PUBG.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\casocha\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\ipykernel\\parentpoller.py:116: UserWarning: Parent poll failed.  If the frontend dies,\n",
      "                the kernel may be left running.  Please let us know\n",
      "                about your system (bitness, Python, etc.) at\n",
      "                ipython-dev@scipy.org\n",
      "  ipython-dev@scipy.org\"\"\")\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import LabelEncoder, OneHotEncoder\n",
    "%matplotlib inline\n",
    "sns.set_style('whitegrid')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>groupId</th>\n",
       "      <th>matchId</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>...</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>0</th>\n",
       "      <td>7f96b2f878858a</td>\n",
       "      <td>4d4b580de459be</td>\n",
       "      <td>a10357fd1a4a91</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>244.80</td>\n",
       "      <td>1</td>\n",
       "      <td>1466</td>\n",
       "      <td>0.4444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>eef90569b9d03c</td>\n",
       "      <td>684d5656442f9e</td>\n",
       "      <td>aeb375fc57110c</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>91.47</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>57</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0045</td>\n",
       "      <td>0</td>\n",
       "      <td>11.04</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1434.00</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0.6400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1eaf90ac73de72</td>\n",
       "      <td>6a4a42c3245a74</td>\n",
       "      <td>110163d8bb94ae</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>68.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>47</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>161.80</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.7755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4616d365dd2853</td>\n",
       "      <td>a930a9c79cd721</td>\n",
       "      <td>f1f1f4ef412d7e</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>32.90</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>75</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>202.70</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.1667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>315c96c26c9aac</td>\n",
       "      <td>de04010b3458dd</td>\n",
       "      <td>6dc8ff871e21e6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>100.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>49.75</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.1875</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               Id         groupId         matchId  assists  boosts  \\\n",
       "0  7f96b2f878858a  4d4b580de459be  a10357fd1a4a91        0       0   \n",
       "1  eef90569b9d03c  684d5656442f9e  aeb375fc57110c        0       0   \n",
       "2  1eaf90ac73de72  6a4a42c3245a74  110163d8bb94ae        1       0   \n",
       "3  4616d365dd2853  a930a9c79cd721  f1f1f4ef412d7e        0       0   \n",
       "4  315c96c26c9aac  de04010b3458dd  6dc8ff871e21e6        0       0   \n",
       "\n",
       "   damageDealt  DBNOs  headshotKills  heals  killPlace      ...       revives  \\\n",
       "0         0.00      0              0      0         60      ...             0   \n",
       "1        91.47      0              0      0         57      ...             0   \n",
       "2        68.00      0              0      0         47      ...             0   \n",
       "3        32.90      0              0      0         75      ...             0   \n",
       "4       100.00      0              0      0         45      ...             0   \n",
       "\n",
       "   rideDistance  roadKills  swimDistance  teamKills vehicleDestroys  \\\n",
       "0        0.0000          0          0.00          0               0   \n",
       "1        0.0045          0         11.04          0               0   \n",
       "2        0.0000          0          0.00          0               0   \n",
       "3        0.0000          0          0.00          0               0   \n",
       "4        0.0000          0          0.00          0               0   \n",
       "\n",
       "   walkDistance  weaponsAcquired  winPoints  winPlacePerc  \n",
       "0        244.80                1       1466        0.4444  \n",
       "1       1434.00                5          0        0.6400  \n",
       "2        161.80                2          0        0.7755  \n",
       "3        202.70                3          0        0.1667  \n",
       "4         49.75                2          0        0.1875  \n",
       "\n",
       "[5 rows x 29 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train = pd.read_csv(r'C:\\Users\\casocha\\Desktop\\Kaggle\\pubg/train_V2.csv')\n",
    "df_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>groupId</th>\n",
       "      <th>matchId</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>...</th>\n",
       "      <th>rankPoints</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9329eb41e215eb</td>\n",
       "      <td>676b23c24e70d6</td>\n",
       "      <td>45b576ab7daa7f</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>51.46</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>73</td>\n",
       "      <td>...</td>\n",
