golden-Copy3.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data = pd.read_csv(\"./data/Training_dataset_Original.csv\")\n",
    "leaderboard_data = pd.read_csv(\"./data/Leaderboard_dataset.csv\")\n",
    "test_data = pd.read_csv(\"./data/Evaluation_dataset.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_data_original = train_data.copy()\n",
    "leaderboard_data_original = leaderboard_data.copy()\n",
    "test_data_original = test_data.copy()"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "application_key        0\n",
       "mvar1                  0\n",
       "mvar2               1954\n",
       "mvar3                170\n",
       "mvar4                170\n",
       "mvar5                170\n",
       "mvar6                  0\n",
       "mvar7                  0\n",
       "mvar8                  0\n",
       "mvar9                  0\n",
       "mvar10                 0\n",
       "mvar11                 0\n",
       "mvar12                 0\n",
       "mvar13                 0\n",
       "mvar14                 0\n",
       "mvar15                 0\n",
       "mvar16                 0\n",
       "mvar17                 0\n",
       "mvar18                 0\n",
       "mvar19                 0\n",
       "mvar20                 0\n",
       "mvar21              7387\n",
       "mvar22              9831\n",
       "mvar23             13399\n",
       "mvar24              6117\n",
       "mvar25                 0\n",
       "mvar26                 0\n",
       "mvar27                 0\n",
       "mvar28                 0\n",
       "mvar29                 0\n",
       "mvar30                 0\n",
       "mvar31                 0\n",
       "mvar32                 0\n",
       "mvar33               616\n",
       "mvar34                 0\n",
       "mvar35                 0\n",
       "mvar36                 0\n",
       "mvar37                 0\n",
       "mvar38                 0\n",
       "mvar39                 0\n",
       "mvar40                 0\n",
       "mvar41                 0\n",
       "mvar42                 0\n",
       "mvar43                 0\n",
       "mvar44              2625\n",
       "mvar45                 0\n",
       "mvar46                 0\n",
       "mvar47                 0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "55439"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data['mvar2'][train_data['default_ind'] == 0].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "19872"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data['mvar3'][train_data['mvar3'] > 6.447].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    61098.000000\n",
       "mean        69.981834\n",
       "std        300.080442\n",
       "min          0.000000\n",
       "25%          9.892500\n",
       "50%         40.440000\n",
       "75%         76.540000\n",
       "max      27867.400000\n",
       "Name: mvar24, dtype: float64"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data['mvar24'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4460"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data['mvar21'][train_data['mvar21'] > 100].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_data['mvar1'] = pd.to_numeric(train_data['mvar1'], errors='coerce')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    76361.000000\n",
       "mean      1745.971085\n",
       "std         94.537617\n",
       "min       1493.000000\n",
       "25%       1679.000000\n",
       "50%       1742.000000\n",
       "75%       1810.000000\n",
       "max       1950.000000\n",
       "Name: mvar1, dtype: float64"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data['mvar1'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_data['mvar6'] = pd.to_numeric(train_data['mvar6'], errors='coerce')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count     61036.000000\n",
       "mean       1606.702372\n",
       "std        3703.654127\n",
       "min           0.000000\n",
       "25%          36.000000\n",
       "50%         297.000000\n",
       "75%        1343.250000\n",
       "max      136595.000000\n",
       "Name: mvar6, dtype: float64"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data['mvar6'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "for column in train_data.columns:\n",
    "    if column != 'application_key':\n",
