Prediction_Player-Copy1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "from __future__ import print_function\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PLAYER</th>\n",
       "      <th>TEAM</th>\n",
       "      <th>AGE</th>\n",
       "      <th>GP</th>\n",
       "      <th>MIN</th>\n",
       "      <th>W</th>\n",
       "      <th>L</th>\n",
       "      <th>PTS</th>\n",
       "      <th>FGM</th>\n",
       "      <th>FGA</th>\n",
       "      <th>...</th>\n",
       "      <th>3PA</th>\n",
       "      <th>FTM</th>\n",
       "      <th>FTA</th>\n",
       "      <th>OREB</th>\n",
       "      <th>DREB</th>\n",
       "      <th>AST</th>\n",
       "      <th>TOV</th>\n",
       "      <th>STL</th>\n",
       "      <th>BLK</th>\n",
       "      <th>SALARY</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Aaron Brooks</td>\n",
       "      <td>MIN</td>\n",
       "      <td>33</td>\n",
       "      <td>32</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Aaron Gordon</td>\n",
       "      <td>ORL</td>\n",
       "      <td>22</td>\n",
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       "      <td>32.9</td>\n",
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       "      <td>39</td>\n",
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       "      <td>5504420.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Abdel Nader</td>\n",
       "      <td>BOS</td>\n",
       "      <td>24</td>\n",
       "      <td>48</td>\n",
       "      <td>10.9</td>\n",
       "      <td>33</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>3.1</td>\n",
       "      <td>...</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.3</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1167333.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Al Horford</td>\n",
       "      <td>BOS</td>\n",
       "      <td>32</td>\n",
       "      <td>72</td>\n",
       "      <td>31.6</td>\n",
       "      <td>47</td>\n",
       "      <td>25</td>\n",
       "      <td>12.9</td>\n",
       "      <td>5.1</td>\n",
       "      <td>10.5</td>\n",
       "      <td>...</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.3</td>\n",
       "      <td>1.7</td>\n",
       "      <td>1.4</td>\n",
       "      <td>5.9</td>\n",
       "      <td>4.7</td>\n",
       "      <td>1.8</td>\n",
       "      <td>0.6</td>\n",
       "      <td>1.1</td>\n",
       "      <td>27734405.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Al Jefferson</td>\n",
       "      <td>IND</td>\n",
       "      <td>33</td>\n",
       "      <td>36</td>\n",
       "      <td>13.4</td>\n",
       "      <td>17</td>\n",
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       "      <td>3.1</td>\n",
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       "      <td>0.8</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.6</td>\n",
       "      <td>9769821.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         PLAYER TEAM  AGE  GP   MIN   W   L   PTS  FGM   FGA     ...      3PA  \\\n",
       "0  Aaron Brooks  MIN   33  32   5.9  19  13   2.3  0.9   2.2     ...      1.0   \n",
       "1  Aaron Gordon  ORL   22  58  32.9  19  39  17.6  6.5  14.9     ...      5.9   \n",
       "2   Abdel Nader  BOS   24  48  10.9  33  15   3.0  1.0   3.1     ...      1.4   \n",
       "3    Al Horford  BOS   32  72  31.6  47  25  12.9  5.1  10.5     ...      3.1   \n",
       "4  Al Jefferson  IND   33  36  13.4  17  19   7.0  3.1   5.8     ...      0.1   \n",
       "\n",
       "   FTM  FTA  OREB  DREB  AST  TOV  STL  BLK      SALARY  \n",
       "0  0.3  0.3   0.2   0.3  0.6  0.3  0.2  0.0   2116955.0  \n",
       "1  2.7  3.9   1.5   6.4  2.3  1.8  1.0  0.8   5504420.0  \n",
       "2  0.5  0.8   0.3   1.2  0.5  0.7  0.3  0.2   1167333.0  \n",
       "3  1.3  1.7   1.4   5.9  4.7  1.8  0.6  1.1  27734405.0  \n",
       "4  0.8  1.0   0.8   3.1  0.8  0.6  0.4  0.6   9769821.0  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "player_file_path='Data/nba_players_stats_salary_2017_2018.csv'\n",
    "player_data_original=pd.read_csv(player_file_path)\n",
    "player_data_original.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>14.9</td>\n",
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       "      <td>Abdel Nader</td>\n",
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       "      <td>0.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1167333.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Al Horford</td>\n",
       "      <td>BOS</td>\n",
       "      <td>32</td>\n",
       "      <td>72</td>\n",
       "      <td>31.6</td>\n",
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       "      <td>Al Jefferson</td>\n",
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      ],
      "text/plain": [
       "         PLAYER TEAM  AGE  GP   MIN   W   L   PTS  FGM   FGA     ...      3PA  \\\n",
       "0  Aaron Brooks  MIN   33  32   5.9  19  13   2.3  0.9   2.2     ...      1.0   \n",
       "1  Aaron Gordon  ORL   22  58  32.9  19  39  17.6  6.5  14.9     ...      5.9   \n",
       "2   Abdel Nader  BOS   24  48  10.9  33  15   3.0  1.0   3.1     ...      1.4   \n",
       "3    Al Horford  BOS   32  72  31.6  47  25  12.9  5.1  10.5     ...      3.1   \n",
       "4  Al Jefferson  IND   33  36  13.4  17  19   7.0  3.1   5.8     ...      0.1   \n",
       "\n",
       "   FTM  FTA  OREB  DREB  AST  TOV  STL  BLK      SALARY  \n",