       "      <td>1500</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>588.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>639bd0dcd7bda8</td>\n",
       "      <td>430933124148dd</td>\n",
       "      <td>42a9a0b906c928</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>179.10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "      <td>...</td>\n",
       "      <td>1503</td>\n",
       "      <td>2</td>\n",
       "      <td>4669.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>63d5c8ef8dfe91</td>\n",
       "      <td>0b45f5db20ba99</td>\n",
       "      <td>87e7e4477a048e</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>23.40</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>49</td>\n",
       "      <td>...</td>\n",
       "      <td>1565</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>787.8</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>cf5b81422591d1</td>\n",
       "      <td>b7497dbdc77f4a</td>\n",
       "      <td>1b9a94f1af67f1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>65.52</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>54</td>\n",
       "      <td>...</td>\n",
       "      <td>1465</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1812.0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ee6a295187ba21</td>\n",
       "      <td>6604ce20a1d230</td>\n",
       "      <td>40754a93016066</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>330.20</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>...</td>\n",
       "      <td>1480</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2963.0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               Id         groupId         matchId  assists  boosts  \\\n",
       "0  9329eb41e215eb  676b23c24e70d6  45b576ab7daa7f        0       0   \n",
       "1  639bd0dcd7bda8  430933124148dd  42a9a0b906c928        0       4   \n",
       "2  63d5c8ef8dfe91  0b45f5db20ba99  87e7e4477a048e        1       0   \n",
       "3  cf5b81422591d1  b7497dbdc77f4a  1b9a94f1af67f1        0       0   \n",
       "4  ee6a295187ba21  6604ce20a1d230  40754a93016066        0       4   \n",
       "\n",
       "   damageDealt  DBNOs  headshotKills  heals  killPlace    ...      rankPoints  \\\n",
       "0        51.46      0              0      0         73    ...            1500   \n",
       "1       179.10      0              0      2         11    ...            1503   \n",
       "2        23.40      0              0      4         49    ...            1565   \n",
       "3        65.52      0              0      0         54    ...            1465   \n",
       "4       330.20      1              2      1          7    ...            1480   \n",
       "\n",
       "   revives  rideDistance  roadKills  swimDistance teamKills  vehicleDestroys  \\\n",
       "0        0           0.0          0           0.0         0                0   \n",
       "1        2        4669.0          0           0.0         0                0   \n",
       "2        0           0.0          0           0.0         0                0   \n",
       "3        0           0.0          0           0.0         0                0   \n",
       "4        1           0.0          0           0.0         0                0   \n",
       "\n",
       "   walkDistance  weaponsAcquired  winPoints  \n",
       "0         588.0                1          0  \n",
       "1        2017.0                6          0  \n",
       "2         787.8                4          0  \n",
       "3        1812.0                3          0  \n",
       "4        2963.0                4          0  \n",
       "\n",
       "[5 rows x 28 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test = pd.read_csv(r'C:\\Users\\casocha\\Desktop\\Kaggle\\pubg/test_V2.csv')\n",
    "df_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 4446966 entries, 0 to 4446965\n",
      "Data columns (total 28 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",
      "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(18), object(4)\n",
      "memory usage: 950.0+ MB\n"
     ]
    }
   ],
   "source": [
    "df_train = df_train.drop('rankPoints',axis=1)\n",
    "df_train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "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",
      "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       1\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Check number of nulls in each feature column\n",
    "nulls_per_column = df_train.isnull().sum()\n",
    "print(nulls_per_column)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train1 = df_train[['Id','groupId','matchId', 'boosts','damageDealt','heals','killPlace','kills','killStreaks','longestKill',\n",
    "                    'walkDistance','weaponsAcquired','swimDistance','rideDistance','headshotKills','matchType','winPlacePerc']]\n",
    "X_test1 = df_test[['Id','groupId','matchId', 'boosts','damageDealt','heals','killPlace','kills','killStreaks','longestKill',\n",
    "                    'walkDistance','weaponsAcquired','swimDistance','rideDistance','headshotKills','matchType']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Heatmap of Attribute Correlation with Winning')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axarr = plt.subplots(1, 1, figsize=(12, 8))\n",
    "#Subsetting the data\n",
    "cor = X_train1.corr() #Calculate the correlation of the above variables\n",
    "mask = np.zeros_like(cor)\n",
    "mask[np.triu_indices_from(mask)] = True\n",