    "        train_data[column] = pd.to_numeric(train_data[column], errors='coerce')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "scrolled": true
   },
   "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>application_key</th>\n",
       "      <th>mvar1</th>\n",
       "      <th>mvar2</th>\n",
       "      <th>mvar3</th>\n",
       "      <th>mvar4</th>\n",
       "      <th>mvar5</th>\n",
       "      <th>mvar6</th>\n",
       "      <th>mvar7</th>\n",
       "      <th>mvar8</th>\n",
       "      <th>mvar9</th>\n",
       "      <th>...</th>\n",
       "      <th>mvar39</th>\n",
       "      <th>mvar40</th>\n",
       "      <th>mvar41</th>\n",
       "      <th>mvar42</th>\n",
       "      <th>mvar43</th>\n",
       "      <th>mvar44</th>\n",
       "      <th>mvar45</th>\n",
       "      <th>mvar46</th>\n",
       "      <th>mvar47</th>\n",
       "      <th>default_ind</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>80000.000000</td>\n",
       "      <td>76361.000000</td>\n",
       "      <td>74447.000000</td>\n",
       "      <td>79499.000000</td>\n",
       "      <td>79499.000000</td>\n",
       "      <td>79499.00000</td>\n",
       "      <td>61036.000000</td>\n",
       "      <td>7.260600e+04</td>\n",
       "      <td>61036.00000</td>\n",
       "      <td>6.859200e+04</td>\n",
       "      <td>...</td>\n",
       "      <td>73960.000000</td>\n",
       "      <td>17443.000000</td>\n",
       "      <td>25422.000000</td>\n",
       "      <td>78099.000000</td>\n",
       "      <td>79182.000000</td>\n",
       "      <td>72255.000000</td>\n",
       "      <td>35145.000000</td>\n",
       "      <td>57211.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>80000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>270031.500000</td>\n",
       "      <td>1745.971085</td>\n",
       "      <td>1.105629</td>\n",
       "      <td>5.280955</td>\n",
       "      <td>0.474516</td>\n",
       "      <td>1.12472</td>\n",
       "      <td>1606.702372</td>\n",
       "      <td>1.759581e+04</td>\n",
       "      <td>6773.68666</td>\n",
       "      <td>3.409744e+04</td>\n",
       "      <td>...</td>\n",
       "      <td>0.090819</td>\n",
       "      <td>98.497452</td>\n",
       "      <td>72.096758</td>\n",
       "      <td>0.335142</td>\n",
       "      <td>6.552777</td>\n",
       "      <td>0.705285</td>\n",
       "      <td>0.070736</td>\n",
       "      <td>0.096135</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.246213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>23094.155105</td>\n",
       "      <td>94.537617</td>\n",
       "      <td>1.645299</td>\n",
       "      <td>10.815463</td>\n",
       "      <td>1.796261</td>\n",
       "      <td>5.99545</td>\n",
       "      <td>3703.654127</td>\n",
       "      <td>4.708164e+04</td>\n",
       "      <td>10127.74526</td>\n",
       "      <td>5.337692e+04</td>\n",
       "      <td>...</td>\n",
       "      <td>0.419301</td>\n",
       "      <td>29.762399</td>\n",
       "      <td>24.442156</td>\n",
       "      <td>0.375656</td>\n",
       "      <td>6.311087</td>\n",
       "      <td>0.245879</td>\n",
       "      <td>0.287365</td>\n",
       "      <td>0.346109</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.430806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>230032.000000</td>\n",
       "      <td>1493.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.014000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.060660</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>250031.750000</td>\n",
       "      <td>1679.000000</td>\n",
       "      <td>0.140100</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>1.756000e+03</td>\n",
       "      <td>490.00000</td>\n",
       "      <td>3.498000e+03</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>90.689000</td>\n",
       "      <td>58.153000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.515220</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>270031.500000</td>\n",
       "      <td>1742.000000</td>\n",
       "      <td>0.538900</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>297.000000</td>\n",
       "      <td>6.967000e+03</td>\n",
       "      <td>2478.00000</td>\n",
