCTS.IN.LAST.SIX.MONTHS'].transform('count')\n",
    "alldata['emp_pri_current_bal_sum']= alldata.groupby('Employee_code_ID')['PRI.CURRENT.BALANCE'].transform('sum')\n",
    "alldata['emp_pri_disbursed_sum']= alldata.groupby('Employee_code_ID')['PRI.DISBURSED.AMOUNT'].transform('sum')\n",
    "alldata['emp_pri_sanctioned_sum']= alldata.groupby('Employee_code_ID')['PRI.SANCTIONED.AMOUNT'].transform('sum')\n",
    "alldata['emp_pri_instal_sum']= alldata.groupby('Employee_code_ID')['PRIMARY.INSTAL.AMT'].transform('sum')\n",
    "alldata['emp_cns_score']= alldata.groupby('Employee_code_ID')['PERFORM_CNS.SCORE'].transform('max')\n",
    "#sec\n",
    "alldata['emp_sec_no_accts_sum']= alldata.groupby('Employee_code_ID')['SEC.NO.OF.ACCTS'].transform('sum')\n",
    "alldata['emp_sec_no_accts_count']= alldata.groupby('Employee_code_ID')['SEC.NO.OF.ACCTS'].transform('count')\n",
    "alldata['emp_sec_overdue_accts_sum']= alldata.groupby('Employee_code_ID')['SEC.OVERDUE.ACCTS'].transform('sum')\n",
    "alldata['emp_sec_overdue_accts_count']= alldata.groupby('Employee_code_ID')['SEC.OVERDUE.ACCTS'].transform('count')\n",
    "alldata['emp_sec_active_accts_sum']= alldata.groupby('Employee_code_ID')['SEC.ACTIVE.ACCTS'].transform('sum')\n",
    "alldata['emp_sec_active_accts_count']= alldata.groupby('Employee_code_ID')['SEC.ACTIVE.ACCTS'].transform('count')\n",
    "alldata['emp_sec_current_bal_sum']= alldata.groupby('Employee_code_ID')['SEC.CURRENT.BALANCE'].transform('sum')\n",
    "alldata['emp_sec_disbursed_sum']= alldata.groupby('Employee_code_ID')['SEC.DISBURSED.AMOUNT'].transform('sum')\n",
    "alldata['emp_sec_sanctioned_sum']= alldata.groupby('Employee_code_ID')['SEC.SANCTIONED.AMOUNT'].transform('sum')\n",
    "alldata['emp_sec_instal_sum']= alldata.groupby('Employee_code_ID')['SEC.INSTAL.AMT'].transform('sum')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "4z07mwKqhsvK"
   },
   "outputs": [],
   "source": [
    "alldata['state_disbursed_sum']= alldata.groupby('State_ID')['disbursed_amount'].transform('sum')\n",
    "alldata['state_pri_no_accts_sum']= alldata.groupby('State_ID')['PRI.NO.OF.ACCTS'].transform('sum')\n",
    "alldata['state_pri_no_accts_count']= alldata.groupby('State_ID')['PRI.NO.OF.ACCTS'].transform('count')\n",
    "alldata['state_pri_overdue_accts_sum']= alldata.groupby('State_ID')['PRI.OVERDUE.ACCTS'].transform('sum')\n",
    "alldata['state_pri_overdue_accts_count']= alldata.groupby('State_ID')['PRI.OVERDUE.ACCTS'].transform('count')\n",
    "alldata['state_pri_active_accts_sum']= alldata.groupby('State_ID')['PRI.ACTIVE.ACCTS'].transform('sum')\n",
    "alldata['state_pri_active_accts_count']= alldata.groupby('State_ID')['PRI.ACTIVE.ACCTS'].transform('count')\n",
    "alldata['state_new_accts_six_sum']= alldata.groupby('State_ID')['NEW.ACCTS.IN.LAST.SIX.MONTHS'].transform('sum')\n",
    "alldata['state_new_accts_six_count']= alldata.groupby('State_ID')['NEW.ACCTS.IN.LAST.SIX.MONTHS'].transform('count')\n",
    "alldata['state_delinq_accts_six_sum']= alldata.groupby('State_ID')['DELINQUENT.ACCTS.IN.LAST.SIX.MONTHS'].transform('sum')\n",
    "alldata['state_delinq_accts_six_count']= alldata.groupby('State_ID')['DELINQUENT.ACCTS.IN.LAST.SIX.MONTHS'].transform('count')\n",
    "alldata['state_pri_current_bal_sum']= alldata.groupby('State_ID')['PRI.CURRENT.BALANCE'].transform('sum')\n",
