Kowope Solution.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "eVlNzQ58W0fg"
   },
   "outputs": [],
   "source": [
    "# Importing neccesary libraries\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set()\n",
    "pd.set_option('display.max_columns', None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 436
    },
    "id": "1DVT-SFBXrxJ",
    "outputId": "4839beaa-f100-47db-fb9e-95424b38c83a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting catboost\n",
      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/90/86/c3dcb600b4f9e7584ed90ea9d30a717fb5c0111574675f442c3e7bc19535/catboost-0.24.1-cp36-none-manylinux1_x86_64.whl (66.1MB)\n",
      "\u001b[K     |████████████████████████████████| 66.1MB 56kB/s \n",
      "\u001b[?25hCollecting rgf_python\n",
      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/7d/b5/9ba527ce20a1a5a6c279318be856aa4996e0d1b59035d466173198ffd468/rgf_python-3.9.0-py2.py3-none-manylinux1_x86_64.whl (757kB)\n",
      "\u001b[K     |████████████████████████████████| 768kB 46.2MB/s \n",
      "\u001b[?25hRequirement already satisfied: numpy>=1.16.0 in /usr/local/lib/python3.6/dist-packages (from catboost) (1.18.5)\n",
      "Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from catboost) (1.15.0)\n",
      "Requirement already satisfied: scipy in /usr/local/lib/python3.6/dist-packages (from catboost) (1.4.1)\n",
      "Requirement already satisfied: graphviz in /usr/local/lib/python3.6/dist-packages (from catboost) (0.10.1)\n",
      "Requirement already satisfied: matplotlib in /usr/local/lib/python3.6/dist-packages (from catboost) (3.2.2)\n",
      "Requirement already satisfied: pandas>=0.24.0 in /usr/local/lib/python3.6/dist-packages (from catboost) (1.1.2)\n",
      "Requirement already satisfied: plotly in /usr/local/lib/python3.6/dist-packages (from catboost) (4.4.1)\n",
      "Requirement already satisfied: joblib in /usr/local/lib/python3.6/dist-packages (from rgf_python) (0.16.0)\n",
      "Requirement already satisfied: scikit-learn>=0.18 in /usr/local/lib/python3.6/dist-packages (from rgf_python) (0.22.2.post1)\n",
      "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib->catboost) (2.4.7)\n",
      "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.6/dist-packages (from matplotlib->catboost) (0.10.0)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib->catboost) (1.2.0)\n",
      "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib->catboost) (2.8.1)\n",
      "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.6/dist-packages (from pandas>=0.24.0->catboost) (2018.9)\n",
      "Requirement already satisfied: retrying>=1.3.3 in /usr/local/lib/python3.6/dist-packages (from plotly->catboost) (1.3.3)\n",
      "Installing collected packages: catboost, rgf-python\n",
      "Successfully installed catboost-0.24.1 rgf-python-3.9.0\n"
     ]
    }
   ],
   "source": [
    "# Insatlling catboost and Regularide Greedy Forest\n",
    "\n",
    "!pip install catboost rgf_python"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "OL143UQOXwl8"
   },
   "outputs": [],
   "source": [
    "# importing the train and test data\n",
    "\n",
    "train_df = pd.read_csv('/content/drive/My Drive/Zindi/Train.csv')\n",
    "test_df = pd.read_csv('/content/drive/My Drive/Zindi/Test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 226
    },
    "id": "RhH7DBs0iEkm",
    "outputId": "d351e786-53c1-422b-ec5d-e393db8f97c7"
   },
   "outputs": [
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       "      <th>0</th>\n",
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       "      <td>3436.0</td>\n",
       "      <td>0.28505</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>134.655</td>\n",
       "      <td>4000.5</td>\n",
       "      <td>17064.0</td>\n",
       "      <td>11376.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>17064.0</td>\n",
       "      <td>5962.0</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>0.2342</td>\n",
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       "      <td>1.620483</td>\n",
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       "      <td>no</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
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       "      <td>0.53845</td>\n",
       "      <td>3.1510</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.282</td>\n",
       "      <td>NaN</td>\n",
       "      <td>956940.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>192944.0</td>\n",
       "      <td>1079864.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16576457</td>\n",
       "      <td>22452691.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1597.5</td>\n",
       "      <td>4746.0</td>\n",
       "      <td>3164.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>15696.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14508.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>0.6666</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>1.513370</td>\n",
       "      <td>0.011640</td>\n",
       "      <td>yes</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
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       "      <td>3372.0</td>\n",
       "      <td>0.17005</td>\n",
