06_preparing_real_test_set.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Loan Prediction 06 - Testing Final Dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the real world, it could occur that the input service did not provide all the necessary information for the model. \n",
    "\n",
    "For these cases, we can skip the classification and either notify other systems about the inconsistency or fill in missing data.\n",
    "\n",
    "For the test set of this challenge, variables other than Self_employed, LoanAmount_Terms and CoapplicantIncome will be imputed with their Mean or Mode.\n",
    "\n",
    "Variables:\n",
    "- Creation of Base_Loan_Installment and Remaining_Income\n",
    "\n",
    "Missing data treatment:\n",
    "- Fill in the Self_employed with 'No'.\n",
    "- Fill in the LoanAmount_Terms with 360 for clients who have this value missing.\n",
    "- Fill in the CoapplicantIncome with 0.\n",
    "\n",
    "Dataset preprocess:\n",
    "- Encode categorical variables\n",
    "- Scale variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "import math\n",
    "from joblib import dump, load\n",
    "sys.path.append('utils')\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "plt.style.use('seaborn')\n",
    "\n",
    "from sklearn import preprocessing\n",
    "from sklearn.preprocessing import OrdinalEncoder\n",
    "\n",
    "import metrics_utils \n",
    "import model_utils\n",
    "import preprocess_utils"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\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_ID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Married</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>Education</th>\n",
       "      <th>Self_Employed</th>\n",
       "      <th>ApplicantIncome</th>\n",
       "      <th>CoapplicantIncome</th>\n",
       "      <th>LoanAmount</th>\n",
       "      <th>Loan_Amount_Term</th>\n",
       "      <th>Credit_History</th>\n",
       "      <th>Property_Area</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LP001015</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>5720</td>\n",
       "      <td>0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>LP001022</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>3076</td>\n",
       "      <td>1500</td>\n",
       "      <td>126.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LP001031</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>5000</td>\n",
       "      <td>1800</td>\n",
       "      <td>208.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>LP001035</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>2340</td>\n",
       "      <td>2546</td>\n",
       "      <td>100.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>LP001051</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Not Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>3276</td>\n",
       "      <td>0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>362</th>\n",
       "      <td>LP002971</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>3+</td>\n",
       "      <td>Not Graduate</td>\n",
       "      <td>Yes</td>\n",
       "      <td>4009</td>\n",
       "      <td>1777</td>\n",
       "      <td>113.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>363</th>\n",
       "      <td>LP002975</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>4158</td>\n",
       "      <td>709</td>\n",
       "      <td>115.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>364</th>\n",
       "      <td>LP002980</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>3250</td>\n",
       "      <td>1993</td>\n",
       "      <td>126.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Semiurban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>365</th>\n",
       "      <td>LP002986</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>5000</td>\n",
       "      <td>2393</td>\n",
       "      <td>158.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Rural</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>366</th>\n",
       "      <td>LP002989</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>Yes</td>\n",
       "      <td>9200</td>\n",
       "      <td>0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Rural</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>367 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Loan_ID Gender Married Dependents     Education Self_Employed  \\\n",
       "0    LP001015   Male     Yes          0      Graduate            No   \n",
       "1    LP001022   Male     Yes          1      Graduate            No   \n",
       "2    LP001031   Male     Yes          2      Graduate            No   \n",
       "3    LP001035   Male     Yes          2      Graduate            No   \n",
       "4    LP001051   Male      No          0  Not Graduate            No   \n",
       "..        ...    ...     ...        ...           ...           ...   \n",
       "362  LP002971   Male     Yes         3+  Not Graduate           Yes   \n",
       "363  LP002975   Male     Yes          0      Graduate            No   \n",
       "364  LP002980   Male      No          0      Graduate            No   \n",
       "365  LP002986   Male     Yes          0      Graduate            No   \n",
       "366  LP002989   Male      No          0      Graduate           Yes   \n",
       "\n",
       "     ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\n",