    "alldata['state_pri_disbursed_sum']= alldata.groupby('State_ID')['PRI.DISBURSED.AMOUNT'].transform('sum')\n",
    "alldata['state_pri_sanctioned_sum']= alldata.groupby('State_ID')['PRI.SANCTIONED.AMOUNT'].transform('sum')\n",
    "alldata['state_pri_instal_sum']= alldata.groupby('State_ID')['PRIMARY.INSTAL.AMT'].transform('sum')\n",
    "alldata['state_cns_score']= alldata.groupby('State_ID')['PERFORM_CNS.SCORE'].transform('max')\n",
    "#sec\n",
    "alldata['state_sec_no_accts_sum']= alldata.groupby('State_ID')['SEC.NO.OF.ACCTS'].transform('sum')\n",
    "alldata['state_sec_no_accts_count']= alldata.groupby('State_ID')['SEC.NO.OF.ACCTS'].transform('count')\n",
    "alldata['state_sec_overdue_accts_sum']= alldata.groupby('State_ID')['SEC.OVERDUE.ACCTS'].transform('sum')\n",
    "alldata['state_sec_overdue_accts_count']= alldata.groupby('State_ID')['SEC.OVERDUE.ACCTS'].transform('count')\n",
    "alldata['state_sec_active_accts_sum']= alldata.groupby('State_ID')['SEC.ACTIVE.ACCTS'].transform('sum')\n",
    "alldata['state_sec_active_accts_count']= alldata.groupby('State_ID')['SEC.ACTIVE.ACCTS'].transform('count')\n",
    "alldata['state_sec_current_bal_sum']= alldata.groupby('State_ID')['SEC.CURRENT.BALANCE'].transform('sum')\n",
    "alldata['state_sec_disbursed_sum']= alldata.groupby('State_ID')['SEC.DISBURSED.AMOUNT'].transform('sum')\n",
    "alldata['state_sec_sanctioned_sum']= alldata.groupby('State_ID')['SEC.SANCTIONED.AMOUNT'].transform('sum')\n",
    "alldata['state_sec_instal_sum']= alldata.groupby('State_ID')['SEC.INSTAL.AMT'].transform('sum')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "E-pKqnIKuE7d"
   },
   "outputs": [],
   "source": [
    "alldata['pc_total_current_balance']=alldata['pc_pri_current_bal_sum']+alldata['pc_sec_current_bal_sum']\n",
    "alldata['state_total_current_balance']=alldata['state_pri_current_bal_sum']+alldata['state_sec_current_bal_sum']\n",
    "alldata['branch_total_current_balance']=alldata['branch_pri_current_bal_sum']+alldata['branch_sec_current_bal_sum']\n",
    "alldata['supp_total_current_balance']=alldata['supp_pri_current_bal_sum']+alldata['supp_sec_current_bal_sum']\n",
    "alldata['emp_total_current_balance']=alldata['emp_pri_current_bal_sum']+alldata['emp_sec_current_bal_sum']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "eamnFL0quy1V"
   },
   "outputs": [],
   "source": [
    "alldata['pc_total_disbursed']=alldata['branch_pri_disbursed_sum']+alldata['branch_sec_disbursed_sum']\n",
    "alldata['branch_total_disbursed']=alldata['branch_pri_disbursed_sum']+alldata['branch_sec_disbursed_sum']\n",
    "alldata['supp_total_disbursed']=alldata['branch_pri_disbursed_sum']+alldata['branch_sec_disbursed_sum']\n",
    "alldata['state_total_disbursed']=alldata['branch_pri_disbursed_sum']+alldata['branch_sec_disbursed_sum']\n",
    "alldata['emp_total_disbursed']=alldata['branch_pri_disbursed_sum']+alldata['branch_sec_disbursed_sum']\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "58gJUI7n_TF7"
   },
   "outputs": [],
   "source": [
    "alldata['pc_total_paid']=alldata['pc_total_disbursed']- alldata['pc_total_current_balance']\n",
    "alldata['state_total_paid']=alldata['state_total_current_balance']+alldata['state_total_disbursed']\n",
