Adanced_Stats_Model-LogisticRegressionV1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pyspark\n",
    "from pyspark.sql import SparkSession\n",
    "from pyspark.sql import functions as f\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import time\n",
    "from itertools import chain"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from nba_api.stats.endpoints import leaguegamefinder\n",
    "from nba_api.stats.endpoints import teamdashboardbylastngames\n",
    "from nba_api.stats.endpoints import leaguedashteamstats\n",
    "from nba_api.stats.endpoints import boxscoreadvancedv2\n",
    "from nba_api.stats.static import teams"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get a dataframe that contains team ids, abbervations, and nicknames for later use."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "team_df = pd.DataFrame(teams.get_teams())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "team_df = team_df[['id', 'abbreviation', 'nickname']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get a dataframe of last 5 games advanced stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "team_adv = leaguedashteamstats.LeagueDashTeamStats(last_n_games=5, measure_type_detailed_defense='Advanced').get_data_frames()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TEAM_ID</th>\n",
       "      <th>TEAM_NAME</th>\n",
       "      <th>GP</th>\n",
       "      <th>W</th>\n",
       "      <th>L</th>\n",
       "      <th>W_PCT</th>\n",
       "      <th>MIN</th>\n",
       "      <th>E_OFF_RATING</th>\n",
       "      <th>OFF_RATING</th>\n",
       "      <th>E_DEF_RATING</th>\n",
       "      <th>...</th>\n",
       "      <th>OREB_PCT_RANK</th>\n",
       "      <th>DREB_PCT_RANK</th>\n",
       "      <th>REB_PCT_RANK</th>\n",
       "      <th>TM_TOV_PCT_RANK</th>\n",
       "      <th>EFG_PCT_RANK</th>\n",
       "      <th>TS_PCT_RANK</th>\n",
       "      <th>PACE_RANK</th>\n",
       "      <th>PIE_RANK</th>\n",
       "      <th>CFID</th>\n",
       "      <th>CFPARAMS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1610612737</td>\n",
       "      <td>Atlanta Hawks</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0.8</td>\n",
       "      <td>240.0</td>\n",
       "      <td>122.2</td>\n",
       "      <td>122.7</td>\n",
       "      <td>111.6</td>\n",
       "      <td>...</td>\n",
       "      <td>14</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>Atlanta Hawks</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1610612738</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>240.0</td>\n",
       "      <td>112.0</td>\n",
       "      <td>113.3</td>\n",
       "      <td>114.1</td>\n",
       "      <td>...</td>\n",
       "      <td>24</td>\n",
       "      <td>10</td>\n",
       "      <td>22</td>\n",
       "      <td>7</td>\n",
       "      <td>14</td>\n",
       "      <td>18</td>\n",
       "      <td>10</td>\n",
       "      <td>21</td>\n",
       "      <td>10</td>\n",
       "      <td>Boston Celtics</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1610612751</td>\n",
       "      <td>Brooklyn Nets</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0.6</td>\n",
       "      <td>240.0</td>\n",
       "      <td>119.9</td>\n",
       "      <td>118.5</td>\n",
       "      <td>111.7</td>\n",
       "      <td>...</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>25</td>\n",
       "      <td>14</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>Brooklyn Nets</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 48 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      TEAM_ID       TEAM_NAME  GP  W  L  W_PCT    MIN  E_OFF_RATING  \\\n",
       "0  1610612737   Atlanta Hawks   5  4  1    0.8  240.0         122.2   \n",
       "1  1610612738  Boston Celtics   5  1  4    0.2  240.0         112.0   \n",
       "2  1610612751   Brooklyn Nets   5  3  2    0.6  240.0         119.9   \n",
       "\n",
       "   OFF_RATING  E_DEF_RATING  ...  OREB_PCT_RANK  DREB_PCT_RANK  REB_PCT_RANK  \\\n",
       "0       122.7         111.6  ...             14              5             4   \n",
       "1       113.3         114.1  ...             24             10            22   \n",
       "2       118.5         111.7  ...             29             29            25   \n",