       "      <td>0.5050</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>192166.0</td>\n",
       "      <td>3044703.0</td>\n",
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       "      <td>3986472.0</td>\n",
       "      <td>3621979.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>771776.0</td>\n",
       "      <td>1186839.0</td>\n",
       "      <td>48187375</td>\n",
       "      <td>136297820.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>120.120</td>\n",
       "      <td>8580.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>85.800</td>\n",
       "      <td>1701.0</td>\n",
       "      <td>4014.0</td>\n",
       "      <td>2676.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>11679.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>522.0</td>\n",
       "      <td>7.8334</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.488884</td>\n",
       "      <td>2.02</td>\n",
       "      <td>0.685168</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>charge</td>\n",
       "      <td>89.940100</td>\n",
       "      <td>0.664452</td>\n",
       "      <td>0.082729</td>\n",
       "      <td>no</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Apcnt_1000016</td>\n",
       "      <td>3370.0</td>\n",
       "      <td>0.77270</td>\n",
       "      <td>1.1010</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>1556.0</td>\n",
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       "      <td>1284089.0</td>\n",
       "      <td>361770.0</td>\n",
       "      <td>396391.0</td>\n",
       "      <td>818456.0</td>\n",
       "      <td>511535.0</td>\n",
       "      <td>28141427</td>\n",
       "      <td>15292757.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>104.535</td>\n",
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       "      <td>115.335</td>\n",
       "      <td>1551.0</td>\n",
       "      <td>3285.0</td>\n",
       "      <td>2190.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>19437.0</td>\n",
       "      <td>12958.0</td>\n",
       "      <td>5202.0</td>\n",
       "      <td>272.0</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>24.0</td>\n",
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       "      <td>150.0135</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.275000</td>\n",
       "      <td>12.12</td>\n",
       "      <td>0.438168</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>charge</td>\n",
       "      <td>97.887502</td>\n",
       "      <td>1.427891</td>\n",
       "      <td>0.045630</td>\n",
       "      <td>no</td>\n",
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       "3  Apcnt_1000012       3372.0      0.17005       0.5050          0.0   \n",
       "4  Apcnt_1000016       3370.0      0.77270       1.1010          0.0   \n",
       "\n",
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       "0        0.000          0.0   10689720.0     252072.0    4272776.0   \n",
       "1        0.000          0.0     898979.0     497531.0    9073814.0   \n",
       "2        6.282          NaN     956940.0          NaN     192944.0   \n",
       "3        0.000     192166.0    3044703.0     385499.0    3986472.0   \n",
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       "\n",
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       "0    11333126.0     4397256.0     2301324.0     7875694.0      96375139   \n",
       "1     2533168.0      244292.0     3982971.0     2141445.0      28141427   \n",
       "2     1079864.0           NaN           NaN           0.0      16576457   \n",
       "3     3621979.0           NaN      771776.0     1186839.0      48187375   \n",
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       "\n",
       "   form_field15  form_field16  form_field17  form_field18  form_field19  \\\n",
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       "1    45169902.0           1.0           1.0           1.0           2.0   \n",
       "2    22452691.0           NaN           NaN           NaN           0.0   \n",
       "3   136297820.0           0.0           0.0           0.0           1.0   \n",
       "4    15292757.0           0.0           0.0           0.0           3.0   \n",
       "\n",
       "   form_field20  form_field21  form_field22  form_field23  form_field24  \\\n",
       "0           3.0       134.655           NaN           NaN       134.655   \n",
       "1           2.0       109.230      11385.24      14584.29        86.685   \n",
       "2           0.0           NaN           NaN           NaN           NaN   \n",
       "3           1.0       120.120       8580.00          0.00        85.800   \n",
       "4           3.0       104.535       8360.19      10326.45       115.335   \n",
       "\n",
       "   form_field25  form_field26  form_field27  form_field28  form_field29  \\\n",
       "0        4000.5       17064.0       11376.0          60.0       17064.0   \n",
       "1        4161.0       13413.0        8942.0         304.0       13413.0   \n",
       "2        1597.5        4746.0        3164.0          60.0       15696.0   \n",
       "3        1701.0        4014.0        2676.0          60.0       11679.0   \n",
       "4        1551.0        3285.0        2190.0          60.0       19437.0   \n",
