linear_regression.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sb\n",
    "import matplotlib.pyplot as plt\n",
    "import plotly.plotly as py\n",
    "import plotly.graph_objs as go\n",
    "import colorlover as cl\n",
    "import plotly.io as pio\n",
    "\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.metrics import r2_score\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "from numpy.polynomial.polynomial import polyfit\n",
    "\n",
    "sb.set()\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"Datasets/train_V2.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 4446966 entries, 0 to 4446965\n",
      "Data columns (total 29 columns):\n",
      "Id                 object\n",
      "groupId            object\n",
      "matchId            object\n",
      "assists            int64\n",
      "boosts             int64\n",
      "damageDealt        float64\n",
      "DBNOs              int64\n",
      "headshotKills      int64\n",
      "heals              int64\n",
      "killPlace          int64\n",
      "killPoints         int64\n",
      "kills              int64\n",
      "killStreaks        int64\n",
      "longestKill        float64\n",
      "matchDuration      int64\n",
      "matchType          object\n",
      "maxPlace           int64\n",
      "numGroups          int64\n",
      "rankPoints         int64\n",
      "revives            int64\n",
      "rideDistance       float64\n",
      "roadKills          int64\n",
      "swimDistance       float64\n",
      "teamKills          int64\n",
      "vehicleDestroys    int64\n",
      "walkDistance       float64\n",
      "weaponsAcquired    int64\n",
      "winPoints          int64\n",
      "winPlacePerc       float64\n",
      "dtypes: float64(6), int64(19), object(4)\n",
      "memory usage: 983.9+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Id                 0\n",
       "groupId            0\n",
       "matchId            0\n",
       "assists            0\n",
       "boosts             0\n",
       "damageDealt        0\n",
       "DBNOs              0\n",
       "headshotKills      0\n",
       "heals              0\n",
       "killPlace          0\n",
       "killPoints         0\n",
       "kills              0\n",
       "killStreaks        0\n",
       "longestKill        0\n",
       "matchDuration      0\n",
       "matchType          0\n",
       "maxPlace           0\n",
       "numGroups          0\n",
       "rankPoints         0\n",
       "revives            0\n",
       "rideDistance       0\n",
       "roadKills          0\n",
       "swimDistance       0\n",
       "teamKills          0\n",
       "vehicleDestroys    0\n",
       "walkDistance       0\n",
       "weaponsAcquired    0\n",
       "winPoints          0\n",
       "winPlacePerc       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.dropna(how='any',axis=0)\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Id', 'groupId', 'matchId', 'assists', 'boosts', 'damageDealt', 'DBNOs',\n",
       "       'headshotKills', 'heals', 'killPlace', 'killPoints', 'kills',\n",
       "       'killStreaks', 'longestKill', 'matchDuration', 'matchType', 'maxPlace',\n",
       "       'numGroups', 'rankPoints', 'revives', 'rideDistance', 'roadKills',\n",
       "       'swimDistance', 'teamKills', 'vehicleDestroys', 'walkDistance',\n",
       "       'weaponsAcquired', 'winPoints', 'winPlacePerc'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>assists</th>\n",
       "      <th>boosts</th>\n",
       "      <th>damageDealt</th>\n",
       "      <th>DBNOs</th>\n",
       "      <th>headshotKills</th>\n",
       "      <th>heals</th>\n",
       "      <th>killPlace</th>\n",
       "      <th>killPoints</th>\n",
       "      <th>kills</th>\n",
       "      <th>killStreaks</th>\n",
       "      <th>...</th>\n",
       "      <th>revives</th>\n",
       "      <th>rideDistance</th>\n",
       "      <th>roadKills</th>\n",
       "      <th>swimDistance</th>\n",
       "      <th>teamKills</th>\n",
       "      <th>vehicleDestroys</th>\n",
       "      <th>walkDistance</th>\n",
       "      <th>weaponsAcquired</th>\n",
       "      <th>winPoints</th>\n",
       "      <th>winPlacePerc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>assists</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.307683</td>\n",
       "      <td>0.406726</td>\n",
       "      <td>0.301057</td>\n",
       "      <td>0.198289</td>\n",
       "      <td>0.228556</td>\n",
       "      <td>0.290062</td>\n",
       "      <td>0.039066</td>\n",
       "      <td>0.319690</td>\n",
       "      <td>0.243471</td>\n",
       "      <td>...</td>\n",
       "      <td>0.198320</td>\n",
       "      <td>0.110644</td>\n",
       "      <td>0.011903</td>\n",
       "      <td>0.023372</td>\n",
       "      <td>0.006081</td>\n",
       "      <td>0.057921</td>\n",
       "      <td>0.290305</td>\n",
       "      <td>0.243882</td>\n",
       "      <td>0.024014</td>\n",
