Predicting An NBA-All Star.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import the packages, algorithms and metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "from sklearn.ensemble import VotingClassifier\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.pipeline import Pipeline\n",
    "\n",
    "from sklearn.metrics import recall_score\n",
    "from sklearn.metrics import precision_score\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import f1_score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load in data and create dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2360, 36) ['Season', 'Player', 'Pos', 'Age', 'Tm', 'G', 'GS', 'MP', 'FG', 'FGA', 'FG%', '3P', '3PA', '3P%', '2P', '2PA', '2P%', 'eFG%', 'FT', 'FTA', 'FT%', 'ORB', 'DRB', 'TRB', 'AST', 'STL', 'BLK', 'TOV', 'PF', 'PTS', 'PER', 'TS%', 'USG%', 'WS', 'BPM', 'All-Star']\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Season</th>\n",
       "      <th>Player</th>\n",
       "      <th>Pos</th>\n",
       "      <th>Age</th>\n",
       "      <th>Tm</th>\n",
       "      <th>G</th>\n",
       "      <th>GS</th>\n",
       "      <th>MP</th>\n",
       "      <th>FG</th>\n",
       "      <th>FGA</th>\n",
       "      <th>FG%</th>\n",
       "      <th>3P</th>\n",
       "      <th>3PA</th>\n",
       "      <th>3P%</th>\n",
       "      <th>2P</th>\n",
       "      <th>2PA</th>\n",
       "      <th>2P%</th>\n",
       "      <th>eFG%</th>\n",
       "      <th>FT</th>\n",
       "      <th>FTA</th>\n",
       "      <th>FT%</th>\n",
       "      <th>ORB</th>\n",
       "      <th>DRB</th>\n",
       "      <th>TRB</th>\n",
       "      <th>AST</th>\n",
       "      <th>STL</th>\n",
       "      <th>BLK</th>\n",
       "      <th>TOV</th>\n",
       "      <th>PF</th>\n",
       "      <th>PTS</th>\n",
       "      <th>PER</th>\n",
       "      <th>TS%</th>\n",
       "      <th>USG%</th>\n",
       "      <th>WS</th>\n",
       "      <th>BPM</th>\n",
       "      <th>All-Star</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016</td>\n",
       "      <td>Álex Abrines</td>\n",
       "      <td>SG</td>\n",
       "      <td>23</td>\n",
       "      <td>OKC</td>\n",
       "      <td>68</td>\n",
       "      <td>6</td>\n",
       "      <td>15.5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.393</td>\n",
       "      <td>1.4</td>\n",
       "      <td>3.6</td>\n",
       "      <td>0.381</td>\n",
       "      <td>0.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.426</td>\n",
       "      <td>0.531</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.898</td>\n",
       "      <td>0.3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.7</td>\n",
       "      <td>6.0</td>\n",
       "      <td>10.1</td>\n",
       "      <td>0.560</td>\n",
       "      <td>15.9</td>\n",
       "      <td>2.1</td>\n",
       "      <td>-1.6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016</td>\n",
       "      <td>Quincy Acy</td>\n",
       "      <td>PF</td>\n",
       "      <td>26</td>\n",
       "      <td>DAL</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>2.8</td>\n",
       "      <td>0.294</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0.143</td>\n",
       "      <td>0.7</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.400</td>\n",
       "      <td>0.324</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.667</td>\n",
       "      <td>0.3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>1.5</td>\n",
       "      <td>2.2</td>\n",
       "      <td>-1.4</td>\n",
       "      <td>0.355</td>\n",
       "      <td>20.0</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>-14.3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016</td>\n",
       "      <td>Quincy Acy</td>\n",
       "      <td>PF</td>\n",
       "      <td>26</td>\n",
       "      <td>BRK</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "      <td>15.9</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.8</td>\n",
       "      <td>0.425</td>\n",
       "      <td>1.1</td>\n",
       "      <td>2.6</td>\n",
       "      <td>0.434</td>\n",
       "      <td>0.9</td>\n",
       "      <td>2.2</td>\n",
       "      <td>0.414</td>\n",
       "      <td>0.542</td>\n",
       "      <td>1.3</td>\n",
       "      <td>1.8</td>\n",
       "      <td>0.754</td>\n",
       "      <td>0.6</td>\n",
       "      <td>2.8</td>\n",
       "      <td>3.3</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.6</td>\n",
       "      <td>1.8</td>\n",
       "      <td>6.5</td>\n",
       "      <td>13.1</td>\n",
       "      <td>0.587</td>\n",
       "      <td>16.5</td>\n",
       "      <td>1.1</td>\n",
       "      <td>-0.9</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016</td>\n",
       "      <td>Steven Adams</td>\n",
       "      <td>C</td>\n",
       "      <td>23</td>\n",
       "      <td>OKC</td>\n",
       "      <td>80</td>\n",
       "      <td>80</td>\n",
       "      <td>29.9</td>\n",
       "      <td>4.7</td>\n",
       "      <td>8.2</td>\n",
       "      <td>0.571</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>4.7</td>\n",
       "      <td>8.2</td>\n",
       "      <td>0.572</td>\n",
       "      <td>0.571</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.2</td>\n",
       "      <td>0.611</td>\n",
       "      <td>3.5</td>\n",
       "      <td>4.2</td>\n",
       "      <td>7.7</td>\n",
       "      <td>1.1</td>\n",
       "      <td>1.1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.8</td>\n",
       "      <td>2.4</td>\n",
       "      <td>11.3</td>\n",
       "      <td>16.5</td>\n",
       "      <td>0.589</td>\n",
       "      <td>16.2</td>\n",
       "      <td>6.5</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016</td>\n",
       "      <td>Arron Afflalo</td>\n",
       "      <td>SG</td>\n",
       "      <td>31</td>\n",
       "      <td>SAC</td>\n",
       "      <td>61</td>\n",
       "      <td>45</td>\n",
       "      <td>25.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.9</td>\n",
       "      <td>0.440</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.411</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.4</td>\n",
       "      <td>0.457</td>\n",
       "      <td>0.514</td>\n",
       "      <td>1.4</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.892</td>\n",
       "      <td>0.1</td>\n",
       "      <td>1.9</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.7</td>\n",
       "      <td>1.7</td>\n",
       "      <td>8.4</td>\n",
       "      <td>8.9</td>\n",
       "      <td>0.559</td>\n",
       "      <td>14.4</td>\n",
       "      <td>1.4</td>\n",
       "      <td>-3.6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2016</td>\n",
       "      <td>Alexis Ajinça</td>\n",
       "      <td>C</td>\n",
       "      <td>28</td>\n",
       "      <td>NOP</td>\n",
       "      <td>39</td>\n",
       "      <td>15</td>\n",
       "      <td>15.0</td>\n",
       "      <td>2.3</td>\n",
       "      <td>4.6</td>\n",
       "      <td>0.500</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.000</td>\n",
       "      <td>2.3</td>\n",
       "      <td>4.5</td>\n",
       "      <td>0.511</td>\n",
       "      <td>0.500</td>\n",
       "      <td>0.7</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.725</td>\n",
       "      <td>1.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>4.5</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.8</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.3</td>\n",
       "      <td>12.9</td>\n",
       "      <td>0.529</td>\n",
       "      <td>17.2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-3.3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2016</td>\n",
       "      <td>Cole Aldrich</td>\n",
       "      <td>C</td>\n",
       "      <td>28</td>\n",
       "      <td>MIN</td>\n",
       "      <td>62</td>\n",
       "      <td>0</td>\n",
       "      <td>8.6</td>\n",
