Final Models (with WL).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Necessary Upgrades\n",
    "Run the cells in this section to make sure you have the latest version of sklearn and joblib.\n",
    "\n",
    "Restart your kernel after installing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Update sklearn to prevent version mismatches\n",
    "# !pip install sklearn --upgrade"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "## install joblib. This will be used to save your model. \n",
    "# !pip install joblib\n",
    "## Restart your kernel after installing "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import Dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.simplefilter('ignore')\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.svm import SVC \n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.metrics import classification_report\n",
    "\n",
    "import joblib"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Read the CSV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_df = pd.read_csv(\"NBA_data/preprocessed_80-19_WL.csv\", index_col = 0)\n",
    "test_df = pd.read_csv(\"NBA_data/preprocessed_2020_WL.csv\", index_col = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['RK', 'Team', 'Year', 'Conf', 'playoffs_y_n', 'G', 'W%', 'W', 'L', 'MP',\n",
       "       'FG', 'FGA', 'FG%', '3P', '3PA', '3P%', '2P', '2PA', '2P%', 'FT', 'FTA',\n",
       "       'FT%', 'ORB', 'DRB', 'AST', 'STL', 'BLK', 'TOV', 'PF', 'PTS',\n",
       "       'W_scalnorm', 'L_scalnorm', 'MP_scalnorm', 'FG_scalnorm',\n",
       "       'FGA_scalnorm', 'FG%_scalnorm', '3P_scalnorm', '3PA_scalnorm',\n",
       "       '3P%_scalnorm', '2P_scalnorm', '2PA_scalnorm', '2P%_scalnorm',\n",
       "       'FT_scalnorm', 'FTA_scalnorm', 'FT%_scalnorm', 'ORB_scalnorm',\n",
       "       'DRB_scalnorm', 'AST_scalnorm', 'STL_scalnorm', 'BLK_scalnorm',\n",
       "       'TOV_scalnorm', 'PF_scalnorm', 'PTS_scalnorm'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_df.head()\n",
    "test_df.head()\n",
    "model_df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Train Test Split Based on Year and Conference (East)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Select X and Y, Split (East)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['W_scalnorm', 'L_scalnorm', 'MP_scalnorm', 'FG_scalnorm',\n",
       "       'FGA_scalnorm', 'FG%_scalnorm', '3P_scalnorm', '3PA_scalnorm',\n",
       "       '3P%_scalnorm', '2P_scalnorm', '2PA_scalnorm', '2P%_scalnorm',\n",
       "       'FT_scalnorm', 'FTA_scalnorm', 'FT%_scalnorm', 'ORB_scalnorm',\n",
       "       'DRB_scalnorm', 'AST_scalnorm', 'STL_scalnorm', 'BLK_scalnorm',\n",
       "       'TOV_scalnorm', 'PF_scalnorm', 'PTS_scalnorm'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drop_columns = ['playoffs_y_n', 'RK', 'Team', 'Year', 'Conf',\n",
    "                'G', 'W', 'L', \n",
    "                'MP', 'FG', 'FGA', 'FG%', '3P', '3PA', '3P%', '2P', '2PA', '2P%', 'FT', 'FTA', 'FT%', \n",
    "                'ORB', 'DRB', 'AST', 'STL', 'BLK', 'TOV', 'PF', 'PTS',\n",
    "                'W%',\n",
    "               ]\n",
    "\n",
    "df_east = model_df.loc[model_df[\"Conf\"] == \"East\"]\n",
    "\n",
    "train_M3E = df_east.loc[df_east[\"Year\"] < 2019]\n",
    "test_M3E = df_east.loc[df_east[\"Year\"] >= 2019]\n",
    "\n",
    "X_train_M3E = train_M3E.drop(columns=drop_columns)\n",
    "X_test_M3E = test_M3E.drop(columns=drop_columns)\n",
    "\n",
    "y_train_M3E = train_M3E[['playoffs_y_n']]\n",
    "y_test_M3E = test_M3E[['playoffs_y_n']]\n",
    "\n",
    "# columns included in training the model\n",
    "X_train_M3E.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Scores (East)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic - East\n",
      "Training Data Score: 0.9107806691449815\n",
      "Testing Data Score: 0.9333333333333333\n",
      "RFC - East\n",
      "Training Data Score: 1.0\n",
      "Testing Data Score: 1.0\n",
      "SVC - East\n",
      "Training Data Score: 0.9256505576208178\n",
      "Testing Data Score: 1.0\n"
     ]
    },
    {
     "data": {
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       "    }\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>Type</th>\n",
       "      <th>Logistic</th>\n",
       "      <th>RFC</th>\n",
       "      <th>SVM</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Train</td>\n",
       "      <td>0.910781</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.925651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Test</td>\n",
       "      <td>0.933333</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Type  Logistic  RFC       SVM\n",
       "0  Train  0.910781  1.0  0.925651\n",
       "1   Test  0.933333  1.0  1.000000"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print (\"Logistic - East\")\n",
    "log_m3East = LogisticRegression()\n",
    "log_m3East.fit(X_train_M3E, y_train_M3E)\n",
    "\n",
    "log_east_train_score = log_m3East.score(X_train_M3E, y_train_M3E)\n",
    "log_east_test_score = log_m3East.score(X_test_M3E, y_test_M3E)\n",
    "\n",
    "print(f\"Training Data Score: {log_east_train_score}\")\n",
    "print(f\"Testing Data Score: {log_east_test_score}\")\n",
    "\n",
    "print (\"RFC - East\")\n",
    "rfc_m3East= RandomForestClassifier()\n",
    "rfc_m3East.fit(X_train_M3E, y_train_M3E)\n",
    "\n",
    "rfc_east_train_score = rfc_m3East.score(X_train_M3E, y_train_M3E)\n",
    "rfc_east_test_score = rfc_m3East.score(X_test_M3E, y_test_M3E)\n",
    "\n",
    "print(f\"Training Data Score: {rfc_east_train_score}\")\n",
    "print(f\"Testing Data Score: {rfc_east_test_score}\")\n",
    "\n",
    "print (\"SVC - East\")\n",
    "svc_m3East = SVC(kernel='linear', probability=True)\n",
    "svc_m3East.fit(X_train_M3E, y_train_M3E)\n",
    "\n",
    "svc_east_train_score = svc_m3East.score(X_train_M3E, y_train_M3E)\n",
    "svc_east_test_score = svc_m3East.score(X_test_M3E, y_test_M3E)\n",
    "\n",
    "print(f\"Training Data Score: {svc_east_train_score}\")\n",
    "print(f\"Testing Data Score: {svc_east_test_score}\")\n",
    "\n",