    "alldata['branch_total_paid']=alldata['branch_total_disbursed'] - alldata['branch_total_current_balance']\n",
    "alldata['supp_total_paid']=alldata['supp_total_current_balance']+alldata['supp_total_disbursed']\n",
    "alldata['emp_total_paid']=alldata['emp_total_current_balance']+alldata['emp_total_disbursed']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "PBET3i7z2Vqq"
   },
   "outputs": [],
   "source": [
    "alldata['total_branch_pri_no_accts_sum']=alldata['branch_pri_no_accts_sum'] +alldata['branch_sec_no_accts_sum']\n",
    "alldata['total_supp_pri_no_accts_sum']=alldata['supp_pri_no_accts_sum'] +alldata['supp_sec_no_accts_sum']\n",
    "alldata['total_state_pri_no_accts_sum']=alldata['state_pri_no_accts_sum'] +alldata['state_sec_no_accts_sum']\n",
    "alldata['total_emp_pri_no_accts_sum']=alldata['branch_pri_no_accts_sum'] +alldata['branch_sec_no_accts_sum']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "q-WjC_yI1AcU"
   },
   "outputs": [],
   "source": [
    "alldata['pc_total_no_inq']=alldata.groupby('Current_pincode_ID')['NO.OF_INQUIRIES'].transform('sum')\n",
    "alldata['branch_total_no_inq']=alldata.groupby('branch_id')['NO.OF_INQUIRIES'].transform('sum')\n",
    "alldata['supp_total_no_inq']=alldata.groupby('supplier_id')['NO.OF_INQUIRIES'].transform('sum')\n",
    "alldata['state_total_no_inq']=alldata.groupby('State_ID')['NO.OF_INQUIRIES'].transform('sum')\n",
    "alldata['emp_total_no_inq']=alldata.groupby('Employee_code_ID')['NO.OF_INQUIRIES'].transform('sum')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "_D2vg00n1i7e"
   },
   "outputs": [],
   "source": [
    "alldata['pc_total_overdue_accts']=alldata['pc_pri_overdue_accts_sum']+alldata['pc_sec_overdue_accts_sum']\n",
    "alldata['branch_total_overdue_accts']=alldata['branch_pri_overdue_accts_sum']+alldata['branch_sec_overdue_accts_sum']\n",
    "alldata['supp_total_overdue_accts']=alldata['supp_pri_overdue_accts_sum']+alldata['supp_sec_overdue_accts_sum']\n",
    "alldata['state_total_overdue_accts']=alldata['state_pri_overdue_accts_sum']+alldata['state_sec_overdue_accts_sum']\n",
    "alldata['emp_total_overdue_accts']=alldata['emp_pri_overdue_accts_sum']+alldata['emp_sec_overdue_accts_sum']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "XnwJfNUy2GsC"
   },
   "outputs": [],
   "source": [
    "alldata['pc_no_overdue_by_total_accts']= alldata['pc_total_overdue_accts'] / alldata['total_pc_pri_no_accts_sum']\n",
    "alldata['branch_no_overdue_by_total_accts']= alldata['branch_total_overdue_accts'] / alldata['total_branch_pri_no_accts_sum']\n",
    "alldata['supp_no_overdue_by_total_accts']= alldata['supp_total_overdue_accts'] / alldata['total_supp_pri_no_accts_sum']\n",
    "alldata['state_no_overdue_by_total_accts']= alldata['state_total_overdue_accts'] / alldata['total_state_pri_no_accts_sum']\n",
    "alldata['emp_no_overdue_by_total_accts']= alldata['emp_total_overdue_accts'] / alldata['total_emp_pri_no_accts_sum']\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "P8E3Rsylhsvj",