       "      <td>0.7</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.523</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.7</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.523</td>\n",
       "      <td>0.523</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.682</td>\n",
       "      <td>0.8</td>\n",
       "      <td>1.7</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.3</td>\n",
       "      <td>1.4</td>\n",
       "      <td>1.7</td>\n",
       "      <td>12.7</td>\n",
       "      <td>0.549</td>\n",
       "      <td>9.4</td>\n",
       "      <td>1.3</td>\n",
       "      <td>-0.8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2016</td>\n",
       "      <td>LaMarcus Aldridge</td>\n",
       "      <td>PF</td>\n",
       "      <td>31</td>\n",
       "      <td>SAS</td>\n",
       "      <td>72</td>\n",
       "      <td>72</td>\n",
       "      <td>32.4</td>\n",
       "      <td>6.9</td>\n",
       "      <td>14.6</td>\n",
       "      <td>0.477</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.411</td>\n",
       "      <td>6.6</td>\n",
       "      <td>13.8</td>\n",
       "      <td>0.480</td>\n",
       "      <td>0.488</td>\n",
       "      <td>3.1</td>\n",
       "      <td>3.8</td>\n",
       "      <td>0.812</td>\n",
       "      <td>2.4</td>\n",
       "      <td>4.9</td>\n",
       "      <td>7.3</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.6</td>\n",
       "      <td>1.2</td>\n",
       "      <td>1.4</td>\n",
       "      <td>2.2</td>\n",
       "      <td>17.3</td>\n",
       "      <td>18.6</td>\n",
       "      <td>0.532</td>\n",
       "      <td>24.5</td>\n",
       "      <td>7.2</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2016</td>\n",
       "      <td>Lavoy Allen</td>\n",
       "      <td>PF</td>\n",
       "      <td>27</td>\n",
       "      <td>IND</td>\n",
       "      <td>61</td>\n",
       "      <td>5</td>\n",
       "      <td>14.3</td>\n",
       "      <td>1.3</td>\n",
       "      <td>2.8</td>\n",
       "      <td>0.458</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.3</td>\n",
       "      <td>2.7</td>\n",
       "      <td>0.461</td>\n",
       "      <td>0.458</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.697</td>\n",
       "      <td>1.7</td>\n",
       "      <td>1.9</td>\n",
       "      <td>3.6</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.3</td>\n",
       "      <td>2.9</td>\n",
       "      <td>11.6</td>\n",
       "      <td>0.485</td>\n",
       "      <td>10.9</td>\n",
       "      <td>1.7</td>\n",
       "      <td>-1.7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2016</td>\n",
       "      <td>Tony Allen</td>\n",
       "      <td>SG</td>\n",
       "      <td>35</td>\n",
       "      <td>MEM</td>\n",
       "      <td>71</td>\n",
       "      <td>66</td>\n",
       "      <td>27.0</td>\n",
       "      <td>3.9</td>\n",
       "      <td>8.4</td>\n",
       "      <td>0.461</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.278</td>\n",
       "      <td>3.6</td>\n",
       "      <td>7.6</td>\n",
       "      <td>0.479</td>\n",
       "      <td>0.473</td>\n",
       "      <td>1.1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>0.615</td>\n",
       "      <td>2.3</td>\n",
       "      <td>3.2</td>\n",
       "      <td>5.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1.4</td>\n",
       "      <td>2.5</td>\n",
       "      <td>9.1</td>\n",
       "      <td>13.3</td>\n",
       "      <td>0.493</td>\n",
       "      <td>17.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Season             Player Pos  Age   Tm   G  GS    MP   FG   FGA    FG%  \\\n",
       "0    2016       Álex Abrines  SG   23  OKC  68   6  15.5  2.0   5.0  0.393   \n",
       "1    2016         Quincy Acy  PF   26  DAL   6   0   8.0  0.8   2.8  0.294   \n",
       "2    2016         Quincy Acy  PF   26  BRK  32   1  15.9  2.0   4.8  0.425   \n",
       "3    2016       Steven Adams   C   23  OKC  80  80  29.9  4.7   8.2  0.571   \n",
       "4    2016      Arron Afflalo  SG   31  SAC  61  45  25.9  3.0   6.9  0.440   \n",
       "5    2016      Alexis Ajinça   C   28  NOP  39  15  15.0  2.3   4.6  0.500   \n",
       "6    2016       Cole Aldrich   C   28  MIN  62   0   8.6  0.7   1.4  0.523   \n",
       "7    2016  LaMarcus Aldridge  PF   31  SAS  72  72  32.4  6.9  14.6  0.477   \n",
       "8    2016        Lavoy Allen  PF   27  IND  61   5  14.3  1.3   2.8  0.458   \n",
       "9    2016         Tony Allen  SG   35  MEM  71  66  27.0  3.9   8.4  0.461   \n",
       "\n",
       "    3P  3PA    3P%   2P   2PA    2P%   eFG%   FT  FTA    FT%  ORB  DRB  TRB  \\\n",
       "0  1.4  3.6  0.381  0.6   1.4  0.426  0.531  0.6  0.7  0.898  0.3  1.0  1.3   \n",
       "1  0.2  1.2  0.143  0.7   1.7  0.400  0.324  0.3  0.5  0.667  0.3  1.0  1.3   \n",
       "2  1.1  2.6  0.434  0.9   2.2  0.414  0.542  1.3  1.8  0.754  0.6  2.8  3.3   \n",
       "3  0.0  0.0  0.000  4.7   8.2  0.572  0.571  2.0  3.2  0.611  3.5  4.2  7.7   \n",
       "4  1.0  2.5  0.411  2.0   4.4  0.457  0.514  1.4  1.5  0.892  0.1  1.9  2.0   \n",
       "5  0.0  0.1  0.000  2.3   4.5  0.511  0.500  0.7  1.0  0.725  1.2  3.4  4.5   \n",
       "6  0.0  0.0  0.000  0.7   1.4  0.523  0.523  0.2  0.4  0.682  0.8  1.7  2.5   \n",
       "7  0.3  0.8  0.411  6.6  13.8  0.480  0.488  3.1  3.8  0.812  2.4  4.9  7.3   \n",
       "8  0.0  0.0  0.000  1.3   2.7  0.461  0.458  0.4  0.5  0.697  1.7  1.9  3.6   \n",
       "9  0.2  0.8  0.278  3.6   7.6  0.479  0.473  1.1  1.8  0.615  2.3  3.2  5.5   \n",
       "\n",
       "   AST  STL  BLK  TOV   PF   PTS   PER    TS%  USG%   WS   BPM  All-Star  \n",
       "0  0.6  0.5  0.1  0.5  1.7   6.0  10.1  0.560  15.9  2.1  -1.6         0  \n",
       "1  0.0  0.0  0.0  0.3  1.5   2.2  -1.4  0.355  20.0 -0.1 -14.3         0  \n",
       "2  0.6  0.4  0.5  0.6  1.8   6.5  13.1  0.587  16.5  1.1  -0.9         0  \n",
       "3  1.1  1.1  1.0  1.8  2.4  11.3  16.5  0.589  16.2  6.5  -0.2         0  \n",
       "4  1.3  0.3  0.1  0.7  1.7   8.4   8.9  0.559  14.4  1.4  -3.6         0  \n",
       "5  0.3  0.5  0.6  0.8  2.0   5.3  12.9  0.529  17.2  1.0  -3.3         0  \n",
       "6  0.4  0.4  0.4  0.3  1.4   1.7  12.7  0.549   9.4  1.3  -0.8         0  \n",
       "7  1.9  0.6  1.2  1.4  2.2  17.3  18.6  0.532  24.5  7.2   0.8         0  \n",
       "8  0.9  0.3  0.4  0.5  1.3   2.9  11.6  0.485  10.9  1.7  -1.7         0  \n",
       "9  1.4  1.6  0.4  1.4  2.5   9.1  13.3  0.493  17.9  3.1  -1.0         0  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('2016-2019_total stats.csv')\n",
    "print(df.shape, df.columns.to_list())\n",
    "\n",
    "#Checking out the data\n",
    "pd.set_option(\"display.max.columns\", None)\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2360 entries, 0 to 2359\n",
      "Data columns (total 36 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   Season    2360 non-null   int64  \n",
      " 1   Player    2360 non-null   object \n",
      " 2   Pos       2360 non-null   object \n",
      " 3   Age       2360 non-null   int64  \n",
      " 4   Tm        2360 non-null   object \n",
      " 5   G         2360 non-null   int64  \n",
      " 6   GS        2360 non-null   int64  \n",
      " 7   MP        2360 non-null   float64\n",
      " 8   FG        2360 non-null   float64\n",
      " 9   FGA       2360 non-null   float64\n",
      " 10  FG%       2350 non-null   float64\n",
      " 11  3P        2360 non-null   float64\n",
      " 12  3PA       2360 non-null   float64\n",
      " 13  3P%       2269 non-null   float64\n",
      " 14  2P        2360 non-null   float64\n",
      " 15  2PA       2360 non-null   float64\n",
      " 16  2P%       2339 non-null   float64\n",
      " 17  eFG%      2351 non-null   float64\n",
      " 18  FT        2360 non-null   float64\n",
      " 19  FTA       2360 non-null   float64\n",
      " 20  FT%       2274 non-null   float64\n",
      " 21  ORB       2360 non-null   float64\n",
      " 22  DRB       2360 non-null   float64\n",
      " 23  TRB       2360 non-null   float64\n",
      " 24  AST       2360 non-null   float64\n",
