editorial.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Editorial"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.model_selection import StratifiedKFold"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read training data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>monthly_income</th>\n",
       "      <th>age</th>\n",
       "      <th>experience</th>\n",
       "      <th>years_employed</th>\n",
       "      <th>age_house</th>\n",
       "      <th>ineligible_for_funds</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>189000.000000</td>\n",
       "      <td>171287.000000</td>\n",
       "      <td>189000.000000</td>\n",
       "      <td>189000.000000</td>\n",
       "      <td>189000.000000</td>\n",
       "      <td>171679.000000</td>\n",
       "      <td>189000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>94500.500000</td>\n",
       "      <td>415841.471194</td>\n",
       "      <td>49.938423</td>\n",
       "      <td>11.148342</td>\n",
       "      <td>6.331286</td>\n",
       "      <td>11.999563</td>\n",
       "      <td>0.123000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>54559.744776</td>\n",
       "      <td>239876.709190</td>\n",
       "      <td>17.065674</td>\n",
       "      <td>6.024009</td>\n",
       "      <td>3.647843</td>\n",
       "      <td>1.399825</td>\n",
       "      <td>0.328438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>815.733899</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>0.000387</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>47250.750000</td>\n",
       "      <td>207600.489499</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>5.996552</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>94500.500000</td>\n",
       "      <td>415772.673367</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>11.145800</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>141750.250000</td>\n",
       "      <td>622848.639009</td>\n",
       "      <td>65.000000</td>\n",
       "      <td>16.347906</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>189000.000000</td>\n",
       "      <td>833500.487477</td>\n",
       "      <td>79.000000</td>\n",
       "      <td>23.351739</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Id  monthly_income            age     experience  \\\n",
       "count  189000.000000   171287.000000  189000.000000  189000.000000   \n",
       "mean    94500.500000   415841.471194      49.938423      11.148342   \n",
       "std     54559.744776   239876.709190      17.065674       6.024009   \n",
       "min         1.000000      815.733899      21.000000       0.000387   \n",
       "25%     47250.750000   207600.489499      35.000000       5.996552   \n",
       "50%     94500.500000   415772.673367      50.000000      11.145800   \n",
       "75%    141750.250000   622848.639009      65.000000      16.347906   \n",
       "max    189000.000000   833500.487477      79.000000      23.351739   \n",
       "\n",
       "       years_employed      age_house  ineligible_for_funds  \n",
       "count   189000.000000  171679.000000         189000.000000  \n",
       "mean         6.331286      11.999563              0.123000  \n",
       "std          3.647843       1.399825              0.328438  \n",
       "min          0.000000      10.000000              0.000000  \n",
       "25%          3.000000      11.000000              0.000000  \n",
       "50%          6.000000      12.000000              0.000000  \n",
       "75%          9.000000      13.000000              0.000000  \n",
       "max         14.000000      14.000000              1.000000  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('train_dataset.csv')\n",
    "data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Split data into train and val set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "skf = StratifiedKFold(random_state=12, n_splits=8, shuffle=True)\n",
    "\n",
    "X, y = data.drop('ineligible_for_funds', axis=1), data['ineligible_for_funds']\n",
    "\n",
    "train_index, val_index = next(skf.split(X, y))\n",
    "\n",
    "train = data.iloc[train_index].copy(deep=True)\n",
    "val = data.iloc[val_index].copy(deep=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>monthly_income</th>\n",
       "      <th>age</th>\n",
       "      <th>experience</th>\n",
       "      <th>has_child</th>\n",
       "      <th>residential_status</th>\n",
       "      <th>owns_bike</th>\n",
       "      <th>profession</th>\n",
       "      <th>home_city</th>\n",
       "      <th>home_state</th>\n",
       "      <th>years_employed</th>\n",
       "      <th>age_house</th>\n",
       "      <th>ineligible_for_funds</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>386858.796687</td>\n",
       "      <td>55</td>\n",
       "      <td>14.843088</td>\n",
       "      <td>no</td>\n",
       "      <td>NaN</td>\n",
       "      <td>no</td>\n",
       "      <td>Drafter</td>\n",
       "      <td>Whittier</td>\n",
       "      <td>Virginia</td>\n",
       "      <td>13</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>701707.897308</td>\n",
       "      <td>31</td>\n",
       "      <td>0.753429</td>\n",
       "      <td>no</td>\n",
       "      <td>on_rent</td>\n",
       "      <td>no</td>\n",
       "      <td>Geologist</td>\n",
       "      <td>Johnstown</td>\n",
       "      <td>North_Carolina</td>\n",
       "      <td>0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>47</td>\n",
       "      <td>2.082146</td>\n",
       "      <td>no</td>\n",
       "      <td>on_rent</td>\n",
       "      <td>no</td>\n",
       "      <td>Surveyor</td>\n",
       "      <td>Paragould</td>\n",
       "      <td>Indiana</td>\n",
       "      <td>1</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>436576.423621</td>\n",
       "      <td>65</td>\n",
       "      <td>5.110069</td>\n",
       "      <td>no</td>\n",
       "      <td>on_rent</td>\n",
       "      <td>no</td>\n",
       "      <td>Police_officer</td>\n",
       "      <td>Reno</td>\n",
       "      <td>Tennessee</td>\n",
       "      <td>3</td>\n",
       "      <td>12.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>81047.323778</td>\n",
       "      <td>54</td>\n",
       "      <td>14.085262</td>\n",
       "      <td>no</td>\n",
       "      <td>on_rent</td>\n",
       "      <td>yes</td>\n",
       "      <td>Comedian</td>\n",
       "      <td>Koyukuk</td>\n",
       "      <td>North Carolina</td>\n",
       "      <td>13</td>\n",
       "      <td>12.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Id  monthly_income  age  experience has_child residential_status owns_bike  \\\n",