       "\n",
       "   form_field30  form_field31  form_field32  form_field33  form_field34  \\\n",
       "0        5962.0           NaN        1052.0        8.0000           2.0   \n",
       "1        5780.0           NaN        2554.0       17.5000           0.0   \n",
       "2           NaN       14508.0          70.0        0.6666           0.0   \n",
       "3           NaN           NaN         522.0        7.8334           1.0   \n",
       "4       12958.0        5202.0         272.0        1.0000           0.0   \n",
       "\n",
       "   form_field35  form_field36  form_field37  form_field38  form_field39  \\\n",
       "0           NaN          12.0          12.0           4.0           0.0   \n",
       "1           0.0          16.0          24.0           8.0           0.0   \n",
       "2           NaN          12.0           0.0           6.0           0.0   \n",
       "3           1.0           4.0           8.0           4.0           1.0   \n",
       "4           0.0          24.0           2.0          24.0           0.0   \n",
       "\n",
       "   form_field40  form_field41  form_field42  form_field43  form_field44  \\\n",
       "0           NaN           NaN      0.392854          2.02      0.711632   \n",
       "1           NaN           NaN      0.314281          8.08      0.183584   \n",
       "2      149.6805           NaN      0.162965         18.18      0.791136   \n",
       "3           NaN           NaN      0.488884          2.02      0.685168   \n",
       "4      150.0135           NaN      0.275000         12.12      0.438168   \n",
       "\n",
       "   form_field45  form_field46 form_field47  form_field48  form_field49  \\\n",
       "0           0.0           0.0       charge           NaN      1.129518   \n",
       "1           NaN           0.0       charge    349.805730      1.620483   \n",
       "2           0.0           0.0       charge           NaN      1.513370   \n",
       "3           NaN           0.0       charge     89.940100      0.664452   \n",
       "4           0.0           0.0       charge     97.887502      1.427891   \n",
       "\n",
       "   form_field50 default_status  \n",
       "0      0.044335             no  \n",
       "1      0.322436             no  \n",
       "2      0.011640            yes  \n",
       "3      0.082729             no  \n",
       "4      0.045630             no  "
      ]
     },
     "execution_count": 16,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 226
    },
    "id": "Vxv0lGQ1iJ-8",
    "outputId": "5d131ad4-588d-488e-b2a4-51296b9be861"
   },
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Applicant_ID</th>\n",
       "      <th>form_field1</th>\n",
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       "      <th>form_field49</th>\n",
       "      <th>form_field50</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Apcnt_1000032</td>\n",
       "      <td>3236.0</td>\n",
       "      <td>0.34875</td>\n",
       "      <td>10.2006</td>\n",
       "      <td>0.0000</td>\n",
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       "      <td>1649749.0</td>\n",
       "      <td>5446.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>48187375</td>\n",
       "      <td>114686147.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1086.0</td>\n",
       "      <td>6204.0</td>\n",
       "      <td>2676.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>14691.0</td>\n",
       "      <td>9794.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>238.0</td>\n",
       "      <td>1.8334</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.825000</td>\n",
       "      <td>1.01</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>charge</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.011221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Apcnt_1000048</td>\n",
       "      <td>3284.0</td>\n",
       "      <td>1.27360</td>\n",
       "      <td>2.9606</td>\n",
       "      <td>9.0198</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9858816.0</td>\n",
       "      <td>49014.0</td>\n",
       "      <td>1510098.0</td>\n",
       "      <td>18308285.0</td>\n",
       "      <td>622789.0</td>\n",
       "      <td>1025793.0</td>\n",
       "      <td>1396510.0</td>\n",
       "      <td>34694910</td>\n",
       "      <td>87479487.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>142.560</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>570.585</td>\n",
       "      <td>3082.5</td>\n",
       "      <td>10221.0</td>\n",
       "      <td>6814.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>11955.0</td>\n",
       "      <td>3528.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1216.0</td>\n",
       "      <td>19.5000</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18.8415</td>\n",
       "      <td>0.507694</td>\n",
       "      <td>4.04</td>\n",
       "      <td>0.623248</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>lending</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.504974</td>\n",
       "      <td>0.043525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Apcnt_1000052</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.27505</td>\n",