       "0               5720                  0       110.0             360.0   \n",
       "1               3076               1500       126.0             360.0   \n",
       "2               5000               1800       208.0             360.0   \n",
       "3               2340               2546       100.0             360.0   \n",
       "4               3276                  0        78.0             360.0   \n",
       "..               ...                ...         ...               ...   \n",
       "362             4009               1777       113.0             360.0   \n",
       "363             4158                709       115.0             360.0   \n",
       "364             3250               1993       126.0             360.0   \n",
       "365             5000               2393       158.0             360.0   \n",
       "366             9200                  0        98.0             180.0   \n",
       "\n",
       "     Credit_History Property_Area  \n",
       "0               1.0         Urban  \n",
       "1               1.0         Urban  \n",
       "2               1.0         Urban  \n",
       "3               NaN         Urban  \n",
       "4               1.0         Urban  \n",
       "..              ...           ...  \n",
       "362             1.0         Urban  \n",
       "363             1.0         Urban  \n",
       "364             NaN     Semiurban  \n",
       "365             1.0         Rural  \n",
       "366             1.0         Rural  \n",
       "\n",
       "[367 rows x 12 columns]"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_import = pd.read_csv('dataset/test_loan.csv')\n",
    "df_import"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Filling Missing Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_import.loc[df_import['Loan_Amount_Term'].isnull(),'Loan_Amount_Term'] = 360\n",
    "df_import.loc[df_import['Self_Employed'].isnull(),'Self_Employed'] = 'No'\n",
    "df_import.loc[df_import['CoapplicantIncome'].isnull(),'CoapplicantIncome'] = 0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calculating New Variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculate_new_variables(df):\n",
    "    base_loan_installment = df['LoanAmount'] * 1000 / df['Loan_Amount_Term']\n",
    "\n",
    "    total_income = df['ApplicantIncome'] + df['CoapplicantIncome']\n",
    "    remaining_income = (total_income - base_loan_installment) / total_income\n",
    "\n",
    "    df['Base_Loan_Installment'] = base_loan_installment\n",
    "    df['Remaining_Income'] = remaining_income\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_import = calculate_new_variables(df_import)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Loan_ID                  367\n",
       "Gender                   356\n",
       "Married                  367\n",
       "Dependents               357\n",
       "Education                367\n",
       "Self_Employed            367\n",
       "ApplicantIncome          367\n",
       "CoapplicantIncome        367\n",
       "LoanAmount               362\n",
       "Loan_Amount_Term         367\n",
       "Credit_History           338\n",
       "Property_Area            367\n",
       "Base_Loan_Installment    362\n",
       "Remaining_Income         362\n",
       "dtype: int64"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_import.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test = df_import.drop(columns = ['Loan_ID']).copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Encoding Variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Married</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>Education</th>\n",
       "      <th>Self_Employed</th>\n",
       "      <th>ApplicantIncome</th>\n",
       "      <th>CoapplicantIncome</th>\n",
       "      <th>LoanAmount</th>\n",
       "      <th>Loan_Amount_Term</th>\n",
       "      <th>Credit_History</th>\n",
       "      <th>Property_Area</th>\n",
       "      <th>Base_Loan_Installment</th>\n",
       "      <th>Remaining_Income</th>\n",
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       "  </thead>\n",
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       "      <th>0</th>\n",
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       "      <td>5720</td>\n",
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       "      <td>360.0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3076</td>\n",
       "      <td>1500</td>\n",
       "      <td>126.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>350.000000</td>\n",
       "      <td>0.923514</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5000</td>\n",
       "      <td>1800</td>\n",
       "      <td>208.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>577.777778</td>\n",
       "      <td>0.915033</td>\n",
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       "      <td>100.0</td>\n",
       "      <td>360.0</td>\n",
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       "      <td>277.777778</td>\n",
       "      <td>0.943148</td>\n",
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       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3276</td>\n",
       "      <td>0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>216.666667</td>\n",
       "      <td>0.933862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>362</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4009</td>\n",