       "1   2   386858.796687   55   14.843088        no                NaN        no   \n",
       "2   3   701707.897308   31    0.753429        no            on_rent        no   \n",
       "3   4             NaN   47    2.082146        no            on_rent        no   \n",
       "5   6   436576.423621   65    5.110069        no            on_rent        no   \n",
       "6   7    81047.323778   54   14.085262        no            on_rent       yes   \n",
       "\n",
       "       profession  home_city      home_state  years_employed  age_house  \\\n",
       "1         Drafter   Whittier        Virginia              13        NaN   \n",
       "2       Geologist  Johnstown  North_Carolina               0       14.0   \n",
       "3        Surveyor  Paragould         Indiana               1       10.0   \n",
       "5  Police_officer       Reno       Tennessee               3       12.0   \n",
       "6        Comedian    Koyukuk  North Carolina              13       12.0   \n",
       "\n",
       "   ineligible_for_funds  \n",
       "1                     1  \n",
       "2                     1  \n",
       "3                     1  \n",
       "5                     1  \n",
       "6                     1  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>monthly_income</th>\n",
       "      <th>age</th>\n",
       "      <th>experience</th>\n",
       "      <th>has_child</th>\n",
       "      <th>residential_status</th>\n",
       "      <th>owns_bike</th>\n",
       "      <th>profession</th>\n",
       "      <th>home_city</th>\n",
       "      <th>home_state</th>\n",
       "      <th>years_employed</th>\n",
       "      <th>age_house</th>\n",
       "      <th>ineligible_for_funds</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>467513.915421</td>\n",
       "      <td>52</td>\n",
       "      <td>8.001408</td>\n",
       "      <td>no</td>\n",
       "      <td>on_rent</td>\n",
       "      <td>no</td>\n",
       "      <td>Physician</td>\n",
       "      <td>Newport</td>\n",
       "      <td>Iowa</td>\n",
       "      <td>7</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>669171.434848</td>\n",
       "      <td>65</td>\n",
       "      <td>7.381569</td>\n",
       "      <td>no</td>\n",
       "      <td>NaN</td>\n",
       "      <td>no</td>\n",
       "      <td>Civil_engineer</td>\n",
       "      <td>Richmond</td>\n",
       "      <td>Virginia</td>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>29</td>\n",
       "      <td>532012.792857</td>\n",
       "      <td>68</td>\n",
       "      <td>8.165787</td>\n",
       "      <td>no</td>\n",
       "      <td>on_rent</td>\n",
       "      <td>yes</td>\n",
       "      <td>Financial_Analyst</td>\n",
       "      <td>Alice</td>\n",
       "      <td>Virginia</td>\n",
       "      <td>4</td>\n",
       "      <td>13.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>32</td>\n",
       "      <td>196510.328475</td>\n",
       "      <td>59</td>\n",
       "      <td>1.240000</td>\n",
       "      <td>no</td>\n",
       "      <td>on_rent</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Army_officer</td>\n",
       "      <td>Winston-Salem</td>\n",
       "      <td>California</td>\n",
       "      <td>1</td>\n",
       "      <td>12.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>44</td>\n",
       "      <td>35134.357516</td>\n",
       "      <td>41</td>\n",
       "      <td>2.079418</td>\n",
       "      <td>no</td>\n",
       "      <td>on_rent</td>\n",
       "      <td>no</td>\n",
       "      <td>Politician</td>\n",
       "      <td>Dublin</td>\n",
       "      <td>Indiana</td>\n",
       "      <td>1</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Id  monthly_income  age  experience has_child residential_status  \\\n",
       "0    1   467513.915421   52    8.001408        no            on_rent   \n",
       "4    5   669171.434848   65    7.381569        no                NaN   \n",
       "28  29   532012.792857   68    8.165787        no            on_rent   \n",
       "31  32   196510.328475   59    1.240000        no            on_rent   \n",
       "43  44    35134.357516   41    2.079418        no            on_rent   \n",
       "\n",
       "   owns_bike         profession      home_city  home_state  years_employed  \\\n",
       "0         no          Physician        Newport        Iowa               7   \n",
       "4         no     Civil_engineer       Richmond    Virginia               6   \n",
       "28       yes  Financial_Analyst          Alice    Virginia               4   \n",
       "31       NaN       Army_officer  Winston-Salem  California               1   \n",
       "43        no         Politician         Dublin     Indiana               1   \n",
       "\n",
       "    age_house  ineligible_for_funds  \n",
       "0        14.0                     1  \n",
       "4         NaN                     1  \n",
       "28       13.0                     1  \n",
       "31       12.0                     1  \n",
       "43       11.0                     1  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "val.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='monthly_income', ylabel='Count'>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(data=train['monthly_income'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='age', ylabel='Count'>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(data=train['age'] )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Id 0\n",
      "monthly_income 15504\n",
      "age 0\n",
      "experience 0\n",
      "has_child 0\n",
      "residential_status 15120\n",
      "owns_bike 15668\n",
      "profession 0\n",
      "home_city 0\n",
      "home_state 0\n",
      "years_employed 0\n",
      "age_house 15120\n",
      "ineligible_for_funds 0\n"
     ]
    }
   ],
   "source": [
    "for i in train.columns:\n",
    "    print(i, train[i].isnull().sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Imputing value for `monthly income`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "age = {i: 0 for i in range(21, 80)}\n",
    "tot_sal = {i: 0 for i in range(21, 80)}\n",
    "for i, r in train.iterrows():\n",
    "    age[r['age']] += 1\n",
    "    if pd.notna(r['monthly_income']):\n",