       "      <td>0.299441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>boosts</th>\n",
       "      <td>0.307683</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.521947</td>\n",
       "      <td>0.358907</td>\n",
       "      <td>0.334661</td>\n",
       "      <td>0.535854</td>\n",
       "      <td>0.554844</td>\n",
       "      <td>0.008347</td>\n",
       "      <td>0.502024</td>\n",
       "      <td>0.405327</td>\n",
       "      <td>...</td>\n",
       "      <td>0.253125</td>\n",
       "      <td>0.328855</td>\n",
       "      <td>0.035124</td>\n",
       "      <td>0.107993</td>\n",
       "      <td>0.013068</td>\n",
       "      <td>0.087387</td>\n",
       "      <td>0.640150</td>\n",
       "      <td>0.406609</td>\n",
       "      <td>0.007645</td>\n",
       "      <td>0.634234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>damageDealt</th>\n",
       "      <td>0.406726</td>\n",
       "      <td>0.521947</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.735763</td>\n",
       "      <td>0.613409</td>\n",
       "      <td>0.342987</td>\n",
       "      <td>0.677398</td>\n",
       "      <td>0.049904</td>\n",
       "      <td>0.888783</td>\n",
       "      <td>0.703599</td>\n",
       "      <td>...</td>\n",
       "      <td>0.256951</td>\n",
       "      <td>0.140883</td>\n",
       "      <td>0.052487</td>\n",
       "      <td>0.036729</td>\n",
       "      <td>0.015468</td>\n",
       "      <td>0.081182</td>\n",
       "      <td>0.398199</td>\n",
       "      <td>0.353248</td>\n",
       "      <td>0.017762</td>\n",
       "      <td>0.440507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DBNOs</th>\n",
       "      <td>0.301057</td>\n",
       "      <td>0.358907</td>\n",
       "      <td>0.735763</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.469923</td>\n",
       "      <td>0.265485</td>\n",
       "      <td>0.555290</td>\n",
       "      <td>0.042616</td>\n",
       "      <td>0.707848</td>\n",
       "      <td>0.646872</td>\n",
       "      <td>...</td>\n",
       "      <td>0.300999</td>\n",
       "      <td>0.102537</td>\n",
       "      <td>0.036093</td>\n",
       "      <td>0.017320</td>\n",
       "      <td>0.069430</td>\n",
       "      <td>0.060210</td>\n",
       "      <td>0.284660</td>\n",
       "      <td>0.218161</td>\n",
       "      <td>0.011108</td>\n",
       "      <td>0.279970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>headshotKills</th>\n",
       "      <td>0.198289</td>\n",
       "      <td>0.334661</td>\n",
       "      <td>0.613409</td>\n",
       "      <td>0.469923</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.199917</td>\n",
       "      <td>0.469829</td>\n",
       "      <td>0.023988</td>\n",
       "      <td>0.674275</td>\n",
       "      <td>0.512882</td>\n",
       "      <td>...</td>\n",
       "      <td>0.150145</td>\n",
       "      <td>0.076495</td>\n",
       "      <td>0.013369</td>\n",
       "      <td>0.028531</td>\n",
       "      <td>0.008658</td>\n",
       "      <td>0.039200</td>\n",
       "      <td>0.250982</td>\n",
       "      <td>0.217552</td>\n",
       "      <td>0.004982</td>\n",
       "      <td>0.277722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>heals</th>\n",
       "      <td>0.228556</td>\n",
       "      <td>0.535854</td>\n",
       "      <td>0.342987</td>\n",
       "      <td>0.265485</td>\n",
       "      <td>0.199917</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.386032</td>\n",
       "      <td>0.002585</td>\n",
       "      <td>0.311781</td>\n",
       "      <td>0.270412</td>\n",
       "      <td>...</td>\n",
       "      <td>0.236680</td>\n",
       "      <td>0.297484</td>\n",
       "      <td>0.024619</td>\n",
       "      <td>0.079587</td>\n",
       "      <td>0.035386</td>\n",
       "      <td>0.062870</td>\n",
       "      <td>0.430266</td>\n",
       "      <td>0.309071</td>\n",
       "      <td>0.010990</td>\n",
       "      <td>0.427857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>killPlace</th>\n",
       "      <td>0.290062</td>\n",
       "      <td>0.554844</td>\n",
       "      <td>0.677398</td>\n",
       "      <td>0.555290</td>\n",
       "      <td>0.469829</td>\n",
       "      <td>0.386032</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.023278</td>\n",
       "      <td>0.730499</td>\n",
       "      <td>0.810517</td>\n",
       "      <td>...</td>\n",
       "      <td>0.269804</td>\n",
       "      <td>0.236202</td>\n",
       "      <td>0.057356</td>\n",
       "      <td>0.089396</td>\n",
       "      <td>0.037797</td>\n",
       "      <td>0.073569</td>\n",
       "      <td>0.590236</td>\n",
       "      <td>0.490144</td>\n",
       "      <td>0.002415</td>\n",
       "      <td>0.719069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>killPoints</th>\n",
       "      <td>0.039066</td>\n",
       "      <td>0.008347</td>\n",