       "      <td>0.0600</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>118256.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21973443</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>852.0</td>\n",
       "      <td>4836.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.8334</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>charge</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Apcnt_1000076</td>\n",
       "      <td>3232.0</td>\n",
       "      <td>0.28505</td>\n",
       "      <td>2.8032</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>473802.0</td>\n",
       "      <td>473802.0</td>\n",
       "      <td>1724437.0</td>\n",
       "      <td>493641.0</td>\n",
       "      <td>1391064.0</td>\n",
       "      <td>1176725.0</td>\n",
       "      <td>1220293.0</td>\n",
       "      <td>16576457</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>109.005</td>\n",
       "      <td>13050.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>109.005</td>\n",
       "      <td>1414.5</td>\n",
       "      <td>4197.0</td>\n",
       "      <td>2798.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>14964.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>248.0</td>\n",
       "      <td>9.0000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.916663</td>\n",
       "      <td>2.02</td>\n",
       "      <td>0.464224</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>charge</td>\n",
       "      <td>90.163742</td>\n",
       "      <td>0.788809</td>\n",
       "      <td>0.104029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Apcnt_1000080</td>\n",
       "      <td>3466.0</td>\n",
       "      <td>2.09545</td>\n",
       "      <td>0.8318</td>\n",
       "      <td>2.5182</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19839.0</td>\n",
       "      <td>1150662.0</td>\n",
       "      <td>1150662.0</td>\n",
       "      <td>7860523.0</td>\n",
       "      <td>5752921.0</td>\n",
       "      <td>271133.0</td>\n",
       "      <td>613064.0</td>\n",
       "      <td>4779643.0</td>\n",
       "      <td>48187375</td>\n",
       "      <td>72049802.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>139.065</td>\n",
       "      <td>469.29</td>\n",
       "      <td>469.29</td>\n",
       "      <td>15.180</td>\n",
       "      <td>2724.0</td>\n",
       "      <td>9855.0</td>\n",
       "      <td>6570.0</td>\n",
       "      <td>182.0</td>\n",
       "      <td>11679.0</td>\n",
       "      <td>5900.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2388.0</td>\n",
       "      <td>16.6666</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.234047</td>\n",
       "      <td>23.23</td>\n",
       "      <td>0.726688</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>lending</td>\n",
       "      <td>1303.587148</td>\n",
       "      <td>1.637733</td>\n",
       "      <td>0.163124</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Applicant_ID  form_field1  form_field2  form_field3  form_field4  \\\n",
       "0  Apcnt_1000032       3236.0      0.34875      10.2006       0.0000   \n",
       "1  Apcnt_1000048       3284.0      1.27360       2.9606       9.0198   \n",
       "2  Apcnt_1000052          NaN      0.27505       0.0600       0.0000   \n",
       "3  Apcnt_1000076       3232.0      0.28505       2.8032       0.0000   \n",
       "4  Apcnt_1000080       3466.0      2.09545       0.8318       2.5182   \n",
       "\n",
       "   form_field5  form_field6  form_field7  form_field8  form_field9  \\\n",
       "0          0.0     418564.0     418564.0     418564.0     540710.0   \n",
       "1          0.0          0.0    9858816.0      49014.0    1510098.0   \n",
       "2          0.0          NaN          NaN          NaN          NaN   \n",
       "3          0.0          0.0     473802.0     473802.0    1724437.0   \n",
       "4          0.0      19839.0    1150662.0    1150662.0    7860523.0   \n",
       "\n",
       "   form_field10  form_field11  form_field12  form_field13  form_field14  \\\n",
       "0           0.0     1649749.0        5446.0           0.0      48187375   \n",
       "1    18308285.0      622789.0     1025793.0     1396510.0      34694910   \n",
       "2           0.0      118256.0           NaN           NaN      21973443   \n",
       "3      493641.0     1391064.0     1176725.0     1220293.0      16576457   \n",
       "4     5752921.0      271133.0      613064.0     4779643.0      48187375   \n",
       "\n",
       "   form_field15  form_field16  form_field17  form_field18  form_field19  \\\n",
       "0   114686147.0           0.0           0.0           0.0           0.0   \n",
       "1    87479487.0           1.0           1.0           1.0           2.0   \n",
       "2           NaN           NaN           NaN           NaN           0.0   \n",
       "3           NaN           2.0           2.0           2.0           3.0   \n",
       "4    72049802.0           0.0           0.0           0.0           3.0   \n",
       "\n",
       "   form_field20  form_field21  form_field22  form_field23  form_field24  \\\n",
       "0           0.0           NaN           NaN           NaN           NaN   \n",
       "1           2.0       142.560           NaN           NaN       570.585   \n",