    "        tot_sal[r['age']] += r['monthly_income']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{21: 2738, 22: 2914, 23: 2823, 24: 2859, 25: 2990, 26: 2775, 27: 3364, 28: 2575, 29: 2700, 30: 2929, 31: 2800, 32: 2524, 33: 3171, 34: 2923, 35: 3045, 36: 2294, 37: 2755, 38: 2512, 39: 2490, 40: 2750, 41: 3109, 42: 2733, 43: 2774, 44: 2405, 45: 3135, 46: 2445, 47: 2925, 48: 3234, 49: 2771, 50: 3022, 51: 2275, 52: 2126, 53: 2922, 54: 3120, 55: 2985, 56: 2886, 57: 2864, 58: 2865, 59: 2946, 60: 2919, 61: 2759, 62: 2830, 63: 2561, 64: 2733, 65: 2618, 66: 3299, 67: 2631, 68: 3101, 69: 3005, 70: 2815, 71: 2760, 72: 2941, 73: 2348, 74: 2608, 75: 2533, 76: 3015, 77: 2561, 78: 2899, 79: 2966} {21: 1010611458.0603219, 22: 1131588204.386621, 23: 1042600512.6611443, 24: 1117006088.9472513, 25: 1167250051.8807886, 26: 1131372378.2770247, 27: 1239086406.811223, 28: 949203866.3834547, 29: 1011465380.2808902, 30: 1117332157.3362148, 31: 1077571319.0557508, 32: 877006132.1650648, 33: 1027744442.789163, 34: 1081220244.4195287, 35: 1175257763.1085255, 36: 861369844.7011477, 37: 1036953782.1203816, 38: 1012742858.9426733, 39: 882603276.6086478, 40: 1066328661.5994072, 41: 1212768782.3021169, 42: 1053596813.244691, 43: 1094418832.8028967, 44: 884653811.5293268, 45: 1084824766.7088733, 46: 930170543.2420555, 47: 1086264746.6965947, 48: 1287025608.9483154, 49: 1083350438.911309, 50: 1088682789.1722684, 51: 916955274.4946611, 52: 790065131.281195, 53: 1128425224.1105478, 54: 1114459297.2862656, 55: 1230635992.7185824, 56: 1070841818.6607814, 57: 1085766368.3833482, 58: 1095480203.367732, 59: 1085064418.351598, 60: 1127604158.2028112, 61: 1166846653.6046786, 62: 1059860777.4712497, 63: 1030695992.9704915, 64: 894812561.8207695, 65: 953306565.5333416, 66: 1223932777.6862643, 67: 1002483166.480706, 68: 1138942902.7480054, 69: 1085430825.9443445, 70: 1128635347.1459024, 71: 990800340.2107966, 72: 1114031262.475253, 73: 776826739.6938249, 74: 1052167128.0557384, 75: 1002202414.0072675, 76: 1110896566.102138, 77: 969394345.8070664, 78: 1087181446.4211082, 79: 1139148956.5699537}\n"
     ]
    }
   ],
   "source": [
    "print(age, tot_sal)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{21: 369105.71879485826, 22: 388328.1415190875, 23: 369323.5964084818, 24: 390698.17731628235, 25: 390384.6327360497, 26: 407701.75793766655, 27: 368337.2196228368, 28: 368622.86073143873, 29: 374616.8075114408, 30: 381472.22852038743, 31: 384846.8996627682, 32: 347466.77185620635, 33: 324107.3613337001, 34: 369900.8704822199, 35: 385963.1405939328, 36: 375488.1624678063, 37: 376389.7575754561, 38: 403161.9661395993, 39: 354459.14723238867, 40: 387755.87694523897, 41: 390083.2365075963, 42: 385509.2620727007, 43: 394527.3369873456, 44: 367839.42267331673, 45: 346036.6082005975, 46: 380437.8499967507, 47: 371372.56297319476, 48: 397967.1023340493, 49: 390960.1006536662, 50: 360252.4120358267, 51: 403057.2635141367, 52: 371620.47567318677, 53: 386182.48600634764, 54: 357198.4927199569, 55: 412273.36439483496, 56: 371047.061213022, 57: 379108.3688489344, 58: 382366.56313009845, 59: 368317.8609475893, 60: 386298.10147407034, 61: 422923.75991470774, 62: 374509.1086470847, 63: 402458.4119369354, 64: 327410.37754144514, 65: 364135.4337407722, 66: 371001.14510041353, 67: 381027.4292971136, 68: 367282.4581580153, 69: 361208.26154553896, 70: 400936.18015840225, 71: 358985.6305111582, 72: 378793.3568429966, 73: 330846.1412665353, 74: 403438.31597229233, 75: 395658.2763550207, 76: 368456.57250485505, 77: 378521.8062503188, 78: 375019.47099727776, 79: 384069.10201279627}\n"
     ]
    }
   ],
   "source": [
    "for i in tot_sal:\n",
    "        tot_sal[i] = tot_sal[i]/age[i]\n",
    "print(tot_sal)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "train.loc[:, 'monthly_income'] = train.apply(lambda x: tot_sal[x.age] if pd.isna(x.monthly_income) else x.monthly_income, axis=1)\n",
    "val.loc[:, 'monthly_income'] = val.apply(lambda x: tot_sal[x.age] if pd.isna(x.monthly_income) else x.monthly_income, axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Imputing value for `owns_bike`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Id 0\n",
      "monthly_income 0\n",
      "age 0\n",
      "experience 0\n",
      "has_child 0\n",
      "residential_status 15120\n",
      "owns_bike 15668\n",
      "profession 0\n",
      "home_city 0\n",
      "home_state 0\n",
      "years_employed 0\n",
      "age_house 15120\n",
      "ineligible_for_funds 0\n"
     ]
    }
   ],
   "source": [
    "for i in train.columns:\n",
    "    print(i, train[i].isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "no     104590\n",
       "yes     45117\n",
       "Name: owns_bike, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.owns_bike.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='age', ylabel='Count'>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(train, x='age', binwidth=10, hue='owns_bike', multiple='layer')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "train.loc[:, 'owns_bike'] = train.apply(lambda x: 'no' if pd.isna(x.owns_bike) else x.owns_bike, axis=1) # we're imputing 'no' as its highest probable class\n",
    "val.loc[:, 'owns_bike'] = val.apply(lambda x: 'no' if pd.isna(x.owns_bike) else x.owns_bike, axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Imputing value for `monthly_income`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Id 0\n",
      "monthly_income 0\n",
      "age 0\n",
      "experience 0\n",
      "has_child 0\n",
      "residential_status 15120\n",
      "owns_bike 0\n",
      "profession 0\n",
      "home_city 0\n",
      "home_state 0\n",
      "years_employed 0\n",
      "age_house 15120\n",
      "ineligible_for_funds 0\n"
     ]
    }
   ],
   "source": [
    "for i in train.columns:\n",
    "    print(i, train[i].isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "on_rent      138353\n",
       "own_house      7673\n",
       "no_house       4229\n",