    "east_scores = [[\"Train\", log_east_train_score, rfc_east_train_score, svc_east_train_score],\n",
    "               [\"Test\", log_east_test_score, rfc_east_test_score, svc_east_test_score]]\n",
    "df_east_scores = pd.DataFrame(east_scores, columns = ['Type','Logistic', 'RFC', 'SVM']) \n",
    "df_east_scores"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Importances (East)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Log - East\n",
      "('L_scalnorm', -5.232476580045679, 5.232476580045679)\n",
      "('W_scalnorm', 5.2188268706713385, 5.2188268706713385)\n",
      "('DRB_scalnorm', 1.6090842664937262, 1.6090842664937262)\n",
      "('2PA_scalnorm', -1.444607493911746, 1.444607493911746)\n",
      "('TOV_scalnorm', -1.1452382726270225, 1.1452382726270225)\n",
      "RFC - East\n",
      "(0.3446181599098721, 'L_scalnorm')\n",
      "(0.28348054501358894, 'W_scalnorm')\n",
      "(0.048344558491776386, '2P%_scalnorm')\n",
      "(0.03257904744902838, 'FG%_scalnorm')\n",
      "(0.03091667456543585, 'DRB_scalnorm')\n",
      "SVM - East\n",
      "('L_scalnorm', -5.565088587963406, 5.565088587963406)\n",
      "('W_scalnorm', 5.515295984498768, 5.515295984498768)\n",
      "('2PA_scalnorm', -1.676708892794669, 1.676708892794669)\n",
      "('DRB_scalnorm', 1.2267792176705155, 1.2267792176705155)\n",
      "('ORB_scalnorm', 1.1200429899274418, 1.1200429899274418)\n"
     ]
    }
   ],
   "source": [
    "feature_names_M3E = X_train_M3E.columns\n",
    "\n",
    "print (\"Log - East\")\n",
    "coeff_log = log_m3East.coef_\n",
    "importance_log = coeff_log[0]\n",
    "abs_importance_log = abs(importance_log)\n",
    "importance_list_log = list(zip(feature_names_M3E,importance_log, abs_importance_log))\n",
    "importance_list_log.sort(key=lambda x:x[2], reverse = True)\n",
    "for i in range (5):\n",
    "    print (importance_list_log[i])\n",
    "\n",
    "print (\"RFC - East\")\n",
    "rfc_importances = rfc_m3East.feature_importances_\n",
    "rfc_importances = sorted(zip(rfc_importances, feature_names_M3E), reverse=True)\n",
    "for i in range (5):\n",
    "    print (rfc_importances[i])\n",
    "\n",
    "print (\"SVM - East\")\n",
    "coeff_svm = svc_m3East.coef_\n",
    "importance_svm = coeff_svm[0]\n",
    "abs_importance_svm = abs(importance_svm)\n",
    "importance_list_svm = list(zip(feature_names_M3E,importance_svm, abs_importance_svm))\n",
    "importance_list_svm.sort(key=lambda x:x[2], reverse = True)\n",
    "for i in range (5):\n",
    "    print (importance_list_svm[i])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2019 Predictions (East)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\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>Team</th>\n",
       "      <th>Year</th>\n",
       "      <th>playoffs_y_n</th>\n",
       "      <th>prediction_log</th>\n",
       "      <th>prob_log</th>\n",
       "      <th>prediction_rfc</th>\n",
       "      <th>prob_rfc</th>\n",
       "      <th>prediction_svm</th>\n",
       "      <th>prob_svm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Toronto Raptors</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.998693</td>\n",
       "      <td>1</td>\n",
       "      <td>0.99</td>\n",
       "      <td>1</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Milwaukee Bucks</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.998574</td>\n",
       "      <td>1</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Philadelphia 76ers</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.979378</td>\n",
       "      <td>1</td>\n",
       "      <td>0.98</td>\n",
       "      <td>1</td>\n",
       "      <td>0.999999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.956835</td>\n",
       "      <td>1</td>\n",
       "      <td>0.96</td>\n",
       "      <td>1</td>\n",
       "      <td>0.996200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Indiana Pacers</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.752063</td>\n",
       "      <td>1</td>\n",
       "      <td>0.93</td>\n",
       "      <td>1</td>\n",
       "      <td>0.852494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Brooklyn Nets</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.650197</td>\n",
       "      <td>1</td>\n",
       "      <td>0.88</td>\n",
       "      <td>1</td>\n",
       "      <td>0.848447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Orlando Magic</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.678030</td>\n",
       "      <td>1</td>\n",
       "      <td>0.90</td>\n",
       "      <td>1</td>\n",
       "      <td>0.803858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Detroit Pistons</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.464175</td>\n",
       "      <td>1</td>\n",
       "      <td>0.80</td>\n",
       "      <td>1</td>\n",
       "      <td>0.665789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Charlotte Hornets</td>\n",
       "      <td>2019</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.495580</td>\n",
       "      <td>0</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0</td>\n",
       "      <td>0.373916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Miami Heat</td>\n",
       "      <td>2019</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.421633</td>\n",
       "      <td>0</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0</td>\n",
       "      <td>0.310564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Atlanta Hawks</td>\n",
       "      <td>2019</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.174032</td>\n",
       "      <td>0</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0</td>\n",
       "      <td>0.046599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Chicago Bulls</td>\n",
       "      <td>2019</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.130277</td>\n",
       "      <td>0</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0</td>\n",
       "      <td>0.018606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Washington Wizards</td>\n",
       "      <td>2019</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.108463</td>\n",
       "      <td>0</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0</td>\n",