       "2           0.0           NaN           NaN           NaN           NaN   \n",
       "3           3.0       109.005      13050.00           NaN       109.005   \n",
       "4           3.0       139.065        469.29        469.29        15.180   \n",
       "\n",
       "   form_field25  form_field26  form_field27  form_field28  form_field29  \\\n",
       "0        1086.0        6204.0        2676.0          60.0       14691.0   \n",
       "1        3082.5       10221.0        6814.0          60.0       11955.0   \n",
       "2           NaN           NaN           NaN         852.0        4836.0   \n",
       "3        1414.5        4197.0        2798.0          60.0       14964.0   \n",
       "4        2724.0        9855.0        6570.0         182.0       11679.0   \n",
       "\n",
       "   form_field30  form_field31  form_field32  form_field33  form_field34  \\\n",
       "0        9794.0           NaN         238.0        1.8334           0.0   \n",
       "1        3528.0           NaN        1216.0       19.5000           3.0   \n",
       "2           NaN           NaN           NaN        7.8334           0.0   \n",
       "3           NaN           NaN         248.0        9.0000           1.0   \n",
       "4        5900.0           NaN        2388.0       16.6666           1.0   \n",
       "\n",
       "   form_field35  form_field36  form_field37  form_field38  form_field39  \\\n",
       "0           NaN          22.0           2.0          14.0           0.0   \n",
       "1           NaN          30.0          18.0          14.0           2.0   \n",
       "2           NaN           2.0           NaN           0.0           NaN   \n",
       "3           1.0          36.0           0.0          26.0           0.0   \n",
       "4           0.0          24.0          28.0          20.0           0.0   \n",
       "\n",
       "   form_field40  form_field41  form_field42  form_field43  form_field44  \\\n",
       "0           NaN           NaN      0.825000          1.01      0.800000   \n",
       "1           NaN       18.8415      0.507694          4.04      0.623248   \n",
       "2           NaN           NaN           NaN          0.00           NaN   \n",
       "3           NaN           NaN      0.916663          2.02      0.464224   \n",
       "4           NaN           NaN      0.234047         23.23      0.726688   \n",
       "\n",
       "   form_field45  form_field46 form_field47  form_field48  form_field49  \\\n",
       "0           NaN           0.0       charge           NaN      0.000000   \n",
       "1           1.0           0.0      lending           NaN      0.504974   \n",
       "2           NaN           NaN       charge           NaN      0.000000   \n",
       "3           NaN           NaN       charge     90.163742      0.788809   \n",
       "4           0.0           0.0      lending   1303.587148      1.637733   \n",
       "\n",
       "   form_field50  \n",
       "0      0.011221  \n",
       "1      0.043525  \n",
       "2           NaN  \n",
       "3      0.104029  \n",
       "4      0.163124  "
      ]
     },
     "execution_count": 17,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 719
    },
    "id": "9vIY6W-EiadQ",
    "outputId": "bc7e0f1a-abb0-4d9a-bb26-9f6a573dc2eb"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# A plot to check percentage of missing values for each column in the train data\n",
    "\n",
    "ax_train = train_df.isna().sum().transform(lambda x:x/train_df.shape[0]).sort_values().plot.barh(figsize=(16, 12), title='% missing values (Train)', color='purple', alpha=.8)\n",
    "for i in ax_train.patches:\n",
    "    ax_train.text(i.get_width()+0.005, i.get_y(), str(int(round(i.get_width(), 2)*100)) + '%', fontsize=13, color='red', alpha=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 719
    },
    "id": "Haw0VhgfkNxW",
    "outputId": "7b5770bb-63bf-42ff-c2de-8ce3d9030de5"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# A plot to check percentage of missing values for each column in the test data\n",
    "ax_test = test_df.isna().sum().transform(lambda x:x/test_df.shape[0]).sort_values().plot.barh(figsize=(16, 12), title='% missing values (Test)', color='orange', alpha=.8)\n",
    "for i in ax_test.patches:\n",
    "    ax_test.text(i.get_width()+0.005, i.get_y(), str(int(round(i.get_width(), 2)*100)) + '%', fontsize=13, color='indigo', alpha=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "id": "hPsX4DC9X3Tv"
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.metrics import roc_auc_score, plot_confusion_matrix, plot_roc_curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "O7owXY-vYDHW"
   },
   "outputs": [],
   "source": [
    "# A function to print train and validation score and also return the validation score of a model\n",
    "def model_auc(model):\n",
    "    train_auc = roc_auc_score(y_train, model.predict_proba(x_train)[:, 1])\n",
    "    test_auc = roc_auc_score(y_test, model.predict_proba(x_test)[:, 1])\n",
    "    print(\"Train AUC : {} \\t Val AUC : {}\".format(train_auc, test_auc))\n",
    "    return test_auc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "id": "X96FrjAyYFqr"
   },
   "outputs": [],
   "source": [
    "# Importing neccesary libraries for modelling\n",