       "Name: residential_status, dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.residential_status.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='monthly_income', ylabel='Count'>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZIAAAEHCAYAAACEKcAKAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8vihELAAAACXBIWXMAAAsTAAALEwEAmpwYAAAml0lEQVR4nO3deZRU1bn38e/D2EZFkahBmzHiwNAyCyqDegNGiIgjJrmCiiTSKNHAK8hdDjeXN06vEIJD1AhGiSIo0USNUYYwiLR0bjODgCI2shRBEDQgDc/7R22aaqge4HR1VXf/PmudVaeec/Y5+2yaeuoMtbe5OyIiIkerRqorICIilZsSiYiIRKJEIiIikSiRiIhIJEokIiISSa1UV6Ciff/73/emTZumuhoiIpVKbm7ul+5+cqJl1S6RNG3alMWLF6e6GiIilYqZfVLcMl3aEhGRSJRIREQkEiUSERGJpNrdIxGR9LZ3717y8/PZvXt3qqtSLWVkZJCZmUnt2rXLXEaJRETSSn5+PscffzxNmzbFzFJdnWrF3dm6dSv5+fk0a9aszOV0aUtE0sru3btp0KCBkkgKmBkNGjQ44rNBJRIRSTtKIqlzNG2vRCIiIpEokYiISCRKJFLpNWrcBDNL+dSocZNUN4UkMHjwYFauXHlYfPLkyQwbNuyotrl9+3Yef/zxwvefffYZV199dYllNmzYQOvWrSPtJ+p6yaKntqTSy/90I4/+Y02qq8Gdvc5KdRWqBXfH3alRo2zfg5955plyr8OBD+6hQ4cCcNpppzF9+vSk7yfqesmiMxIRSXsbNmzgnHPOYejQobRv357f/OY3dOrUiaysLO69914AvvnmG/r06cO5555L69atmTp1KgA9e/Ys7F9v0qRJnHnmmfTo0YMFCxYUbn/Lli1cddVVdOrUiU6dOhUuu++++7jpppvo2bMnzZs3Z8KECQCMGjWK9evX07ZtW0aOHFnkbGPDhg1069aN9u3b0759e957770yHeOKFSvo3Lkzbdu2JSsri7Vr1x62n127dnHJJZfQvn172rRpw2uvvZawPnPmzKFv376F2x42bBiTJ08uXLdly5ZkZWUxYsSIo/0nKUJnJCJSKaxZs4ZJkyZxxRVXMH36dHJycnB3Lr/8cubOncuWLVs47bTTeOONNwDYsWNHkfKbN2/m3nvvJTc3lxNOOIGLLrqIdu3aATB8+HDuuOMOLrzwQjZu3Ejv3r1ZtWoVAKtXr2b27Nns3LmTs846i1tvvZUHHniA5cuXk5eXB8SSxwGnnHIK77zzDhkZGaxdu5brr7++TB3FPvnkkwwfPpyf/exnfPfdd+zbt++w/RQUFDBjxgzq1avHl19+SZcuXbj88ssPW2/OnDkJ97Ft2zZmzJjB6tWrMTO2b99etsYvRdISiZk1Av4E/ADYDzzl7r8zs/uAW4AtYdW73f3NUGY0cDOwD7jd3d8O8Q7AZOAY4E1guLu7mdUN++gAbAWuc/cNyTomEUmdJk2a0KVLF0aMGME//vGPwiSwa9cu1q5dS7du3RgxYgR33XUXffv2pVu3bkXKL1q0iJ49e3LyybGe0K+77jo+/PBDAN59990i91G+/vprdu7cCUCfPn2oW7cudevW5ZRTTuHzzz8vsZ579+5l2LBh5OXlUbNmzcJ9lKZr166MHTuW/Px8rrzySlq0aHHYOu7O3Xffzdy5c6lRowabNm0qtT7x6tWrR0ZGBoMHD6ZPnz5FzlqiSOYZSQHwa3f/l5kdD+Sa2Tth2Th3fyR+ZTNrCQwAWgGnAe+a2Znuvg94AhgCvE8skVwKvEUs6Xzl7meY2QDgQeC6JB6TiKTIscceC8Q+TEePHs0vfvGLw9bJzc3lzTffZPTo0fTq1Yt77rmnyPLifiOxf/9+Fi5cyDHHHHPYsrp16xbO16xZk4KCghLrOW7cOE499VSWLFnC/v37ycjIKPXYAH76059y3nnn8cYbb9C7d2+eeeYZmjdvXmSdKVOmsGXLFnJzc6lduzZNmzZN+OPBWrVqsX///sL3B9apVasWOTk5zJw5k5deeomJEycya9asMtWvJEm7R+Lum939X2F+J7AKOL2EIv2Al9x9j7t/DKwDOptZQ6Ceuy90dyd2BnJFXJnnwvx04BLTL5lEqrTevXvz7LPPsmvXLgA2bdrEF198wWeffcb3vvc9fv7znzNixAj+9a9/FSl33nnnMWfOHLZu3crevXuZNm1a4bJevXoxceLEwvcHLhEV5/jjjy88YznUjh07aNiwITVq1OD5559n3759ZTqujz76iObNm3P77bdz+eWXs3Tp0sP2s2PHDk455RRq167N7Nmz+eSTTxLWp0mTJqxcuZI9e/awY8cOZs6cCcTO3nbs2MFll13G+PHjSz3OsqqQeyRm1hRoBywCLgCGmdkNwGJiZy1fEUsy78cVyw+xvWH+0Djh9VMAdy8wsx1AA+DLQ/Y/hNgZDY0bNy7PQxORCtarVy9WrVpF165dATjuuON44YUXWLduHSNHjqRGjRrUrl2bJ554oki5hg0bct9999G1a1caNmxI+/btCz/kJ0yYQHZ2NllZWRQUFNC9e3eefPLJYuvQoEEDLrjgAlq3bs2Pf/xjsrOzC5cNHTqUq666imnTpnHRRRcVnkmVZurUqbzwwgvUrl2bH/zgB9xzzz2cdNJJRfZz11138ZOf/ISOHTvStm1bzj777IT1efjhh7n22mvJysqiRYsWhZcBd+7cSb9+/di9ezfuzrhx48re8CWw2Jf85DGz44B/AmPd/VUzO5XYB70DvwEauvtNZvYYsNDdXwjl/kjsMtZG4Lfu/h8h3g34P+7+EzNbAfR29/ywbD3Q2d23Flefjh07ukZIrFrMLG0e/032/6fqYNWqVZxzzjmprka1lujfwMxy3b1jovWT+vivmdUGXgGmuPurAO7+ubvvc/f9wNNA57B6PtAorngm8FmIZyaIFyljZrWAE4BtyTkaERFJJGmJJNyr+COwyt0fjYs3jFutP7A8zL8ODDCzumbWDGgB5Lj7ZmCnmXUJ27wBeC2uzMAwfzUwy/WVUETS2Ntvv03btm2LTP379091tSJJ5j2SC4D/BJaZWV6I3Q1cb2ZtiV3a2gD8AsDdV5jZy8BKYk98ZYcntgBu5eDjv2+FCWKJ6nkzW0fsTGRAEo9HRCSy3r1707t371RXo1wlLZG4+3wg0RNUb5ZQZiwwNkF8MXBYJzXuvhu4JkI1RUQkInWRIiIikSiRiIhIJEokIlJplPeQAZWh6/+8vDzefLPYOwJpQZ02ikilUd5DBlR01/8FBQXUqnVkH7t5eXksXryYyy67LEm1ik5nJCIiJXj00Udp3bo1rVu3Zvz48YVd2t9yyy20atWKXr168e9//7vY8j179uTuu++mR48e/O53vyM3N5cePXrQoUMHevfuzebNmwvXu+uuu+jcuTNnnnkm8+bN47vvvuOee+5h6tSptG3btrBr/HSjRCIiUozc3FwmTZrEokWLeP/993n66af56quvWLt2LdnZ2axYsYITTzyRV155pcTtbN++nX/+85/cfvvt3HbbbUyfPp3c3FxuuukmxowZU7heQUEBOTk5jB8/nvvvv586derw3//931x33XXk5eVx3XXp2SetLm2JiBRj/vz59O/fv7C/rCuvvJJ58+bRrFkz2rZtC0CHDh2KjEeSyIEEsGbNGpYvX86PfvQjAPbt20fDhgd/o33llVeWeZvpRIlERKQYxXWUcWjX8iVd2oKiXeC3atWKhQsXlrjdsnRXn050aUtEpBjdu3fnL3/5C99++y3ffPMNM2bMOGzArCNx1llnsWXLlsJEsnfvXlasWFFimZK6rE8XOiMRkUojs1Hjcn3SKrNRycNKtG/fnkGDBtG5c6xv2cGDB1O/fv2j3l+dOnWYPn06t99+Ozt27KCgoIBf/epXtGrVqtgyF110EQ888ABt27Zl9OjRaXmfJOndyKcbdSNf9agb+apF3cinXlp1Iy8iIlWfLm2JiJSD7OxsFixYUCQ2fPhwbrzxxhTVqOIokYiIlIPHHnss1VVIGV3aEhGRSJRIREQkEiUSERGJRIlEREQiUSIRkUqjaePMch2PpGnjzFQfUpWgp7ZEpNL45NNN+Kz/W27bs4vvLrdtHa1BgwbRt29frr766lRX5ajpjERERCJRIhERKcGhA1s99NBDTJgwAYA77riDiy++GICZM2fy85//HIDjjjuOMWPGcO6559KlSxc+//zzEvcxd+5czj//fJo3b8706dOBWE/BI0eOpHXr1rRp06ZwUKs5c+bQt2/fwrLDhg1j8uTJAIwaNYqWLVuSlZXFiBEjANiyZQtXXXUVnTp1olOnTof9aLI8KJGIiBQj0cBW3bp1Y968eQAsXryYXbt2sXfvXubPn1/YM/A333xDly5dWLJkCd27d+fpp58ucT+bN29m/vz5/O1vf2PUqFEAvPrqq+Tl5bFkyRLeffddRo4cWTiaYiLbtm1jxowZrFixgqVLl/Jf//VfQOzX9XfccQcffPABr7zyCoMHDy6PpilC90hERIqRaGCrnJwccnNz2blzJ3Xr1qV9+/YsXryYefPmFZ6p1KlTp/CsoUOHDrzzzjsl7ueKK66gRo0atGzZsvDsZf78+Vx//fXUrFmTU089lR49evDBBx9Qr169hNuoV68eGRkZDB48mD59+hTu/91332XlypWF63399dfs3LmT448/PlrjxFEiEREpRqLenM2Mpk2bMmnSJM4//3yysrKYPXs269evL+wxt3bt2pgZULZBquIHyjqwz+J6kq5Vqxb79+8vfL979+7CeE5ODjNnzuSll15i4sSJzJo1i/3797Nw4UKOOeaYIzjyI6NEIiKVRpNGp5frk1ZNGp1e4vLu3bszaNAgRo0ahbszY8YMnn/+ebZt28YjjzzCs88+S5s2bbjzzjvp0KFDYfIoD927d+cPf/gDAwcOZNu2bcydO5eHH36YvXv3snLlSvbs2cPu3buZOXMmF154Ibt27eLbb7/lsssuo0uXLpxxxhkA9OrVi4kTJzJy5EgA8vLyCocJLi9KJCJSaWzYmF+h+0s0sFW7du3Ytm0bY8eOpWvXrhx77LFkZGREGjkxkf79+7Nw4ULOPfdczIyHHnqIH/zgBwBce+21ZGVl0aJFC9q1awfAzp076devH7t378bdGTduHAATJkwgOzubrKwsCgoK6N69O08++WS51lUDW0mlp4GtqhYNbJV6GthKREQqlC5tiYhUgLFjxzJt2rQisWuuuYYxY8akqEblR4lERKQCjBkzpkokjUSSdmnLzBqZ2WwzW2VmK8xseIifZGbvmNna8Fo/rsxoM1tnZmvMrHdcvIOZLQvLJlh4NMLM6prZ1BBfZGZNk3U8IiKSWDLvkRQAv3b3c4AuQLaZtQRGATPdvQUwM7wnLBsAtAIuBR43s5phW08AQ4AWYbo0xG8GvnL3M4BxwINJPB4REUkgaYnE3Te7+7/C/E5gFXA60A94Lqz2HHBFmO8HvOTue9z9Y2Ad0NnMGgL13H2hxx6J+dMhZQ5sazpwyYGzFRERqRgV8tRWuOTUDlgEnOrumyGWbIBTwmqnA5/GFcsPsdPD/KHxImXcvQDYATRIsP8hZrbYzBZv2bKlnI5KRCpaoyaNynU8kkZNGqX6kKqEpN9sN7PjgFeAX7n71yWcMCRa4CXESypTNOD+FPAUxH5HUlqdRSQ95W/M57H/fazctpfdLrvctlWSnj178sgjj9CxY8KfYVR6ST0jMbPaxJLIFHd/NYQ/D5erCK9fhHg+EP/1IBP4LMQzE8SLlDGzWsAJwLbyPxIRESlOMp/aMuCPwCp3fzRu0evAwDA/EHgtLj4gPInVjNhN9Zxw+WunmXUJ27zhkDIHtnU1MMv102IRKUcbNmzgnHPO4ZZbbqFVq1b06tWLf//73+Tl5dGlSxeysrLo378/X331VYnbmTZtGp07d+bMM88s7IZ+9+7d3HjjjbRp04Z27doxe/ZsACZPnsywYcMKy/bt25c5c+awb98+Bg0aVDhGyYFuUNavX8+ll15Khw4d6NatG6tXr05SaySWzDOSC4D/BC42s7wwXQY8APzIzNYCPwrvcfcVwMvASuDvQLa77wvbuhV4htgN+PXAWyH+R6CBma0D7iQ8ASYiUp7Wrl1LdnY2K1as4MQTT+SVV17hhhtu4MEHH2Tp0qW0adOG+++/v8RtFBQUkJOTw/jx4wvXfeyx2GW6ZcuW8eKLLzJw4MDC3nwTycvLY9OmTSxfvpxly5Zx4403AjBkyBB+//vfk5ubyyOPPMLQoUPL6cjLJmn3SNx9PonvYQBcUkyZscDYBPHFQOsE8d3ANRGqKSJSqmbNmhX2mNuhQwfWr1/P9u3b6dGjBwADBw7kmmtK/ii68sorC8tv2LABiI05cttttwFw9tln06RJEz788MNit9G8eXM++ugjbrvtNvr06UOvXr3YtWsX7733XpH979mz52gP9ajol+0iIqWIHy+kZs2abN++/ai3ET8+yZGOOVK/fn2WLFnC22+/zWOPPcbLL7/M+PHjOfHEE8nLyzviOpUXJRIRqTQyG2eW65NWmY0zS18pgRNOOIH69eszb948unXrxvPPP194dnIkunfvzpQpU7j44ov58MMP2bhxI2eddRZff/01jz/+OPv372fTpk3k5OQA8OWXX1KnTh2uuuoqfvjDHzJo0CDq1atHs2bNmDZtGtdccw3uztKlSzn33HOP6tiOhhKJiFQan37yaekrVZDnnnuOX/7yl3z77bc0b96cSZMmHfE2hg4dyi9/+UvatGlDrVq1mDx5MnXr1uWCCy6gWbNmtGnThtatW9O+fXsANm3axI033lh4tvLb3/4WgClTpnDrrbfyP//zP+zdu5cBAwZUaCLReCRS6Wk8kqpF45GknsYjERGRCqVLWyIi5SQ7O5sFCxYUiQ0fPrzwMd2qSolERNKOu1MZ+1898LuQyuxoLs/q0paIpJWMjAy2bt2q+00p4O5s3bqVjIyMIyqnMxIRSSuZmZnk5+ejnrpTIyMjg8zMI3ssWolERNJK7dq1adasWaqrIUdAl7ZERCQSJRIREYlEiURERCJRIhERkUiUSEREJBIlEhERiUSJREREIlEiERGRSJRIREQkEiUSERGJRF2kiJQXq5HyHmszGzXm042fpLQOUv0okYiUF9+f8pEa7+x1Vkr3L9WTLm2JiEgkSiQiIhKJEomIiESiRCIiIpEokYiISCRKJCIiEokSiYiIRKJEIiIikSiRiIhIJElLJGb2rJl9YWbL42L3mdkmM8sL02Vxy0ab2TozW2NmvePiHcxsWVg2wUIfFGZW18ymhvgiM2uarGORxBo1boKZpXwSkdRKZhcpk4GJwJ8OiY9z90fiA2bWEhgAtAJOA941szPdfR/wBDAEeB94E7gUeAu4GfjK3c8wswHAg8B1yTscOVT+pxtT3iUIqFsQKV6jxk3I/3RjqqtR5ftAK1MiMbML3H1BabF47j73CM4S+gEvufse4GMzWwd0NrMNQD13Xxj2+SfgCmKJpB9wXyg/HZhoZubuXsZ9HrF0+aOsWas2+wr2proaImlPX3YqRlnPSH4PtC9DrCyGmdkNwGLg1+7+FXA6sTOOA/JDbG+YPzROeP0UwN0LzGwH0AD48tAdmtkQYmc1NG7c+CiqHHaeRn+U6VIPSTNp0AMx6MtOdVNiIjGzrsD5wMlmdmfconpAzaPY3xPAbwAPr/8PuAlI9JfvJcQpZVnRoPtTwFMAHTt2TNoZi0jKpUEPxKAvO9VNaTfb6wDHEUs4x8dNXwNXH+nO3P1zd9/n7vuBp4HOYVE+0Chu1UzgsxDPTBAvUsbMagEnANuOtE4iIhJNiWck7v5P4J9mNtndI98pMrOG7r45vO0PHHii63Xgz2b2KLGb7S2AHHffZ2Y7zawLsAi4gdgltQNlBgILiSW1Wcm8PyIiIomV9R5JXTN7CmgaX8bdLy6ugJm9CPQEvm9m+cC9QE8za0vsEtQG4BdhOyvM7GVgJVAAZIcntgBuJfYE2DHEbrK/FeJ/BJ4PN+a3EXvqS0REKlhZE8k04EngGWBfKesC4O7XJwj/sYT1xwJjE8QXA60TxHcD15SlLiIikjxlTSQF7v5EUmsiIiKVUll/2f5XMxtqZg3N7KQDU1JrJiIilUJZz0gGhteRcTEHmpdvdUREpLIpUyJx92bJroiIiFROZe0i5YZEcXc/tB8tERGpZsp6aatT3HwGcAnwLw7vkFFERKqZsl7aui3+vZmdADyflBqJiEilcrTjkXxL7NfnIiJSzZX1HslfOdghYk3gHODlZFVKREQqj7LeI4kfiKoA+MTd84tbWUREqo8yXdoKnTeuJtbzb33gu2RWSkREKo8yJRIzuxbIIda31bXAIjM74m7kRUSk6inrpa0xQCd3/wLAzE4G3iU2xK2IiFRjZX1qq8aBJBJsPYKyIiJShZX1jOTvZvY28GJ4fx3wZnKqJCIilUlpY7afAZzq7iPN7ErgQmJjpS8EplRA/UREJM2VdnlqPLATwN1fdfc73f0OYmcj45NbNRERqQxKSyRN3X3pocEwamHTpNRIREQqldISSUYJy44pz4qIiEjlVFoi+cDMbjk0aGY3A7nJqZKIiFQmpT219Stghpn9jIOJoyNQB+ifxHqJiEglUWIicffPgfPN7CKgdQi/4e6zkl4zERGpFMo6HslsYHaS6yIiIpWQfp0uIiKRKJGIiEgkSiQiIhKJEomIiESiRCIiIpEokYiISCRKJCIiEokSiYiIRJK0RGJmz5rZF2a2PC52kpm9Y2Zrw2v9uGWjzWydma0xs95x8Q5mtiwsm2BmFuJ1zWxqiC8ys6bJOhYRESleMs9IJgOXHhIbBcx09xbAzPAeM2sJDABahTKPm1nNUOYJYAjQIkwHtnkz8JW7nwGMAx5M2pGIiEixkpZI3H0usO2QcD/guTD/HHBFXPwld9/j7h8D64DOZtYQqOfuC93dgT8dUubAtqYDlxw4WxERkYpT0fdITnX3zQDh9ZQQPx34NG69/BA7PcwfGi9Sxt0LgB1Ag0Q7NbMhZrbYzBZv2bKlnA5FREQgfW62JzqT8BLiJZU5POj+lLt3dPeOJ5988lFWUUREEqnoRPJ5uFxFeP0ixPOBRnHrZQKfhXhmgniRMmZWCziBwy+liYhIklV0InkdGBjmBwKvxcUHhCexmhG7qZ4TLn/tNLMu4f7HDYeUObCtq4FZ4T6KiIhUoDKNR3I0zOxFoCfwfTPLB+4FHgBeDkP1bgSuAXD3FWb2MrASKACy3X1f2NStxJ4AOwZ4K0wAfwSeN7N1xM5EBiTrWEREpHhJSyTufn0xiy4pZv2xwNgE8cUcHJ0xPr6bkIhERCR10uVmu4iIVFJKJCIiEokSiYiIRKJEIiIikSiRiIhIJEokIiISiRKJiIhEokQiIiKRKJGIiEgkSiQiIhKJEomIiESiRCIiIpEokYiISCRKJCIiEokSiYiIRKJEIiIikSiRiIhIJEokIiISiRKJiIhEokQiIiKRKJGIiEgkSiQiIhKJEomIiESiRCIiIpEokYiISCRKJCIiEokSiYiIRKJEIiIikSiRiIhIJEokIiISiRKJiIhEkpJEYmYbzGyZmeWZ2eIQO8nM3jGzteG1ftz6o81snZmtMbPecfEOYTvrzGyCmVkqjkdEpDpL5RnJRe7e1t07hvejgJnu3gKYGd5jZi2BAUAr4FLgcTOrGco8AQwBWoTp0gqsv4iIkF6XtvoBz4X554Ar4uIvufsed/8YWAd0NrOGQD13X+juDvwproyIiFSQVCUSB/5hZrlmNiTETnX3zQDh9ZQQPx34NK5sfoidHuYPjR/GzIaY2WIzW7xly5ZyPAwREamVov1e4O6fmdkpwDtmtrqEdRPd9/AS4ocH3Z8CngLo2LFjwnVERJLGapAOt3AzGzXm042flPt2U5JI3P2z8PqFmc0AOgOfm1lDd98cLlt9EVbPBxrFFc8EPgvxzARxEZH04vt59B9rUl0L7ux1VlK2W+GXtszsWDM7/sA80AtYDrwODAyrDQReC/OvAwPMrK6ZNSN2Uz0nXP7aaWZdwtNaN8SVERGRCpKKM5JTgRnhNK8W8Gd3/7uZfQC8bGY3AxuBawDcfYWZvQysBAqAbHffF7Z1KzAZOAZ4K0wiIlKBKjyRuPtHwLkJ4luBS4opMxYYmyC+GGhd3nUUEZGyS6fHf0VEpBJSIhERkUiUSEREJBIlEhERiUSJREREIlEiERGRSJRIREQkEiUSERGJRIlEREQiUSIREZFIlEhERCQSJRIREYlEiURERCJRIhERkUiUSEREJBIlEhERiUSJREREIlEiERGRSJRIREQkEiUSERGJRIlEREQiUSIREZFIaqW6AiLl4f777091FUSqLSUSqRLuHXhJqqvAnQv+nOoqiKSELm2JiEgkOiMRqWJ0mU8qmhKJSBWjy3xS0ZRIKil96yxqzj/npLoKgP5dpHpSIjlC6fJBkS7fOtOlPc7s0CLVVYApqf93SaczgXT525DkUyI5Qqn+oIDYh0W6fAPv0bRmqqvA6wtSXQNJJF3+ryihJZ8SyRFKlw/wdPkGni71kIPS5W80XaRLQqvKKn0iMbNLgd8BNYFn3P2BZO5PH5xSknT4EE+Xv9F0aAtIn3pUZZU6kZhZTeAx4EdAPvCBmb3u7itTWzOprlL+IZ5GXzJS3haQVmfNVfkSW6VOJEBnYJ27fwRgZi8B/QAlEhFJK1X5fqK5e3K2XAHM7GrgUncfHN7/J3Ceuw87ZL0hwJDw9ixgzVHu8vvAl0dZtipSexyktihK7VFUVWiPJu5+cqIFlf2MxBLEDsuM7v4U8FTknZktdveOUbdTVag9DlJbFKX2KKqqt0dl72srH2gU9z4T+CxFdRERqZYqeyL5AGhhZs3MrA4wAHg9xXUSEalWKvWlLXcvMLNhwNvEHv991t1XJHGXkS+PVTFqj4PUFkWpPYqq0u1RqW+2i4hI6lX2S1siIpJiSiQiIhKJEkkZmNmlZrbGzNaZ2ahU1ycKM2tkZrPNbJWZrTCz4SF+kpm9Y2Zrw2v9uDKjw7GvMbPecfEOZrYsLJtgZhbidc1saogvMrOmcWUGhn2sNbOBFXjoJTKzmmb2v2b2t/C+2raHmZ1oZtPNbHX4O+laXdvDzO4I/0+Wm9mLZpZRXduiRO6uqYSJ2E389UBzoA6wBGiZ6npFOJ6GQPswfzzwIdASeAgYFeKjgAfDfMtwzHWBZqEtaoZlOUBXYr/neQv4cYgPBZ4M8wOAqWH+JOCj8Fo/zNdPdZuEut0J/Bn4W3hfbdsDeA4YHObrACdWx/YATgc+Bo4J718GBlXHtii1rVJdgXSfwj/+23HvRwOjU12vcjy+14j1VbYGaBhiDYE1iY6X2BNyXcM6q+Pi1wN/iF8nzNci9otei18nLPsDcH0atEEmMBO4mIOJpFq2B1AvfHjaIfFq1x7EEsmn4cO8FvA3oFd1bIvSJl3aKt2BP6YD8kOs0gun0e2ARcCp7r4ZILyeElYr7vhPD/OHxouUcfcCYAfQoIRtpdp44P8A++Ni1bU9mgNbgEnhUt8zZnYs1bA93H0T8AiwEdgM7HD3f1AN26I0SiSlK1M3LJWNmR0HvAL8yt2/LmnVBDEvIX60ZVLCzPoCX7h7blmLJIhVmfYg9q24PfCEu7cDviF2+aY4VbY9wr2PfsQuU50GHGtmPy+pSIJYlWiL0iiRlK7KdcNiZrWJJZEp7v5qCH9uZg3D8obAFyFe3PHnh/lD40XKmFkt4ARgWwnbSqULgMvNbAPwEnCxmb1A9W2PfCDf3ReF99OJJZbq2B7/AXzs7lvcfS/wKnA+1bMtSpbqa2vpPhH7hvYRsW8lB262t0p1vSIcjwF/AsYfEn+YojcQHwrzrSh6A/EjDt5A/ADowsEbiJeFeDZFbyC+HOZPInb9vX6YPgZOSnWbxLVBTw7eI6m27QHMA84K8/eFtqh27QGcB6wAvheO4TngturYFqW2VaorUBkm4DJiTzetB8akuj4Rj+VCYqfIS4G8MF1G7LrsTGBteD0prsyYcOxrCE+bhHhHYHlYNpGDPSVkANOAdcSeVmkeV+amEF8H3Jjq9jikbXpyMJFU2/YA2gKLw9/IX8IHWbVsD+B+YHU4jueJJYlq2RYlTeoiRUREItE9EhERiUSJREREIlEiERGRSJRIREQkEiUSERGJRIlEREQiUSIRiSB0uT407n3PA13RJ1h3jpl1PIp9vBeljiLJpkQiEs2JxLoCTxp3Pz+Z2xeJSolEqg0zaxoGa3omDFQ0xcz+w8wWhMGDOodBi/5iZkvN7H0zywpl7zOzZ8NZxUdmdnvY7APAD80sz8weDrHj4gaGmnJgEKO4etxsZuPi3t9iZo+WUO9d4bVn2P9h2zazTmb2npktMbMcMzs+DMI0KQyo9L9mdlFYd1A4xr+a2cdmNszM7gzrvG9mJ4X1fmhmfzezXDObZ2Znl9M/hVQ1qf5pvSZNFTUBTYECoA2xL1G5wLPE+j/qR6w7kN8D94b1Lwbywvx9wHvEusj4PrAVqB22uTxuHz2JdQWeGfaxELgwLJtDrKuMY4l1lVE7xN8D2pRQ710lbZtYH3AfAZ3CevWI9RH3a2BSiJ1NrDv0DGKDM60jNrDZyWGbvwzrjSPWIzTEuv9oEebPA2al+t9QU3pOtY4s7YhUeh+7+zIAM1sBzHR3N7NlxJJCE+AqAHefZWYNzOyEUPYNd98D7DGzL4BTi9lHjrvnh33khe3OP7DQ3b8xs1lAXzNbRSyhLCtj/RNtewew2d0/CNv/Oiy/kFhixN1Xm9knwJlhO7PdfSew08x2AH8N8WVAVhhm4HxgWtwJVd0y1lGqGSUSqW72xM3vj3u/n9j/h4IEZQ50SBdfdh/F//8py3rPAHcT6xBwUslVLnXbRuKxKhKNaZFoO4naoQaw3d3bHkHdpJrSPRKRouYCP4PYPQngSy954K+dxC4RHRGPjffRCPgp8OIR17Ko1cBpZtYJINwfqUXRYzkTaEysV9qy1O9r4GMzuyaUNzM7N2I9pYpSIhEp6j6go5ktJXYjfWBJK7v7VmBBuHn/cEnrJvAysMDdvzqqmh6sw3fAdcDvzWwJ8A6xeyGPAzXDZbupwKBwaa6sfgbcHLa5gth9JJHDqBt5kRQJvzcZ5+4zU10XkSh0RiJSwcKPGD8E/q0kIlWBzkhE0oCZHRh171CXhMtnImlLiURERCLRpS0REYlEiURERCJRIhERkUiUSEREJJL/D5JjxH329/YBAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(train, x='monthly_income', binwidth=100000, hue='residential_status', multiple='layer')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "train.residential_status = train.apply(lambda x: 'on_rent' if pd.isna(x.residential_status) else x.residential_status, axis=1)\n",
    "val.residential_status = val.apply(lambda x: 'on_rent' if pd.isna(x.residential_status) else x.residential_status, axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### We drop `age_house` as the values are distributed almost equally over all bins"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Id 0\n",
      "monthly_income 0\n",
      "age 0\n",
      "experience 0\n",
      "has_child 0\n",
      "residential_status 0\n",
      "owns_bike 0\n",