       "      <td>1.417100e+04</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>78.883500</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.737030</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>290031.250000</td>\n",
       "      <td>1810.000000</td>\n",
       "      <td>1.445050</td>\n",
       "      <td>6.447000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>1343.250000</td>\n",
       "      <td>1.805575e+04</td>\n",
       "      <td>9684.25000</td>\n",
       "      <td>4.385375e+04</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>111.698000</td>\n",
       "      <td>91.639750</td>\n",
       "      <td>0.571430</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.943475</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>310031.000000</td>\n",
       "      <td>1950.000000</td>\n",
       "      <td>44.630100</td>\n",
       "      <td>286.858000</td>\n",
       "      <td>114.696000</td>\n",
       "      <td>226.52700</td>\n",
       "      <td>136595.000000</td>\n",
       "      <td>5.549600e+06</td>\n",
       "      <td>291810.00000</td>\n",
       "      <td>3.711880e+06</td>\n",
       "      <td>...</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>430.300000</td>\n",
       "      <td>170.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>91.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 49 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       application_key         mvar1         mvar2         mvar3  \\\n",
       "count     80000.000000  76361.000000  74447.000000  79499.000000   \n",
       "mean     270031.500000   1745.971085      1.105629      5.280955   \n",
       "std       23094.155105     94.537617      1.645299     10.815463   \n",
       "min      230032.000000   1493.000000      0.000000      0.000000   \n",
       "25%      250031.750000   1679.000000      0.140100      0.000000   \n",
       "50%      270031.500000   1742.000000      0.538900      0.300000   \n",
       "75%      290031.250000   1810.000000      1.445050      6.447000   \n",
       "max      310031.000000   1950.000000     44.630100    286.858000   \n",
       "\n",
       "              mvar4        mvar5          mvar6         mvar7         mvar8  \\\n",
       "count  79499.000000  79499.00000   61036.000000  7.260600e+04   61036.00000   \n",
       "mean       0.474516      1.12472    1606.702372  1.759581e+04    6773.68666   \n",
       "std        1.796261      5.99545    3703.654127  4.708164e+04   10127.74526   \n",
       "min        0.000000      0.00000       0.000000  0.000000e+00       0.00000   \n",
       "25%        0.000000      0.00000      36.000000  1.756000e+03     490.00000   \n",
       "50%        0.000000      0.00000     297.000000  6.967000e+03    2478.00000   \n",
       "75%        0.000000      0.00000    1343.250000  1.805575e+04    9684.25000   \n",
       "max      114.696000    226.52700  136595.000000  5.549600e+06  291810.00000   \n",
       "\n",
       "              mvar9      ...             mvar39        mvar40        mvar41  \\\n",
       "count  6.859200e+04      ...       73960.000000  17443.000000  25422.000000   \n",
       "mean   3.409744e+04      ...           0.090819     98.497452     72.096758   \n",
       "std    5.337692e+04      ...           0.419301     29.762399     24.442156   \n",
       "min    0.000000e+00      ...           0.000000      0.014000      0.000000   \n",
       "25%    3.498000e+03      ...           0.000000     90.689000     58.153000   \n",
       "50%    1.417100e+04      ...           0.000000    100.000000     78.883500   \n",
       "75%    4.385375e+04      ...           0.000000    111.698000     91.639750   \n",
       "max    3.711880e+06      ...          21.000000    430.300000    170.000000   \n",
       "\n",
       "             mvar42        mvar43        mvar44        mvar45        mvar46  \\\n",
       "count  78099.000000  79182.000000  72255.000000  35145.000000  57211.000000   \n",
       "mean       0.335142      6.552777      0.705285      0.070736      0.096135   \n",
       "std        0.375656      6.311087      0.245879      0.287365      0.346109   \n",
       "min        0.000000      0.000000      0.060660      0.000000      0.000000   \n",
       "25%        0.000000      2.000000      0.515220      0.000000      0.000000   \n",
       "50%        0.200000      5.000000      0.737030      0.000000      0.000000   \n",