       "\n",
       "   TM_TOV_PCT_RANK  EFG_PCT_RANK  TS_PCT_RANK  PACE_RANK  PIE_RANK  CFID  \\\n",
       "0                5             6            8          8         7    10   \n",
       "1                7            14           18         10        21    10   \n",
       "2               14             2            3         13         6    10   \n",
       "\n",
       "         CFPARAMS  \n",
       "0   Atlanta Hawks  \n",
       "1  Boston Celtics  \n",
       "2   Brooklyn Nets  \n",
       "\n",
       "[3 rows x 48 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "team_adv[0].head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pull all games so we can store the game ids & build rows of matchups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_games = leaguegamefinder.LeagueGameFinder(season_nullable='2020-21', league_id_nullable='00', season_type_nullable='Regular Season').get_data_frames()[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_game_ids = set(all_games['GAME_ID'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Use games ids to create a dataframe of advanced metrics for every game"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "season_advanced=[]\n",
    "for i in all_game_ids:\n",
    "    season_advanced.append(boxscoreadvancedv2.BoxScoreAdvancedV2(game_id=i).get_normalized_dict()['TeamStats'])\n",
    "    time.sleep(.6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "szn_adv_df = pd.DataFrame(list(chain.from_iterable(season_advanced)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(all_games) == len(szn_adv_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Implement Spark"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "spark = SparkSession.builder.appName('NBA_Advanced_Stats_Model').getOrCreate()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "team_spark = spark.createDataFrame(team_df)\n",
    "team_adv_spark = spark.createDataFrame(team_adv[0])\n",
    "all_games_spark = spark.createDataFrame(all_games)\n",
    "szn_adv_spark = spark.createDataFrame(szn_adv_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SEASON_ID</th>\n",
       "      <th>TEAM_ID</th>\n",
       "      <th>TEAM_ABBREVIATION</th>\n",
       "      <th>TEAM_NAME</th>\n",
       "      <th>GAME_ID</th>\n",
       "      <th>GAME_DATE</th>\n",
       "      <th>MATCHUP</th>\n",
       "      <th>WL</th>\n",
       "      <th>MIN</th>\n",
       "      <th>PTS</th>\n",
       "      <th>...</th>\n",
       "      <th>FT_PCT</th>\n",
       "      <th>OREB</th>\n",
       "      <th>DREB</th>\n",
       "      <th>REB</th>\n",
       "      <th>AST</th>\n",
       "      <th>STL</th>\n",
       "      <th>BLK</th>\n",
       "      <th>TOV</th>\n",
       "      <th>PF</th>\n",
       "      <th>PLUS_MINUS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>22020</td>\n",
       "      <td>1610612740</td>\n",
       "      <td>NOP</td>\n",
       "      <td>New Orleans Pelicans</td>\n",
       "      <td>0022001059</td>\n",
       "      <td>2021-05-14</td>\n",
       "      <td>NOP @ GSW</td>\n",
       "      <td>L</td>\n",
       "      <td>241</td>\n",
       "      <td>122</td>\n",
       "      <td>...</td>\n",
       "      <td>0.679</td>\n",
       "      <td>12</td>\n",
       "      <td>39</td>\n",
       "      <td>51</td>\n",
       "      <td>21</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>23</td>\n",
       "      <td>-3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>22020</td>\n",
       "      <td>1610612744</td>\n",
       "      <td>GSW</td>\n",
       "      <td>Golden State Warriors</td>\n",
       "      <td>0022001059</td>\n",
       "      <td>2021-05-14</td>\n",
       "      <td>GSW vs. NOP</td>\n",
       "      <td>W</td>\n",
       "      <td>240</td>\n",
       "      <td>125</td>\n",
       "      <td>...</td>\n",
       "      <td>0.800</td>\n",
       "      <td>8</td>\n",
       "      <td>38</td>\n",
       "      <td>46</td>\n",
       "      <td>29</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>21</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>22020</td>\n",
       "      <td>1610612746</td>\n",
       "      <td>LAC</td>\n",
       "      <td>LA Clippers</td>\n",
       "      <td>0022001058</td>\n",
       "      <td>2021-05-14</td>\n",