       "0  0.3  0.3   0.2   0.3  0.6  0.3  0.2  0.0   2116955.0  \n",
       "1  2.7  3.9   1.5   6.4  2.3  1.8  1.0  0.8   5504420.0  \n",
       "2  0.5  0.8   0.3   1.2  0.5  0.7  0.3  0.2   1167333.0  \n",
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       "4  0.8  1.0   0.8   3.1  0.8  0.6  0.4  0.6   9769821.0  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "player_data=player_data_original.copy()\n",
    "player_data.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Categorical variables:\n",
      "['PLAYER', 'TEAM']\n"
     ]
    }
   ],
   "source": [
    "# Get list of categorical variables\n",
    "s = (player_data.dtypes == 'object')\n",
    "object_cols = list(s[s].index)\n",
    "\n",
    "print(\"Categorical variables:\")\n",
    "print(object_cols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
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       "      <td>Aaron Gordon</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Abdel Nader</td>\n",
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       "      <td>1.4</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.3</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1167333.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Al Horford</td>\n",
       "      <td>1</td>\n",
       "      <td>32</td>\n",
       "      <td>72</td>\n",
       "      <td>31.6</td>\n",
       "      <td>47</td>\n",
       "      <td>25</td>\n",
       "      <td>12.9</td>\n",
       "      <td>5.1</td>\n",
       "      <td>10.5</td>\n",
       "      <td>...</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.3</td>\n",
       "      <td>1.7</td>\n",
       "      <td>1.4</td>\n",
       "      <td>5.9</td>\n",
       "      <td>4.7</td>\n",
       "      <td>1.8</td>\n",
       "      <td>0.6</td>\n",
       "      <td>1.1</td>\n",
       "      <td>27734405.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Al Jefferson</td>\n",
       "      <td>9</td>\n",
       "      <td>33</td>\n",
       "      <td>36</td>\n",
       "      <td>13.4</td>\n",
       "      <td>17</td>\n",
       "      <td>19</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.1</td>\n",
       "      <td>5.8</td>\n",
       "      <td>...</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.8</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>3.1</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.6</td>\n",
       "      <td>9769821.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         PLAYER  TEAM  AGE  GP   MIN   W   L   PTS  FGM   FGA     ...      \\\n",
       "0  Aaron Brooks    15   33  32   5.9  19  13   2.3  0.9   2.2     ...       \n",
       "1  Aaron Gordon    19   22  58  32.9  19  39  17.6  6.5  14.9     ...       \n",
       "2   Abdel Nader     1   24  48  10.9  33  15   3.0  1.0   3.1     ...       \n",
       "3    Al Horford     1   32  72  31.6  47  25  12.9  5.1  10.5     ...       \n",
       "4  Al Jefferson     9   33  36  13.4  17  19   7.0  3.1   5.8     ...       \n",
       "\n",
       "   3PA  FTM  FTA  OREB  DREB  AST  TOV  STL  BLK      SALARY  \n",
       "0  1.0  0.3  0.3   0.2   0.3  0.6  0.3  0.2  0.0   2116955.0  \n",
       "1  5.9  2.7  3.9   1.5   6.4  2.3  1.8  1.0  0.8   5504420.0  \n",
       "2  1.4  0.5  0.8   0.3   1.2  0.5  0.7  0.3  0.2   1167333.0  \n",
       "3  3.1  1.3  1.7   1.4   5.9  4.7  1.8  0.6  1.1  27734405.0  \n",
       "4  0.1  0.8  1.0   0.8   3.1  0.8  0.6  0.4  0.6   9769821.0  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "# Apply label encoder to each column with categorical data\n",
    "label_encoder = LabelEncoder()\n",
    "player_data[object_cols[1]] = label_encoder.fit_transform(player_data[object_cols[1]])\n",
    "player_data[object_cols[1]] = label_encoder.transform(player_data[object_cols[1]])\n",
    "player_data.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Choose target and features\n",
    "y = player_data.SALARY\n",
    "player_features = ['AGE','FGM','OREB','FTA','GP','FGA','3PM','MIN','W','BLK','STL','PTS','TOV','L','FTM','3PA', 'AST','TEAM','DREB' ]\n",
    "X = player_data[player_features]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Cross validation\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.impute import SimpleImputer\n",
    "\n",
    "\n",
    "my_pipeline = Pipeline(steps=[('preprocessor', SimpleImputer()),\n",
    "                              ('model', RandomForestRegressor(n_estimators=75,\n",
    "                                                              random_state=0))\n",
    "                             ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE scores:\n",
      " [3568817.01811966 2964373.15965812 3641500.03282051 4014980.37025641\n",
      " 3434056.04376068 3958594.02119658 4304066.68752137 3128027.92526316\n",
      " 3206078.7154386  3534604.23017544]\n",
      "Average MAE score (across experiments):\n",
      "3575509.8204210526\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "# Multiply by -1 since sklearn calculates *negative* MAE\n",
    "scores = -1 * cross_val_score(my_pipeline, X, y,\n",
    "                              cv=10,\n",
    "                              scoring='neg_mean_absolute_error')\n",
    "\n",
    "print(\"MAE scores:\\n\", scores)\n",
    "print(\"Average MAE score (across experiments):\")\n",
    "print(scores.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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