    "sns.heatmap(cor, mask = mask,square = True,annot=True, linewidths=.5, fmt= '.1f') #Plot the correlation as heat map\n",
    "plt.title('Heatmap of Attribute Correlation with Winning')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Walk distance, weapons acquired, boosts, & damage dealt seem to have the highest correlations with win. Which makes sense since they are game mechanic actions so the longer you're alive / still in the game the more you use. DBNOS, headshots, assists, heals, kills, kill streaks, & longestKill also seem to be important. The additional movement variables like ride & swim are low but total movement could be important"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\casocha\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n",
      "C:\\Users\\casocha\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \n",
      "C:\\Users\\casocha\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  after removing the cwd from sys.path.\n",
      "C:\\Users\\casocha\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \"\"\"\n",
      "C:\\Users\\casocha\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:7: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  import sys\n",
      "C:\\Users\\casocha\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:8: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "X_train1['total_distance'] = X_train1['walkDistance'] + X_train1['swimDistance'] + X_train1['rideDistance']\n",
    "X_test1['total_distance'] = X_test1['walkDistance'] + X_test1['swimDistance'] + X_test1['rideDistance']\n",
    "\n",
    "X_train1['total_item'] = X_train1['boosts'] + X_train1['heals'] \n",
    "X_test1['total_item'] = X_test1['boosts'] + X_test1['heals'] \n",
    "\n",
    "X_train1['headshot_percentage'] = (X_train1['headshotKills'] / X_train1['kills']) * 100\n",
    "X_test1['headshot_percentage'] = (X_test1['headshotKills'] / X_test1['kills']) * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>matchType_crashfpp</th>\n",
       "      <th>matchType_crashtpp</th>\n",
       "      <th>matchType_duo</th>\n",
       "      <th>matchType_duo-fpp</th>\n",
       "      <th>matchType_flarefpp</th>\n",
       "      <th>matchType_flaretpp</th>\n",
       "      <th>matchType_normal-duo</th>\n",
       "      <th>matchType_normal-duo-fpp</th>\n",
       "      <th>matchType_normal-solo</th>\n",
       "      <th>matchType_normal-solo-fpp</th>\n",
       "      <th>matchType_normal-squad</th>\n",
       "      <th>matchType_normal-squad-fpp</th>\n",
       "      <th>matchType_solo</th>\n",
       "      <th>matchType_solo-fpp</th>\n",
       "      <th>matchType_squad</th>\n",
       "      <th>matchType_squad-fpp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   matchType_crashfpp  matchType_crashtpp  matchType_duo  matchType_duo-fpp  \\\n",
       "0                   0                   0              0                  0   \n",
       "1                   0                   0              0                  0   \n",
       "2                   0                   0              1                  0   \n",
       "3                   0                   0              0                  0   \n",
       "4                   0                   0              0                  0   \n",
       "\n",
       "   matchType_flarefpp  matchType_flaretpp  matchType_normal-duo  \\\n",
       "0                   0                   0                     0   \n",
       "1                   0                   0                     0   \n",
       "2                   0                   0                     0   \n",
       "3                   0                   0                     0   \n",
       "4                   0                   0                     0   \n",
       "\n",
       "   matchType_normal-duo-fpp  matchType_normal-solo  matchType_normal-solo-fpp  \\\n",
       "0                         0                      0                          0   \n",
       "1                         0                      0                          0   \n",
       "2                         0                      0                          0   \n",
       "3                         0                      0                          0   \n",
       "4                         0                      0                          0   \n",
       "\n",
       "   matchType_normal-squad  matchType_normal-squad-fpp  matchType_solo  \\\n",
       "0                       0                           0               0   \n",
       "1                       0                           0               0   \n",
       "2                       0                           0               0   \n",
       "3                       0                           0               0   \n",
       "4                       0                           0               0   \n",
       "\n",
       "   matchType_solo-fpp  matchType_squad  matchType_squad-fpp  \n",
       "0                   0                0                    1  \n",
       "1                   0                0                    1  \n",
       "2                   0                0                    0  \n",
       "3                   0                0                    1  \n",
       "4                   1                0                    0  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# One hot encode matchType\n",
    "X_train1 = pd.get_dummies(X_train1, columns=['matchType'])\n",
    "X_test1 = pd.get_dummies(X_test1, columns=['matchType'])\n",