       "      <td>LAC @ HOU</td>\n",
       "      <td>L</td>\n",
       "      <td>240</td>\n",
       "      <td>115</td>\n",
       "      <td>...</td>\n",
       "      <td>0.933</td>\n",
       "      <td>7</td>\n",
       "      <td>28</td>\n",
       "      <td>35</td>\n",
       "      <td>29</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>13</td>\n",
       "      <td>15</td>\n",
       "      <td>-7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>22020</td>\n",
       "      <td>1610612745</td>\n",
       "      <td>HOU</td>\n",
       "      <td>Houston Rockets</td>\n",
       "      <td>0022001058</td>\n",
       "      <td>2021-05-14</td>\n",
       "      <td>HOU vs. LAC</td>\n",
       "      <td>W</td>\n",
       "      <td>239</td>\n",
       "      <td>122</td>\n",
       "      <td>...</td>\n",
       "      <td>0.727</td>\n",
       "      <td>8</td>\n",
       "      <td>33</td>\n",
       "      <td>41</td>\n",
       "      <td>30</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>15</td>\n",
       "      <td>20</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   SEASON_ID     TEAM_ID TEAM_ABBREVIATION              TEAM_NAME     GAME_ID  \\\n",
       "14     22020  1610612740               NOP   New Orleans Pelicans  0022001059   \n",
       "4      22020  1610612744               GSW  Golden State Warriors  0022001059   \n",
       "12     22020  1610612746               LAC            LA Clippers  0022001058   \n",
       "0      22020  1610612745               HOU        Houston Rockets  0022001058   \n",
       "\n",
       "     GAME_DATE      MATCHUP WL  MIN  PTS  ...  FT_PCT  OREB  DREB  REB  AST  \\\n",
       "14  2021-05-14    NOP @ GSW  L  241  122  ...   0.679    12    39   51   21   \n",
       "4   2021-05-14  GSW vs. NOP  W  240  125  ...   0.800     8    38   46   29   \n",
       "12  2021-05-14    LAC @ HOU  L  240  115  ...   0.933     7    28   35   29   \n",
       "0   2021-05-14  HOU vs. LAC  W  239  122  ...   0.727     8    33   41   30   \n",
       "\n",
       "    STL  BLK  TOV  PF  PLUS_MINUS  \n",
       "14    6    3    9  23        -3.0  \n",
       "4     4    4   11  21         3.0  \n",
       "12    9    5   13  15        -7.0  \n",
       "0     4    4   15  20         7.0  \n",
       "\n",
       "[4 rows x 28 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_games_spark.toPandas().sort_values('GAME_ID', ascending=False).head(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>GAME_ID</th>\n",
       "      <th>TEAM_ID</th>\n",
       "      <th>TEAM_NAME</th>\n",
       "      <th>TEAM_ABBREVIATION</th>\n",
       "      <th>TEAM_CITY</th>\n",
       "      <th>MIN</th>\n",
       "      <th>E_OFF_RATING</th>\n",
       "      <th>OFF_RATING</th>\n",
       "      <th>E_DEF_RATING</th>\n",
       "      <th>DEF_RATING</th>\n",
       "      <th>...</th>\n",
       "      <th>TM_TOV_PCT</th>\n",
       "      <th>EFG_PCT</th>\n",
       "      <th>TS_PCT</th>\n",
       "      <th>USG_PCT</th>\n",
       "      <th>E_USG_PCT</th>\n",
       "      <th>E_PACE</th>\n",
       "      <th>PACE</th>\n",
       "      <th>PACE_PER40</th>\n",
       "      <th>POSS</th>\n",
       "      <th>PIE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1305</th>\n",
       "      <td>0022001059</td>\n",
       "      <td>1610612744</td>\n",
       "      <td>Warriors</td>\n",
       "      <td>GSW</td>\n",
       "      <td>Golden State</td>\n",
       "      <td>240:00</td>\n",
       "      <td>120.0</td>\n",
       "      <td>120.2</td>\n",
       "      <td>112.6</td>\n",
       "      <td>116.2</td>\n",
       "      <td>...</td>\n",
       "      <td>11.5</td>\n",
       "      <td>0.580</td>\n",
       "      <td>0.624</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.198</td>\n",
       "      <td>106.26</td>\n",
       "      <td>104.5</td>\n",
       "      <td>87.08</td>\n",
       "      <td>104</td>\n",
       "      <td>0.538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1304</th>\n",
       "      <td>0022001059</td>\n",
       "      <td>1610612740</td>\n",
       "      <td>Pelicans</td>\n",
       "      <td>NOP</td>\n",
       "      <td>New Orleans</td>\n",
       "      <td>240:00</td>\n",
       "      <td>112.6</td>\n",