    "outputId": "5f37fc78-2315-477d-c9fd-00677bce8f63"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(345546, 214)"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alldata.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### We added quite a few variables using this method.  My first approach I added the PRI and SEC totals and numerous other combonations. LGBM would not break that 0.65ish mark.  After checking feature_importances these variables alone would always be at the top of the list. So I thought creating more variables from them would maybe help the model. I finally was able to break the 0.66 mark on the public LB during the last day. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "8UGC2rayqzjf"
   },
   "outputs": [],
   "source": [
    "alldata['disbursed_amount'] = np.log(alldata['disbursed_amount'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "C3Fq5M7NYvT4"
   },
   "outputs": [],
   "source": [
    "alldata.drop('UniqueID', axis = 1,inplace=True)                      "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "L7fvECyRYvVE"
   },
   "outputs": [],
   "source": [
    "train = alldata.loc[alldata['source']=='train']\n",
    "test = alldata.loc[alldata['source']=='test']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "u5qFej1kYvVL",
    "outputId": "8286d9dd-fc06-4aa9-f9a5-38b1448548bc"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((233154, 213), (112392, 213))"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.shape, test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "dTzUlYhVYvVZ"
   },
   "outputs": [],
   "source": [
    "train.drop('source',axis=1, inplace=True)\n",
    "test.drop('source',axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "yVk5ZO7XhswQ"
   },
   "outputs": [],
   "source": [
    "#train.to_csv('train_ltfs_mod.csv', index=False)\n",
    "#test.to_csv('test_ltfs_mod.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ckSaaxpJYvVg"
   },
   "outputs": [],
   "source": [
    "test = test.drop('loan_default', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "4_pAjDJKYvVm"
   },
   "outputs": [],
   "source": [
    "X = train.drop('loan_default', 1)\n",
    "y = train.loan_default"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "kiyCdimQYvWC"
   },
   "outputs": [],
   "source": [
    "import lightgbm as lgb\n",
    "from sklearn.model_selection import StratifiedKFold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "2ZGjvc2kYvWL"
   },
   "outputs": [],
   "source": [
    "#params were fintuned using BayesianOpt\n",
    "\n",
    "params={'num_leaves': 50,\n",
    "        'learning_rate':0.1,\n",
    "        'max_depth': 3,\n",
    "        'objective':'binary',\n",
    "        'metric':'auc',\n",
    "        'num_threads':-1,\n",
    "        'tree_learner': 'serial',\n",
    "        'verbosity':1,\n",
    "        'min_data_in_leaf': 1000,\n",
    "        'min_sum_hessian_in_leaf':455\n",
    "      \n",
    "        \n",
    "       \n",
    "         }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1037
    },
    "colab_type": "code",
    "id": "6EO9ieKlYvWk",
    "outputId": "fb5c1624-ee7d-4ca2-cb5f-a19589a167d5",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "1 of kfold 5\n",
      "Training until validation scores don't improve for 500 rounds.\n",
      "[200]\ttraining's auc: 0.680739\tvalid_1's auc: 0.667027\n",
      "[400]\ttraining's auc: 0.6914\tvalid_1's auc: 0.670751\n",
      "[600]\ttraining's auc: 0.698434\tvalid_1's auc: 0.672394\n",
      "[800]\ttraining's auc: 0.70435\tvalid_1's auc: 0.673742\n",