    "\n",
    "# Take a look at the encoding\n",
    "matchType_encoding = X_train1.filter(regex='matchType')\n",
    "matchType_encoding.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>groupId</th>\n",
       "      <th>matchId</th>\n",
       "      <th>boosts</th>\n",
       "      <th>damageDealt</th>\n",
       "      <th>heals</th>\n",
       "      <th>killPlace</th>\n",
       "      <th>kills</th>\n",
       "      <th>killStreaks</th>\n",
       "      <th>longestKill</th>\n",
       "      <th>...</th>\n",
       "      <th>matchType_normal-duo</th>\n",
       "      <th>matchType_normal-duo-fpp</th>\n",
       "      <th>matchType_normal-solo</th>\n",
       "      <th>matchType_normal-solo-fpp</th>\n",
       "      <th>matchType_normal-squad</th>\n",
       "      <th>matchType_normal-squad-fpp</th>\n",
       "      <th>matchType_solo</th>\n",
       "      <th>matchType_solo-fpp</th>\n",
       "      <th>matchType_squad</th>\n",
       "      <th>matchType_squad-fpp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7f96b2f878858a</td>\n",
       "      <td>4d4b580de459be</td>\n",
       "      <td>a10357fd1a4a91</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>eef90569b9d03c</td>\n",
       "      <td>684d5656442f9e</td>\n",
       "      <td>aeb375fc57110c</td>\n",
       "      <td>0</td>\n",
       "      <td>91.47</td>\n",
       "      <td>0</td>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1eaf90ac73de72</td>\n",
       "      <td>6a4a42c3245a74</td>\n",
       "      <td>110163d8bb94ae</td>\n",
       "      <td>0</td>\n",
       "      <td>68.00</td>\n",
       "      <td>0</td>\n",
       "      <td>47</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4616d365dd2853</td>\n",
       "      <td>a930a9c79cd721</td>\n",
       "      <td>f1f1f4ef412d7e</td>\n",
       "      <td>0</td>\n",
       "      <td>32.90</td>\n",
       "      <td>0</td>\n",
       "      <td>75</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>315c96c26c9aac</td>\n",
       "      <td>de04010b3458dd</td>\n",
       "      <td>6dc8ff871e21e6</td>\n",
       "      <td>0</td>\n",
       "      <td>100.00</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>58.53</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 35 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               Id         groupId         matchId  boosts  damageDealt  heals  \\\n",
       "0  7f96b2f878858a  4d4b580de459be  a10357fd1a4a91       0         0.00      0   \n",
       "1  eef90569b9d03c  684d5656442f9e  aeb375fc57110c       0        91.47      0   \n",
       "2  1eaf90ac73de72  6a4a42c3245a74  110163d8bb94ae       0        68.00      0   \n",
       "3  4616d365dd2853  a930a9c79cd721  f1f1f4ef412d7e       0        32.90      0   \n",
       "4  315c96c26c9aac  de04010b3458dd  6dc8ff871e21e6       0       100.00      0   \n",
       "\n",
       "   killPlace  kills  killStreaks  longestKill         ...           \\\n",
       "0         60      0            0         0.00         ...            \n",
       "1         57      0            0         0.00         ...            \n",
       "2         47      0            0         0.00         ...            \n",
       "3         75      0            0         0.00         ...            \n",
       "4         45      1            1        58.53         ...            \n",
       "\n",
       "   matchType_normal-duo  matchType_normal-duo-fpp  matchType_normal-solo  \\\n",
       "0                     0                         0                      0   \n",
       "1                     0                         0                      0   \n",
       "2                     0                         0                      0   \n",
       "3                     0                         0                      0   \n",
       "4                     0                         0                      0   \n",
       "\n",
       "   matchType_normal-solo-fpp  matchType_normal-squad  \\\n",
       "0                          0                       0   \n",
       "1                          0                       0   \n",
       "2                          0                       0   \n",
       "3                          0                       0   \n",
       "4                          0                       0   \n",
       "\n",
       "   matchType_normal-squad-fpp  matchType_solo  matchType_solo-fpp  \\\n",
       "0                           0               0                   0   \n",
       "1                           0               0                   0   \n",
       "2                           0               0                   0   \n",
       "3                           0               0                   0   \n",
       "4                           0               0                   1   \n",
       "\n",
       "   matchType_squad  matchType_squad-fpp  \n",
       "0                0                    1  \n",
       "1                0                    1  \n",
       "2                0                    0  \n",
       "3                0                    1  \n",
       "4                0                    0  \n",
       "\n",
       "[5 rows x 35 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>groupId_cat</th>\n",
       "      <th>matchId_cat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>613775</td>\n",
       "      <td>30086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>827823</td>\n",