       "      <td>116.2</td>\n",
       "      <td>120.0</td>\n",
       "      <td>120.2</td>\n",
       "      <td>...</td>\n",
       "      <td>8.6</td>\n",
       "      <td>0.520</td>\n",
       "      <td>0.548</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.198</td>\n",
       "      <td>106.26</td>\n",
       "      <td>104.5</td>\n",
       "      <td>87.08</td>\n",
       "      <td>105</td>\n",
       "      <td>0.462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>161</th>\n",
       "      <td>0022001058</td>\n",
       "      <td>1610612745</td>\n",
       "      <td>Rockets</td>\n",
       "      <td>HOU</td>\n",
       "      <td>Houston</td>\n",
       "      <td>240:00</td>\n",
       "      <td>117.7</td>\n",
       "      <td>120.8</td>\n",
       "      <td>107.9</td>\n",
       "      <td>112.7</td>\n",
       "      <td>...</td>\n",
       "      <td>15.8</td>\n",
       "      <td>0.616</td>\n",
       "      <td>0.638</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.199</td>\n",
       "      <td>105.14</td>\n",
       "      <td>101.5</td>\n",
       "      <td>84.58</td>\n",
       "      <td>101</td>\n",
       "      <td>0.509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>160</th>\n",
       "      <td>0022001058</td>\n",
       "      <td>1610612746</td>\n",
       "      <td>Clippers</td>\n",
       "      <td>LAC</td>\n",
       "      <td>LA</td>\n",
       "      <td>240:00</td>\n",
       "      <td>107.9</td>\n",
       "      <td>112.7</td>\n",
       "      <td>117.7</td>\n",
       "      <td>120.8</td>\n",
       "      <td>...</td>\n",
       "      <td>13.7</td>\n",
       "      <td>0.543</td>\n",
       "      <td>0.577</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.197</td>\n",
       "      <td>105.14</td>\n",
       "      <td>101.5</td>\n",
       "      <td>84.58</td>\n",
       "      <td>102</td>\n",
       "      <td>0.491</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         GAME_ID     TEAM_ID TEAM_NAME TEAM_ABBREVIATION     TEAM_CITY  \\\n",
       "1305  0022001059  1610612744  Warriors               GSW  Golden State   \n",
       "1304  0022001059  1610612740  Pelicans               NOP   New Orleans   \n",
       "161   0022001058  1610612745   Rockets               HOU       Houston   \n",
       "160   0022001058  1610612746  Clippers               LAC            LA   \n",
       "\n",
       "         MIN  E_OFF_RATING  OFF_RATING  E_DEF_RATING  DEF_RATING  ...  \\\n",
       "1305  240:00         120.0       120.2         112.6       116.2  ...   \n",
       "1304  240:00         112.6       116.2         120.0       120.2  ...   \n",
       "161   240:00         117.7       120.8         107.9       112.7  ...   \n",
       "160   240:00         107.9       112.7         117.7       120.8  ...   \n",
       "\n",
       "      TM_TOV_PCT  EFG_PCT  TS_PCT  USG_PCT  E_USG_PCT  E_PACE   PACE  \\\n",
       "1305        11.5    0.580   0.624      1.0      0.198  106.26  104.5   \n",
       "1304         8.6    0.520   0.548      1.0      0.198  106.26  104.5   \n",
       "161         15.8    0.616   0.638      1.0      0.199  105.14  101.5   \n",
       "160         13.7    0.543   0.577      1.0      0.197  105.14  101.5   \n",
       "\n",
       "      PACE_PER40  POSS    PIE  \n",
       "1305       87.08   104  0.538  \n",
       "1304       87.08   105  0.462  \n",
       "161        84.58   101  0.509  \n",
       "160        84.58   102  0.491  \n",
       "\n",
       "[4 rows x 29 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "szn_adv_spark.toPandas().sort_values('GAME_ID', ascending=False).head(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "root\n",
      " |-- SEASON_ID: string (nullable = true)\n",
      " |-- TEAM_ID: long (nullable = true)\n",
      " |-- TEAM_ABBREVIATION: string (nullable = true)\n",
      " |-- TEAM_NAME: string (nullable = true)\n",
      " |-- GAME_ID: string (nullable = true)\n",
      " |-- GAME_DATE: string (nullable = true)\n",
      " |-- MATCHUP: string (nullable = true)\n",
      " |-- WL: string (nullable = true)\n",
      " |-- MIN: long (nullable = true)\n",
      " |-- PTS: long (nullable = true)\n",
      " |-- FGM: long (nullable = true)\n",
      " |-- FGA: long (nullable = true)\n",