       "      <td>0.049904</td>\n",
       "      <td>0.042616</td>\n",
       "      <td>0.023988</td>\n",
       "      <td>0.002585</td>\n",
       "      <td>0.023278</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.036192</td>\n",
       "      <td>0.037091</td>\n",
       "      <td>...</td>\n",
       "      <td>0.012041</td>\n",
       "      <td>0.029316</td>\n",
       "      <td>0.004156</td>\n",
       "      <td>0.004604</td>\n",
       "      <td>0.039739</td>\n",
       "      <td>0.009658</td>\n",
       "      <td>0.004630</td>\n",
       "      <td>0.003978</td>\n",
       "      <td>0.983417</td>\n",
       "      <td>0.012908</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>kills</th>\n",
       "      <td>0.319690</td>\n",
       "      <td>0.502024</td>\n",
       "      <td>0.888783</td>\n",
       "      <td>0.707848</td>\n",
       "      <td>0.674275</td>\n",
       "      <td>0.311781</td>\n",
       "      <td>0.730499</td>\n",
       "      <td>0.036192</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.802500</td>\n",
       "      <td>...</td>\n",
       "      <td>0.253323</td>\n",
       "      <td>0.114750</td>\n",
       "      <td>0.063651</td>\n",
       "      <td>0.039127</td>\n",
       "      <td>0.017601</td>\n",
       "      <td>0.079991</td>\n",
       "      <td>0.374774</td>\n",
       "      <td>0.336414</td>\n",
       "      <td>0.006929</td>\n",
       "      <td>0.419916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>killStreaks</th>\n",
       "      <td>0.243471</td>\n",
       "      <td>0.405327</td>\n",
       "      <td>0.703599</td>\n",
       "      <td>0.646872</td>\n",
       "      <td>0.512882</td>\n",
       "      <td>0.270412</td>\n",
       "      <td>0.810517</td>\n",
       "      <td>0.037091</td>\n",
       "      <td>0.802500</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.241695</td>\n",
       "      <td>0.098420</td>\n",
       "      <td>0.065575</td>\n",
       "      <td>0.034588</td>\n",
       "      <td>0.027612</td>\n",
       "      <td>0.064374</td>\n",
       "      <td>0.327639</td>\n",
       "      <td>0.275373</td>\n",
       "      <td>0.008552</td>\n",
       "      <td>0.377566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>longestKill</th>\n",
       "      <td>0.261425</td>\n",
       "      <td>0.423290</td>\n",
       "      <td>0.563837</td>\n",
       "      <td>0.451422</td>\n",
       "      <td>0.447261</td>\n",
       "      <td>0.263278</td>\n",
       "      <td>0.541146</td>\n",
       "      <td>0.014084</td>\n",
       "      <td>0.602936</td>\n",
       "      <td>0.515097</td>\n",
       "      <td>...</td>\n",
       "      <td>0.170134</td>\n",
       "      <td>0.190853</td>\n",
       "      <td>0.025214</td>\n",
       "      <td>0.053455</td>\n",
       "      <td>0.007604</td>\n",
       "      <td>0.083227</td>\n",
       "      <td>0.413810</td>\n",
       "      <td>0.276264</td>\n",
       "      <td>0.003720</td>\n",
       "      <td>0.410154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>matchDuration</th>\n",
       "      <td>0.019450</td>\n",
       "      <td>0.072107</td>\n",
       "      <td>0.006756</td>\n",
       "      <td>0.014486</td>\n",
       "      <td>0.017657</td>\n",
       "      <td>0.108901</td>\n",
       "      <td>0.005806</td>\n",
       "      <td>0.099542</td>\n",
       "      <td>0.030688</td>\n",
       "      <td>0.034645</td>\n",
       "      <td>...</td>\n",
       "      <td>0.016990</td>\n",
       "      <td>0.365212</td>\n",
       "      <td>0.020016</td>\n",
       "      <td>0.008737</td>\n",
       "      <td>0.047969</td>\n",
       "      <td>0.060924</td>\n",
       "      <td>0.145062</td>\n",
       "      <td>0.129080</td>\n",
       "      <td>0.087361</td>\n",
       "      <td>0.005171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>maxPlace</th>\n",
       "      <td>0.147916</td>\n",
       "      <td>0.013686</td>\n",
       "      <td>0.040708</td>\n",
       "      <td>0.267710</td>\n",
       "      <td>0.009211</td>\n",
       "      <td>0.064759</td>\n",
       "      <td>0.016791</td>\n",
       "      <td>0.044802</td>\n",
       "      <td>0.004847</td>\n",
       "      <td>0.037339</td>\n",
       "      <td>...</td>\n",
       "      <td>0.160369</td>\n",
       "      <td>0.047784</td>\n",
       "      <td>0.011504</td>\n",
       "      <td>0.007788</td>\n",
       "      <td>0.038057</td>\n",
       "      <td>0.018888</td>\n",
       "      <td>0.083624</td>\n",
       "      <td>0.039121</td>\n",
       "      <td>0.029295</td>\n",
       "      <td>0.037377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>numGroups</th>\n",
       "      <td>0.146805</td>\n",
       "      <td>0.012929</td>\n",
       "      <td>0.040079</td>\n",
       "      <td>0.265789</td>\n",
       "      <td>0.009389</td>\n",
       "      <td>0.064204</td>\n",
       "      <td>0.018786</td>\n",