      " 25  STL       2360 non-null   float64\n",
      " 26  BLK       2360 non-null   float64\n",
      " 27  TOV       2360 non-null   float64\n",
      " 28  PF        2360 non-null   float64\n",
      " 29  PTS       2360 non-null   float64\n",
      " 30  PER       2360 non-null   float64\n",
      " 31  TS%       2347 non-null   float64\n",
      " 32  USG%      2360 non-null   float64\n",
      " 33  WS        2360 non-null   float64\n",
      " 34  BPM       2360 non-null   float64\n",
      " 35  All-Star  2360 non-null   int64  \n",
      "dtypes: float64(28), int64(5), object(3)\n",
      "memory usage: 663.9+ KB\n"
     ]
    }
   ],
   "source": [
    "#Checking out the data\n",
    "\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualizing the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Import matplotlib\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "PTS_column = df[\"PTS\"]\n",
    "TRB_column = df[\"TRB\"]\n",
    "AST_column = df[\"AST\"]\n",
    "WS_Column = df[\"WS\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Frequency'>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Points Histogram\n",
    "# Right skew here with majority of players scoring around 5 points per game\n",
    "PTS_column.plot(kind=\"hist\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Frequency'>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Rebounds Histogram\n",
    "# Right skew here with majority of players getting between 2-4 rebounds a game\n",
    "\n",
    "TRB_column.plot(kind=\"hist\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Frequency'>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Assists Histogram\n",
    "# Right skew here with majority of players getting between 0-2 assists a game\n",
    "\n",
    "AST_column.plot(kind=\"hist\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Frequency'>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Win shares Histogram\n",
    "WS_Column.plot(kind=\"hist\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Player'>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Players with highest points per game seasons between 2016-2019\n",
    "\n",
    "#James Harden\n",
    "#James Harden\n",
    "#Russell Westbrook\n",
    "#Bradley Beal\n",
    "#James Harden\n",
    "\n",
    "top_5 = df.sort_values(by=\"PTS\", ascending=False).head()\n",
    "\n",
    "top_5.plot(x=\"Player\", y=\"PTS\", kind=\"bar\", rot=10, fontsize=8.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Player'>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Players with highest assists per game seasons between 2016-2019\n",
    "\n",
    "#James Harden\n",
    "#Russell Westbrook\n",
    "#John Wall\n",
    "#Russell Westbrook\n",
    "#Russell Westbrook\n",
    "\n",
    "top_5ast = df.sort_values(by=\"AST\", ascending=False).head()\n",
    "\n",
    "top_5ast.plot(x=\"Player\", y=\"AST\", kind=\"bar\", rot=10, fontsize=8.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Player'>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Players with highest rebounds per game seasons between 2016-2019\n",
    "\n",
    "#Andre Drummond\n",
    "#Andre Drummond\n",
    "#Andre Drummond\n",
    "#DeAndre Jordann\n",
    "#Hassan Whiteside\n",
    "\n",
    "top_5reb = df.sort_values(by=\"TRB\", ascending=False).head()\n",
    "\n",
    "top_5reb.plot(x=\"Player\", y=\"TRB\", kind=\"bar\", rot=10, fontsize=8.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Player'>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Players with highest win shares between 2016-2019\n",
    "\n",
    "#James Harden\n",
    "#James Harden\n",
    "#James Harden\n",
    "#Rudy Gobert\n",
    "#Giannis Antetokounmpo\n",
    "\n",
    "top_5W = df.sort_values(by=\"WS\", ascending=False).head()\n",
    "\n",
    "top_5W.plot(x=\"Player\", y=\"WS\", kind=\"bar\", rot=10, fontsize=8.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='USG%', ylabel='PTS'>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Correlation between usage rate and points scored\n",
    "#Fairly strong correlation\n",
    "\n",
    "df.plot(x=\"USG%\", y=\"PTS\", kind=\"scatter\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='3PA', ylabel='PTS'>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Correlation between 3 points attempted per game and points scored\n",
    "#Stronger correlation than usage rate\n",
    "\n",
    "df.plot(x=\"3PA\", y=\"PTS\", kind=\"scatter\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='3P', ylabel='PTS'>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Correlation between 3 points made and points scored\n",
    "#Strong correlation\n",
    "\n",
    "df.plot(x=\"3P\", y=\"PTS\", kind=\"scatter\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='WS', ylabel='PTS'>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Correlation between win shares and points scored\n",
    "#Strong correlation\n",
    "\n",
    "df.plot(x=\"WS\", y=\"PTS\", kind=\"scatter\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Replace null/blank values with 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.fillna(0, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Putting all the predictive features in a list (xcols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['MP', 'FG', 'FGA', 'FG%', '3P', '3PA', '3P%', '2P', '2PA', '2P%', 'eFG%', 'FT', 'FTA', 'FT%', 'ORB', 'DRB', 'TRB', 'AST', 'STL', 'BLK', 'TOV', 'PF', 'PTS', 'PER', 'TS%', 'USG%', 'WS', 'BPM'] \n",
      "\n"
     ]
    }
   ],
   "source": [
    "xcols = df.columns[7 : 35].to_list()\n",
    "print(xcols,'\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Train-test split\n",
    "\n",
    "## Split the data into training (80%) and testing (20%) portions\n",
    "\n",
    "#### Notes: train_test_split shuffles the data prior to splitting, so should be randomized\n",
    "#### The 'random_state' feature ensures that you get the same split of the data every time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "training data: (1888, 28)\n",
      "test data: (472, 28)\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(df[xcols], df['All-Star'], \n",
    "                                                    train_size=0.8, random_state=1)\n",
    "print('training data:', X_train.shape)\n",
    "print('test data:', X_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Logistic regression part 1 using all of the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "training accuracy: 0.98\n",
      "test accuracy: 0.987\n"
     ]
    }
   ],
   "source": [
    "#Pretty good accuracy although there is a slight indication of overfitting\n",
    "\n",
    "log_reg = LogisticRegression(solver='lbfgs', max_iter=3000)\n",
    "\n",
    "# Fit the model to the training data\n",
    "clf = log_reg.fit(X_train, y_train)\n",
    "\n",
    "# Get accuracy stats\n",
    "print('training accuracy: {}'.format(clf.score(X_train, y_train).round(3)))\n",
    "print('test accuracy: {}'.format(clf.score(X_test, y_test).round(3)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploring the coefficients"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ten largest positive features:\n",
      "    feature  coefficient\n",
      "19     BLK     0.896932\n",
      "15     DRB     0.699265\n",
      "26      WS     0.576855\n",
      "18     STL     0.502342\n",
      "17     AST     0.324525\n",
      "25    USG%     0.310784\n",
      "4       3P     0.305152\n",
      "6      3P%     0.257870\n",
      "0       MP     0.205625\n",
      "8      2PA     0.168880 \n",
      "\n",
      "Ten largest negative features:\n",
      "    feature  coefficient\n",
      "20     TOV     0.035314\n",
      "16     TRB     0.024485\n",
      "2      FGA     0.018399\n",
      "12     FTA    -0.077998\n",
      "11      FT    -0.143209\n",
      "5      3PA    -0.170199\n",
      "23     PER    -0.272222\n",
      "21      PF    -0.441302\n",
      "7       2P    -0.538853\n",
      "14     ORB    -0.692044\n"
     ]
    }
   ],
   "source": [