      " |-- FG_PCT: double (nullable = true)\n",
      " |-- FG3M: long (nullable = true)\n",
      " |-- FG3A: long (nullable = true)\n",
      " |-- FG3_PCT: double (nullable = true)\n",
      " |-- FTM: long (nullable = true)\n",
      " |-- FTA: long (nullable = true)\n",
      " |-- FT_PCT: double (nullable = true)\n",
      " |-- OREB: long (nullable = true)\n",
      " |-- DREB: long (nullable = true)\n",
      " |-- REB: long (nullable = true)\n",
      " |-- AST: long (nullable = true)\n",
      " |-- STL: long (nullable = true)\n",
      " |-- BLK: long (nullable = true)\n",
      " |-- TOV: long (nullable = true)\n",
      " |-- PF: long (nullable = true)\n",
      " |-- PLUS_MINUS: double (nullable = true)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "all_games_spark.printSchema()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Combine DFs to get mathcup data for splitting and rejoinin home and away"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_games_spark.createOrReplaceTempView('all_games_V')\n",
    "szn_adv_spark.createOrReplaceTempView('szn_adv_V')\n",
    "df = spark.sql('SELECT a.MATCHUP, a.WL, a.PF, s.* FROM all_games_V a RIGHT JOIN szn_adv_V s \\\n",
    "                        ON a.GAME_ID = s.GAME_ID AND a.TEAM_ID = s.TEAM_ID')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "root\n",
      " |-- MATCHUP: string (nullable = true)\n",
      " |-- WL: string (nullable = true)\n",
      " |-- PF: long (nullable = true)\n",
      " |-- GAME_ID: string (nullable = true)\n",
      " |-- TEAM_ID: long (nullable = true)\n",
      " |-- TEAM_NAME: string (nullable = true)\n",
      " |-- TEAM_ABBREVIATION: string (nullable = true)\n",
      " |-- TEAM_CITY: string (nullable = true)\n",
      " |-- MIN: string (nullable = true)\n",
      " |-- E_OFF_RATING: double (nullable = true)\n",
      " |-- OFF_RATING: double (nullable = true)\n",
      " |-- E_DEF_RATING: double (nullable = true)\n",
      " |-- DEF_RATING: double (nullable = true)\n",
      " |-- E_NET_RATING: double (nullable = true)\n",
      " |-- NET_RATING: double (nullable = true)\n",
      " |-- AST_PCT: double (nullable = true)\n",
      " |-- AST_TOV: double (nullable = true)\n",
      " |-- AST_RATIO: double (nullable = true)\n",
      " |-- OREB_PCT: double (nullable = true)\n",
      " |-- DREB_PCT: double (nullable = true)\n",
      " |-- REB_PCT: double (nullable = true)\n",
      " |-- E_TM_TOV_PCT: double (nullable = true)\n",
      " |-- TM_TOV_PCT: double (nullable = true)\n",
      " |-- EFG_PCT: double (nullable = true)\n",
      " |-- TS_PCT: double (nullable = true)\n",
      " |-- USG_PCT: double (nullable = true)\n",
      " |-- E_USG_PCT: double (nullable = true)\n",
      " |-- E_PACE: double (nullable = true)\n",
      " |-- PACE: double (nullable = true)\n",
      " |-- PACE_PER40: double (nullable = true)\n",
      " |-- POSS: long (nullable = true)\n",
      " |-- PIE: double (nullable = true)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "df.printSchema()"
   ]
  },
  {
   "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>MATCHUP</th>\n",
       "      <th>WL</th>\n",
       "      <th>PF</th>\n",
       "      <th>GAME_ID</th>\n",
       "      <th>TEAM_ID</th>\n",
       "      <th>TEAM_NAME</th>\n",
       "      <th>TEAM_ABBREVIATION</th>\n",
       "      <th>TEAM_CITY</th>\n",
       "      <th>MIN</th>\n",
       "      <th>E_OFF_RATING</th>\n",
       "      <th>...</th>\n",
       "      <th>TM_TOV_PCT</th>\n",
       "      <th>EFG_PCT</th>\n",
       "      <th>TS_PCT</th>\n",
       "      <th>USG_PCT</th>\n",
       "      <th>E_USG_PCT</th>\n",
       "      <th>E_PACE</th>\n",
       "      <th>PACE</th>\n",
       "      <th>PACE_PER40</th>\n",
       "      <th>POSS</th>\n",
       "      <th>PIE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>TOR @ SAS</td>\n",
       "      <td>L</td>\n",
       "      <td>23</td>\n",
       "      <td>0022000027</td>\n",
       "      <td>1610612761</td>\n",
       "      <td>Raptors</td>\n",
       "      <td>TOR</td>\n",
       "      <td>Toronto</td>\n",
       "      <td>240:00</td>\n",
       "      <td>109.0</td>\n",
       "      <td>...</td>\n",
       "      <td>11.5</td>\n",
       "      <td>0.536</td>\n",
       "      <td>0.556</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.199</td>\n",