      "profession 0\n",
      "home_city 0\n",
      "home_state 0\n",
      "years_employed 0\n",
      "age_house 15120\n",
      "ineligible_for_funds 0\n"
     ]
    }
   ],
   "source": [
    "for i in train.columns:\n",
    "    print(i, train[i].isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12.0    30840\n",
       "11.0    30837\n",
       "13.0    30338\n",
       "14.0    29265\n",
       "10.0    28975\n",
       "Name: age_house, dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.age_house.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='age', ylabel='Count'>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(train, x='age', hue='age_house', multiple='layer')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = train.drop(['age_house'], axis=1)\n",
    "val = val.drop(['age_house'], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Transform features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Id', 'monthly_income', 'age', 'experience', 'has_child',\n",
       "       'residential_status', 'owns_bike', 'profession', 'home_city',\n",
       "       'home_state', 'years_employed', 'ineligible_for_funds'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Physician                     3886\n",
       "Statistician                  3802\n",
       "Magistrate                    3524\n",
       "Web_designer                  3519\n",
       "Drafter                       3519\n",
       "Computer_hardware_engineer    3493\n",
       "Psychologist                  3491\n",
       "Air_traffic_controller        3485\n",
       "Fashion_Designer              3467\n",
       "Technical_writer              3452\n",
       "Comedian                      3449\n",
       "Chemical_engineer             3425\n",
       "Industrial_Engineer           3410\n",
       "Hotel_Manager                 3405\n",
       "Financial_Analyst             3400\n",
       "Graphic_Designer              3383\n",
       "Mechanical_engineer           3373\n",
       "Biomedical_Engineer           3349\n",
       "Software_Developer            3338\n",
       "Flight_attendant              3320\n",
       "Petroleum_Engineer            3292\n",
       "Police_officer                3287\n",
       "Secretary                     3271\n",
       "Politician                    3234\n",
       "Microbiologist                3223\n",
       "Computer_operator             3223\n",
       "Lawyer                        3185\n",
       "Artist                        3183\n",
       "Technician                    3181\n",
       "Scientist                     3180\n",
       "Dentist                       3137\n",
       "Technology_specialist         3133\n",
       "Aviator                       3128\n",
       "Consultant                    3126\n",
       "Surgeon                       3124\n",
       "Surveyor                      3120\n",
       "Design_Engineer               3102\n",
       "Analyst                       3093\n",
       "Geologist                     3080\n",
       "Architect                     3055\n",
       "Civil_engineer                3055\n",
       "Army_officer                  3053\n",
       "Designer                      3050\n",
       "Librarian                     3041\n",
       "Chef                          3039\n",
       "Firefighter                   3014\n",
       "Economist                     3011\n",
       "Chartered_Accountant          2955\n",
       "Civil_servant                 2928\n",
       "Official                      2738\n",
       "Engineer                      2644\n",
       "Name: profession, dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.profession.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Winter Haven       1012\n",
       "Gary                937\n",
       "Harrisonburg        753\n",
       "Burlington          739\n",
       "Bozeman             702\n",
       "                   ... \n",
       "Columbus[36]          1\n",
       "Scottsbluff[17]       1\n",
       "Waycross[63]          1\n",
       "Pensacola[58]         1\n",
       "Monterey[64]          1\n",
       "Name: home_city, Length: 9833, dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.home_city.value_counts() # it can be seen that some strings have things appended to them"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>monthly_income</th>\n",
       "      <th>age</th>\n",
       "      <th>experience</th>\n",
       "      <th>years_employed</th>\n",
       "      <th>age_house</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>63000.000000</td>\n",
       "      <td>63000.000000</td>\n",
       "      <td>63000.000000</td>\n",
       "      <td>63000.000000</td>\n",
       "      <td>63000.000000</td>\n",
       "      <td>63000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>220500.500000</td>\n",
       "      <td>417263.437393</td>\n",
       "      <td>50.001016</td>\n",
       "      <td>11.191371</td>\n",
       "      <td>6.341651</td>\n",
       "      <td>11.994413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>18186.677816</td>\n",
       "      <td>239689.206316</td>\n",
       "      <td>17.058478</td>\n",
       "      <td>6.018964</td>\n",
       "      <td>3.644700</td>\n",
       "      <td>1.395776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>189001.000000</td>\n",
       "      <td>837.316831</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>0.002326</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>204750.750000</td>\n",
       "      <td>209549.405094</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>6.047270</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>11.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>220500.500000</td>\n",
       "      <td>418093.907188</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>11.218748</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>12.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>236250.250000</td>\n",
       "      <td>623312.435637</td>\n",
       "      <td>65.000000</td>\n",
       "      <td>16.389425</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>13.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>252000.000000</td>\n",
       "      <td>833453.440655</td>\n",
       "      <td>79.000000</td>\n",
       "      <td>22.975283</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>14.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Id  monthly_income           age    experience  \\\n",
       "count   63000.000000    63000.000000  63000.000000  63000.000000   \n",
       "mean   220500.500000   417263.437393     50.001016     11.191371   \n",