       "      <td>0.005110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Cleveland Cavaliers</td>\n",
       "      <td>2019</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.060170</td>\n",
       "      <td>0</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0</td>\n",
       "      <td>0.005019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>New York Knicks</td>\n",
       "      <td>2019</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.007124</td>\n",
       "      <td>0</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000021</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Team  Year  playoffs_y_n  prediction_log  prob_log  \\\n",
       "7       Toronto Raptors  2019             1               1  0.998693   \n",
       "0       Milwaukee Bucks  2019             1               1  0.998574   \n",
       "3    Philadelphia 76ers  2019             1               1  0.979378   \n",
       "13       Boston Celtics  2019             1               1  0.956835   \n",
       "21       Indiana Pacers  2019             1               1  0.752063   \n",
       "14        Brooklyn Nets  2019             1               1  0.650197   \n",
       "23        Orlando Magic  2019             1               1  0.678030   \n",
       "24      Detroit Pistons  2019             1               0  0.464175   \n",
       "18    Charlotte Hornets  2019             0               0  0.495580   \n",
       "25           Miami Heat  2019             0               0  0.421633   \n",
       "11        Atlanta Hawks  2019             0               0  0.174032   \n",
       "26        Chicago Bulls  2019             0               0  0.130277   \n",
       "9    Washington Wizards  2019             0               0  0.108463   \n",
       "28  Cleveland Cavaliers  2019             0               0  0.060170   \n",
       "27      New York Knicks  2019             0               0  0.007124   \n",
       "\n",
       "    prediction_rfc  prob_rfc  prediction_svm  prob_svm  \n",
       "7                1      0.99               1  1.000000  \n",
       "0                1      1.00               1  1.000000  \n",
       "3                1      0.98               1  0.999999  \n",
       "13               1      0.96               1  0.996200  \n",
       "21               1      0.93               1  0.852494  \n",
       "14               1      0.88               1  0.848447  \n",
       "23               1      0.90               1  0.803858  \n",
       "24               1      0.80               1  0.665789  \n",
       "18               0      0.17               0  0.373916  \n",
       "25               0      0.17               0  0.310564  \n",
       "11               0      0.16               0  0.046599  \n",
       "26               0      0.08               0  0.018606  \n",
       "9                0      0.20               0  0.005110  \n",
       "28               0      0.04               0  0.005019  \n",
       "27               0      0.01               0  0.000021  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "east_predictions_2019 = test_M3E[[\"Team\", \"Year\", \"playoffs_y_n\"]]\n",
    "\n",
    "log_probability = log_m3East.predict_proba(X_test_M3E)[:,1].tolist()\n",
    "rfc_probability = rfc_m3East.predict_proba(X_test_M3E)[:,1].tolist()\n",
    "svm_probability = svc_m3East.predict_proba(X_test_M3E)[:,1].tolist()\n",
    "\n",
    "log_prediction = log_m3East.predict(X_test_M3E).tolist()\n",
    "rfc_prediction = rfc_m3East.predict(X_test_M3E).tolist()\n",
    "svm_prediction = svc_m3East.predict(X_test_M3E).tolist()\n",
    "\n",
    "east_predictions_2019[\"prediction_log\"] = log_prediction\n",
    "east_predictions_2019[\"prob_log\"] = log_probability\n",
    "east_predictions_2019[\"prediction_rfc\"] = rfc_prediction\n",
    "east_predictions_2019[\"prob_rfc\"] = rfc_probability\n",
    "east_predictions_2019[\"prediction_svm\"] = svm_prediction\n",
    "east_predictions_2019[\"prob_svm\"] = svm_probability\n",
    "\n",
    "east_predictions_2019.loc[east_predictions_2019[\"playoffs_y_n\"] == 1]\n",
    "east_predictions_2019.sort_values(\"prob_log\", ascending = False)\n",
    "east_predictions_2019.sort_values(\"prob_rfc\", ascending = False)\n",
    "east_predictions_2019.sort_values(\"prob_svm\", ascending = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2020 Predictions (East)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Team</th>\n",
       "      <th>Year</th>\n",
       "      <th>playoffs_y_n</th>\n",
       "      <th>prediction_log</th>\n",
       "      <th>prob_log</th>\n",
       "      <th>prediction_rfc</th>\n",
       "      <th>prob_rfc</th>\n",
       "      <th>prediction_svm</th>\n",
       "      <th>prob_svm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Toronto Raptors</td>\n",
       "      <td>2020</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.996742</td>\n",
       "      <td>1</td>\n",
       "      <td>0.99</td>\n",
       "      <td>1</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>2020</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.995791</td>\n",
       "      <td>1</td>\n",
       "      <td>0.97</td>\n",
       "      <td>1</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Milwaukee Bucks</td>\n",
       "      <td>2020</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.997051</td>\n",
       "      <td>1</td>\n",
       "      <td>0.98</td>\n",
       "      <td>1</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Philadelphia 76ers</td>\n",
       "      <td>2020</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.952284</td>\n",
       "      <td>1</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0.996814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Miami Heat</td>\n",
       "      <td>2020</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.904437</td>\n",
       "      <td>1</td>\n",
       "      <td>0.98</td>\n",
       "      <td>1</td>\n",
       "      <td>0.988748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Brooklyn Nets</td>\n",