    "# Put the coefficients into a new dataframe\n",
    "coef = pd.concat([pd.DataFrame(xcols),pd.DataFrame(np.transpose(clf.coef_))], axis = 1)\n",
    "coef.columns = ['feature','coefficient']\n",
    "coef.sort_values(by=['coefficient'], ascending=False, inplace=True)\n",
    "\n",
    "# Examine the features/stats with the 10 largest positive and negative coefficients\n",
    "print('Ten largest positive features:\\n', coef.head(10), '\\n')\n",
    "print('Ten largest negative features:\\n', coef.tail(10))\n",
    "\n",
    "#The coefficients here show the best weights to maximize the 1s and minimize the 0s."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['BLK', 'DRB', 'WS', 'STL', 'AST', 'USG%', '3P', '3P%', 'MP', '2PA', 'TOV', 'TRB', 'FGA', 'FTA', 'FT', '3PA', 'PER', 'PF', '2P', 'ORB']\n"
     ]
    }
   ],
   "source": [
    "#Adding the features with the 10 largest positive and negative coefficients to a new list for training purposes\n",
    "\n",
    "xcols2 = coef.feature[0:10].to_list()\n",
    "xcols2 += coef.feature[-10:].to_list()\n",
    "print(xcols2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Logistic regression part 2 using only the list of features with the largest coefficients (xcols2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "training data: (1888, 20)\n",
      "test data: (472, 20)\n",
      "training accuracy: 0.979\n",
      "test accuracy: 0.987\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(df[xcols2], df['All-Star'], \n",
    "                                                    train_size=0.8, random_state=1)\n",
    "print('training data:', X_train.shape)\n",
    "print('test data:', X_test.shape)\n",
    "\n",
    "# fit the model to the training data\n",
    "clf2 = log_reg.fit(X_train, y_train)\n",
    "\n",
    "# get accuracy stats\n",
    "print('training accuracy: {}'.format(clf2.score(X_train, y_train).round(3)))\n",
    "print('test accuracy: {}'.format(clf2.score(X_test, y_test).round(3)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C = 0.001\n",
      "training accuracy: 0.976\n",
      "test accuracy: 0.985 \n",
      "\n",
      "C = 0.01\n",
      "training accuracy: 0.978\n",
      "test accuracy: 0.987 \n",
      "\n",
      "C = 0.1\n",
      "training accuracy: 0.977\n",
      "test accuracy: 0.989 \n",
      "\n",
      "C = 1\n",
      "training accuracy: 0.979\n",
      "test accuracy: 0.987 \n",
      "\n",
      "C = 10\n",
      "training accuracy: 0.98\n",
      "test accuracy: 0.985 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Regularization w/ logistic regression & C parameter with logistic regression\n",
    "\n",
    "cset = [.001, .01, .1, 1, 10]\n",
    "for i in cset:\n",
    "    print('C =', i)\n",
    "    log_reg = LogisticRegression(solver='lbfgs', max_iter=3000, C=i)\n",
    "    clf = log_reg.fit(X_train, y_train)\n",
    "    print('training accuracy: {}'.format(clf.score(X_train, y_train).round(3)))\n",
    "    print('test accuracy: {}'.format(clf.score(X_test, y_test).round(3)), '\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C = 0.001\n",
      "[0.98305085 0.97669492 0.97881356 0.97881356 0.96822034]\n",
      "Accuracy: 0.977 (+/- 0.010) \n",
      "\n",
      "C = 0.01\n",
      "[0.97669492 0.98305085 0.97457627 0.98305085 0.97245763]\n",
      "Accuracy: 0.978 (+/- 0.009) \n",
      "\n",
      "C = 0.1\n",
      "[0.97669492 0.9809322  0.97457627 0.9809322  0.97669492]\n",
      "Accuracy: 0.978 (+/- 0.005) \n",
      "\n",
      "C = 1\n",
      "[0.97669492 0.97245763 0.97457627 0.9809322  0.97245763]\n",
      "Accuracy: 0.975 (+/- 0.006) \n",
      "\n",
      "C = 10\n",
      "[0.97245763 0.97457627 0.97457627 0.9809322  0.96610169]\n",
      "Accuracy: 0.974 (+/- 0.010) \n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Cross-validation w/ tuning regularization in logistic regression\n",
    "\n",
    "for i in cset:\n",
    "    print('C =', i)\n",
    "    log_reg = LogisticRegression(solver='lbfgs', max_iter=3000, C=i)\n",
    "    scores = cross_val_score(log_reg, df[xcols2], df['All-Star'], cv=5)\n",
    "    print(scores)\n",
    "    print(\"Accuracy: %0.3f (+/- %0.3f)\" % (scores.mean(), scores.std() * 2), '\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Recall: 0.800\n",
      "  Precision: 0.842\n",
      "  F1 score: 0.821\n",
      "  Specificity: 0.993\n"
     ]
    }
   ],
   "source": [
    "# Let's check out some other stats for the test data\n",
    "# Pretty good recall, precision, F1 score and specificity overall\n",
    "\n",
    "y_pred_test = clf.predict(X_test)\n",
    "print('  Recall: {:.3f}'.format(recall_score(y_test, y_pred_test)))\n",
    "print('  Precision: {:.3f}'.format(precision_score(y_test, y_pred_test)))\n",
    "print('  F1 score: {:.3f}'.format(f1_score(y_test, y_pred_test)))\n",
    "cm = confusion_matrix(y_test, y_pred_test)\n",
    "tn, fp, fn, tp = cm.ravel()\n",
    "specificity = tn / (tn+fp)\n",
    "print('  Specificity: {:.3f}'.format(specificity))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CM key:\n",
      " [['TN' 'FP']\n",
      " ['FN' 'TP']] \n",
      "\n",
      "CM for test:\n",
      " [[449   3]\n",
      " [  4  16]]\n"
     ]
    }
   ],
   "source": [
    "# Confusion matrix\n",
    "\n",
    "cm = confusion_matrix(y_test, y_pred_test)\n",
    "sample = np.array([['TN', 'FP'], ['FN', 'TP']])\n",
    "print('CM key:\\n', sample, '\\n')\n",
    "print('CM for test:\\n', cm)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Implementing Grid search attempting to to compute the optimum values of hyperparameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Grid search\n",
    "\n",
    "# Define x_scaler\n",
    "\n",
    "scaler = MinMaxScaler()\n",
    "\n",
    "X = df[xcols2]\n",
    "scaled_X = scaler.fit_transform(X)\n",
    "y = df['All-Star']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean of accuracies: [0.95815732 0.97192823 0.97404463 0.97404463 0.97404463 0.97510283\n",
      " 0.97616272]\n",
      "std dev of accuracies: [0.0014831  0.00197893 0.00328017 0.0052545  0.00418325 0.00457164\n",
      " 0.00390663]\n",
      "best parameters: {'C': 100}\n"
     ]
    }
   ],
   "source": [
    "# Grid search\n",
    "\n",
    "# Positive results\n",
    "\n",
    "scaled_X = scaler.fit_transform(X_train)\n",
    "\n",
    "tuned_parameters = {'C': [0.1, 0.5, 1, 5, 10, 50, 100]}\n",
    "\n",
    "grid = GridSearchCV(LogisticRegression(solver='liblinear'), tuned_parameters, cv=3, scoring=\"accuracy\")\n",
    "\n",
    "grid.fit(scaled_X, y_train)\n",
    "\n",
    "print('mean of accuracies:', grid.cv_results_['mean_test_score'])\n",
    "print('std dev of accuracies:', grid.cv_results_['std_test_score'])\n",
    "\n",
    "# print best parameter after tuning \n",
    "print('best parameters:', grid.best_params_) \n",
    "\n",
    "# store the best estimator (In this case it's 'C': 100)\n",
    "best_logreg = grid.best_estimator_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ensemble Model: Random Forests"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "params: {'max_samples': 0.25, 'n_estimators': 10}\n",
      "mean of accuracies: 0.9783876672777417\n",
      "std dev of accuracies: 0.004163786241168384\n",
      "\n",
      "params: {'max_samples': 0.25, 'n_estimators': 100}\n",
      "mean of accuracies: 0.978810677754391\n",
      "std dev of accuracies: 0.004919325353848497\n",
      "\n",
      "params: {'max_samples': 0.25, 'n_estimators': 250}\n",
      "mean of accuracies: 0.980504875128385\n",
      "std dev of accuracies: 0.006095597012227296\n",
      "\n",
      "params: {'max_samples': 0.5, 'n_estimators': 10}\n",
      "mean of accuracies: 0.9771154026466985\n",
      "std dev of accuracies: 0.0055077881963055434\n",
      "\n",
      "params: {'max_samples': 0.5, 'n_estimators': 100}\n",
      "mean of accuracies: 0.9792347659647387\n",
      "std dev of accuracies: 0.004208928110305293\n",
      "\n",
      "params: {'max_samples': 0.5, 'n_estimators': 250}\n",
      "mean of accuracies: 0.9796583153082373\n",