       "75%        0.571430     10.000000      0.943475      0.000000      0.000000   \n",
       "max        2.000000     91.000000      1.000000      5.000000     19.000000   \n",
       "\n",
       "       mvar47   default_ind  \n",
       "count     0.0  80000.000000  \n",
       "mean      NaN      0.246213  \n",
       "std       NaN      0.430806  \n",
       "min       NaN      0.000000  \n",
       "25%       NaN      0.000000  \n",
       "50%       NaN      0.000000  \n",
       "75%       NaN      0.000000  \n",
       "max       NaN      1.000000  \n",
       "\n",
       "[8 rows x 49 columns]"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "application_key        0\n",
       "mvar1               3639\n",
       "mvar2               5553\n",
       "mvar3                501\n",
       "mvar4                501\n",
       "mvar5                501\n",
       "mvar6              18964\n",
       "mvar7               7394\n",
       "mvar8              18964\n",
       "mvar9              11408\n",
       "mvar10               502\n",
       "mvar11             44819\n",
       "mvar12             14078\n",
       "mvar13              8352\n",
       "mvar14                 0\n",
       "mvar15             32067\n",
       "mvar16             18510\n",
       "mvar17             15846\n",
       "mvar18             14771\n",
       "mvar19                 4\n",
       "mvar20               502\n",
       "mvar21             22561\n",
       "mvar22             29124\n",
       "mvar23             40248\n",
       "mvar24             18902\n",
       "mvar25              7706\n",
       "mvar26             10610\n",
       "mvar27             13209\n",
       "mvar28               502\n",
       "mvar29               502\n",
       "mvar30             36417\n",
       "mvar31             56218\n",
       "mvar32              7706\n",
       "mvar33              1751\n",
       "mvar34               502\n",
       "mvar35             33014\n",
       "mvar36              2898\n",
       "mvar37              7706\n",
       "mvar38               502\n",
       "mvar39              6040\n",
       "mvar40             62557\n",
       "mvar41             54578\n",
       "mvar42              1901\n",
       "mvar43               818\n",
       "mvar44              7745\n",
       "mvar45             44855\n",
       "mvar46             22789\n",
       "mvar47             80000\n",
       "default_ind            0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_data.fillna(train_data.mean(),inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "application_key        0\n",
       "mvar1                  0\n",
       "mvar2                  0\n",
       "mvar3                  0\n",
       "mvar4                  0\n",
       "mvar5                  0\n",
       "mvar6                  0\n",
       "mvar7                  0\n",
       "mvar8                  0\n",
       "mvar9                  0\n",
       "mvar10                 0\n",
       "mvar11                 0\n",
       "mvar12                 0\n",
       "mvar13                 0\n",
       "mvar14                 0\n",
       "mvar15                 0\n",
       "mvar16                 0\n",
       "mvar17                 0\n",
       "mvar18                 0\n",
       "mvar19                 0\n",
       "mvar20                 0\n",
       "mvar21                 0\n",
       "mvar22                 0\n",
       "mvar23                 0\n",
       "mvar24                 0\n",
       "mvar25                 0\n",
       "mvar26                 0\n",
       "mvar27                 0\n",
       "mvar28                 0\n",
       "mvar29                 0\n",
       "mvar30                 0\n",
       "mvar31                 0\n",
       "mvar32                 0\n",
       "mvar33                 0\n",
       "mvar34                 0\n",
       "mvar35                 0\n",
       "mvar36                 0\n",
       "mvar37                 0\n",
       "mvar38                 0\n",
       "mvar39                 0\n",
       "mvar40                 0\n",
       "mvar41                 0\n",
       "mvar42                 0\n",
       "mvar43                 0\n",
       "mvar44                 0\n",
       "mvar45                 0\n",
       "mvar46                 0\n",
       "mvar47             80000\n",