       "      <td>0.042367</td>\n",
       "      <td>0.004060</td>\n",
       "      <td>0.036054</td>\n",
       "      <td>...</td>\n",
       "      <td>0.159173</td>\n",
       "      <td>0.049880</td>\n",
       "      <td>0.010435</td>\n",
       "      <td>0.007922</td>\n",
       "      <td>0.038429</td>\n",
       "      <td>0.019173</td>\n",
       "      <td>0.082667</td>\n",
       "      <td>0.039984</td>\n",
       "      <td>0.027483</td>\n",
       "      <td>0.039621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rankPoints</th>\n",
       "      <td>0.016407</td>\n",
       "      <td>0.023202</td>\n",
       "      <td>0.001459</td>\n",
       "      <td>0.003451</td>\n",
       "      <td>0.005119</td>\n",
       "      <td>0.019142</td>\n",
       "      <td>0.014805</td>\n",
       "      <td>0.975555</td>\n",
       "      <td>0.008200</td>\n",
       "      <td>0.003426</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000613</td>\n",
       "      <td>0.036813</td>\n",
       "      <td>0.006138</td>\n",
       "      <td>0.004724</td>\n",
       "      <td>0.039626</td>\n",
       "      <td>0.013400</td>\n",
       "      <td>0.015790</td>\n",
       "      <td>0.014756</td>\n",
       "      <td>0.993845</td>\n",
       "      <td>0.013523</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>revives</th>\n",
       "      <td>0.198320</td>\n",
       "      <td>0.253125</td>\n",
       "      <td>0.256951</td>\n",
       "      <td>0.300999</td>\n",
       "      <td>0.150145</td>\n",
       "      <td>0.236680</td>\n",
       "      <td>0.269804</td>\n",
       "      <td>0.012041</td>\n",
       "      <td>0.253323</td>\n",
       "      <td>0.241695</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.109946</td>\n",
       "      <td>0.005920</td>\n",
       "      <td>0.020338</td>\n",
       "      <td>0.032989</td>\n",
       "      <td>0.038019</td>\n",
       "      <td>0.236796</td>\n",
       "      <td>0.167281</td>\n",
       "      <td>0.002434</td>\n",
       "      <td>0.240881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rideDistance</th>\n",
       "      <td>0.110644</td>\n",
       "      <td>0.328855</td>\n",
       "      <td>0.140883</td>\n",
       "      <td>0.102537</td>\n",
       "      <td>0.076495</td>\n",
       "      <td>0.297484</td>\n",
       "      <td>0.236202</td>\n",
       "      <td>0.029316</td>\n",
       "      <td>0.114750</td>\n",
       "      <td>0.098420</td>\n",
       "      <td>...</td>\n",
       "      <td>0.109946</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.103518</td>\n",
       "      <td>0.049448</td>\n",
       "      <td>0.050179</td>\n",
       "      <td>0.119253</td>\n",
       "      <td>0.310936</td>\n",
       "      <td>0.282246</td>\n",
       "      <td>0.027809</td>\n",
       "      <td>0.342915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>roadKills</th>\n",
       "      <td>0.011903</td>\n",
       "      <td>0.035124</td>\n",
       "      <td>0.052487</td>\n",
       "      <td>0.036093</td>\n",
       "      <td>0.013369</td>\n",
       "      <td>0.024619</td>\n",
       "      <td>0.057356</td>\n",
       "      <td>0.004156</td>\n",
       "      <td>0.063651</td>\n",
       "      <td>0.065575</td>\n",
       "      <td>...</td>\n",
       "      <td>0.005920</td>\n",
       "      <td>0.103518</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.004411</td>\n",
       "      <td>0.009684</td>\n",
       "      <td>0.031071</td>\n",
       "      <td>0.016736</td>\n",
       "      <td>0.016494</td>\n",
       "      <td>0.004750</td>\n",
       "      <td>0.034544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>swimDistance</th>\n",
       "      <td>0.023372</td>\n",
       "      <td>0.107993</td>\n",
       "      <td>0.036729</td>\n",
       "      <td>0.017320</td>\n",
       "      <td>0.028531</td>\n",
       "      <td>0.079587</td>\n",
       "      <td>0.089396</td>\n",
       "      <td>0.004604</td>\n",
       "      <td>0.039127</td>\n",
       "      <td>0.034588</td>\n",
       "      <td>...</td>\n",
       "      <td>0.020338</td>\n",
       "      <td>0.049448</td>\n",
       "      <td>0.004411</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.006199</td>\n",
       "      <td>0.008090</td>\n",
       "      <td>0.167509</td>\n",
       "      <td>0.075698</td>\n",
       "      <td>0.002680</td>\n",
       "      <td>0.149607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>teamKills</th>\n",
       "      <td>0.006081</td>\n",
       "      <td>0.013068</td>\n",
       "      <td>0.015468</td>\n",
       "      <td>0.069430</td>\n",
       "      <td>0.008658</td>\n",
       "      <td>0.035386</td>\n",
       "      <td>0.037797</td>\n",
       "      <td>0.039739</td>\n",
       "      <td>0.017601</td>\n",
       "      <td>0.027612</td>\n",
       "      <td>...</td>\n",
       "      <td>0.032989</td>\n",