      "std dev of accuracies: 0.004538167749342911\n",
      "\n",
      "params: {'max_samples': 1, 'n_estimators': 10}\n",
      "mean of accuracies: 0.9563560961467786\n",
      "std dev of accuracies: 0.0005864634258649128\n",
      "\n",
      "params: {'max_samples': 1, 'n_estimators': 100}\n",
      "mean of accuracies: 0.9563560961467786\n",
      "std dev of accuracies: 0.0005864634258649128\n",
      "\n",
      "params: {'max_samples': 1, 'n_estimators': 250}\n",
      "mean of accuracies: 0.9563560961467786\n",
      "std dev of accuracies: 0.0005864634258649128\n",
      "\n",
      "***best parameters: {'max_samples': 0.25, 'n_estimators': 250}\n",
      "best score: 0.980504875128385\n"
     ]
    }
   ],
   "source": [
    "#Bagging with random forests\n",
    "\n",
    "# set-up grid of parameters to search\n",
    "param_grid = {'n_estimators': [10, 100, 250], 'max_samples': [.25, .5, 1]} \n",
    "\n",
    "\n",
    "# instantiate grid search object\n",
    "grid = GridSearchCV(RandomForestClassifier(), param_grid, cv = 3)\n",
    "\n",
    "# fitting the model for grid search \n",
    "grid.fit(X, y)\n",
    "\n",
    "# print parameters, mean, and standard deviation of scores by iteration\n",
    "for z in range(0, len(grid.cv_results_['params'])):\n",
    "    print('\\nparams:', grid.cv_results_['params'][z])\n",
    "    print('mean of accuracies:', grid.cv_results_['mean_test_score'][z])\n",
    "    print('std dev of accuracies:', grid.cv_results_['std_test_score'][z])\n",
    "\n",
    "# print best parameter after tuning \n",
    "print('\\n***best parameters:', grid.best_params_)\n",
    "print('best score:', grid.best_score_)\n",
    "\n",
    "# store the best estimator (in this case it's 'max_samples': 0.5, 'n_estimators': 100)\n",
    "best_rf = grid.best_estimator_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Let's Start Predicting (Using the Logistic Regression Model due to it having slightly more accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(626, 35) ['Rk', 'Player', 'Pos', 'Age', 'Tm', 'G', 'GS', 'MP', 'FG', 'FGA', 'FG%', '3P', '3PA', '3P%', '2P', '2PA', '2P%', 'eFG%', 'FT', 'FTA', 'FT%', 'ORB', 'DRB', 'TRB', 'AST', 'STL', 'BLK', 'TOV', 'PF', 'PTS', 'PER', 'TS%', 'USG%', 'WS', 'BPM'] \n",
      "\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Rk</th>\n",
       "      <th>Player</th>\n",
       "      <th>Pos</th>\n",
       "      <th>Age</th>\n",
       "      <th>Tm</th>\n",
       "      <th>G</th>\n",
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       "      <th>MP</th>\n",
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       "      <th>FGA</th>\n",
       "      <th>FG%</th>\n",
       "      <th>3P</th>\n",
       "      <th>3PA</th>\n",
       "      <th>3P%</th>\n",
       "      <th>2P</th>\n",
       "      <th>2PA</th>\n",
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       "      <th>eFG%</th>\n",
       "      <th>FT</th>\n",
       "      <th>FTA</th>\n",
       "      <th>FT%</th>\n",
       "      <th>ORB</th>\n",
       "      <th>DRB</th>\n",
       "      <th>TRB</th>\n",
       "      <th>AST</th>\n",
       "      <th>STL</th>\n",
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       "      <th>PTS</th>\n",
       "      <th>PER</th>\n",
       "      <th>TS%</th>\n",
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       "      <th>BPM</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Precious Achiuwa</td>\n",
       "      <td>PF</td>\n",
       "      <td>21</td>\n",
       "      <td>MIA</td>\n",
       "      <td>61</td>\n",
       "      <td>4</td>\n",
       "      <td>12.1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.7</td>\n",
       "      <td>0.544</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.9</td>\n",
       "      <td>1.8</td>\n",
       "      <td>0.509</td>\n",
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       "      <td>2.2</td>\n",
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       "      <td>0.5</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.5</td>\n",
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       "      <td>14.2</td>\n",
       "      <td>0.550</td>\n",
       "      <td>19.5</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Jaylen Adams</td>\n",
       "      <td>PG</td>\n",
       "      <td>24</td>\n",
       "      <td>MIL</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>2.6</td>\n",
       "      <td>0.1</td>\n",
       "      <td>1.1</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.167</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.3</td>\n",
       "      <td>-6.5</td>\n",
       "      <td>0.125</td>\n",
       "      <td>18.6</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>-19.8</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Steven Adams</td>\n",
       "      <td>C</td>\n",
       "      <td>27</td>\n",
       "      <td>NOP</td>\n",
       "      <td>58</td>\n",
       "      <td>58</td>\n",
       "      <td>27.7</td>\n",
       "      <td>3.3</td>\n",
       "      <td>5.3</td>\n",
       "      <td>0.614</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.000</td>\n",
       "      <td>3.3</td>\n",
       "      <td>5.3</td>\n",
       "      <td>0.620</td>\n",
       "      <td>0.614</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0.444</td>\n",
       "      <td>3.7</td>\n",
       "      <td>5.2</td>\n",
       "      <td>8.9</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.7</td>\n",
       "      <td>1.3</td>\n",
       "      <td>1.9</td>\n",
       "      <td>7.6</td>\n",
       "      <td>15.1</td>\n",
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       "      <td>4.0</td>\n",
       "      <td>-0.8</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Bam Adebayo</td>\n",
       "      <td>C</td>\n",
       "      <td>23</td>\n",
       "      <td>MIA</td>\n",
       "      <td>64</td>\n",
       "      <td>64</td>\n",
       "      <td>33.5</td>\n",
       "      <td>7.1</td>\n",
       "      <td>12.5</td>\n",
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       "      <td>0.250</td>\n",
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       "      <td>12.4</td>\n",
       "      <td>0.573</td>\n",
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       "      <td>4.4</td>\n",
       "      <td>5.5</td>\n",
       "      <td>0.799</td>\n",
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       "      <td>6.7</td>\n",
       "      <td>9.0</td>\n",
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       "      <td>1.2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.6</td>\n",
       "      <td>2.3</td>\n",
       "      <td>18.7</td>\n",
       "      <td>22.7</td>\n",
       "      <td>0.626</td>\n",
       "      <td>23.7</td>\n",
       "      <td>8.8</td>\n",
       "      <td>4.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>LaMarcus Aldridge</td>\n",
       "      <td>C</td>\n",
       "      <td>35</td>\n",
       "      <td>SAS</td>\n",
       "      <td>21</td>\n",
       "      <td>18</td>\n",
       "      <td>25.9</td>\n",
       "      <td>5.5</td>\n",
       "      <td>11.8</td>\n",
       "      <td>0.464</td>\n",
       "      <td>1.3</td>\n",
       "      <td>3.6</td>\n",
       "      <td>0.360</td>\n",
       "      <td>4.2</td>\n",
       "      <td>8.2</td>\n",
       "      <td>0.509</td>\n",
       "      <td>0.518</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1.8</td>\n",
       "      <td>0.838</td>\n",
       "      <td>0.8</td>\n",
       "      <td>3.7</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.9</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.7</td>\n",
       "      <td>13.7</td>\n",
       "      <td>15.1</td>\n",
       "      <td>0.545</td>\n",
       "      <td>22.7</td>\n",
       "      <td>0.8</td>\n",
       "      <td>-1.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>621</th>\n",
       "      <td>536</td>\n",
       "      <td>Delon Wright</td>\n",
       "      <td>PG</td>\n",