       "default_ind            0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_data['mvar47'] = pd.get_dummies(train_data_original['mvar47'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x_train = train_data.loc[:,'mvar1':'mvar47']\n",
    "y_train = train_data['default_ind']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>application_key</th>\n",
       "      <th>mvar2</th>\n",
       "      <th>mvar3</th>\n",
       "      <th>mvar4</th>\n",
       "      <th>mvar5</th>\n",
       "      <th>mvar14</th>\n",
       "      <th>mvar21</th>\n",
       "      <th>mvar22</th>\n",
       "      <th>mvar23</th>\n",
       "      <th>mvar24</th>\n",
       "      <th>mvar33</th>\n",
       "      <th>mvar44</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>25000.000000</td>\n",
       "      <td>23294.000000</td>\n",
       "      <td>24842.000000</td>\n",
       "      <td>24842.000000</td>\n",
       "      <td>24842.000000</td>\n",
       "      <td>2.500000e+04</td>\n",
       "      <td>18106.000000</td>\n",
       "      <td>15919.000000</td>\n",
       "      <td>12359.000000</td>\n",
       "      <td>19223.000000</td>\n",
       "      <td>24461.000000</td>\n",
       "      <td>22538.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>362552.500000</td>\n",
       "      <td>1.118127</td>\n",
       "      <td>5.243267</td>\n",
       "      <td>0.463290</td>\n",
       "      <td>1.080183</td>\n",
       "      <td>1.523549e+05</td>\n",
       "      <td>57.885791</td>\n",
       "      <td>4427.876770</td>\n",
       "      <td>4270.918441</td>\n",
       "      <td>72.500520</td>\n",
       "      <td>6.920566</td>\n",
       "      <td>0.702558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>7217.022701</td>\n",
       "      <td>1.658707</td>\n",
       "      <td>10.605688</td>\n",
       "      <td>1.688155</td>\n",
       "      <td>5.813250</td>\n",
       "      <td>6.337980e+06</td>\n",
       "      <td>35.344775</td>\n",
       "      <td>3813.637964</td>\n",
       "      <td>3774.419701</td>\n",
       "      <td>305.673992</td>\n",
       "      <td>7.655343</td>\n",
       "      <td>0.246612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>350053.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.117000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.083300</td>\n",
       "      <td>0.072760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>356302.750000</td>\n",
       "      <td>0.142725</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.261300e+04</td>\n",
       "      <td>27.244750</td>\n",
       "      <td>750.845000</td>\n",
       "      <td>532.705000</td>\n",
       "      <td>10.345000</td>\n",
       "      <td>1.166700</td>\n",
       "      <td>0.509132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>362552.500000</td>\n",
       "      <td>0.542400</td>\n",
       "      <td>0.286000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.234300e+04</td>\n",
       "      <td>59.569500</td>\n",
       "      <td>3978.570000</td>\n",
       "      <td>3707.500000</td>\n",
       "      <td>41.220000</td>\n",
       "      <td>3.833300</td>\n",
       "      <td>0.732740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>368802.250000</td>\n",
       "      <td>1.469375</td>\n",
       "      <td>6.480000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.238750e+05</td>\n",
       "      <td>86.366500</td>\n",
       "      <td>7523.980000</td>\n",
       "      <td>7405.470000</td>\n",
       "      <td>76.250000</td>\n",
       "      <td>10.500000</td>\n",
       "      <td>0.942372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>375052.000000</td>\n",
       "      <td>31.018100</td>\n",
       "      <td>167.740000</td>\n",
       "      <td>21.245200</td>\n",
       "      <td>156.878000</td>\n",
       "      <td>9.910000e+08</td>\n",
       "      <td>565.800000</td>\n",
       "      <td>99800.000000</td>\n",
       "      <td>41737.500000</td>\n",
       "      <td>22621.130000</td>\n",
       "      <td>76.500000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       application_key         mvar2         mvar3         mvar4  \\\n",
       "count     25000.000000  23294.000000  24842.000000  24842.000000   \n",
       "mean     362552.500000      1.118127      5.243267      0.463290   \n",