       "      <td>2020</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.643515</td>\n",
       "      <td>1</td>\n",
       "      <td>0.86</td>\n",
       "      <td>1</td>\n",
       "      <td>0.841734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Indiana Pacers</td>\n",
       "      <td>2020</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.640746</td>\n",
       "      <td>1</td>\n",
       "      <td>0.94</td>\n",
       "      <td>1</td>\n",
       "      <td>0.738822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Orlando Magic</td>\n",
       "      <td>2020</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.659642</td>\n",
       "      <td>1</td>\n",
       "      <td>0.92</td>\n",
       "      <td>1</td>\n",
       "      <td>0.696887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Charlotte Hornets</td>\n",
       "      <td>2020</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.318675</td>\n",
       "      <td>0</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0</td>\n",
       "      <td>0.298180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Chicago Bulls</td>\n",
       "      <td>2020</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.231856</td>\n",
       "      <td>0</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0</td>\n",
       "      <td>0.111513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>New York Knicks</td>\n",
       "      <td>2020</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.249925</td>\n",
       "      <td>0</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0</td>\n",
       "      <td>0.090511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Cleveland Cavaliers</td>\n",
       "      <td>2020</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.110362</td>\n",
       "      <td>0</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0</td>\n",
       "      <td>0.027865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Atlanta Hawks</td>\n",
       "      <td>2020</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.136861</td>\n",
       "      <td>0</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0</td>\n",
       "      <td>0.018727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Washington Wizards</td>\n",
       "      <td>2020</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.109624</td>\n",
       "      <td>0</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0</td>\n",
       "      <td>0.006142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Detroit Pistons</td>\n",
       "      <td>2020</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.019693</td>\n",
       "      <td>0</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000309</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Team  Year  playoffs_y_n  prediction_log  prob_log  \\\n",
       "13      Toronto Raptors  2020             1               1  0.996742   \n",
       "12       Boston Celtics  2020             1               1  0.995791   \n",
       "1       Milwaukee Bucks  2020             1               1  0.997051   \n",
       "17   Philadelphia 76ers  2020             0               1  0.952284   \n",
       "10           Miami Heat  2020             0               1  0.904437   \n",
       "21        Brooklyn Nets  2020             0               1  0.643515   \n",
       "18       Indiana Pacers  2020             0               1  0.640746   \n",
       "27        Orlando Magic  2020             0               1  0.659642   \n",
       "29    Charlotte Hornets  2020             0               0  0.318675   \n",
       "26        Chicago Bulls  2020             0               0  0.231856   \n",
       "23      New York Knicks  2020             0               0  0.249925   \n",
       "25  Cleveland Cavaliers  2020             0               0  0.110362   \n",
       "4         Atlanta Hawks  2020             0               0  0.136861   \n",
       "7    Washington Wizards  2020             0               0  0.109624   \n",
       "22      Detroit Pistons  2020             0               0  0.019693   \n",
       "\n",
       "    prediction_rfc  prob_rfc  prediction_svm  prob_svm  \n",
       "13               1      0.99               1  1.000000  \n",
       "12               1      0.97               1  1.000000  \n",
       "1                1      0.98               1  1.000000  \n",
       "17               1      1.00               1  0.996814  \n",
       "10               1      0.98               1  0.988748  \n",
       "21               1      0.86               1  0.841734  \n",
       "18               1      0.94               1  0.738822  \n",
       "27               1      0.92               1  0.696887  \n",
       "29               0      0.12               0  0.298180  \n",
       "26               0      0.04               0  0.111513  \n",
       "23               0      0.07               0  0.090511  \n",
       "25               0      0.01               0  0.027865  \n",
       "4                0      0.15               0  0.018727  \n",
       "7                0      0.17               0  0.006142  \n",
       "22               0      0.04               0  0.000309  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df_east = test_df.loc[test_df[\"Conf\"] == \"East\"]\n",
    "east_predictions_2020 = test_df_east[[\"Team\", \"Year\", \"playoffs_y_n\"]]\n",
    "\n",
    "X_test_M3E_2020 = test_df_east.drop(columns=drop_columns)\n",
    "\n",
    "log_probability = log_m3East.predict_proba(X_test_M3E_2020)[:,1].tolist()\n",
    "rfc_probability = rfc_m3East.predict_proba(X_test_M3E_2020)[:,1].tolist()\n",
    "svm_probability = svc_m3East.predict_proba(X_test_M3E_2020)[:,1].tolist()\n",
    "\n",
    "log_prediction = log_m3East.predict(X_test_M3E_2020).tolist()\n",
    "rfc_prediction = rfc_m3East.predict(X_test_M3E_2020).tolist()\n",
    "svm_prediction = svc_m3East.predict(X_test_M3E_2020).tolist()\n",
    "\n",
    "east_predictions_2020[\"prediction_log\"] = log_prediction\n",
    "east_predictions_2020[\"prob_log\"] = log_probability\n",
    "east_predictions_2020[\"prediction_rfc\"] = rfc_prediction\n",
    "east_predictions_2020[\"prob_rfc\"] = rfc_probability\n",