       "      <td>0.050179</td>\n",
       "      <td>0.009684</td>\n",
       "      <td>0.006199</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.112164</td>\n",
       "      <td>0.024858</td>\n",
       "      <td>0.035918</td>\n",
       "      <td>0.040943</td>\n",
       "      <td>0.015943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vehicleDestroys</th>\n",
       "      <td>0.057921</td>\n",
       "      <td>0.087387</td>\n",
       "      <td>0.081182</td>\n",
       "      <td>0.060210</td>\n",
       "      <td>0.039200</td>\n",
       "      <td>0.062870</td>\n",
       "      <td>0.073569</td>\n",
       "      <td>0.009658</td>\n",
       "      <td>0.079991</td>\n",
       "      <td>0.064374</td>\n",
       "      <td>...</td>\n",
       "      <td>0.038019</td>\n",
       "      <td>0.119253</td>\n",
       "      <td>0.031071</td>\n",
       "      <td>0.008090</td>\n",
       "      <td>0.112164</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.076237</td>\n",
       "      <td>0.061012</td>\n",
       "      <td>0.011426</td>\n",
       "      <td>0.073436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>walkDistance</th>\n",
       "      <td>0.290305</td>\n",
       "      <td>0.640150</td>\n",
       "      <td>0.398199</td>\n",
       "      <td>0.284660</td>\n",
       "      <td>0.250982</td>\n",
       "      <td>0.430266</td>\n",
       "      <td>0.590236</td>\n",
       "      <td>0.004630</td>\n",
       "      <td>0.374774</td>\n",
       "      <td>0.327639</td>\n",
       "      <td>...</td>\n",
       "      <td>0.236796</td>\n",
       "      <td>0.310936</td>\n",
       "      <td>0.016736</td>\n",
       "      <td>0.167509</td>\n",
       "      <td>0.024858</td>\n",
       "      <td>0.076237</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.541222</td>\n",
       "      <td>0.002044</td>\n",
       "      <td>0.810888</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>weaponsAcquired</th>\n",
       "      <td>0.243882</td>\n",
       "      <td>0.406609</td>\n",
       "      <td>0.353248</td>\n",
       "      <td>0.218161</td>\n",
       "      <td>0.217552</td>\n",
       "      <td>0.309071</td>\n",
       "      <td>0.490144</td>\n",
       "      <td>0.003978</td>\n",
       "      <td>0.336414</td>\n",
       "      <td>0.275373</td>\n",
       "      <td>...</td>\n",
       "      <td>0.167281</td>\n",
       "      <td>0.282246</td>\n",
       "      <td>0.016494</td>\n",
       "      <td>0.075698</td>\n",
       "      <td>0.035918</td>\n",
       "      <td>0.061012</td>\n",
       "      <td>0.541222</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.003815</td>\n",
       "      <td>0.583806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>winPoints</th>\n",
       "      <td>0.024014</td>\n",
       "      <td>0.007645</td>\n",
       "      <td>0.017762</td>\n",
       "      <td>0.011108</td>\n",
       "      <td>0.004982</td>\n",
       "      <td>0.010990</td>\n",
       "      <td>0.002415</td>\n",
       "      <td>0.983417</td>\n",
       "      <td>0.006929</td>\n",
       "      <td>0.008552</td>\n",
       "      <td>...</td>\n",
       "      <td>0.002434</td>\n",
       "      <td>0.027809</td>\n",
       "      <td>0.004750</td>\n",
       "      <td>0.002680</td>\n",
       "      <td>0.040943</td>\n",
       "      <td>0.011426</td>\n",
       "      <td>0.002044</td>\n",
       "      <td>0.003815</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.007061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>winPlacePerc</th>\n",
       "      <td>0.299441</td>\n",
       "      <td>0.634234</td>\n",
       "      <td>0.440507</td>\n",
       "      <td>0.279970</td>\n",
       "      <td>0.277722</td>\n",
       "      <td>0.427857</td>\n",
       "      <td>0.719069</td>\n",
       "      <td>0.012908</td>\n",
       "      <td>0.419916</td>\n",
       "      <td>0.377566</td>\n",
       "      <td>...</td>\n",
       "      <td>0.240881</td>\n",
       "      <td>0.342915</td>\n",
       "      <td>0.034544</td>\n",
       "      <td>0.149607</td>\n",
       "      <td>0.015943</td>\n",
       "      <td>0.073436</td>\n",
       "      <td>0.810888</td>\n",
       "      <td>0.583806</td>\n",
       "      <td>0.007061</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>25 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  assists    boosts  damageDealt     DBNOs  headshotKills  \\\n",
       "assists          1.000000  0.307683     0.406726  0.301057       0.198289   \n",
       "boosts           0.307683  1.000000     0.521947  0.358907       0.334661   \n",
       "damageDealt      0.406726  0.521947     1.000000  0.735763       0.613409   \n",
       "DBNOs            0.301057  0.358907     0.735763  1.000000       0.469923   \n",
       "headshotKills    0.198289  0.334661     0.613409  0.469923       1.000000   \n",
       "heals            0.228556  0.535854     0.342987  0.265485       0.199917   \n",