       "std     18186.677816   239689.206316     17.058478      6.018964   \n",
       "min    189001.000000      837.316831     21.000000      0.002326   \n",
       "25%    204750.750000   209549.405094     35.000000      6.047270   \n",
       "50%    220500.500000   418093.907188     50.000000     11.218748   \n",
       "75%    236250.250000   623312.435637     65.000000     16.389425   \n",
       "max    252000.000000   833453.440655     79.000000     22.975283   \n",
       "\n",
       "       years_employed     age_house  \n",
       "count    63000.000000  63000.000000  \n",
       "mean         6.341651     11.994413  \n",
       "std          3.644700      1.395776  \n",
       "min          0.000000     10.000000  \n",
       "25%          4.000000     11.000000  \n",
       "50%          6.000000     12.000000  \n",
       "75%          9.000000     13.000000  \n",
       "max         14.000000     14.000000  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test = pd.read_csv('test_dataset.csv')\n",
    "test.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Id 0\n",
      "monthly_income 0\n",
      "age 0\n",
      "experience 0\n",
      "has_child 0\n",
      "residential_status 0\n",
      "owns_bike 0\n",
      "profession 0\n",
      "home_city 0\n",
      "home_state 0\n",
      "years_employed 0\n",
      "age_house 0\n"
     ]
    }
   ],
   "source": [
    "for i in test.columns:\n",
    "    print(i, test[i].isna().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "import re\n",
    "'''\n",
    "We're doing this to remove those '[6]' type things appended to some strings \n",
    "    and also replacing spaces with '-' as a few strings are seperated by this rather than spaces.\n",
    "'''\n",
    "train['profession'] = train.profession.apply(lambda x: re.sub(r'\\[[0-9*]*\\]', '',  '_'.join(x.split())))\n",
    "train['home_city'] = train.home_city.apply(lambda x: re.sub(r'\\[[0-9*]*\\]', '',  '_'.join(x.split())))\n",
    "train['home_state'] = train.home_state.apply(lambda x: re.sub(r'\\[[0-9*]*\\]', '', '_'.join(x.split())))\n",
    "\n",
    "test['profession'] = test.profession.apply(lambda x: re.sub(r'\\[[0-9*]*\\]', '',  '_'.join(x.split())))\n",
    "test['home_city'] = test.home_city.apply(lambda x: re.sub(r'\\[[0-9*]*\\]', '',  '_'.join(x.split())))\n",
    "test['home_state'] = test.home_state.apply(lambda x: re.sub(r'\\[[0-9*]*\\]', '', '_'.join(x.split())))\n",
    "\n",
    "test['profession'] = test.profession.apply(lambda x: re.sub(r'\\[[0-9*]*\\]', '',  '_'.join(x.split())))\n",
    "test['home_city'] = test.home_city.apply(lambda x: re.sub(r'\\[[0-9*]*\\]', '',  '_'.join(x.split())))\n",
    "test['home_state'] = test.home_state.apply(lambda x: re.sub(r'\\[[0-9*]*\\]', '',  '_'.join(x.split())))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "re = LabelEncoder().fit(train['residential_status'])\n",
    "\n",
    "train['resi'] = re.transform(train['residential_status'])\n",
    "val['resi'] = re.transform(val['residential_status'])\n",
    "test['resi'] = re.transform(test['residential_status'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "hc = LabelEncoder().fit(train['has_child'])\n",
    "\n",
    "train['hc'] = hc.transform(train['has_child'])\n",
    "val['hc'] = hc.transform(val['has_child'])\n",
    "test['hc'] = hc.transform(test['has_child'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "ob = LabelEncoder().fit(data['owns_bike'])\n",
    "\n",
    "train['ob'] = ob.transform(train['owns_bike'])\n",
    "val['ob'] = ob.transform(val['owns_bike'])\n",
    "test['ob'] = ob.transform(test['owns_bike'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "pf = LabelEncoder().fit(data['profession'])\n",
    "\n",
    "train['pf'] = pf.transform(train['profession'])\n",
    "val['pf'] = pf.transform(val['profession'])\n",
    "test['pf'] = pf.transform(test['profession'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "hci = LabelEncoder().fit(data['home_city'])\n",
    "\n",
    "train['hci'] = hci.transform(train['home_city'])\n",
    "val['hci'] = hci.transform(val['home_city'])\n",
    "test['hci'] = hci.transform(test['home_city'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "hs = LabelEncoder().fit(data['home_state'])\n",
    "\n",
    "train['hs'] = hs.transform(train['home_state'])\n",
    "val['hs'] = hs.transform(val['home_state'])\n",
    "test['hs'] = hs.transform(test['home_state'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### We select the numeric and encoded features to be passed to the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_cols = ['monthly_income', 'age', 'experience', 'hc', 'resi', 'ob', 'pf', 'hci', 'hs', 'years_employed']\n",
    "\n",
    "X_train = train[X_cols]\n",
    "y_train = train['ineligible_for_funds']\n",
    "\n",
    "X_val = val[X_cols]\n",
    "y_val = val['ineligible_for_funds']\n",
    "\n",
    "X_test = test[X_cols]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Since the dataset is imbalanced we upsample 1 and downsample 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "from imblearn.combine import SMOTEENN\n",
    "\n",
    "smt = SMOTEENN(random_state=0)\n",
    "X_sm, y_sm = smt.fit_resample(X_train, y_train)"
   ]
  },
  {
   "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>monthly_income</th>\n",
       "      <th>age</th>\n",
       "      <th>experience</th>\n",
       "      <th>hc</th>\n",
       "      <th>resi</th>\n",
       "      <th>ob</th>\n",
       "      <th>pf</th>\n",
       "      <th>hci</th>\n",
       "      <th>hs</th>\n",
       "      <th>years_employed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>724400.308991</td>\n",
       "      <td>61</td>\n",
       "      <td>3.741440</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>9062</td>\n",
       "      <td>2119</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>54254.886628</td>\n",
       "      <td>72</td>\n",
       "      <td>16.745524</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>7497</td>\n",
       "      <td>628</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>753938.108478</td>\n",
       "      <td>63</td>\n",
       "      <td>4.335981</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>44</td>\n",
       "      <td>9778</td>\n",
       "      <td>2423</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>257949.201949</td>\n",
       "      <td>45</td>\n",
       "      <td>10.712152</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>26</td>\n",
       "      <td>6888</td>\n",
       "      <td>897</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>54356.550522</td>\n",