    "\n",
    "from sklearn.model_selection import train_test_split, StratifiedKFold\n",
    "from sklearn.ensemble import RandomForestClassifier, VotingClassifier\n",
    "from sklearn.preprocessing import StandardScaler, MinMaxScaler\n",
    "from catboost import CatBoostClassifier\n",
    "from sklearn.pipeline import Pipeline\n",
    "from rgf import RGFClassifier\n",
    "from xgboost import XGBClassifier\n",
    "from lightgbm import LGBMClassifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "U7UxdwWYp1pL"
   },
   "source": [
    "    The only cleaning done on the train and test data was to fill missing values in each column with -999"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "YKr_PwqQYIHq"
   },
   "outputs": [],
   "source": [
    "features = train_df.columns.drop(['Applicant_ID', 'default_status'])\n",
    "X = pd.get_dummies(train_df[features])\n",
    "X.fillna(-999, inplace=True)\n",
    "y = train_df.default_status\n",
    "\n",
    "test_x = pd.get_dummies(test_df[features])\n",
    "test_x.fillna(-999, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "r-csLA4BqQrk"
   },
   "source": [
    " After filling missing values, i tried feature engineering, tried creating logical columns from domain understanding, it wasnt bringing improvement on the leaderboard."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "BnhxJyVivcbB"
   },
   "source": [
    "My model was a voting classifier comprising of two catboosts, two Regularized greedy forest and two random forest models.\n",
    "some untuned, others slightly tuned.\n",
    "\n",
    "Ran each model with a differnet seed value for random state, values are 32, 64 and 45. For each seed, used the voting classifier explained above. Also ran a diffierent variation of the Voting Classifier on seed value containing two catboosts, one regularized greedy forest and two random forest classifier. All in a stratfied kfold of 30 splits."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##            voting classifiers with two catboosts, two Regularized greedy forest and tow random forests"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "OnE9GOjOYKhc",
    "outputId": "bd6e196e-ca68-43b4-efd0-ad9167437e62"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of splits left 30 to train.\n",
      "0.850497385042755\n",
      "Number of splits left 29 to train.\n",
      "0.8485745767183451\n",
      "Number of splits left 28 to train.\n",
      "0.8328444837593307\n",
      "Number of splits left 27 to train.\n",
      "0.8395812964601083\n",
      "Number of splits left 26 to train.\n",
      "0.824090196626162\n",
      "Number of splits left 25 to train.\n",
      "0.8508294923723949\n",
      "Number of splits left 24 to train.\n",
      "0.8351956174247714\n",
      "Number of splits left 23 to train.\n",
      "0.8368375312320562\n",
      "Number of splits left 22 to train.\n",
      "0.8491844747582911\n",
      "Number of splits left 21 to train.\n",
      "0.8601936775455095\n",
      "Number of splits left 20 to train.\n",
      "0.8415180719152041\n",
      "Number of splits left 19 to train.\n",
      "0.8411813088753356\n",
      "Number of splits left 18 to train.\n",
      "0.8431910237906792\n",
      "Number of splits left 17 to train.\n",
      "0.8466440088768876\n",
      "Number of splits left 16 to train.\n",
      "0.8472523550134241\n",
      "Number of splits left 15 to train.\n",
      "0.8414698398628901\n",
      "Number of splits left 14 to train.\n",
      "0.8412033062564115\n",
      "Number of splits left 13 to train.\n",
      "0.8365157239331682\n",
      "Number of splits left 12 to train.\n",
      "0.8410560929272519\n",
      "Number of splits left 11 to train.\n",
      "0.8341076237909137\n",
      "Number of splits left 10 to train.\n",
      "0.8379967480078832\n",
      "Number of splits left 9 to train.\n",
      "0.8462866877978857\n",
      "Number of splits left 8 to train.\n",
      "0.8493336832770886\n",
      "Number of splits left 7 to train.\n",
      "0.8334045127214392\n",
      "Number of splits left 6 to train.\n",
      "0.8452710226381515\n",
      "Number of splits left 5 to train.\n",
      "0.8384968155635932\n",
      "Number of splits left 4 to train.\n",
      "0.8508354389490506\n",
      "Number of splits left 3 to train.\n",
      "0.8340303736684924\n",
      "Number of splits left 2 to train.\n",
      "0.8456608268508323\n",
      "Number of splits left 1 to train.\n",
      "0.8364468491861478\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.8419910348614154"
      ]
     },
     "execution_count": 9,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# for seed value 64\n",
    "\n",
    " preds, scores = [], []\n",
    " \n",
    "seed = 64\n",
    " \n",
    "Vc = VotingClassifier(estimators=[\n",
    "('cat',  CatBoostClassifier(verbose=False, thread_count=-1, random_state=seed)), \n",
    "('rgf', RGFClassifier()), (\"rgf1\", RGFClassifier(algorithm=\"RGF_Sib\")), ('cat1', CatBoostClassifier(bagging_temperature = 0.8609895558337871, border_count=116, depth=5, iterations=270, l2_leaf_reg=3, \n",
    "                         learning_rate=0.07085617391173239, random_strength=0.08820672130534879, random_state=seed, scale_pos_weight=0.390675124341283, verbose=False, thread_count=-1)), \n",