    "east_predictions_2020[\"prediction_svm\"] = svm_prediction\n",
    "east_predictions_2020[\"prob_svm\"] = svm_probability\n",
    "\n",
    "east_predictions_2020.loc[east_predictions_2020[\"playoffs_y_n\"] == 1]\n",
    "east_predictions_2020.sort_values(\"prob_log\", ascending = False)\n",
    "east_predictions_2020.sort_values(\"prob_rfc\", ascending = False)\n",
    "east_predictions_2020.sort_values(\"prob_svm\", ascending = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Train Test Split Based on Year and Conference (West)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Select X and Y, Split (West)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "drop_columns = ['playoffs_y_n', 'RK', 'Team', 'Year', 'Conf',\n",
    "                'G', 'W', 'L', \n",
    "                'MP', 'FG', 'FGA', 'FG%', '3P', '3PA', '3P%', '2P', '2PA', '2P%', 'FT', 'FTA', 'FT%', \n",
    "                'ORB', 'DRB', 'AST', 'STL', 'BLK', 'TOV', 'PF', 'PTS',\n",
    "                'W%',\n",
    "               ]\n",
    "\n",
    "df_west = model_df.loc[model_df[\"Conf\"] == \"West\"]\n",
    "\n",
    "train_M3W = df_west.loc[df_west[\"Year\"] < 2019]\n",
    "test_M3W = df_west.loc[df_west[\"Year\"] >= 2019]\n",
    "\n",
    "X_train_M3W = train_M3W.drop(columns=drop_columns)\n",
    "X_test_M3W = test_M3W.drop(columns=drop_columns)\n",
    "\n",
    "y_train_M3W = train_M3W[['playoffs_y_n']]\n",
    "y_test_M3W = test_M3W[['playoffs_y_n']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Scores (West)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic - West\n",
      "Training Data Score: 0.9235074626865671\n",
      "Testing Data Score: 0.9333333333333333\n",
      "RFC - West\n",
      "Training Data Score: 1.0\n",
      "Testing Data Score: 1.0\n",
      "SVC - West\n",
      "Training Data Score: 0.9328358208955224\n",
      "Testing Data Score: 0.9333333333333333\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Type</th>\n",
       "      <th>Logistic</th>\n",
       "      <th>RFC</th>\n",
       "      <th>SVM</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Train</td>\n",
       "      <td>0.923507</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.932836</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Test</td>\n",
       "      <td>0.933333</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.933333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Type  Logistic  RFC       SVM\n",
       "0  Train  0.923507  1.0  0.932836\n",
       "1   Test  0.933333  1.0  0.933333"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print (\"Logistic - West\")\n",
    "log_m3West = LogisticRegression()\n",
    "log_m3West.fit(X_train_M3W, y_train_M3W)\n",
    "\n",
    "log_west_train_score = log_m3West.score(X_train_M3W, y_train_M3W)\n",
    "log_west_test_score = log_m3West.score(X_test_M3W, y_test_M3W)\n",
    "\n",
    "print(f\"Training Data Score: {log_west_train_score}\")\n",
    "print(f\"Testing Data Score: {log_west_test_score}\")\n",
    "\n",
    "print (\"RFC - West\")\n",
    "rfc_m3West= RandomForestClassifier()\n",
    "rfc_m3West.fit(X_train_M3W, y_train_M3W)\n",
    "\n",
    "rfc_west_train_score = rfc_m3West.score(X_train_M3W, y_train_M3W)\n",
    "rfc_west_test_score = rfc_m3West.score(X_test_M3W, y_test_M3W)\n",
    "\n",
    "print(f\"Training Data Score: {rfc_west_train_score}\")\n",
    "print(f\"Testing Data Score: {rfc_west_test_score}\")\n",
    "\n",
    "print (\"SVC - West\")\n",
    "svc_m3West = SVC(kernel='linear', probability=True)\n",
    "svc_m3West.fit(X_train_M3W, y_train_M3W)\n",
    "\n",
    "svc_west_train_score = svc_m3West.score(X_train_M3W, y_train_M3W)\n",
    "svc_west_test_score = svc_m3West.score(X_test_M3W, y_test_M3W)\n",
    "\n",
    "print(f\"Training Data Score: {svc_west_train_score}\")\n",
    "print(f\"Testing Data Score: {svc_west_test_score}\")\n",
    "\n",
    "west_scores = [[\"Train\", log_west_train_score, rfc_west_train_score, svc_west_train_score],\n",
    "               [\"Test\", log_west_test_score, rfc_west_test_score, svc_west_test_score]]\n",
    "df_west_scores = pd.DataFrame(west_scores, columns = ['Type','Logistic', 'RFC', 'SVM']) \n",
    "df_west_scores"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Importances (West)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Log - West\n",
      "('L_scalnorm', -4.83943331887569, 4.83943331887569)\n",
      "('W_scalnorm', 4.808103191034908, 4.808103191034908)\n",
      "('STL_scalnorm', 2.064722698503162, 2.064722698503162)\n",
      "('TOV_scalnorm', -1.881081073295122, 1.881081073295122)\n",
      "('DRB_scalnorm', 1.6369379687574714, 1.6369379687574714)\n",
      "RFC - West\n",
      "(0.3199725888724758, 'W_scalnorm')\n",
      "(0.23479362123262085, 'L_scalnorm')\n",
      "(0.07062335456350548, '2P%_scalnorm')\n",
      "(0.0479195869475173, 'FG%_scalnorm')\n",
      "(0.04113421067213359, 'DRB_scalnorm')\n",
      "SVM - West\n",
      "('L_scalnorm', -4.645370576068627, 4.645370576068627)\n",
      "('W_scalnorm', 4.6130182415752365, 4.6130182415752365)\n",
      "('STL_scalnorm', 1.6745577417052768, 1.6745577417052768)\n",
      "('TOV_scalnorm', -1.157067377774653, 1.157067377774653)\n",
      "('FTA_scalnorm', 1.139316806190769, 1.139316806190769)\n"
     ]
    }
   ],
   "source": [
    "feature_names_M3W = X_train_M3W.columns\n",
    "\n",
    "print (\"Log - West\")\n",
    "coeff_log = log_m3West.coef_\n",
    "importance_log = coeff_log[0]\n",
    "abs_importance_log = abs(importance_log)\n",
    "importance_list_log = list(zip(feature_names_M3W,importance_log, abs_importance_log))\n",
    "importance_list_log.sort(key=lambda x:x[2], reverse = True)\n",
    "for i in range (5):\n",
    "    print (importance_list_log[i])\n",
    "\n",
    "print (\"RFC - West\")\n",
    "rfc_importances = rfc_m3West.feature_importances_\n",
    "rfc_importances = sorted(zip(rfc_importances, feature_names_M3W), reverse=True)\n",
    "for i in range (5):\n",
    "    print (rfc_importances[i])\n",
    "\n",
    "print (\"SVM - West\")\n",
    "coeff_svm = svc_m3West.coef_\n",
    "importance_svm = coeff_svm[0]\n",