       "killPlace        0.290062  0.554844     0.677398  0.555290       0.469829   \n",
       "killPoints       0.039066  0.008347     0.049904  0.042616       0.023988   \n",
       "kills            0.319690  0.502024     0.888783  0.707848       0.674275   \n",
       "killStreaks      0.243471  0.405327     0.703599  0.646872       0.512882   \n",
       "longestKill      0.261425  0.423290     0.563837  0.451422       0.447261   \n",
       "matchDuration    0.019450  0.072107     0.006756  0.014486       0.017657   \n",
       "maxPlace         0.147916  0.013686     0.040708  0.267710       0.009211   \n",
       "numGroups        0.146805  0.012929     0.040079  0.265789       0.009389   \n",
       "rankPoints       0.016407  0.023202     0.001459  0.003451       0.005119   \n",
       "revives          0.198320  0.253125     0.256951  0.300999       0.150145   \n",
       "rideDistance     0.110644  0.328855     0.140883  0.102537       0.076495   \n",
       "roadKills        0.011903  0.035124     0.052487  0.036093       0.013369   \n",
       "swimDistance     0.023372  0.107993     0.036729  0.017320       0.028531   \n",
       "teamKills        0.006081  0.013068     0.015468  0.069430       0.008658   \n",
       "vehicleDestroys  0.057921  0.087387     0.081182  0.060210       0.039200   \n",
       "walkDistance     0.290305  0.640150     0.398199  0.284660       0.250982   \n",
       "weaponsAcquired  0.243882  0.406609     0.353248  0.218161       0.217552   \n",
       "winPoints        0.024014  0.007645     0.017762  0.011108       0.004982   \n",
       "winPlacePerc     0.299441  0.634234     0.440507  0.279970       0.277722   \n",
       "\n",
       "                    heals  killPlace  killPoints     kills  killStreaks  \\\n",
       "assists          0.228556   0.290062    0.039066  0.319690     0.243471   \n",
       "boosts           0.535854   0.554844    0.008347  0.502024     0.405327   \n",
       "damageDealt      0.342987   0.677398    0.049904  0.888783     0.703599   \n",
       "DBNOs            0.265485   0.555290    0.042616  0.707848     0.646872   \n",
       "headshotKills    0.199917   0.469829    0.023988  0.674275     0.512882   \n",
       "heals            1.000000   0.386032    0.002585  0.311781     0.270412   \n",
       "killPlace        0.386032   1.000000    0.023278  0.730499     0.810517   \n",
       "killPoints       0.002585   0.023278    1.000000  0.036192     0.037091   \n",
       "kills            0.311781   0.730499    0.036192  1.000000     0.802500   \n",
       "killStreaks      0.270412   0.810517    0.037091  0.802500     1.000000   \n",
       "longestKill      0.263278   0.541146    0.014084  0.602936     0.515097   \n",
       "matchDuration    0.108901   0.005806    0.099542  0.030688     0.034645   \n",
       "maxPlace         0.064759   0.016791    0.044802  0.004847     0.037339   \n",
       "numGroups        0.064204   0.018786    0.042367  0.004060     0.036054   \n",
       "rankPoints       0.019142   0.014805    0.975555  0.008200     0.003426   \n",
       "revives          0.236680   0.269804    0.012041  0.253323     0.241695   \n",
       "rideDistance     0.297484   0.236202    0.029316  0.114750     0.098420   \n",
       "roadKills        0.024619   0.057356    0.004156  0.063651     0.065575   \n",
       "swimDistance     0.079587   0.089396    0.004604  0.039127     0.034588   \n",
       "teamKills        0.035386   0.037797    0.039739  0.017601     0.027612   \n",
       "vehicleDestroys  0.062870   0.073569    0.009658  0.079991     0.064374   \n",
       "walkDistance     0.430266   0.590236    0.004630  0.374774     0.327639   \n",
       "weaponsAcquired  0.309071   0.490144    0.003978  0.336414     0.275373   \n",
       "winPoints        0.010990   0.002415    0.983417  0.006929     0.008552   \n",
       "winPlacePerc     0.427857   0.719069    0.012908  0.419916     0.377566   \n",
       "\n",
       "                     ...        revives  rideDistance  roadKills  \\\n",
       "assists              ...       0.198320      0.110644   0.011903   \n",
       "boosts               ...       0.253125      0.328855   0.035124   \n",
       "damageDealt          ...       0.256951      0.140883   0.052487   \n",
       "DBNOs                ...       0.300999      0.102537   0.036093   \n",
       "headshotKills        ...       0.150145      0.076495   0.013369   \n",
       "heals                ...       0.236680      0.297484   0.024619   \n",
       "killPlace            ...       0.269804      0.236202   0.057356   \n",
       "killPoints           ...       0.012041      0.029316   0.004156   \n",
       "kills                ...       0.253323      0.114750   0.063651   \n",
       "killStreaks          ...       0.241695      0.098420   0.065575   \n",
       "longestKill          ...       0.170134      0.190853   0.025214   \n",