       "std        7217.022701      1.658707     10.605688      1.688155   \n",
       "min      350053.000000      0.000000      0.000000      0.000000   \n",
       "25%      356302.750000      0.142725      0.000000      0.000000   \n",
       "50%      362552.500000      0.542400      0.286000      0.000000   \n",
       "75%      368802.250000      1.469375      6.480000      0.000000   \n",
       "max      375052.000000     31.018100    167.740000     21.245200   \n",
       "\n",
       "              mvar5        mvar14        mvar21        mvar22        mvar23  \\\n",
       "count  24842.000000  2.500000e+04  18106.000000  15919.000000  12359.000000   \n",
       "mean       1.080183  1.523549e+05     57.885791   4427.876770   4270.918441   \n",
       "std        5.813250  6.337980e+06     35.344775   3813.637964   3774.419701   \n",
       "min        0.000000  0.000000e+00      0.117000      0.000000      0.000000   \n",
       "25%        0.000000  4.261300e+04     27.244750    750.845000    532.705000   \n",
       "50%        0.000000  7.234300e+04     59.569500   3978.570000   3707.500000   \n",
       "75%        0.000000  1.238750e+05     86.366500   7523.980000   7405.470000   \n",
       "max      156.878000  9.910000e+08    565.800000  99800.000000  41737.500000   \n",
       "\n",
       "             mvar24        mvar33        mvar44  \n",
       "count  19223.000000  24461.000000  22538.000000  \n",
       "mean      72.500520      6.920566      0.702558  \n",
       "std      305.673992      7.655343      0.246612  \n",
       "min        0.000000      0.083300      0.072760  \n",
       "25%       10.345000      1.166700      0.509132  \n",
       "50%       41.220000      3.833300      0.732740  \n",
       "75%       76.250000     10.500000      0.942372  \n",
       "max    22621.130000     76.500000      1.000000  "
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "leaderboard_data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "for column in leaderboard_data.columns:\n",
    "    if column != 'application_key':\n",
    "        leaderboard_data[column] = pd.to_numeric(leaderboard_data[column], errors='coerce')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>application_key</th>\n",
       "      <th>mvar1</th>\n",
       "      <th>mvar2</th>\n",
       "      <th>mvar3</th>\n",
       "      <th>mvar4</th>\n",
       "      <th>mvar5</th>\n",
       "      <th>mvar6</th>\n",
       "      <th>mvar7</th>\n",
       "      <th>mvar8</th>\n",
       "      <th>mvar9</th>\n",
       "      <th>...</th>\n",
       "      <th>mvar38</th>\n",
       "      <th>mvar39</th>\n",
       "      <th>mvar40</th>\n",
       "      <th>mvar41</th>\n",
       "      <th>mvar42</th>\n",
       "      <th>mvar43</th>\n",
       "      <th>mvar44</th>\n",
       "      <th>mvar45</th>\n",
       "      <th>mvar46</th>\n",
       "      <th>mvar47</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>25000.000000</td>\n",
       "      <td>23850.000000</td>\n",
       "      <td>23294.000000</td>\n",
       "      <td>24842.000000</td>\n",
       "      <td>24842.000000</td>\n",
       "      <td>24842.000000</td>\n",
       "      <td>19193.000000</td>\n",
       "      <td>2.269400e+04</td>\n",
       "      <td>19193.000000</td>\n",
       "      <td>21523.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>24842.000000</td>\n",
       "      <td>23078.000000</td>\n",
       "      <td>5458.000000</td>\n",
       "      <td>7748.000000</td>\n",
       "      <td>24367.000000</td>\n",
       "      <td>24723.000000</td>\n",
       "      <td>22538.000000</td>\n",
       "      <td>11027.000000</td>\n",
       "      <td>17851.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>362552.500000</td>\n",
       "      <td>1746.901551</td>\n",
       "      <td>1.118127</td>\n",
       "      <td>5.243267</td>\n",
       "      <td>0.463290</td>\n",
       "      <td>1.080183</td>\n",
       "      <td>1624.069192</td>\n",
       "      <td>1.775037e+04</td>\n",
       "      <td>6753.202313</td>\n",
       "      <td>33825.554662</td>\n",
       "      <td>...</td>\n",
       "      <td>4.251349</td>\n",
       "      <td>0.089912</td>\n",
       "      <td>98.491140</td>\n",
       "      <td>71.990942</td>\n",
       "      <td>0.331992</td>\n",
       "      <td>6.546091</td>\n",