       "      <td>1777</td>\n",
       "      <td>113.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>313.888889</td>\n",
       "      <td>0.945750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>363</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4158</td>\n",
       "      <td>709</td>\n",
       "      <td>115.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>319.444444</td>\n",
       "      <td>0.934365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>364</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3250</td>\n",
       "      <td>1993</td>\n",
       "      <td>126.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>350.000000</td>\n",
       "      <td>0.933244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>365</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5000</td>\n",
       "      <td>2393</td>\n",
       "      <td>158.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>438.888889</td>\n",
       "      <td>0.940635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>366</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9200</td>\n",
       "      <td>0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>544.444444</td>\n",
       "      <td>0.940821</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>367 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Gender  Married Dependents  Education  Self_Employed  ApplicantIncome  \\\n",
       "0        1        1          0          0              0             5720   \n",
       "1        1        1          1          0              0             3076   \n",
       "2        1        1          2          0              0             5000   \n",
       "3        1        1          2          0              0             2340   \n",
       "4        1        0          0          1              0             3276   \n",
       "..     ...      ...        ...        ...            ...              ...   \n",
       "362      1        1          3          1              1             4009   \n",
       "363      1        1          0          0              0             4158   \n",
       "364      1        0          0          0              0             3250   \n",
       "365      1        1          0          0              0             5000   \n",
       "366      1        0          0          0              1             9200   \n",
       "\n",
       "     CoapplicantIncome  LoanAmount  Loan_Amount_Term  Credit_History  \\\n",
       "0                    0       110.0             360.0             1.0   \n",
       "1                 1500       126.0             360.0             1.0   \n",
       "2                 1800       208.0             360.0             1.0   \n",
       "3                 2546       100.0             360.0             NaN   \n",
       "4                    0        78.0             360.0             1.0   \n",
       "..                 ...         ...               ...             ...   \n",
       "362               1777       113.0             360.0             1.0   \n",
       "363                709       115.0             360.0             1.0   \n",
       "364               1993       126.0             360.0             NaN   \n",
       "365               2393       158.0             360.0             1.0   \n",
       "366                  0        98.0             180.0             1.0   \n",
       "\n",
       "     Property_Area  Base_Loan_Installment  Remaining_Income  \n",
       "0                2             305.555556          0.946581  \n",
       "1                2             350.000000          0.923514  \n",
       "2                2             577.777778          0.915033  \n",
       "3                2             277.777778          0.943148  \n",
       "4                2             216.666667          0.933862  \n",
       "..             ...                    ...               ...  \n",
       "362              2             313.888889          0.945750  \n",
       "363              2             319.444444          0.934365  \n",
       "364              1             350.000000          0.933244  \n",
       "365              0             438.888889          0.940635  \n",
       "366              0             544.444444          0.940821  \n",
       "\n",
       "[367 rows x 13 columns]"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ordinal_encoder = OrdinalEncoder()\n",
    "ordinal_encoder.categories_ = np.load('saves/variable_encoder_categories.npy', allow_pickle= True)\n",
    "categorical_columns = ['Gender', 'Married', 'Dependents', 'Education', 'Self_Employed', 'Property_Area','Credit_History','Loan_Amount_Term']\n",
    "df_encoded_nans = preprocess_utils.encode_with_nan(X_test, categorical_columns, ordinal_encoder)\n",
    "df_encoded_nans"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Scaling Test Set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Gender</th>\n",
       "      <th>Married</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>Education</th>\n",
       "      <th>Self_Employed</th>\n",
       "      <th>ApplicantIncome</th>\n",
       "      <th>CoapplicantIncome</th>\n",
       "      <th>LoanAmount</th>\n",
       "      <th>Loan_Amount_Term</th>\n",
       "      <th>Credit_History</th>\n",
       "      <th>Property_Area</th>\n",