       "matchDuration        ...       0.016990      0.365212   0.020016   \n",
       "maxPlace             ...       0.160369      0.047784   0.011504   \n",
       "numGroups            ...       0.159173      0.049880   0.010435   \n",
       "rankPoints           ...       0.000613      0.036813   0.006138   \n",
       "revives              ...       1.000000      0.109946   0.005920   \n",
       "rideDistance         ...       0.109946      1.000000   0.103518   \n",
       "roadKills            ...       0.005920      0.103518   1.000000   \n",
       "swimDistance         ...       0.020338      0.049448   0.004411   \n",
       "teamKills            ...       0.032989      0.050179   0.009684   \n",
       "vehicleDestroys      ...       0.038019      0.119253   0.031071   \n",
       "walkDistance         ...       0.236796      0.310936   0.016736   \n",
       "weaponsAcquired      ...       0.167281      0.282246   0.016494   \n",
       "winPoints            ...       0.002434      0.027809   0.004750   \n",
       "winPlacePerc         ...       0.240881      0.342915   0.034544   \n",
       "\n",
       "                 swimDistance  teamKills  vehicleDestroys  walkDistance  \\\n",
       "assists              0.023372   0.006081         0.057921      0.290305   \n",
       "boosts               0.107993   0.013068         0.087387      0.640150   \n",
       "damageDealt          0.036729   0.015468         0.081182      0.398199   \n",
       "DBNOs                0.017320   0.069430         0.060210      0.284660   \n",
       "headshotKills        0.028531   0.008658         0.039200      0.250982   \n",
       "heals                0.079587   0.035386         0.062870      0.430266   \n",
       "killPlace            0.089396   0.037797         0.073569      0.590236   \n",
       "killPoints           0.004604   0.039739         0.009658      0.004630   \n",
       "kills                0.039127   0.017601         0.079991      0.374774   \n",
       "killStreaks          0.034588   0.027612         0.064374      0.327639   \n",
       "longestKill          0.053455   0.007604         0.083227      0.413810   \n",
       "matchDuration        0.008737   0.047969         0.060924      0.145062   \n",
       "maxPlace             0.007788   0.038057         0.018888      0.083624   \n",
       "numGroups            0.007922   0.038429         0.019173      0.082667   \n",
       "rankPoints           0.004724   0.039626         0.013400      0.015790   \n",
       "revives              0.020338   0.032989         0.038019      0.236796   \n",
       "rideDistance         0.049448   0.050179         0.119253      0.310936   \n",
       "roadKills            0.004411   0.009684         0.031071      0.016736   \n",
       "swimDistance         1.000000   0.006199         0.008090      0.167509   \n",
       "teamKills            0.006199   1.000000         0.112164      0.024858   \n",
       "vehicleDestroys      0.008090   0.112164         1.000000      0.076237   \n",
       "walkDistance         0.167509   0.024858         0.076237      1.000000   \n",
       "weaponsAcquired      0.075698   0.035918         0.061012      0.541222   \n",
       "winPoints            0.002680   0.040943         0.011426      0.002044   \n",
       "winPlacePerc         0.149607   0.015943         0.073436      0.810888   \n",
       "\n",
       "                 weaponsAcquired  winPoints  winPlacePerc  \n",
       "assists                 0.243882   0.024014      0.299441  \n",
       "boosts                  0.406609   0.007645      0.634234  \n",
       "damageDealt             0.353248   0.017762      0.440507  \n",
       "DBNOs                   0.218161   0.011108      0.279970  \n",
       "headshotKills           0.217552   0.004982      0.277722  \n",
       "heals                   0.309071   0.010990      0.427857  \n",
       "killPlace               0.490144   0.002415      0.719069  \n",
       "killPoints              0.003978   0.983417      0.012908  \n",
       "kills                   0.336414   0.006929      0.419916  \n",
       "killStreaks             0.275373   0.008552      0.377566  \n",
       "longestKill             0.276264   0.003720      0.410154  \n",
       "matchDuration           0.129080   0.087361      0.005171  \n",
       "maxPlace                0.039121   0.029295      0.037377  \n",
       "numGroups               0.039984   0.027483      0.039621  \n",
       "rankPoints              0.014756   0.993845      0.013523  \n",
       "revives                 0.167281   0.002434      0.240881  \n",
       "rideDistance            0.282246   0.027809      0.342915  \n",
       "roadKills               0.016494   0.004750      0.034544  \n",
       "swimDistance            0.075698   0.002680      0.149607  \n",
       "teamKills               0.035918   0.040943      0.015943  \n",
       "vehicleDestroys         0.061012   0.011426      0.073436  \n",