       "      <td>0.702558</td>\n",
       "      <td>0.071280</td>\n",
       "      <td>0.092712</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>7217.022701</td>\n",
       "      <td>94.088195</td>\n",
       "      <td>1.658707</td>\n",
       "      <td>10.605688</td>\n",
       "      <td>1.688155</td>\n",
       "      <td>5.813250</td>\n",
       "      <td>3588.247900</td>\n",
       "      <td>5.485979e+04</td>\n",
       "      <td>10133.583639</td>\n",
       "      <td>51781.414456</td>\n",
       "      <td>...</td>\n",
       "      <td>3.870975</td>\n",
       "      <td>0.444250</td>\n",
       "      <td>30.488996</td>\n",
       "      <td>24.806054</td>\n",
       "      <td>0.374753</td>\n",
       "      <td>6.260583</td>\n",
       "      <td>0.246612</td>\n",
       "      <td>0.302654</td>\n",
       "      <td>0.321896</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>350053.000000</td>\n",
       "      <td>1477.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.018000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.072760</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>356302.750000</td>\n",
       "      <td>1680.000000</td>\n",
       "      <td>0.142725</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>1.701000e+03</td>\n",
       "      <td>494.000000</td>\n",
       "      <td>3518.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>91.338250</td>\n",
       "      <td>58.179500</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.509132</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>362552.500000</td>\n",
       "      <td>1742.000000</td>\n",
       "      <td>0.542400</td>\n",
       "      <td>0.286000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>297.000000</td>\n",
       "      <td>7.011500e+03</td>\n",
       "      <td>2503.000000</td>\n",
       "      <td>14072.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>79.123000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.732740</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>368802.250000</td>\n",
       "      <td>1811.000000</td>\n",
       "      <td>1.469375</td>\n",
       "      <td>6.480000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1371.000000</td>\n",
       "      <td>1.803575e+04</td>\n",
       "      <td>9595.000000</td>\n",
       "      <td>43009.500000</td>\n",
       "      <td>...</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>112.237500</td>\n",
       "      <td>91.666000</td>\n",
       "      <td>0.561950</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.942372</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>375052.000000</td>\n",
       "      <td>1950.000000</td>\n",
       "      <td>31.018100</td>\n",
       "      <td>167.740000</td>\n",
       "      <td>21.245200</td>\n",
       "      <td>156.878000</td>\n",
       "      <td>59192.000000</td>\n",
       "      <td>5.637108e+06</td>\n",
       "      <td>223123.000000</td>\n",
       "      <td>840658.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>73.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>631.360000</td>\n",
       "      <td>182.111000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>63.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 48 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       application_key         mvar1         mvar2         mvar3  \\\n",
       "count     25000.000000  23850.000000  23294.000000  24842.000000   \n",
       "mean     362552.500000   1746.901551      1.118127      5.243267   \n",
       "std        7217.022701     94.088195      1.658707     10.605688   \n",
       "min      350053.000000   1477.000000      0.000000      0.000000   \n",
       "25%      356302.750000   1680.000000      0.142725      0.000000   \n",
       "50%      362552.500000   1742.000000      0.542400      0.286000   \n",
       "75%      368802.250000   1811.000000      1.469375      6.480000   \n",
       "max      375052.000000   1950.000000     31.018100    167.740000   \n",
       "\n",
       "              mvar4         mvar5         mvar6         mvar7          mvar8  \\\n",
       "count  24842.000000  24842.000000  19193.000000  2.269400e+04   19193.000000   \n",
       "mean       0.463290      1.080183   1624.069192  1.775037e+04    6753.202313   \n",