    "('rg',RandomForestClassifier(n_estimators=500, n_jobs=-1, random_state=seed)), ('rg1', RandomForestClassifier(n_estimators=700, n_jobs=-1, random_state=seed))], \n",
    "voting=\"soft\", weights=[3, 1.5,1.5,1,1,1])\n",
    " \n",
    "skf = StratifiedKFold(n_splits=30, shuffle=True, random_state=seed) \n",
    " \n",
    "i = 30\n",
    "for train_index, test_index in skf.split(X, y):\n",
    "    print(\"Number of splits left {0} to train.\".format(i))\n",
    "    x_train, x_test, y_train, y_test = X.iloc[train_index], X.iloc[test_index], y.iloc[train_index], y.iloc[test_index]\n",
    "    pipe = Pipeline([(\"SC\", MinMaxScaler()), (\"clf\", Vc)])\n",
    "    pipe.fit(x_train, y_train)\n",
    "    score = roc_auc_score(y_test, pipe.predict_proba(x_test)[:, 1])\n",
    "    scores.append(score) \n",
    "    pred = pipe.predict_proba(test_x)[:, 1]\n",
    "    preds.append(pred)\n",
    "    print(score)\n",
    "    i -= 1\n",
    " \n",
    "np.mean(scores) #LB Score 0.84532655"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "id": "0I3E8aAUZM-l"
   },
   "outputs": [],
   "source": [
    "ss = pd.read_csv(\"/content/drive/My Drive/Zindi/SampleSubmission.csv\")\n",
    "p = np.mean(preds, axis=0)\n",
    "ss[\"default_status\"] = p\n",
    "ss.to_csv(\"/content/drive/My Drive/Zindi/64.csv\", index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "gE431VqM5qpL"
   },
   "source": [
    "  The rest models were ran in different notebooks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "kQZq31eBGPEu"
   },
   "outputs": [],
   "source": [
    "# For seed value 45\n",
    "\n",
    " preds, scores = [], []\n",
    " \n",
    "seed = 45\n",
    " \n",
    "Vc = VotingClassifier(estimators=[\n",
    "('cat',  CatBoostClassifier(verbose=False, thread_count=-1, random_state=seed)), \n",
    "('rgf', RGFClassifier()), (\"rgf1\", RGFClassifier(algorithm=\"RGF_Sib\")), ('cat1', CatBoostClassifier(bagging_temperature = 0.8609895558337871, border_count=116, depth=5, iterations=270, l2_leaf_reg=3, \n",
    "                         learning_rate=0.07085617391173239, random_strength=0.08820672130534879, random_state=seed, scale_pos_weight=0.390675124341283, verbose=False, thread_count=-1)), \n",
    "('rg',RandomForestClassifier(n_estimators=500, n_jobs=-1, random_state=seed)), ('rg1', RandomForestClassifier(n_estimators=700, n_jobs=-1, random_state=seed))], \n",
    "voting=\"soft\", weights=[3, 1.5,1.5,1,1,1])\n",
    " \n",
    "skf = StratifiedKFold(n_splits=30, shuffle=True, random_state=seed) \n",
    " \n",
    "i = 30\n",
    "for train_index, test_index in skf.split(X, y):\n",
    "    print(\"Number of splits left {0} to train.\".format(i))\n",
    "    x_train, x_test, y_train, y_test = X.iloc[train_index], X.iloc[test_index], y.iloc[train_index], y.iloc[test_index]\n",
    "    pipe = Pipeline([(\"SC\", MinMaxScaler()), (\"clf\", Vc)])\n",
    "    pipe.fit(x_train, y_train)\n",
    "    score = roc_auc_score(y_test, pipe.predict_proba(x_test)[:, 1])\n",
    "    scores.append(score) \n",
    "    pred = pipe.predict_proba(test_x)[:, 1]\n",
    "    preds.append(pred)\n",
    "    print(score)\n",
    "    i -= 1\n",
    " \n",
    "np.mean(scores)    #LB Score 0.8453015"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "xcN1f368OH1N"
   },
   "outputs": [],
   "source": [
    "ss = pd.read_csv('/content/drive/My Drive/Zindi/SampleSubmission.csv')\n",
    "ss['default_status'] = np.mean(preds, axis=0)\n",
    "ss.to_csv('/content/drive/My Drive/Zindi/4ie.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "KLo79KjkN-Tg"
   },
   "outputs": [],
   "source": [
    "# For seed value 32\n",
    "\n",
    " preds, scores = [], []\n",
    " \n",
    "seed = 32\n",
    " \n",
    "Vc = VotingClassifier(estimators=[\n",
    "('cat',  CatBoostClassifier(verbose=False, thread_count=-1, random_state=seed)), \n",
    "('rgf', RGFClassifier()), (\"rgf1\", RGFClassifier(algorithm=\"RGF_Sib\")), ('cat1', CatBoostClassifier(bagging_temperature = 0.8609895558337871, border_count=116, depth=5, iterations=270, l2_leaf_reg=3, \n",
    "                         learning_rate=0.07085617391173239, random_strength=0.08820672130534879, random_state=seed, scale_pos_weight=0.390675124341283, verbose=False, thread_count=-1)), \n",
    "('rg',RandomForestClassifier(n_estimators=500, n_jobs=-1, random_state=seed)), ('rg1', RandomForestClassifier(n_estimators=700, n_jobs=-1, random_state=seed))], \n",
    "voting=\"soft\", weights=[3, 1.5,1.5,1,1,1])\n",
    " \n",
    "skf = StratifiedKFold(n_splits=30, shuffle=True, random_state=seed) \n",
    " \n",
    "i = 30\n",
    "for train_index, test_index in skf.split(X, y):\n",
    "    print(\"Number of splits left {0} to train.\".format(i))\n",
    "    x_train, x_test, y_train, y_test = X.iloc[train_index], X.iloc[test_index], y.iloc[train_index], y.iloc[test_index]\n",
    "    pipe = Pipeline([(\"SC\", MinMaxScaler()), (\"clf\", Vc)])\n",
    "    pipe.fit(x_train, y_train)\n",
    "    score = roc_auc_score(y_test, pipe.predict_proba(x_test)[:, 1])\n",
    "    scores.append(score) \n",
    "    pred = pipe.predict_proba(test_x)[:, 1]\n",
    "    preds.append(pred)\n",
    "    print(score)\n",