       "walkDistance            0.541222   0.002044      0.810888  \n",
       "weaponsAcquired         1.000000   0.003815      0.583806  \n",
       "winPoints               0.003815   1.000000      0.007061  \n",
       "winPlacePerc            0.583806   0.007061      1.000000  \n",
       "\n",
       "[25 rows x 25 columns]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr().abs()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": true,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.metrics import r2_score\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "from numpy.polynomial.polynomial import polyfit\n",
    "\n",
    "columns = ['boosts', 'killPlace','walkDistance', 'weaponsAcquired', 'damageDealt', 'heals', 'kills']\n",
    "\n",
    "X = df[['assists', 'boosts', 'damageDealt', 'DBNOs', 'headshotKills', 'heals', 'killPlace', 'kills', 'killStreaks', 'longestKill', 'revives', 'rideDistance', 'walkDistance', 'weaponsAcquired']]\n",
    "y = df['winPlacePerc']\n",
    "split_size = 0.25\n",
    "\n",
    "X_train, X_test,y_train,y_test= train_test_split(X, y, test_size = split_size)\n",
    "\n",
    "poly = PolynomialFeatures(degree=2)\n",
    "linreg = LinearRegression()\n",
    "linreg.fit(poly.fit_transform(X_train), y_train)\n",
    "y_pred_train = linreg.predict(poly.fit_transform(X_train))\n",
    "y_pred_test = linreg.predict(poly.fit_transform(X_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
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9WCcX7u37eDu0ExVuz9MwnHvdSjKuVhzDvn5DbLfnZlze7H093SpLuzgF9UHZ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\nmPcI+tDXP3Wq8/VPneoZXkfV7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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f3bae48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Goodness of Fit of Model \tTrain Dataset\n",
      "Explained Variance (R^2) \t: 0.867176517023\n",
      "Mean Squared Error (MSE) \t: 0.0125510121426\n",
      "\n",
      "Goodness of Fit of Model \tTest Dataset\n",
      "Explained Variance (R^2) \t: 0.867323103328\n",
      "Mean Squared Error (MSE) \t: 0.0125392177577\n"
     ]
    }
   ],
   "source": [
    "f, axes = plt.subplots(1, 2, figsize=(24, 12))\n",
    "axes[0].scatter(y_train, y_pred_train, color='red')\n",
    "axes[0].scatter(y_train, y_train, color='white')\n",
    "axes[0].set_xlabel(\"Actual Placement (Train)\")\n",
    "axes[0].set_ylabel(\"Predicted Placement (Train)\")\n",
    "axes[1].scatter(y_test, y_pred_test,color='red')\n",
    "axes[1].scatter(y_test, y_test,color='white')\n",
    "axes[1].set_xlabel(\"Actual Placement (Test)\")\n",
    "axes[1].set_ylabel(\"Predicted Placement (Test)\")\n",
    "\n",
    "plt.show()\n",
    "\n",
    "# Check the Goodness of Fit (on Train Data)\n",
    "print(\"Goodness of Fit of Model \\tTrain Dataset\")\n",
    "print(\"Explained Variance (R^2) \\t:\", linreg.score(poly.fit_transform(X_train), y_train))\n",
    "print(\"Mean Squared Error (MSE) \\t:\", mean_squared_error(y_train, y_pred_train))\n",
    "print()\n",
    "\n",
    "# Check the Goodness of Fit (on Test Data)\n",
    "print(\"Goodness of Fit of Model \\tTest Dataset\")\n",
    "print(\"Explained Variance (R^2) \\t:\", linreg.score(poly.fit_transform(X_test), y_test))\n",
    "print(\"Mean Squared Error (MSE) \\t:\", mean_squared_error(y_test, y_pred_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>winPlacePerc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9329eb41e215eb</td>\n",
       "      <td>0.304084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>639bd0dcd7bda8</td>\n",
       "      <td>0.868036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>63d5c8ef8dfe91</td>\n",
       "      <td>0.598199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>cf5b81422591d1</td>\n",
       "      <td>0.629374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ee6a295187ba21</td>\n",
       "      <td>0.950364</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Id  winPlacePerc\n",
       "0  9329eb41e215eb      0.304084\n",
       "1  639bd0dcd7bda8      0.868036\n",
       "2  63d5c8ef8dfe91      0.598199\n",
       "3  cf5b81422591d1      0.629374\n",
       "4  ee6a295187ba21      0.950364"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test_orig = pd.read_csv(\"Datasets/test_V2.csv\")\n",
    "\n",
    "df_test = df_test_orig[['assists', 'boosts', 'damageDealt', 'DBNOs', 'headshotKills', 'heals', 'killPlace', 'kills', 'killStreaks', 'longestKill', 'revives', 'rideDistance', 'walkDistance', 'weaponsAcquired']]\n",
    "\n",
    "df_pred_test = linreg.predict(poly.fit_transform(df_test))\n",
    "\n",
    "df_test_orig['winPlacePerc'] = df_pred_test\n",
    "df_test_orig = df_test_orig[['Id', 'winPlacePerc']]\n",
    "\n",
    "df_test_orig.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_test_orig.to_csv('Datasets/submission.csv', index = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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