       "      <td>28</td>\n",
       "      <td>SAC</td>\n",
       "      <td>27</td>\n",
       "      <td>8</td>\n",
       "      <td>25.8</td>\n",
       "      <td>3.9</td>\n",
       "      <td>8.3</td>\n",
       "      <td>0.462</td>\n",
       "      <td>1.2</td>\n",
       "      <td>3.1</td>\n",
       "      <td>0.398</td>\n",
       "      <td>2.6</td>\n",
       "      <td>5.3</td>\n",
       "      <td>0.500</td>\n",
       "      <td>0.536</td>\n",
       "      <td>1.1</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.833</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.9</td>\n",
       "      <td>3.9</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1.3</td>\n",
       "      <td>1.1</td>\n",
       "      <td>10.0</td>\n",
       "      <td>15.9</td>\n",
       "      <td>0.563</td>\n",
       "      <td>17.1</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>622</th>\n",
       "      <td>537</td>\n",
       "      <td>Thaddeus Young</td>\n",
       "      <td>PF</td>\n",
       "      <td>32</td>\n",
       "      <td>CHI</td>\n",
       "      <td>68</td>\n",
       "      <td>23</td>\n",
       "      <td>24.3</td>\n",
       "      <td>5.4</td>\n",
       "      <td>9.7</td>\n",
       "      <td>0.559</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.267</td>\n",
       "      <td>5.3</td>\n",
       "      <td>9.1</td>\n",
       "      <td>0.580</td>\n",
       "      <td>0.568</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.628</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3.8</td>\n",
       "      <td>6.2</td>\n",
       "      <td>4.3</td>\n",
       "      <td>1.1</td>\n",
       "      <td>0.6</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.2</td>\n",
       "      <td>12.1</td>\n",
       "      <td>20.3</td>\n",
       "      <td>0.578</td>\n",
       "      <td>22.3</td>\n",
       "      <td>5.1</td>\n",
       "      <td>3.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>623</th>\n",
       "      <td>538</td>\n",
       "      <td>Trae Young</td>\n",
       "      <td>PG</td>\n",
       "      <td>22</td>\n",
       "      <td>ATL</td>\n",
       "      <td>63</td>\n",
       "      <td>63</td>\n",
       "      <td>33.7</td>\n",
       "      <td>7.7</td>\n",
       "      <td>17.7</td>\n",
       "      <td>0.438</td>\n",
       "      <td>2.2</td>\n",
       "      <td>6.3</td>\n",
       "      <td>0.343</td>\n",
       "      <td>5.6</td>\n",
       "      <td>11.3</td>\n",
       "      <td>0.491</td>\n",
       "      <td>0.499</td>\n",
       "      <td>7.7</td>\n",
       "      <td>8.7</td>\n",
       "      <td>0.886</td>\n",
       "      <td>0.6</td>\n",
       "      <td>3.3</td>\n",
       "      <td>3.9</td>\n",
       "      <td>9.4</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.2</td>\n",
       "      <td>4.1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>25.3</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0.589</td>\n",
       "      <td>33.0</td>\n",
       "      <td>7.2</td>\n",
       "      <td>3.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>624</th>\n",
       "      <td>539</td>\n",
       "      <td>Cody Zeller</td>\n",
       "      <td>C</td>\n",
       "      <td>28</td>\n",
       "      <td>CHO</td>\n",
       "      <td>48</td>\n",
       "      <td>21</td>\n",
       "      <td>20.9</td>\n",
       "      <td>3.8</td>\n",
       "      <td>6.8</td>\n",
       "      <td>0.559</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.143</td>\n",
       "      <td>3.7</td>\n",
       "      <td>6.2</td>\n",
       "      <td>0.598</td>\n",
       "      <td>0.565</td>\n",
       "      <td>1.8</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.714</td>\n",
       "      <td>2.5</td>\n",
       "      <td>4.4</td>\n",
       "      <td>6.8</td>\n",
       "      <td>1.8</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1.1</td>\n",
       "      <td>2.5</td>\n",
       "      <td>9.4</td>\n",
       "      <td>18.2</td>\n",
       "      <td>0.599</td>\n",
       "      <td>18.3</td>\n",
       "      <td>3.3</td>\n",
       "      <td>-0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>625</th>\n",
       "      <td>540</td>\n",
       "      <td>Ivica Zubac</td>\n",
       "      <td>C</td>\n",
       "      <td>23</td>\n",
       "      <td>LAC</td>\n",
       "      <td>72</td>\n",
       "      <td>33</td>\n",
       "      <td>22.3</td>\n",
       "      <td>3.6</td>\n",
       "      <td>5.5</td>\n",
       "      <td>0.652</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.250</td>\n",
       "      <td>3.6</td>\n",
       "      <td>5.4</td>\n",
       "      <td>0.656</td>\n",
       "      <td>0.654</td>\n",
       "      <td>1.9</td>\n",
       "      <td>2.4</td>\n",
       "      <td>0.789</td>\n",
       "      <td>2.6</td>\n",
       "      <td>4.6</td>\n",
       "      <td>7.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.9</td>\n",
       "      <td>1.1</td>\n",
       "      <td>2.6</td>\n",
       "      <td>9.0</td>\n",
       "      <td>19.1</td>\n",
       "      <td>0.693</td>\n",
       "      <td>15.1</td>\n",
       "      <td>6.9</td>\n",
       "      <td>0.7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>626 rows × 35 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Rk             Player Pos  Age   Tm   G  GS    MP   FG   FGA    FG%  \\\n",
       "0      1   Precious Achiuwa  PF   21  MIA  61   4  12.1  2.0   3.7  0.544   \n",
       "1      2       Jaylen Adams  PG   24  MIL   7   0   2.6  0.1   1.1  0.125   \n",
       "2      3       Steven Adams   C   27  NOP  58  58  27.7  3.3   5.3  0.614   \n",
       "3      4        Bam Adebayo   C   23  MIA  64  64  33.5  7.1  12.5  0.570   \n",
       "4      5  LaMarcus Aldridge   C   35  SAS  21  18  25.9  5.5  11.8  0.464   \n",
       "..   ...                ...  ..  ...  ...  ..  ..   ...  ...   ...    ...   \n",
       "621  536       Delon Wright  PG   28  SAC  27   8  25.8  3.9   8.3  0.462   \n",
       "622  537     Thaddeus Young  PF   32  CHI  68  23  24.3  5.4   9.7  0.559   \n",
       "623  538         Trae Young  PG   22  ATL  63  63  33.7  7.7  17.7  0.438   \n",
       "624  539        Cody Zeller   C   28  CHO  48  21  20.9  3.8   6.8  0.559   \n",
       "625  540        Ivica Zubac   C   23  LAC  72  33  22.3  3.6   5.5  0.652   \n",
       "\n",
       "      3P  3PA    3P%   2P   2PA    2P%   eFG%   FT  FTA    FT%  ORB  DRB  TRB  \\\n",
       "0    0.0  0.0  0.000  2.0   3.7  0.546  0.544  0.9  1.8  0.509  1.2  2.2  3.4   \n",
       "1    0.0  0.3  0.000  0.1   0.9  0.167  0.125  0.0  0.0    NaN  0.0  0.4  0.4   \n",
       "2    0.0  0.1  0.000  3.3   5.3  0.620  0.614  1.0  2.3  0.444  3.7  5.2  8.9   \n",
       "3    0.0  0.1  0.250  7.1  12.4  0.573  0.571  4.4  5.5  0.799  2.2  6.7  9.0   \n",
       "4    1.3  3.6  0.360  4.2   8.2  0.509  0.518  1.5  1.8  0.838  0.8  3.7  4.5   \n",
       "..   ...  ...    ...  ...   ...    ...    ...  ...  ...    ...  ...  ...  ...   \n",
       "621  1.2  3.1  0.398  2.6   5.3  0.500  0.536  1.1  1.3  0.833  1.0  2.9  3.9   \n",
       "622  0.2  0.7  0.267  5.3   9.1  0.580  0.568  1.0  1.7  0.628  2.5  3.8  6.2   \n",
       "623  2.2  6.3  0.343  5.6  11.3  0.491  0.499  7.7  8.7  0.886  0.6  3.3  3.9   \n",
       "624  0.1  0.6  0.143  3.7   6.2  0.598  0.565  1.8  2.5  0.714  2.5  4.4  6.8   \n",
       "625  0.0  0.1  0.250  3.6   5.4  0.656  0.654  1.9  2.4  0.789  2.6  4.6  7.2   \n",
       "\n",
       "     AST  STL  BLK  TOV   PF   PTS   PER    TS%  USG%   WS   BPM  \n",
       "0    0.5  0.3  0.5  0.7  1.5   5.0  14.2  0.550  19.5  1.3  -4.5  \n",
       "1    0.3  0.0  0.0  0.0  0.1   0.3  -6.5  0.125  18.6 -0.1 -19.8  \n",
       "2    1.9  0.9  0.7  1.3  1.9   7.6  15.1  0.596  11.7  4.0  -0.8  \n",
       "3    5.4  1.2  1.0  2.6  2.3  18.7  22.7  0.626  23.7  8.8   4.7  \n",
       "4    1.7  0.4  0.9  1.0  1.7  13.7  15.1  0.545  22.7  0.8  -1.2  \n",
       "..   ...  ...  ...  ...  ...   ...   ...    ...   ...  ...   ...  \n",