       "      <td>32752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>843521</td>\n",
       "      <td>3143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1340462</td>\n",
       "      <td>45261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1757849</td>\n",
       "      <td>20532</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   groupId_cat  matchId_cat\n",
       "0       613775        30086\n",
       "1       827823        32752\n",
       "2       843521         3143\n",
       "3      1340462        45261\n",
       "4      1757849        20532"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Turn groupId and match Id into categorical types\n",
    "X_train1['groupId'] = X_train1['groupId'].astype('category')\n",
    "X_train1['matchId'] = X_train1['matchId'].astype('category')\n",
    "\n",
    "# Get category coding for groupId and matchID\n",
    "X_train1['groupId_cat'] = X_train1['groupId'].cat.codes\n",
    "X_train1['matchId_cat'] = X_train1['matchId'].cat.codes\n",
    "\n",
    "# Get rid of old columns\n",
    "X_train1.drop(columns=['groupId', 'matchId'], inplace=True)\n",
    "\n",
    "# Turn groupId and match Id into categorical types\n",
    "X_test1['groupId'] = X_test1['groupId'].astype('category')\n",
    "X_test1['matchId'] = X_test1['matchId'].astype('category')\n",
    "\n",
    "# Get category coding for groupId and matchID\n",
    "X_test1['groupId_cat'] = X_test1['groupId'].cat.codes\n",
    "X_test1['matchId_cat'] = X_test1['matchId'].cat.codes\n",
    "\n",
    "# Get rid of old columns\n",
    "X_test1.drop(columns=['groupId', 'matchId'], inplace=True)\n",
    "\n",
    "# Lets take a look at our newly created features\n",
    "X_train1[['groupId_cat', 'matchId_cat']].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>boosts</th>\n",
       "      <th>damageDealt</th>\n",
       "      <th>heals</th>\n",
       "      <th>killPlace</th>\n",
       "      <th>kills</th>\n",
       "      <th>killStreaks</th>\n",
       "      <th>longestKill</th>\n",
       "      <th>walkDistance</th>\n",
       "      <th>weaponsAcquired</th>\n",
       "      <th>...</th>\n",
       "      <th>matchType_normal-solo</th>\n",
       "      <th>matchType_normal-solo-fpp</th>\n",
       "      <th>matchType_normal-squad</th>\n",
       "      <th>matchType_normal-squad-fpp</th>\n",
       "      <th>matchType_solo</th>\n",
       "      <th>matchType_solo-fpp</th>\n",
       "      <th>matchType_squad</th>\n",
       "      <th>matchType_squad-fpp</th>\n",
       "      <th>groupId_cat</th>\n",
       "      <th>matchId_cat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7f96b2f878858a</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>244.80</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>613775</td>\n",
       "      <td>30086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>eef90569b9d03c</td>\n",
       "      <td>0</td>\n",
       "      <td>91.47</td>\n",
       "      <td>0</td>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1434.00</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>827823</td>\n",
       "      <td>32752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1eaf90ac73de72</td>\n",
       "      <td>0</td>\n",
       "      <td>68.00</td>\n",
       "      <td>0</td>\n",
       "      <td>47</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>161.80</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>843521</td>\n",
       "      <td>3143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4616d365dd2853</td>\n",
       "      <td>0</td>\n",
       "      <td>32.90</td>\n",
       "      <td>0</td>\n",
       "      <td>75</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>202.70</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1340462</td>\n",
       "      <td>45261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>315c96c26c9aac</td>\n",
       "      <td>0</td>\n",
       "      <td>100.00</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>58.53</td>\n",
       "      <td>49.75</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1757849</td>\n",
       "      <td>20532</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 35 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               Id  boosts  damageDealt  heals  killPlace  kills  killStreaks  \\\n",
       "0  7f96b2f878858a       0         0.00      0         60      0            0   \n",
       "1  eef90569b9d03c       0        91.47      0         57      0            0   \n",
       "2  1eaf90ac73de72       0        68.00      0         47      0            0   \n",
       "3  4616d365dd2853       0        32.90      0         75      0            0   \n",
       "4  315c96c26c9aac       0       100.00      0         45      1            1   \n",
       "\n",
       "   longestKill  walkDistance  weaponsAcquired     ...       \\\n",
       "0         0.00        244.80                1     ...        \n",
       "1         0.00       1434.00                5     ...        \n",
       "2         0.00        161.80                2     ...        \n",
       "3         0.00        202.70                3     ...        \n",
       "4        58.53         49.75                2     ...        \n",
       "\n",
       "   matchType_normal-solo  matchType_normal-solo-fpp  matchType_normal-squad  \\\n",
       "0                      0                          0                       0   \n",
       "1                      0                          0                       0   \n",