       "      <td>68</td>\n",
       "      <td>9.710970</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>10743</td>\n",
       "      <td>2423</td>\n",
       "      <td>9</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>229585</th>\n",
       "      <td>881.550562</td>\n",
       "      <td>23</td>\n",
       "      <td>2.543454</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>27</td>\n",
       "      <td>8636</td>\n",
       "      <td>2119</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>229586</th>\n",
       "      <td>701681.804626</td>\n",
       "      <td>31</td>\n",
       "      <td>1.454632</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>26</td>\n",
       "      <td>4961</td>\n",
       "      <td>1579</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>229587</th>\n",
       "      <td>112037.421775</td>\n",
       "      <td>30</td>\n",
       "      <td>1.376202</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>9570</td>\n",
       "      <td>1941</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>229588</th>\n",
       "      <td>711464.196162</td>\n",
       "      <td>33</td>\n",
       "      <td>19.273595</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>48</td>\n",
       "      <td>10395</td>\n",
       "      <td>897</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>229589</th>\n",
       "      <td>87546.676336</td>\n",
       "      <td>53</td>\n",
       "      <td>2.205832</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>32</td>\n",
       "      <td>2764</td>\n",
       "      <td>2676</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>229590 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        monthly_income  age  experience  hc  resi  ob  pf    hci    hs  \\\n",
       "0        724400.308991   61    3.741440   0     1   0  10   9062  2119   \n",
       "1         54254.886628   72   16.745524   0     1   0   4   7497   628   \n",
       "2        753938.108478   63    4.335981   0     1   1  44   9778  2423   \n",
       "3        257949.201949   45   10.712152   0     1   1  26   6888   897   \n",
       "4         54356.550522   68    9.710970   0     1   1   4  10743  2423   \n",
       "...                ...  ...         ...  ..   ...  ..  ..    ...   ...   \n",
       "229585      881.550562   23    2.543454   0     1   1  27   8636  2119   \n",
       "229586   701681.804626   31    1.454632   0     1   0  26   4961  1579   \n",
       "229587   112037.421775   30    1.376202   0     0   0   7   9570  1941   \n",
       "229588   711464.196162   33   19.273595   0     1   0  48  10395   897   \n",
       "229589    87546.676336   53    2.205832   0     1   0  32   2764  2676   \n",
       "\n",
       "        years_employed  \n",
       "0                    3  \n",
       "1                   14  \n",
       "2                    3  \n",
       "3                    4  \n",
       "4                    9  \n",
       "...                ...  \n",
       "229585               1  \n",
       "229586               0  \n",
       "229587               0  \n",
       "229588               5  \n",
       "229589               0  \n",
       "\n",
       "[229590 rows x 10 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_sm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(n_jobs=10, random_state=0)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "# You could hyperparameter tune it to get better results..\n",
    "\n",
    "clf = RandomForestClassifier( random_state=0, n_jobs=10)\n",
    "clf.fit(X_sm, y_sm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9033180010687365"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import roc_auc_score\n",
    "roc_auc_score(y_val, clf.predict_proba(X_val)[:, 1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## We'll see how well it performs on the test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>ineligible_for_funds</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>63000.000000</td>\n",
       "      <td>63000.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>220500.500000</td>\n",
       "      <td>0.12300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>18186.677816</td>\n",
       "      <td>0.32844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>189001.000000</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>204750.750000</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>220500.500000</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>236250.250000</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>252000.000000</td>\n",
       "      <td>1.00000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Id  ineligible_for_funds\n",
       "count   63000.000000           63000.00000\n",
       "mean   220500.500000               0.12300\n",
       "std     18186.677816               0.32844\n",
       "min    189001.000000               0.00000\n",
       "25%    204750.750000               0.00000\n",
       "50%    220500.500000               0.00000\n",
       "75%    236250.250000               0.00000\n",
       "max    252000.000000               1.00000"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_label = pd.read_csv('test_labels_PRIVATE.csv')\n",
    "test_label.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9630566612861603"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "roc_auc_score(test_label.ineligible_for_funds, clf.predict_proba(X_test)[:, 1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "sub = pd.DataFrame(columns=['Id', 'ineligible_for_funds'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "sub['Id'] = test['Id']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "sub['ineligible_for_funds'] = clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>ineligible_for_funds</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>189001</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>189002</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>189003</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>189004</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>189005</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Id  ineligible_for_funds\n",
       "0  189001                     0\n",
       "1  189002                     0\n",
       "2  189003                     0\n",
       "3  189004                     1\n",
       "4  189005                     1"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sub.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "sub.to_csv('submission', index=False)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}