       "621  3.6  1.6  0.4  1.3  1.1  10.0  15.9  0.563  17.1  1.3   0.8  \n",
       "622  4.3  1.1  0.6  2.0  2.2  12.1  20.3  0.578  22.3  5.1   3.2  \n",
       "623  9.4  0.8  0.2  4.1  1.8  25.3  23.0  0.589  33.0  7.2   3.6  \n",
       "624  1.8  0.6  0.4  1.1  2.5   9.4  18.2  0.599  18.3  3.3  -0.3  \n",
       "625  1.3  0.3  0.9  1.1  2.6   9.0  19.1  0.693  15.1  6.9   0.7  \n",
       "\n",
       "[626 rows x 35 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Bringing in new data for this past season (2020-2021)\n",
    "#Cheers to Basketball Reference for having the data, just needed to combine them\n",
    "\n",
    "mydata = pd.read_csv('2020_2021_season.csv')\n",
    "print(mydata.shape, mydata.columns.to_list(),'\\n')\n",
    "mydata"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Once again, replace null/blank values with 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "mydata.fillna(0, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Creating a dataset with only the 10 negative/positive features that we discovered earlier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "mydata2 = mydata[['BLK', 'DRB', 'WS', 'STL', 'AST', 'USG%', '3P', '3P%', 'MP', '2PA', 'TOV', 'TRB', 'FGA', 'FTA', 'FT', '3PA', 'PER', 'PF', '2P', 'ORB']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .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>BLK</th>\n",
       "      <th>DRB</th>\n",
       "      <th>WS</th>\n",
       "      <th>STL</th>\n",
       "      <th>AST</th>\n",
       "      <th>USG%</th>\n",
       "      <th>3P</th>\n",
       "      <th>3P%</th>\n",
       "      <th>MP</th>\n",
       "      <th>2PA</th>\n",
       "      <th>TOV</th>\n",
       "      <th>TRB</th>\n",
       "      <th>FGA</th>\n",
       "      <th>FTA</th>\n",
       "      <th>FT</th>\n",
       "      <th>3PA</th>\n",
       "      <th>PER</th>\n",
       "      <th>PF</th>\n",
       "      <th>2P</th>\n",
       "      <th>ORB</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.5</td>\n",
       "      <td>2.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.5</td>\n",
       "      <td>19.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>12.1</td>\n",
       "      <td>3.7</td>\n",
       "      <td>0.7</td>\n",
       "      <td>3.4</td>\n",
       "      <td>3.7</td>\n",
       "      <td>1.8</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.2</td>\n",
       "      <td>1.5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>18.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>2.6</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>-6.5</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.7</td>\n",
       "      <td>5.2</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.9</td>\n",
       "      <td>1.9</td>\n",
       "      <td>11.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>27.7</td>\n",
       "      <td>5.3</td>\n",
       "      <td>1.3</td>\n",
       "      <td>8.9</td>\n",
       "      <td>5.3</td>\n",
       "      <td>2.3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>15.1</td>\n",
       "      <td>1.9</td>\n",
       "      <td>3.3</td>\n",
       "      <td>3.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>6.7</td>\n",
       "      <td>8.8</td>\n",
       "      <td>1.2</td>\n",
       "      <td>5.4</td>\n",
       "      <td>23.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.250</td>\n",
       "      <td>33.5</td>\n",
       "      <td>12.4</td>\n",
       "      <td>2.6</td>\n",
       "      <td>9.0</td>\n",
       "      <td>12.5</td>\n",
       "      <td>5.5</td>\n",
       "      <td>4.4</td>\n",
       "      <td>0.1</td>\n",
       "      <td>22.7</td>\n",
       "      <td>2.3</td>\n",
       "      <td>7.1</td>\n",
       "      <td>2.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.9</td>\n",
       "      <td>3.7</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1.7</td>\n",
       "      <td>22.7</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.360</td>\n",
       "      <td>25.9</td>\n",
       "      <td>8.2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>11.8</td>\n",
       "      <td>1.8</td>\n",
       "      <td>1.5</td>\n",
       "      <td>3.6</td>\n",
       "      <td>15.1</td>\n",
       "      <td>1.7</td>\n",
       "      <td>4.2</td>\n",
       "      <td>0.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>621</th>\n",
       "      <td>0.4</td>\n",
       "      <td>2.9</td>\n",
       "      <td>1.3</td>\n",
       "      <td>1.6</td>\n",
       "      <td>3.6</td>\n",
       "      <td>17.1</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0.398</td>\n",
       "      <td>25.8</td>\n",
       "      <td>5.3</td>\n",
       "      <td>1.3</td>\n",
       "      <td>3.9</td>\n",
       "      <td>8.3</td>\n",
       "      <td>1.3</td>\n",
       "      <td>1.1</td>\n",
       "      <td>3.1</td>\n",
       "      <td>15.9</td>\n",
       "      <td>1.1</td>\n",
       "      <td>2.6</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>622</th>\n",
       "      <td>0.6</td>\n",
       "      <td>3.8</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.1</td>\n",
       "      <td>4.3</td>\n",
       "      <td>22.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.267</td>\n",
       "      <td>24.3</td>\n",
       "      <td>9.1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.2</td>\n",
       "      <td>9.7</td>\n",
       "      <td>1.7</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.7</td>\n",
       "      <td>20.3</td>\n",
       "      <td>2.2</td>\n",
       "      <td>5.3</td>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>623</th>\n",
       "      <td>0.2</td>\n",
       "      <td>3.3</td>\n",
       "      <td>7.2</td>\n",
       "      <td>0.8</td>\n",
       "      <td>9.4</td>\n",
       "      <td>33.0</td>\n",
       "      <td>2.2</td>\n",
       "      <td>0.343</td>\n",
       "      <td>33.7</td>\n",
       "      <td>11.3</td>\n",
       "      <td>4.1</td>\n",
       "      <td>3.9</td>\n",
       "      <td>17.7</td>\n",
       "      <td>8.7</td>\n",
       "      <td>7.7</td>\n",
       "      <td>6.3</td>\n",
       "      <td>23.0</td>\n",
       "      <td>1.8</td>\n",
       "      <td>5.6</td>\n",
       "      <td>0.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>624</th>\n",
       "      <td>0.4</td>\n",
       "      <td>4.4</td>\n",
       "      <td>3.3</td>\n",
       "      <td>0.6</td>\n",
       "      <td>1.8</td>\n",
       "      <td>18.3</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.143</td>\n",
       "      <td>20.9</td>\n",
       "      <td>6.2</td>\n",
       "      <td>1.1</td>\n",
       "      <td>6.8</td>\n",
       "      <td>6.8</td>\n",
       "      <td>2.5</td>\n",
       "      <td>1.8</td>\n",
       "      <td>0.6</td>\n",
       "      <td>18.2</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3.7</td>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>625</th>\n",
       "      <td>0.9</td>\n",
       "      <td>4.6</td>\n",
       "      <td>6.9</td>\n",
       "      <td>0.3</td>\n",
       "      <td>1.3</td>\n",
       "      <td>15.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.250</td>\n",
       "      <td>22.3</td>\n",
       "      <td>5.4</td>\n",
       "      <td>1.1</td>\n",
       "      <td>7.2</td>\n",
       "      <td>5.5</td>\n",
       "      <td>2.4</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.1</td>\n",
       "      <td>19.1</td>\n",
       "      <td>2.6</td>\n",
       "      <td>3.6</td>\n",
       "      <td>2.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>626 rows × 20 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     BLK  DRB   WS  STL  AST  USG%   3P    3P%    MP   2PA  TOV  TRB   FGA  \\\n",
       "0    0.5  2.2  1.3  0.3  0.5  19.5  0.0  0.000  12.1   3.7  0.7  3.4   3.7   \n",
       "1    0.0  0.4 -0.1  0.0  0.3  18.6  0.0  0.000   2.6   0.9  0.0  0.4   1.1   \n",
       "2    0.7  5.2  4.0  0.9  1.9  11.7  0.0  0.000  27.7   5.3  1.3  8.9   5.3   \n",