      "[1000]\ttraining's auc: 0.709506\tvalid_1's auc: 0.674272\n",
      "[1200]\ttraining's auc: 0.713851\tvalid_1's auc: 0.674378\n",
      "[1400]\ttraining's auc: 0.717725\tvalid_1's auc: 0.6745\n",
      "[1600]\ttraining's auc: 0.721084\tvalid_1's auc: 0.674081\n",
      "[1800]\ttraining's auc: 0.724273\tvalid_1's auc: 0.674072\n",
      "Early stopping, best iteration is:\n",
      "[1333]\ttraining's auc: 0.716339\tvalid_1's auc: 0.674563\n",
      "\n",
      "2 of kfold 5\n",
      "Training until validation scores don't improve for 500 rounds.\n",
      "[200]\ttraining's auc: 0.680299\tvalid_1's auc: 0.668354\n",
      "[400]\ttraining's auc: 0.691167\tvalid_1's auc: 0.672768\n",
      "[600]\ttraining's auc: 0.698716\tvalid_1's auc: 0.674402\n",
      "[800]\ttraining's auc: 0.704489\tvalid_1's auc: 0.675174\n",
      "[1000]\ttraining's auc: 0.70969\tvalid_1's auc: 0.675778\n",
      "[1200]\ttraining's auc: 0.713921\tvalid_1's auc: 0.67616\n",
      "[1400]\ttraining's auc: 0.717853\tvalid_1's auc: 0.676244\n",
      "[1600]\ttraining's auc: 0.721561\tvalid_1's auc: 0.67629\n",
      "[1800]\ttraining's auc: 0.72488\tvalid_1's auc: 0.676318\n",
      "[2000]\ttraining's auc: 0.728052\tvalid_1's auc: 0.67607\n",
      "[2200]\ttraining's auc: 0.730931\tvalid_1's auc: 0.675796\n",
      "Early stopping, best iteration is:\n",
      "[1703]\ttraining's auc: 0.723216\tvalid_1's auc: 0.676411\n",
      "\n",
      "3 of kfold 5\n",
      "Training until validation scores don't improve for 500 rounds.\n",
      "[200]\ttraining's auc: 0.680009\tvalid_1's auc: 0.667562\n",
      "[400]\ttraining's auc: 0.691352\tvalid_1's auc: 0.672159\n",
      "[600]\ttraining's auc: 0.698778\tvalid_1's auc: 0.674245\n",
      "[800]\ttraining's auc: 0.704978\tvalid_1's auc: 0.675397\n",
      "[1000]\ttraining's auc: 0.709998\tvalid_1's auc: 0.676013\n",
      "[1200]\ttraining's auc: 0.714262\tvalid_1's auc: 0.676179\n",
      "[1400]\ttraining's auc: 0.71803\tvalid_1's auc: 0.676537\n",
      "[1600]\ttraining's auc: 0.721509\tvalid_1's auc: 0.676515\n",
      "[1800]\ttraining's auc: 0.724839\tvalid_1's auc: 0.676548\n",
      "[2000]\ttraining's auc: 0.728071\tvalid_1's auc: 0.676325\n",
      "[2200]\ttraining's auc: 0.730942\tvalid_1's auc: 0.676397\n",
      "Early stopping, best iteration is:\n",
      "[1779]\ttraining's auc: 0.724586\tvalid_1's auc: 0.676633\n",
      "\n",
      "4 of kfold 5\n",
      "Training until validation scores don't improve for 500 rounds.\n",
      "[200]\ttraining's auc: 0.680509\tvalid_1's auc: 0.667128\n",
      "[400]\ttraining's auc: 0.691232\tvalid_1's auc: 0.671716\n",
      "[600]\ttraining's auc: 0.698631\tvalid_1's auc: 0.673616\n",
      "[800]\ttraining's auc: 0.704446\tvalid_1's auc: 0.674799\n",
      "[1000]\ttraining's auc: 0.709425\tvalid_1's auc: 0.675526\n",
      "[1200]\ttraining's auc: 0.713868\tvalid_1's auc: 0.676318\n",
      "[1400]\ttraining's auc: 0.717579\tvalid_1's auc: 0.676535\n",
      "[1600]\ttraining's auc: 0.72107\tvalid_1's auc: 0.67644\n",
      "[1800]\ttraining's auc: 0.724212\tvalid_1's auc: 0.676333\n",
      "Early stopping, best iteration is:\n",
      "[1470]\ttraining's auc: 0.718976\tvalid_1's auc: 0.676632\n",
      "\n",
      "5 of kfold 5\n",