    "abs_importance_svm = abs(importance_svm)\n",
    "importance_list_svm = list(zip(feature_names_M3W,importance_svm, abs_importance_svm))\n",
    "importance_list_svm.sort(key=lambda x:x[2], reverse = True)\n",
    "for i in range (5):\n",
    "    print (importance_list_svm[i])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2019 Predictions (West)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>Team</th>\n",
       "      <th>Year</th>\n",
       "      <th>playoffs_y_n</th>\n",
       "      <th>prediction_log</th>\n",
       "      <th>prob_log</th>\n",
       "      <th>prediction_rfc</th>\n",
       "      <th>prob_rfc</th>\n",
       "      <th>prediction_svm</th>\n",
       "      <th>prob_svm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Denver Nuggets</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.978323</td>\n",
       "      <td>1</td>\n",
       "      <td>0.99</td>\n",
       "      <td>1</td>\n",
       "      <td>0.999999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Portland Trail Blazers</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.945955</td>\n",
       "      <td>1</td>\n",
       "      <td>0.89</td>\n",
       "      <td>1</td>\n",
       "      <td>0.994630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Utah Jazz</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.951321</td>\n",
       "      <td>1</td>\n",
       "      <td>0.95</td>\n",
       "      <td>1</td>\n",
       "      <td>0.993939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Golden State Warriors</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.954128</td>\n",
       "      <td>1</td>\n",
       "      <td>0.98</td>\n",
       "      <td>1</td>\n",
       "      <td>0.990790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Oklahoma City Thunder</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.808597</td>\n",
       "      <td>1</td>\n",
       "      <td>0.89</td>\n",
       "      <td>1</td>\n",
       "      <td>0.939246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Houston Rockets</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.708726</td>\n",
       "      <td>1</td>\n",
       "      <td>0.94</td>\n",
       "      <td>1</td>\n",
       "      <td>0.820253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Los Angeles Clippers</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.741402</td>\n",
       "      <td>1</td>\n",
       "      <td>0.85</td>\n",
       "      <td>1</td>\n",
       "      <td>0.780530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>San Antonio Spurs</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.709244</td>\n",
       "      <td>1</td>\n",
       "      <td>0.86</td>\n",
       "      <td>1</td>\n",
       "      <td>0.688928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Sacramento Kings</td>\n",
       "      <td>2019</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.452615</td>\n",
       "      <td>0</td>\n",
       "      <td>0.25</td>\n",
       "      <td>1</td>\n",
       "      <td>0.522707</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Memphis Grizzlies</td>\n",
       "      <td>2019</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.405252</td>\n",
       "      <td>0</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0</td>\n",
       "      <td>0.459591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Los Angeles Lakers</td>\n",
       "      <td>2019</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.413185</td>\n",
       "      <td>0</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0</td>\n",
       "      <td>0.389368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>New Orleans Pelicans</td>\n",
       "      <td>2019</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.534634</td>\n",
       "      <td>0</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0</td>\n",
       "      <td>0.348369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Minnesota Timberwolves</td>\n",
       "      <td>2019</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.385283</td>\n",
       "      <td>0</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0</td>\n",
       "      <td>0.208905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Phoenix Suns</td>\n",
       "      <td>2019</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.081299</td>\n",
       "      <td>0</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0</td>\n",
       "      <td>0.014340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Dallas Mavericks</td>\n",
       "      <td>2019</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.092035</td>\n",
       "      <td>0</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0</td>\n",
       "      <td>0.012272</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Team  Year  playoffs_y_n  prediction_log  prob_log  \\\n",
       "19          Denver Nuggets  2019             1               1  0.978323   \n",
       "5   Portland Trail Blazers  2019             1               1  0.945955   \n",
       "16               Utah Jazz  2019             1               1  0.951321   \n",
       "1    Golden State Warriors  2019             1               1  0.954128   \n",
       "6    Oklahoma City Thunder  2019             1               1  0.808597   \n",
       "10         Houston Rockets  2019             1               1  0.708726   \n",
       "4     Los Angeles Clippers  2019             1               1  0.741402   \n",
       "17       San Antonio Spurs  2019             1               1  0.709244   \n",
       "8         Sacramento Kings  2019             0               0  0.452615   \n",
       "29       Memphis Grizzlies  2019             0               0  0.405252   \n",
       "15      Los Angeles Lakers  2019             0               0  0.413185   \n",
       "2     New Orleans Pelicans  2019             0               1  0.534634   \n",
       "12  Minnesota Timberwolves  2019             0               0  0.385283   \n",
       "22            Phoenix Suns  2019             0               0  0.081299   \n",
       "20        Dallas Mavericks  2019             0               0  0.092035   \n",