       "std        1.688155      5.813250   3588.247900  5.485979e+04   10133.583639   \n",
       "min        0.000000      0.000000      0.000000  0.000000e+00       0.000000   \n",
       "25%        0.000000      0.000000     36.000000  1.701000e+03     494.000000   \n",
       "50%        0.000000      0.000000    297.000000  7.011500e+03    2503.000000   \n",
       "75%        0.000000      0.000000   1371.000000  1.803575e+04    9595.000000   \n",
       "max       21.245200    156.878000  59192.000000  5.637108e+06  223123.000000   \n",
       "\n",
       "               mvar9   ...          mvar38        mvar39       mvar40  \\\n",
       "count   21523.000000   ...    24842.000000  23078.000000  5458.000000   \n",
       "mean    33825.554662   ...        4.251349      0.089912    98.491140   \n",
       "std     51781.414456   ...        3.870975      0.444250    30.488996   \n",
       "min         0.000000   ...        0.000000      0.000000     0.018000   \n",
       "25%      3518.000000   ...        2.000000      0.000000    91.338250   \n",
       "50%     14072.000000   ...        3.000000      0.000000   100.000000   \n",
       "75%     43009.500000   ...        6.000000      0.000000   112.237500   \n",
       "max    840658.000000   ...       73.000000     20.000000   631.360000   \n",
       "\n",
       "            mvar41        mvar42        mvar43        mvar44        mvar45  \\\n",
       "count  7748.000000  24367.000000  24723.000000  22538.000000  11027.000000   \n",
       "mean     71.990942      0.331992      6.546091      0.702558      0.071280   \n",
       "std      24.806054      0.374753      6.260583      0.246612      0.302654   \n",
       "min       0.000000      0.000000      0.000000      0.072760      0.000000   \n",
       "25%      58.179500      0.000000      2.000000      0.509132      0.000000   \n",
       "50%      79.123000      0.200000      5.000000      0.732740      0.000000   \n",
       "75%      91.666000      0.561950     10.000000      0.942372      0.000000   \n",
       "max     182.111000      2.000000     63.000000      1.000000      7.000000   \n",
       "\n",
       "             mvar46  mvar47  \n",
       "count  17851.000000     0.0  \n",
       "mean       0.092712     NaN  \n",
       "std        0.321896     NaN  \n",
       "min        0.000000     NaN  \n",
       "25%        0.000000     NaN  \n",
       "50%        0.000000     NaN  \n",
       "75%        0.000000     NaN  \n",
       "max        3.000000     NaN  \n",
       "\n",
       "[8 rows x 48 columns]"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "leaderboard_data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "leaderboard_data['mvar47'] = pd.get_dummies(leaderboard_data_original['mvar47'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "leaderboard_data.fillna(leaderboard_data.mean(),inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x_test = leaderboard_data.loc[:,'mvar1':]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
       "          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n",
       "          verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "clf = LogisticRegression()\n",
    "clf.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pred = clf.predict_proba(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pred = pd.DataFrame(pred)\n",
    "pred['application_key'] = leaderboard_data['application_key']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(25000, 3)"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pred = pred.sort_values(by = 0,kind = 'mergesort',ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cols = pred.columns.tolist()\n",
    "cols = cols[-1:] + cols[:-1]\n",
    "pred = pred[cols]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pred = pred.loc[:,'application_key':]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "submit = pred['application_key'][:10000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "submit = pd.DataFrame(submit)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "submit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "submit[0] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "submit.to_csv('flabbergasted_IITGuwahati_55.csv',header = False, index = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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