       "      <td>106.82</td>\n",
       "      <td>104.0</td>\n",
       "      <td>86.67</td>\n",
       "      <td>104</td>\n",
       "      <td>0.491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ORL @ MIN</td>\n",
       "      <td>W</td>\n",
       "      <td>17</td>\n",
       "      <td>0022000221</td>\n",
       "      <td>1610612753</td>\n",
       "      <td>Magic</td>\n",
       "      <td>ORL</td>\n",
       "      <td>Orlando</td>\n",
       "      <td>240:00</td>\n",
       "      <td>96.8</td>\n",
       "      <td>...</td>\n",
       "      <td>12.5</td>\n",
       "      <td>0.446</td>\n",
       "      <td>0.479</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.198</td>\n",
       "      <td>99.70</td>\n",
       "      <td>95.5</td>\n",
       "      <td>79.58</td>\n",
       "      <td>96</td>\n",
       "      <td>0.505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>IND @ MIL</td>\n",
       "      <td>L</td>\n",
       "      <td>13</td>\n",
       "      <td>0022000329</td>\n",
       "      <td>1610612754</td>\n",
       "      <td>Pacers</td>\n",
       "      <td>IND</td>\n",
       "      <td>Indiana</td>\n",
       "      <td>240:00</td>\n",
       "      <td>102.2</td>\n",
       "      <td>...</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.511</td>\n",
       "      <td>0.541</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.201</td>\n",
       "      <td>108.88</td>\n",
       "      <td>107.5</td>\n",
       "      <td>89.58</td>\n",
       "      <td>108</td>\n",
       "      <td>0.424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>HOU @ NOP</td>\n",
       "      <td>L</td>\n",
       "      <td>23</td>\n",
       "      <td>0022000377</td>\n",
       "      <td>1610612745</td>\n",
       "      <td>Rockets</td>\n",
       "      <td>HOU</td>\n",
       "      <td>Houston</td>\n",
       "      <td>240:00</td>\n",
       "      <td>100.8</td>\n",
       "      <td>...</td>\n",
       "      <td>12.4</td>\n",
       "      <td>0.489</td>\n",
       "      <td>0.504</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.198</td>\n",
       "      <td>100.78</td>\n",
       "      <td>98.0</td>\n",
       "      <td>81.67</td>\n",
       "      <td>97</td>\n",
       "      <td>0.324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>POR vs. PHI</td>\n",
       "      <td>W</td>\n",
       "      <td>21</td>\n",
       "      <td>0022000394</td>\n",
       "      <td>1610612757</td>\n",
       "      <td>Trail Blazers</td>\n",
       "      <td>POR</td>\n",
       "      <td>Portland</td>\n",
       "      <td>240:00</td>\n",
       "      <td>122.8</td>\n",
       "      <td>...</td>\n",
       "      <td>8.6</td>\n",
       "      <td>0.522</td>\n",
       "      <td>0.572</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.200</td>\n",
       "      <td>96.44</td>\n",
       "      <td>94.0</td>\n",
       "      <td>78.33</td>\n",
       "      <td>93</td>\n",
       "      <td>0.474</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       MATCHUP WL  PF     GAME_ID     TEAM_ID      TEAM_NAME  \\\n",
       "0    TOR @ SAS  L  23  0022000027  1610612761        Raptors   \n",
       "1    ORL @ MIN  W  17  0022000221  1610612753          Magic   \n",
       "2    IND @ MIL  L  13  0022000329  1610612754         Pacers   \n",
       "3    HOU @ NOP  L  23  0022000377  1610612745        Rockets   \n",
       "4  POR vs. PHI  W  21  0022000394  1610612757  Trail Blazers   \n",
       "\n",
       "  TEAM_ABBREVIATION TEAM_CITY     MIN  E_OFF_RATING  ...  TM_TOV_PCT  EFG_PCT  \\\n",
       "0               TOR   Toronto  240:00         109.0  ...        11.5    0.536   \n",
       "1               ORL   Orlando  240:00          96.8  ...        12.5    0.446   \n",
       "2               IND   Indiana  240:00         102.2  ...        13.0    0.511   \n",
       "3               HOU   Houston  240:00         100.8  ...        12.4    0.489   \n",
       "4               POR  Portland  240:00         122.8  ...         8.6    0.522   \n",
       "\n",
       "   TS_PCT  USG_PCT  E_USG_PCT  E_PACE   PACE  PACE_PER40  POSS    PIE  \n",
       "0   0.556      1.0      0.199  106.82  104.0       86.67   104  0.491  \n",