       "\n",
       "    prediction_rfc  prob_rfc  prediction_svm  prob_svm  \n",
       "19               1      0.99               1  0.999999  \n",
       "5                1      0.89               1  0.994630  \n",
       "16               1      0.95               1  0.993939  \n",
       "1                1      0.98               1  0.990790  \n",
       "6                1      0.89               1  0.939246  \n",
       "10               1      0.94               1  0.820253  \n",
       "4                1      0.85               1  0.780530  \n",
       "17               1      0.86               1  0.688928  \n",
       "8                0      0.25               1  0.522707  \n",
       "29               0      0.04               0  0.459591  \n",
       "15               0      0.18               0  0.389368  \n",
       "2                0      0.33               0  0.348369  \n",
       "12               0      0.13               0  0.208905  \n",
       "22               0      0.14               0  0.014340  \n",
       "20               0      0.09               0  0.012272  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "west_predictions_2019 = test_M3W[[\"Team\", \"Year\", \"playoffs_y_n\"]]\n",
    "\n",
    "log_probability = log_m3West.predict_proba(X_test_M3W)[:,1].tolist()\n",
    "rfc_probability = rfc_m3West.predict_proba(X_test_M3W)[:,1].tolist()\n",
    "svm_probability = svc_m3West.predict_proba(X_test_M3W)[:,1].tolist()\n",
    "\n",
    "log_prediction = log_m3West.predict(X_test_M3W).tolist()\n",
    "rfc_prediction = rfc_m3West.predict(X_test_M3W).tolist()\n",
    "svm_prediction = svc_m3West.predict(X_test_M3W).tolist()\n",
    "\n",
    "west_predictions_2019[\"prediction_log\"] = log_prediction\n",
    "west_predictions_2019[\"prob_log\"] = log_probability\n",
    "west_predictions_2019[\"prediction_rfc\"] = rfc_prediction\n",
    "west_predictions_2019[\"prob_rfc\"] = rfc_probability\n",
    "west_predictions_2019[\"prediction_svm\"] = svm_prediction\n",
    "west_predictions_2019[\"prob_svm\"] = svm_probability\n",
    "\n",
    "west_predictions_2019.loc[west_predictions_2019[\"playoffs_y_n\"] == 1]\n",
    "west_predictions_2019.sort_values(\"prob_log\", ascending = False)\n",
    "west_predictions_2019.sort_values(\"prob_rfc\", ascending = False)\n",
    "west_predictions_2019.sort_values(\"prob_svm\", ascending = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2020 Predictions (West)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Team</th>\n",
       "      <th>Year</th>\n",
       "      <th>playoffs_y_n</th>\n",
       "      <th>prediction_log</th>\n",
       "      <th>prob_log</th>\n",
       "      <th>prediction_rfc</th>\n",
       "      <th>prob_rfc</th>\n",
       "      <th>prediction_svm</th>\n",
       "      <th>prob_svm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Los Angeles Lakers</td>\n",
       "      <td>2020</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.981844</td>\n",
       "      <td>1</td>\n",
       "      <td>0.99</td>\n",
       "      <td>1</td>\n",
       "      <td>0.999999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Denver Nuggets</td>\n",
       "      <td>2020</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.976707</td>\n",
       "      <td>1</td>\n",
       "      <td>0.98</td>\n",
       "      <td>1</td>\n",
       "      <td>0.999998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Los Angeles Clippers</td>\n",
       "      <td>2020</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.917355</td>\n",
       "      <td>1</td>\n",
       "      <td>0.97</td>\n",
       "      <td>1</td>\n",
       "      <td>0.984437</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Oklahoma City Thunder</td>\n",
       "      <td>2020</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.868943</td>\n",
       "      <td>1</td>\n",
       "      <td>0.97</td>\n",
       "      <td>1</td>\n",
       "      <td>0.979654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>San Antonio Spurs</td>\n",
       "      <td>2020</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.833116</td>\n",
       "      <td>0</td>\n",
       "      <td>0.23</td>\n",
       "      <td>1</td>\n",
       "      <td>0.870065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Dallas Mavericks</td>\n",
       "      <td>2020</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.822072</td>\n",
       "      <td>1</td>\n",
       "      <td>0.90</td>\n",
       "      <td>1</td>\n",
       "      <td>0.850352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Memphis Grizzlies</td>\n",
       "      <td>2020</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.696814</td>\n",
       "      <td>1</td>\n",
       "      <td>0.90</td>\n",
       "      <td>1</td>\n",
       "      <td>0.802480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Houston Rockets</td>\n",
       "      <td>2020</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.709991</td>\n",
       "      <td>1</td>\n",
       "      <td>0.89</td>\n",
       "      <td>1</td>\n",
       "      <td>0.789839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Phoenix Suns</td>\n",
       "      <td>2020</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.588773</td>\n",
       "      <td>0</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0</td>\n",
       "      <td>0.423971</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Utah Jazz</td>\n",
       "      <td>2020</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.504967</td>\n",
       "      <td>1</td>\n",
       "      <td>0.96</td>\n",
       "      <td>0</td>\n",
       "      <td>0.398855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>New Orleans Pelicans</td>\n",
       "      <td>2020</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.333448</td>\n",
       "      <td>0</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0</td>\n",
       "      <td>0.161939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Portland Trail Blazers</td>\n",
       "      <td>2020</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.208624</td>\n",