       "      <th>Base_Loan_Installment</th>\n",
       "      <th>Remaining_Income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.309187</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.272237</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.183971</td>\n",
       "      <td>0.796543</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.162420</td>\n",
       "      <td>0.167038</td>\n",
       "      <td>0.315364</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.213115</td>\n",
       "      <td>0.692344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.269220</td>\n",
       "      <td>0.200445</td>\n",
       "      <td>0.536388</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.362477</td>\n",
       "      <td>0.654032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.121565</td>\n",
       "      <td>0.283519</td>\n",
       "      <td>0.245283</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.165756</td>\n",
       "      <td>0.781035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.173522</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.185984</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.125683</td>\n",
       "      <td>0.739090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>362</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.214210</td>\n",
       "      <td>0.197884</td>\n",
       "      <td>0.280323</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.189435</td>\n",
       "      <td>0.792789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>363</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.222481</td>\n",
       "      <td>0.078953</td>\n",
       "      <td>0.285714</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.193078</td>\n",
       "      <td>0.741361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>364</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.172079</td>\n",
       "      <td>0.221938</td>\n",
       "      <td>0.315364</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.213115</td>\n",
       "      <td>0.736297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>365</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.269220</td>\n",
       "      <td>0.266481</td>\n",
       "      <td>0.401617</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.271403</td>\n",
       "      <td>0.769680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>366</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.502359</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.239892</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.340619</td>\n",
       "      <td>0.770524</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>367 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Gender  Married  Dependents  Education  Self_Employed  ApplicantIncome  \\\n",
       "0       1.0      1.0    0.000000        0.0            0.0         0.309187   \n",
       "1       1.0      1.0    0.333333        0.0            0.0         0.162420   \n",
       "2       1.0      1.0    0.666667        0.0            0.0         0.269220   \n",
       "3       1.0      1.0    0.666667        0.0            0.0         0.121565   \n",
       "4       1.0      0.0    0.000000        1.0            0.0         0.173522   \n",
       "..      ...      ...         ...        ...            ...              ...   \n",
       "362     1.0      1.0    1.000000        1.0            1.0         0.214210   \n",
       "363     1.0      1.0    0.000000        0.0            0.0         0.222481   \n",
       "364     1.0      0.0    0.000000        0.0            0.0         0.172079   \n",
       "365     1.0      1.0    0.000000        0.0            0.0         0.269220   \n",
       "366     1.0      0.0    0.000000        0.0            1.0         0.502359   \n",
       "\n",
       "     CoapplicantIncome  LoanAmount  Loan_Amount_Term  Credit_History  \\\n",
       "0             0.000000    0.272237          0.666667             1.0   \n",
       "1             0.167038    0.315364          0.666667             1.0   \n",
       "2             0.200445    0.536388          0.666667             1.0   \n",
       "3             0.283519    0.245283          0.666667             NaN   \n",
       "4             0.000000    0.185984          0.666667             1.0   \n",
       "..                 ...         ...               ...             ...   \n",
       "362           0.197884    0.280323          0.666667             1.0   \n",
       "363           0.078953    0.285714          0.666667             1.0   \n",
       "364           0.221938    0.315364          0.666667             NaN   \n",
       "365           0.266481    0.401617          0.666667             1.0   \n",
       "366           0.000000    0.239892          0.166667             1.0   \n",
       "\n",
       "     Property_Area  Base_Loan_Installment  Remaining_Income  \n",
       "0              1.0               0.183971          0.796543  \n",
       "1              1.0               0.213115          0.692344  \n",
       "2              1.0               0.362477          0.654032  \n",
       "3              1.0               0.165756          0.781035  \n",
       "4              1.0               0.125683          0.739090  \n",
       "..             ...                    ...               ...  \n",
       "362            1.0               0.189435          0.792789  \n",