       "1   0.479      1.0      0.198   99.70   95.5       79.58    96  0.505  \n",
       "2   0.541      1.0      0.201  108.88  107.5       89.58   108  0.424  \n",
       "3   0.504      1.0      0.198  100.78   98.0       81.67    97  0.324  \n",
       "4   0.572      1.0      0.200   96.44   94.0       78.33    93  0.474  \n",
       "\n",
       "[5 rows x 32 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.toPandas().head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Split Home and Away"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "home_df = df.filter(df.MATCHUP.contains('.'))\n",
    "away_df = df.filter(df.MATCHUP.contains('@'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Create HomeWin column comprised of binary response: 1 for home team win, 0 for home team loss."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "home_df = home_df.withColumn('HomeWin', f.when(f.col('WL') == 'W', 1).otherwise(0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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>WL</th>\n",
       "      <th>HomeWin</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>W</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>L</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>W</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  WL  HomeWin\n",
       "0  W        1\n",
       "1  L        0\n",
       "2  W        1"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "home_df.toPandas()[['WL', 'HomeWin']].head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Prepare columns for `home_` and `away_` prefixes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [],
   "source": [
    "away_drop = ['MATCHUP', 'WL', 'GAME_ID', 'TEAM_CITY', 'MIN', 'USG_PCT']\n",
    "home_drop = away_drop + ['HomeWin']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [],
   "source": [
    "away_prep = away_df.drop(*away_drop)\n",
    "home_prep = home_df.drop(*home_drop)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [],
   "source": [
    "away_col_names = [f\"away_{i}\" for i in away_prep.columns]\n",
    "home_col_names = [f\"home_{i}\" for i in home_prep.columns]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [],
   "source": [
    "away_prep_df = away_prep.toDF(*away_col_names)\n",
    "home_prep_df = home_prep.toDF(*home_col_names)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Cannot concatenate horizonatlly in PySpark. So, we'll convert some DFs to pandas, concatenate, and convert back to Spark DF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_away = spark.createDataFrame(pd.concat([away_df.toPandas()['GAME_ID'], away_prep_df.toPandas()], axis=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_home = spark.createDataFrame(pd.concat([home_df.toPandas()[['GAME_ID', 'HomeWin']], home_prep_df.toPandas()], axis=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This join method avoids duplicating the 'GAME_ID' column.\n",
    "final_df = final_home.join(final_away, 'GAME_ID')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "root\n",
      " |-- GAME_ID: string (nullable = true)\n",
      " |-- HomeWin: long (nullable = true)\n",
      " |-- home_PF: long (nullable = true)\n",
      " |-- home_TEAM_ID: long (nullable = true)\n",
      " |-- home_TEAM_NAME: string (nullable = true)\n",
      " |-- home_TEAM_ABBREVIATION: string (nullable = true)\n",
      " |-- home_E_OFF_RATING: double (nullable = true)\n",
      " |-- home_OFF_RATING: double (nullable = true)\n",
      " |-- home_E_DEF_RATING: double (nullable = true)\n",
      " |-- home_DEF_RATING: double (nullable = true)\n",
      " |-- home_E_NET_RATING: double (nullable = true)\n",
      " |-- home_NET_RATING: double (nullable = true)\n",
      " |-- home_AST_PCT: double (nullable = true)\n",
      " |-- home_AST_TOV: double (nullable = true)\n",
      " |-- home_AST_RATIO: double (nullable = true)\n",
      " |-- home_OREB_PCT: double (nullable = true)\n",
      " |-- home_DREB_PCT: double (nullable = true)\n",
      " |-- home_REB_PCT: double (nullable = true)\n",
      " |-- home_E_TM_TOV_PCT: double (nullable = true)\n",
      " |-- home_TM_TOV_PCT: double (nullable = true)\n",
      " |-- home_EFG_PCT: double (nullable = true)\n",
      " |-- home_TS_PCT: double (nullable = true)\n",
      " |-- home_E_USG_PCT: double (nullable = true)\n",