       "3    1.0  6.7  8.8  1.2  5.4  23.7  0.0  0.250  33.5  12.4  2.6  9.0  12.5   \n",
       "4    0.9  3.7  0.8  0.4  1.7  22.7  1.3  0.360  25.9   8.2  1.0  4.5  11.8   \n",
       "..   ...  ...  ...  ...  ...   ...  ...    ...   ...   ...  ...  ...   ...   \n",
       "621  0.4  2.9  1.3  1.6  3.6  17.1  1.2  0.398  25.8   5.3  1.3  3.9   8.3   \n",
       "622  0.6  3.8  5.1  1.1  4.3  22.3  0.2  0.267  24.3   9.1  2.0  6.2   9.7   \n",
       "623  0.2  3.3  7.2  0.8  9.4  33.0  2.2  0.343  33.7  11.3  4.1  3.9  17.7   \n",
       "624  0.4  4.4  3.3  0.6  1.8  18.3  0.1  0.143  20.9   6.2  1.1  6.8   6.8   \n",
       "625  0.9  4.6  6.9  0.3  1.3  15.1  0.0  0.250  22.3   5.4  1.1  7.2   5.5   \n",
       "\n",
       "     FTA   FT  3PA   PER   PF   2P  ORB  \n",
       "0    1.8  0.9  0.0  14.2  1.5  2.0  1.2  \n",
       "1    0.0  0.0  0.3  -6.5  0.1  0.1  0.0  \n",
       "2    2.3  1.0  0.1  15.1  1.9  3.3  3.7  \n",
       "3    5.5  4.4  0.1  22.7  2.3  7.1  2.2  \n",
       "4    1.8  1.5  3.6  15.1  1.7  4.2  0.8  \n",
       "..   ...  ...  ...   ...  ...  ...  ...  \n",
       "621  1.3  1.1  3.1  15.9  1.1  2.6  1.0  \n",
       "622  1.7  1.0  0.7  20.3  2.2  5.3  2.5  \n",
       "623  8.7  7.7  6.3  23.0  1.8  5.6  0.6  \n",
       "624  2.5  1.8  0.6  18.2  2.5  3.7  2.5  \n",
       "625  2.4  1.9  0.1  19.1  2.6  3.6  2.6  \n",
       "\n",
       "[626 rows x 20 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mydata2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['BLK', 'DRB', 'WS', 'STL', 'AST', 'USG%', '3P', '3P%', 'MP', '2PA', 'TOV', 'TRB', 'FGA', 'FTA', 'FT', '3PA', 'PER', 'PF', '2P', 'ORB'] \n",
      "\n"
     ]
    }
   ],
   "source": [
    "xcols3 = mydata2.columns.to_list()\n",
    "print(xcols3,'\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Predicting the 2020-2021 all-stars with the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred = clf.predict(mydata[xcols3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      " 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      " 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      " 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0\n",
      " 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      " 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0\n",
      " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      " 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      " 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0\n",
      " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0\n",
      " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0\n",
      " 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0]\n"
     ]
    }
   ],
   "source": [
    "print(pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Adding the All-Star information back to the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "mydata['All-Star'] = pred.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      Rk             Player Pos  Age   Tm   G  GS    MP   FG   FGA    FG%  \\\n",
      "0      1   Precious Achiuwa  PF   21  MIA  61   4  12.1  2.0   3.7  0.544   \n",
      "1      2       Jaylen Adams  PG   24  MIL   7   0   2.6  0.1   1.1  0.125   \n",
      "2      3       Steven Adams   C   27  NOP  58  58  27.7  3.3   5.3  0.614   \n",
      "3      4        Bam Adebayo   C   23  MIA  64  64  33.5  7.1  12.5  0.570   \n",
      "4      5  LaMarcus Aldridge   C   35  SAS  21  18  25.9  5.5  11.8  0.464   \n",
      "..   ...                ...  ..  ...  ...  ..  ..   ...  ...   ...    ...   \n",
      "621  536       Delon Wright  PG   28  SAC  27   8  25.8  3.9   8.3  0.462   \n",
      "622  537     Thaddeus Young  PF   32  CHI  68  23  24.3  5.4   9.7  0.559   \n",
      "623  538         Trae Young  PG   22  ATL  63  63  33.7  7.7  17.7  0.438   \n",
      "624  539        Cody Zeller   C   28  CHO  48  21  20.9  3.8   6.8  0.559   \n",
      "625  540        Ivica Zubac   C   23  LAC  72  33  22.3  3.6   5.5  0.652   \n",
      "\n",
      "      3P  3PA    3P%   2P   2PA    2P%   eFG%   FT  FTA    FT%  ORB  DRB  TRB  \\\n",
      "0    0.0  0.0  0.000  2.0   3.7  0.546  0.544  0.9  1.8  0.509  1.2  2.2  3.4   \n",
      "1    0.0  0.3  0.000  0.1   0.9  0.167  0.125  0.0  0.0  0.000  0.0  0.4  0.4   \n",
      "2    0.0  0.1  0.000  3.3   5.3  0.620  0.614  1.0  2.3  0.444  3.7  5.2  8.9   \n",
      "3    0.0  0.1  0.250  7.1  12.4  0.573  0.571  4.4  5.5  0.799  2.2  6.7  9.0   \n",
      "4    1.3  3.6  0.360  4.2   8.2  0.509  0.518  1.5  1.8  0.838  0.8  3.7  4.5   \n",
      "..   ...  ...    ...  ...   ...    ...    ...  ...  ...    ...  ...  ...  ...   \n",
      "621  1.2  3.1  0.398  2.6   5.3  0.500  0.536  1.1  1.3  0.833  1.0  2.9  3.9   \n",
      "622  0.2  0.7  0.267  5.3   9.1  0.580  0.568  1.0  1.7  0.628  2.5  3.8  6.2   \n",
      "623  2.2  6.3  0.343  5.6  11.3  0.491  0.499  7.7  8.7  0.886  0.6  3.3  3.9   \n",
      "624  0.1  0.6  0.143  3.7   6.2  0.598  0.565  1.8  2.5  0.714  2.5  4.4  6.8   \n",
      "625  0.0  0.1  0.250  3.6   5.4  0.656  0.654  1.9  2.4  0.789  2.6  4.6  7.2   \n",
      "\n",
      "     AST  STL  BLK  TOV   PF   PTS   PER    TS%  USG%   WS   BPM  All-Star  \n",
      "0    0.5  0.3  0.5  0.7  1.5   5.0  14.2  0.550  19.5  1.3  -4.5         0  \n",
      "1    0.3  0.0  0.0  0.0  0.1   0.3  -6.5  0.125  18.6 -0.1 -19.8         0  \n",
      "2    1.9  0.9  0.7  1.3  1.9   7.6  15.1  0.596  11.7  4.0  -0.8         0  \n",
      "3    5.4  1.2  1.0  2.6  2.3  18.7  22.7  0.626  23.7  8.8   4.7         0  \n",
      "4    1.7  0.4  0.9  1.0  1.7  13.7  15.1  0.545  22.7  0.8  -1.2         0  \n",
      "..   ...  ...  ...  ...  ...   ...   ...    ...   ...  ...   ...       ...  \n",
      "621  3.6  1.6  0.4  1.3  1.1  10.0  15.9  0.563  17.1  1.3   0.8         0  \n",
      "622  4.3  1.1  0.6  2.0  2.2  12.1  20.3  0.578  22.3  5.1   3.2         0  \n",
      "623  9.4  0.8  0.2  4.1  1.8  25.3  23.0  0.589  33.0  7.2   3.6         1  \n",
      "624  1.8  0.6  0.4  1.1  2.5   9.4  18.2  0.599  18.3  3.3  -0.3         0  \n",
      "625  1.3  0.3  0.9  1.1  2.6   9.0  19.1  0.693  15.1  6.9   0.7         0  \n",
      "\n",
      "[626 rows x 36 columns]\n"
     ]
    }
   ],
   "source": [
    "print(mydata)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Storing the results\n",
    "\n",
    "mydata.to_csv(r'/Users/tricky/Desktop/Predicting NBA All-Stars project/Data/2020_season_all_stars.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Final Results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2020 All-Stars from my model:\n",
    "#### Giannis Antetokounmpo\n",
    "#### Stephen Curry\n",
    "#### Luka Doncic\n",
    "#### Kevin Durant\n",
    "#### Joel Embiid\n",
    "#### Paul George\n",
    "#### James Harden\n",
    "#### Kyrie Irving\n",
    "#### LeBron James\n",
    "#### Nikola Jokic\n",
    "#### Zach LaVine\n",
    "#### Kawhi Leonard\n",
    "#### Damian Lillard\n",
    "#### Donovan Mitchell\n",
    "#### Chris Paul\n",
    "#### Julius Randle\n",
    "#### Domantas Sabonis\n",
    "#### Jayson Tatum\n",
    "#### Nikola Vucevic\n",
    "#### Russell Westbrook\n",
    "#### Trae Young"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2020 All-Stars in reality:\n",
    "#### Giannis Antetokounmpo\n",
    "#### Stephen Curry\n",
    "#### Luka Doncic\n",
    "#### Kevin Durant\n",
    "#### Joel Embiid\n",
    "#### Paul George\n",
    "#### James Harden\n",
    "#### Kyrie Irving\n",
    "#### LeBron James\n",
    "#### Nikola Jokic\n",
    "#### Zach LaVine\n",
    "#### Kawhi Leonard\n",
    "#### Damian Lillard\n",
    "#### Donovan Mitchell\n",
    "#### Chris Paul\n",
    "#### Julius Randle\n",
    "#### Domantas Sabonis\n",
    "#### Jayson Tatum\n",
    "#### Nikola Vucevic\n",
    "#### Jaylen Brown\n",
    "#### Rudy Gobert\n",
    "#### Ben Simmons\n",
    "#### Bradley Beal\n",
    "#### Mike Conley\n",
    "#### Trae Young"
   ]
  }
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