    "    i -= 1\n",
    " \n",
    "np.mean(scores)  #LB Score 0.845320714"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "qUDG5oSjOLfO"
   },
   "outputs": [],
   "source": [
    "ss = pd.read_csv(\"/content/drive/My Drive/Zindi/SampleSubmission.csv\")\n",
    "p = np.mean(preds, axis=0)\n",
    "ss[\"default_status\"] = p\n",
    "ss.to_csv(\"/content/drive/My Drive/Zindi/t2rg2cat_2rf.csv\", index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##         This was a voting classifier made up of two catboosts, 1 Regularized greedy forest and 2 random forests on seed value of 32"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "AjL8T0FpP_4d"
   },
   "outputs": [],
   "source": [
    "seed = 32\n",
    " \n",
    "skf = StratifiedKFold(n_splits=30, shuffle=True, random_state=seed)\n",
    "scores = []\n",
    "preds = []\n",
    "vc = VotingClassifier(estimators=[('cat', CatBoostClassifier(verbose=False, random_state=seed, thread_count=-1)), \n",
    "                                  ('cat2', CatBoostClassifier(bagging_temperature = 0.8609895558337871, border_count=116, depth=5, iterations=270, l2_leaf_reg=3, learning_rate=0.07085617391173239, \n",
    "                                                              random_strength=0.08820672130534879, scale_pos_weight=0.390675124341283, verbose=False, random_state=seed, thread_count=-1)), \n",
    "                                  ('rgf', RGFClassifier()), ('rf', RandomForestClassifier(n_estimators=500, n_jobs=-1, random_state=seed)), \n",
    "                                  ('rf2', RandomForestClassifier(n_estimators=700, n_jobs=-1, random_state=seed))], voting='soft', weights=[3, 1, 1.5, 1, 1])\n",
    " \n",
    "i = 1\n",
    "for train, test in skf.split(X, y):\n",
    "    pipe = Pipeline([('scaler', MinMaxScaler()), ('model', vc)])\n",
    "    x_train, x_test, y_train, y_test = X.iloc[train], X.iloc[test], y.iloc[train], y.iloc[test]\n",
    "    pipe.fit(x_train, y_train)\n",
    "    print('Number of splits trained {} '.format(i))\n",
    "    score = model_auc(pipe)\n",
    "    pred = pipe.predict_proba(test_x)[:, 1]\n",
    "    scores.append(score)\n",
    "    preds.append(pred)\n",
    "    i += 1\n",
    " \n",
    "pred_32 = np.mean(preds, axis=0)\n",
    "np.mean(scores)   #LB Score 0.84533767"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "ZKleuH_2QdCC"
   },
   "outputs": [],
   "source": [
    "ss = pd.read_csv('/content/drive/My Drive/Zindi/SampleSubmission.csv')\n",
    "ss['default_status'] = pred_32\n",
    "ss.to_csv('/content/drive/My Drive/Zindi/32.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "KbS0a3esUeFh"
   },
   "source": [
    "  ##      Blending predictions\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "id": "6UYvvEaoQx-X"
   },
   "outputs": [],
   "source": [
    "sub1 = pd.read_csv('/content/drive/My Drive/Zindi/32.csv')  #LB Score 0.84533767\n",
    "sub2 = pd.read_csv('/content/drive/My Drive/Zindi/64.csv')  #LB Score 0.84532655\n",
    "sub3 = pd.read_csv('/content/drive/My Drive/Zindi/4ie.csv')  #LB Score 0.8453015\n",
    "sub4 = pd.read_csv('/content/drive/My Drive/Zindi/t2rg2cat_2rf.csv')  #LB Score 0.845320714"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "id": "EKUICeVXWd3b"
   },
   "outputs": [],
   "source": [
    "blend = (sub1['default_status'] + sub2['default_status'] + sub4['default_status'])/3\n",
    "ss = pd.read_csv('/content/drive/My Drive/Zindi/SampleSubmission.csv')\n",
    "ss['default_status'] = blend\n",
    "ss.to_csv('/content/drive/My Drive/Zindi/Final_sub.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "id": "abohnfQnXq1E"
   },
   "outputs": [],
   "source": [
    "blend1 = sub1['default_status']*0.6 + sub2['default_status']*0.4\n",
    "ss = pd.read_csv('/content/drive/My Drive/Zindi/SampleSubmission.csv')\n",
    "ss['default_status'] = blend1\n",
    "ss.to_csv('/content/drive/My Drive/Zindi/Final_sub1.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "id": "Ym87V_qjYdbw"
   },
   "outputs": [],
   "source": [
    "blend2 = sub1['default_status']*0.6 + sub2['default_status']*0.3 + sub4['default_status']*0.1\n",
    "ss = pd.read_csv('/content/drive/My Drive/Zindi/SampleSubmission.csv')\n",
    "ss['default_status'] = blend2\n",
    "ss.to_csv('/content/drive/My Drive/Zindi/Final_sub2.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "gCUvK5B9h7-K"
   },
   "source": [
    "  ####                        Further Improvements"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "      1. Over time i have known a stacking Classifier to work better than a Voting Classifier.  So a stacking Classifier with the right combination of algorithms will beat this Voting Classifiers\n",
    "      \n",
    "      2. A very thorough understanding of the dataset so as to create quality features to help the models get closer to understanding the data better.\n",
    "      \n",
    "      3. Hypertunning each models individually to near perfection. I did little or no hyper tunning, if it were otherwise the models will perform much more better.\n",
    "      \n",
    "      4. Feature selection\n",
    "      \n",
    "      5. Handling outliers and Multi colinearity \n",
    " "
   ]
  }
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