       "363            1.0               0.193078          0.741361  \n",
       "364            0.5               0.213115          0.736297  \n",
       "365            0.0               0.271403          0.769680  \n",
       "366            0.0               0.340619          0.770524  \n",
       "\n",
       "[367 rows x 13 columns]"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaler =  load('saves/minmax_scaler_manual_imputation.bin')\n",
    "\n",
    "X_test_norm = pd.DataFrame(data=scaler.transform(df_encoded_nans),columns=df_encoded_nans.columns)\n",
    "X_test_norm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Gender                   356\n",
       "Married                  367\n",
       "Dependents               357\n",
       "Education                367\n",
       "Self_Employed            367\n",
       "ApplicantIncome          367\n",
       "CoapplicantIncome        367\n",
       "LoanAmount               362\n",
       "Loan_Amount_Term         367\n",
       "Credit_History           338\n",
       "Property_Area            367\n",
       "Base_Loan_Installment    362\n",
       "Remaining_Income         362\n",
       "dtype: int64"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test_norm.count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Marking missing values as -999 (only for XGBoost)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test_xgb = X_test_norm.copy()\n",
    "nulls = X_test_xgb.isnull()\n",
    "X_test_xgb[nulls != 0] = -999"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Replacing Missing Values for Mean/Mode"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test_filled = df_encoded_nans.copy()\n",
    "\n",
    "categorical_columns_to_fill = ['Gender', 'Married', 'Dependents', 'Education', 'Property_Area','Credit_History']\n",
    "for col in categorical_columns_to_fill:\n",
    "    X_test_filled.loc[nulls[col],col] = X_test_filled[col].mode()[0]\n",
    "    \n",
    "X_test_filled.loc[nulls['LoanAmount'],'LoanAmount'] = X_test_filled['LoanAmount'].mean()\n",
    "X_test_filled = calculate_new_variables(X_test_filled)\n",
    "\n",
    "X_test_filled_norm = pd.DataFrame(data=scaler.transform(X_test_filled),columns=X_test_filled.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Gender</th>\n",
       "      <th>Married</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>Education</th>\n",
       "      <th>Self_Employed</th>\n",
       "      <th>ApplicantIncome</th>\n",
       "      <th>CoapplicantIncome</th>\n",
       "      <th>LoanAmount</th>\n",
       "      <th>Loan_Amount_Term</th>\n",
       "      <th>Credit_History</th>\n",
       "      <th>Property_Area</th>\n",
       "      <th>Base_Loan_Installment</th>\n",
       "      <th>Remaining_Income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>367.000000</td>\n",
       "      <td>367.000000</td>\n",
       "      <td>367.000000</td>\n",
       "      <td>367.000000</td>\n",
       "      <td>367.000000</td>\n",
       "      <td>367.000000</td>\n",
       "      <td>367.000000</td>\n",
       "      <td>367.000000</td>\n",
       "      <td>367.000000</td>\n",
       "      <td>367.000000</td>\n",
       "      <td>367.000000</td>\n",
       "      <td>367.000000</td>\n",
       "      <td>367.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.809264</td>\n",
       "      <td>0.634877</td>\n",
       "      <td>0.268847</td>\n",
       "      <td>0.228883</td>\n",
       "      <td>0.100817</td>\n",
       "      <td>0.258429</td>\n",
       "      <td>0.174786</td>\n",
       "      <td>0.342675</td>\n",
       "      <td>0.618952</td>\n",
       "      <td>0.839237</td>\n",
       "      <td>0.539510</td>\n",
       "      <td>0.323627</td>\n",
       "      <td>0.636088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.393417</td>\n",
       "      <td>0.482122</td>\n",
       "      <td>0.355059</td>\n",
       "      <td>0.420687</td>\n",
       "      <td>0.301498</td>\n",
       "      <td>0.272589</td>\n",
       "      <td>0.259937</td>\n",
       "      <td>0.164275</td>\n",
       "      <td>0.179607</td>\n",
       "      <td>0.367814</td>\n",
       "      <td>0.412168</td>\n",
       "      <td>0.914668</td>\n",
       "      <td>0.850912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.008326</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.051213</td>\n",
       "      <td>-0.316667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.047359</td>\n",
       "      <td>-12.479868</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.150652</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.247978</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.173042</td>\n",
       "      <td>0.666104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.201832</td>\n",
       "      <td>0.114143</td>\n",
       "      <td>0.315364</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.220401</td>\n",
       "      <td>0.725935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.272551</td>\n",
       "      <td>0.270657</td>\n",
       "      <td>0.400270</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.301457</td>\n",
       "      <td>0.777737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.017707</td>\n",
       "      <td>2.672606</td>\n",
       "      <td>1.458221</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>14.191257</td>\n",