      " |-- home_E_PACE: double (nullable = true)\n",
      " |-- home_PACE: double (nullable = true)\n",
      " |-- home_PACE_PER40: double (nullable = true)\n",
      " |-- home_POSS: long (nullable = true)\n",
      " |-- home_PIE: double (nullable = true)\n",
      " |-- away_PF: long (nullable = true)\n",
      " |-- away_TEAM_ID: long (nullable = true)\n",
      " |-- away_TEAM_NAME: string (nullable = true)\n",
      " |-- away_TEAM_ABBREVIATION: string (nullable = true)\n",
      " |-- away_E_OFF_RATING: double (nullable = true)\n",
      " |-- away_OFF_RATING: double (nullable = true)\n",
      " |-- away_E_DEF_RATING: double (nullable = true)\n",
      " |-- away_DEF_RATING: double (nullable = true)\n",
      " |-- away_E_NET_RATING: double (nullable = true)\n",
      " |-- away_NET_RATING: double (nullable = true)\n",
      " |-- away_AST_PCT: double (nullable = true)\n",
      " |-- away_AST_TOV: double (nullable = true)\n",
      " |-- away_AST_RATIO: double (nullable = true)\n",
      " |-- away_OREB_PCT: double (nullable = true)\n",
      " |-- away_DREB_PCT: double (nullable = true)\n",
      " |-- away_REB_PCT: double (nullable = true)\n",
      " |-- away_E_TM_TOV_PCT: double (nullable = true)\n",
      " |-- away_TM_TOV_PCT: double (nullable = true)\n",
      " |-- away_EFG_PCT: double (nullable = true)\n",
      " |-- away_TS_PCT: double (nullable = true)\n",
      " |-- away_E_USG_PCT: double (nullable = true)\n",
      " |-- away_E_PACE: double (nullable = true)\n",
      " |-- away_PACE: double (nullable = true)\n",
      " |-- away_PACE_PER40: double (nullable = true)\n",
      " |-- away_POSS: long (nullable = true)\n",
      " |-- away_PIE: double (nullable = true)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "final_df.printSchema()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Standardize Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_cols = final_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [],
   "source": [
    "drop_final_cols = ['GAME_ID', 'HomeWin', 'home_TEAM_ID', 'home_TEAM_NAME', 'home_TEAM_ABBREVIATION', 'away_TEAM_ID', 'away_TEAM_NAME', 'away_TEAM_ABBREVIATION']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [],
   "source": [
    "inputCols = [i for i in final_df.columns if i not in drop_final_cols]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Split data in to training and testing set - 80% and 20%, respectively."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyspark.ml.feature import VectorAssembler\n",
    "from pyspark.ml.feature import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [],
   "source": [
    "va = VectorAssembler(inputCols = inputCols, outputCol = 'features')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [],
   "source": [
    "va_df = va.transform(final_df)\n",
    "va_df = va_df.select(['features', 'HomeWin'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_test_split = va_df.randomSplit([0.8, 0.2], seed=76)\n",
    "train_df = train_test_split[0]\n",
    "test_df = train_test_split[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = StandardScaler(inputCol='features', outputCol='scaledFeats')\n",
    "train_scaled = scaler.fit(train_df).transform(train_df)\n",
    "test_scaled = scaler.fit(train_df).transform(test_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x1008 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (14,14))\n",
    "sns.heatmap(final_df.toPandas()[inputCols].corr())\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Modeling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyspark.ml.classification import LogisticRegression\n",
    "from pyspark.ml.evaluation import BinaryClassificationEvaluator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [],
   "source": [
    "lr = LogisticRegression(featuresCol='scaledFeats', labelCol='HomeWin', maxIter=10, regParam=0.3, elasticNetParam=0.8)\n",
    "lr_model = lr.fit(train_scaled)\n",
    "lr_predictions = lr_model.transform(test_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9932432432432432"
      ]
     },
     "execution_count": 210,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr_evaluator = BinaryClassificationEvaluator(labelCol='HomeWin')\n",
    "lr_evaluator.evaluate(lr_predictions)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}