       "2                      0                          0                       0   \n",
       "3                      0                          0                       0   \n",
       "4                      0                          0                       0   \n",
       "\n",
       "   matchType_normal-squad-fpp  matchType_solo  matchType_solo-fpp  \\\n",
       "0                           0               0                   0   \n",
       "1                           0               0                   0   \n",
       "2                           0               0                   0   \n",
       "3                           0               0                   0   \n",
       "4                           0               0                   1   \n",
       "\n",
       "   matchType_squad  matchType_squad-fpp  groupId_cat  matchId_cat  \n",
       "0                0                    1       613775        30086  \n",
       "1                0                    1       827823        32752  \n",
       "2                0                    0       843521         3143  \n",
       "3                0                    1      1340462        45261  \n",
       "4                0                    0      1757849        20532  \n",
       "\n",
       "[5 rows x 35 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train1 = X_train1.fillna(-1)\n",
    "X_train1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 4446966 entries, 0 to 4446965\n",
      "Data columns (total 33 columns):\n",
      "boosts                        int64\n",
      "damageDealt                   float64\n",
      "heals                         int64\n",
      "killPlace                     int64\n",
      "kills                         int64\n",
      "killStreaks                   int64\n",
      "longestKill                   float64\n",
      "walkDistance                  float64\n",
      "weaponsAcquired               int64\n",
      "swimDistance                  float64\n",
      "rideDistance                  float64\n",
      "headshotKills                 int64\n",
      "total_distance                float64\n",
      "total_item                    int64\n",
      "headshot_percentage           float64\n",
      "matchType_crashfpp            uint8\n",
      "matchType_crashtpp            uint8\n",
      "matchType_duo                 uint8\n",
      "matchType_duo-fpp             uint8\n",
      "matchType_flarefpp            uint8\n",
      "matchType_flaretpp            uint8\n",
      "matchType_normal-duo          uint8\n",
      "matchType_normal-duo-fpp      uint8\n",
      "matchType_normal-solo         uint8\n",
      "matchType_normal-solo-fpp     uint8\n",
      "matchType_normal-squad        uint8\n",
      "matchType_normal-squad-fpp    uint8\n",
      "matchType_solo                uint8\n",
      "matchType_solo-fpp            uint8\n",
      "matchType_squad               uint8\n",
      "matchType_squad-fpp           uint8\n",
      "groupId_cat                   int32\n",
      "matchId_cat                   int32\n",
      "dtypes: float64(7), int32(2), int64(8), uint8(16)\n",
      "memory usage: 610.7 MB\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "#X_train = np.array(X_train1.drop(['winPlacePerc','Id'], axis=1))\n",
    "y_train = X_train1['winPlacePerc'].values\n",
    "\n",
    "X_test = np.array(X_test1.drop(['Id'], axis=1))\n",
    "ids = X_test1['Id'].values\n",
    "\n",
    "\n",
    "X_train1 = X_train1.drop(['winPlacePerc','Id'],axis=1)#.apply(pd.to_numeric)\n",
    "X_train1.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\casocha\\AppData\\Roaming\\Python\\Python37\\site-packages\\sklearn\\preprocessing\\data.py:617: DataConversionWarning: Data with input dtype uint8, int32, int64, float64 were all converted to float64 by StandardScaler.\n",
      "  return self.partial_fit(X, y)\n",
      "C:\\Users\\casocha\\AppData\\Roaming\\Python\\Python37\\site-packages\\sklearn\\base.py:462: DataConversionWarning: Data with input dtype uint8, int32, int64, float64 were all converted to float64 by StandardScaler.\n",
      "  return self.fit(X, **fit_params).transform(X)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "scaler = StandardScaler()\n",
    "X_train = scaler.fit_transform(X_train1)\n",
    "X_test = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_valid, y_train, y_valid = train_test_split(X_train, y_train, test_size=0.7, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Metric used for the PUBG competition (Mean Absolute Error (MAE))\n",
    "from sklearn.metrics import mean_absolute_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "import xgboost as xgb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "d_train = xgb.DMatrix(X_train, label=y_train)\n",
    "d_valid = xgb.DMatrix(X_valid, label=y_valid) \n",
    "d_test = xgb.DMatrix(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create the parameter dictionary: param\n",
    "param = {'max_depth': 10, \"n_estimators\":10,'eval_metric':'mae', 'silent': 1, 'colsample_bytree':0.3}\n",
    "#model = xgb.train(params, train_dmat, maximize=True, verbose_eval=5)\n",
    "\n",
    "model = xgb.train(param,d_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "preds = model.predict(d_valid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.09412530435005978\n"
     ]
    }
   ],
   "source": [
    "print(mean_absolute_error(y_valid,preds))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}