       "      <td>1.013945</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Gender     Married  Dependents   Education  Self_Employed  \\\n",
       "count  367.000000  367.000000  367.000000  367.000000     367.000000   \n",
       "mean     0.809264    0.634877    0.268847    0.228883       0.100817   \n",
       "std      0.393417    0.482122    0.355059    0.420687       0.301498   \n",
       "min      0.000000    0.000000    0.000000    0.000000       0.000000   \n",
       "25%      1.000000    0.000000    0.000000    0.000000       0.000000   \n",
       "50%      1.000000    1.000000    0.000000    0.000000       0.000000   \n",
       "75%      1.000000    1.000000    0.666667    0.000000       0.000000   \n",
       "max      1.000000    1.000000    1.000000    1.000000       1.000000   \n",
       "\n",
       "       ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\n",
       "count       367.000000         367.000000  367.000000        367.000000   \n",
       "mean          0.258429           0.174786    0.342675          0.618952   \n",
       "std           0.272589           0.259937    0.164275          0.179607   \n",
       "min          -0.008326           0.000000    0.051213         -0.316667   \n",
       "25%           0.150652           0.000000    0.247978          0.666667   \n",
       "50%           0.201832           0.114143    0.315364          0.666667   \n",
       "75%           0.272551           0.270657    0.400270          0.666667   \n",
       "max           4.017707           2.672606    1.458221          1.000000   \n",
       "\n",
       "       Credit_History  Property_Area  Base_Loan_Installment  Remaining_Income  \n",
       "count      367.000000     367.000000             367.000000        367.000000  \n",
       "mean         0.839237       0.539510               0.323627          0.636088  \n",
       "std          0.367814       0.412168               0.914668          0.850912  \n",
       "min          0.000000       0.000000               0.047359        -12.479868  \n",
       "25%          1.000000       0.000000               0.173042          0.666104  \n",
       "50%          1.000000       0.500000               0.220401          0.725935  \n",
       "75%          1.000000       1.000000               0.301457          0.777737  \n",
       "max          1.000000       1.000000              14.191257          1.013945  "
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test_filled_norm.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Preparing for submission"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_final_prediction(model, data,id_tags):\n",
    "    df = pd.DataFrame(columns = ['Loan_ID','Loan_Status'])\n",
    "    df['Loan_ID'] = id_tags\n",
    "    df['Loan_Status'] = model.predict(data.values).ravel()\n",
    "    df.loc[df['Loan_Status'] == 1,'Loan_Status'] = 'Y'\n",
    "    df.loc[df['Loan_Status'] == 0,'Loan_Status'] = 'N'\n",
    "    return df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Executing Classifications"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [],
   "source": [
    "logistic_reg =  load('saves/logistic_regression_manual_imputation.bin')\n",
    "df_pred_logistic_reg = make_final_prediction(logistic_reg,X_test_filled_norm, id_tags = df_import['Loan_ID'])\n",
    "df_pred_logistic_reg.to_csv('submission/logistic_regression_prediction_20200215.csv',index = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [],
   "source": [
    "ridge_reg =  load('saves/ridge_regression_manual_imputation.bin')\n",
    "df_pred_ridge_reg = make_final_prediction(ridge_reg,X_test_filled_norm, id_tags = df_import['Loan_ID'])\n",
    "df_pred_ridge_reg.to_csv('submission/ridge_regression_prediction_20200215.csv',index = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [],
   "source": [
    "random_forest =  load('saves/random_forest_manual_imputation.bin')\n",
    "df_pred_random_forest = make_final_prediction(random_forest,X_test_filled_norm, id_tags = df_import['Loan_ID'])\n",
    "df_pred_random_forest.to_csv('submission/random_forest_prediction_20200215.csv',index = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [],
   "source": [
    "gboost =  load('saves/gradient_boosting_manual_imputation.bin')\n",
    "df_pred_gboost = make_final_prediction(gboost,X_test_filled_norm, id_tags = df_import['Loan_ID'])\n",
    "df_pred_gboost.to_csv('submission/gboost_prediction_20200215.csv',index = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [],
   "source": [
    "xgboost =  load('saves/extreme_gradient_boosting_manual_imputation.bin')\n",
    "df_pred_xgb = make_final_prediction(xgboost,X_test_xgb, id_tags = df_import['Loan_ID'])\n",
    "df_pred_xgb.to_csv('submission/xgb_no_fill_prediction_20200215.csv',index = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [],
   "source": [
    "xgboost =  load('saves/extreme_gradient_boosting_manual_imputation.bin')\n",
    "df_pred_xgb = make_final_prediction(xgboost,X_test_filled_norm, id_tags = df_import['Loan_ID'])\n",
    "df_pred_xgb.to_csv('submission/xgb_prediction_20200215.csv',index = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All the predictions were submitted on the following link:\n",
    "\n",
    "https://datahack.analyticsvidhya.com/contest/practice-problem-loan-prediction-iii/\n",
    "\n"
   ]
  }
 ],
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