      "Training until validation scores don't improve for 500 rounds.\n",
      "[200]\ttraining's auc: 0.680027\tvalid_1's auc: 0.669853\n",
      "[400]\ttraining's auc: 0.691047\tvalid_1's auc: 0.674295\n",
      "[600]\ttraining's auc: 0.698667\tvalid_1's auc: 0.676776\n",
      "[800]\ttraining's auc: 0.704405\tvalid_1's auc: 0.677732\n",
      "[1000]\ttraining's auc: 0.709456\tvalid_1's auc: 0.67827\n",
      "[1200]\ttraining's auc: 0.713923\tvalid_1's auc: 0.678319\n",
      "[1400]\ttraining's auc: 0.718079\tvalid_1's auc: 0.678682\n",
      "[1600]\ttraining's auc: 0.721858\tvalid_1's auc: 0.678598\n",
      "[1800]\ttraining's auc: 0.724771\tvalid_1's auc: 0.678263\n",
      "Early stopping, best iteration is:\n",
      "[1428]\ttraining's auc: 0.71869\tvalid_1's auc: 0.678759\n"
     ]
    }
   ],
   "source": [
    "i=1\n",
    "kf = StratifiedKFold(n_splits = 5, random_state =420, shuffle=True)\n",
    "\n",
    "\n",
    "\n",
    "for train_index, test_index in kf.split(X,y):\n",
    "  print('\\n{} of kfold {}'.format(i,kf.n_splits))\n",
    "  \n",
    "  x_tr, x_val = X.iloc[train_index], X.iloc[test_index]\n",
    "  y_tr, y_val = y[train_index], y[test_index]\n",
    "  \n",
    "  tr_data = lgb.Dataset(x_tr, label=y_tr)\n",
    "  val_data = lgb.Dataset(x_val, label=y_val)\n",
    "  val_data = lgb.Dataset(X.iloc[test_index], label=y[test_index])\n",
    "  \n",
    "  \n",
    "  lgb_model1 = lgb.train(params,tr_data,25000,\n",
    "                         valid_sets=[tr_data, val_data],\n",
    "                         verbose_eval = 200, \n",
    "                         early_stopping_rounds=500\n",
    "                        )\n",
    "  \n",
    "  oof_lgb_pred_test = lgb_model1.predict(x_val) \n",
    "  i += 1\n",
    "  lgb_pred_test = lgb_model1.predict(test)\n",
    "  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### This model scored our best. \n",
    "#### Public LB: 0.6604528139 which put us in 112th/1342.  \n",
    "#### Private LB: 0.6631884673 for 118th/1342 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2880x1440 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Showing first 41 important features per LGBM\n",
    "\n",
    "feature_imp = pd.DataFrame(sorted(zip(lgb_model1.feature_importance(),X.columns)), \n",
    "                               columns=['Value','Feature'])\n",
    "plt.figure(figsize=(40, 20))\n",
    "sns.set(font_scale = 3)\n",
    "sns.barplot(x=\"Value\", y=\"Feature\", data=feature_imp.sort_values(by=\"Value\", \n",
    "                                                        ascending=False)[0:40])\n",
    "plt.title('LightGBM Features (avg over folds)')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Zlp4yFITYvW9"
   },
   "outputs": [],
   "source": [
    "#submission = pd.read_csv('sample_submission_24jSKY6.csv')\n",
    "#submission['loan_default']= lgb_pred_test\n",
    "#submission['UniqueID'] = test_og['UniqueID']\n",
    "\n",
    "#pd.DataFrame(submission, columns=['UniqueID', 'loan_default']).to_csv('best_finetune_1.csv', index=False)"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "name": "Copy of finhackaddedfeats (1).ipynb",
   "provenance": [],
   "version": "0.3.2"
  },
  "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": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}