       "      <td>0</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0</td>\n",
       "      <td>0.090028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Sacramento Kings</td>\n",
       "      <td>2020</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.119925</td>\n",
       "      <td>0</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0</td>\n",
       "      <td>0.027464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Minnesota Timberwolves</td>\n",
       "      <td>2020</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.084396</td>\n",
       "      <td>0</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0</td>\n",
       "      <td>0.010358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Golden State Warriors</td>\n",
       "      <td>2020</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.002756</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000005</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Team  Year  playoffs_y_n  prediction_log  prob_log  \\\n",
       "14      Los Angeles Lakers  2020             1               1  0.981844   \n",
       "15          Denver Nuggets  2020             0               1  0.976707   \n",
       "6     Los Angeles Clippers  2020             0               1  0.917355   \n",
       "20   Oklahoma City Thunder  2020             0               1  0.868943   \n",
       "16       San Antonio Spurs  2020             0               1  0.833116   \n",
       "0         Dallas Mavericks  2020             0               1  0.822072   \n",
       "8        Memphis Grizzlies  2020             0               1  0.696814   \n",
       "2          Houston Rockets  2020             0               1  0.709991   \n",
       "9             Phoenix Suns  2020             0               1  0.588773   \n",
       "19               Utah Jazz  2020             0               1  0.504967   \n",
       "5     New Orleans Pelicans  2020             0               0  0.333448   \n",
       "3   Portland Trail Blazers  2020             0               0  0.208624   \n",
       "24        Sacramento Kings  2020             0               0  0.119925   \n",
       "11  Minnesota Timberwolves  2020             0               0  0.084396   \n",
       "28   Golden State Warriors  2020             0               0  0.002756   \n",
       "\n",
       "    prediction_rfc  prob_rfc  prediction_svm  prob_svm  \n",
       "14               1      0.99               1  0.999999  \n",
       "15               1      0.98               1  0.999998  \n",
       "6                1      0.97               1  0.984437  \n",
       "20               1      0.97               1  0.979654  \n",
       "16               0      0.23               1  0.870065  \n",
       "0                1      0.90               1  0.850352  \n",
       "8                1      0.90               1  0.802480  \n",
       "2                1      0.89               1  0.789839  \n",
       "9                0      0.30               0  0.423971  \n",
       "19               1      0.96               0  0.398855  \n",
       "5                0      0.36               0  0.161939  \n",
       "3                0      0.31               0  0.090028  \n",
       "24               0      0.08               0  0.027464  \n",
       "11               0      0.15               0  0.010358  \n",
       "28               0      0.00               0  0.000005  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df_west = test_df.loc[test_df[\"Conf\"] == \"West\"]\n",
    "west_predictions_2020 = test_df_west[[\"Team\", \"Year\", \"playoffs_y_n\"]]\n",
    "\n",
    "X_test_M3W_2020 = test_df_west.drop(columns=drop_columns)\n",
    "\n",
    "log_probability = log_m3West.predict_proba(X_test_M3W_2020)[:,1].tolist()\n",
    "rfc_probability = rfc_m3West.predict_proba(X_test_M3W_2020)[:,1].tolist()\n",
    "svm_probability = svc_m3West.predict_proba(X_test_M3W_2020)[:,1].tolist()\n",
    "\n",
    "log_prediction = log_m3West.predict(X_test_M3W_2020).tolist()\n",
    "rfc_prediction = rfc_m3West.predict(X_test_M3W_2020).tolist()\n",
    "svm_prediction = svc_m3West.predict(X_test_M3W_2020).tolist()\n",
    "\n",
    "west_predictions_2020[\"prediction_log\"] = log_prediction\n",
    "west_predictions_2020[\"prob_log\"] = log_probability\n",
    "west_predictions_2020[\"prediction_rfc\"] = rfc_prediction\n",
    "west_predictions_2020[\"prob_rfc\"] = rfc_probability\n",
    "west_predictions_2020[\"prediction_svm\"] = svm_prediction\n",
    "west_predictions_2020[\"prob_svm\"] = svm_probability\n",
    "\n",
    "west_predictions_2020.loc[west_predictions_2020[\"playoffs_y_n\"] == 1]\n",
    "west_predictions_2020.sort_values(\"prob_log\", ascending = False)\n",
    "west_predictions_2020.sort_values(\"prob_rfc\", ascending = False)\n",
    "west_predictions_2020.sort_values(\"prob_svm\", ascending = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Save the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['models/west_svm_WL.h5']"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "east_log = 'models/east_log_WL.h5'\n",
    "east_rfc = 'models/east_rfc_WL.h5'\n",
    "east_svm = 'models/east_svm_WL.h5'\n",
    "\n",
    "joblib.dump(log_m3East, east_log)\n",
    "joblib.dump(rfc_m3East, east_rfc)\n",
    "joblib.dump(svc_m3East, east_svm)\n",
    "\n",
    "west_log = 'models/west_log_WL.h5'\n",
    "west_rfc = 'models/west_rfc_WL.h5'\n",
    "west_svm = 'models/west_svm_WL.h5'\n",
    "\n",
    "joblib.dump(log_m3West, west_log)\n",
    "joblib.dump(rfc_m3West, west_rfc)\n",
    "joblib.dump(svc_m3West, west_svm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9333333333333333\n",
      "1.0\n",
      "1.0\n",
      "0.9333333333333333\n",
      "1.0\n",
      "0.9333333333333333\n"
     ]
    }
   ],
   "source": [
    "loaded_model = joblib.load(\"models/east_log_WL.h5\")\n",
    "print(f\"{loaded_model.score(X_test_M3E, y_test_M3E)}\")\n",
    "loaded_model = joblib.load(\"models/east_rfc_WL.h5\")\n",
    "print(f\"{loaded_model.score(X_test_M3E, y_test_M3E)}\")\n",
    "loaded_model = joblib.load(\"models/east_svm_WL.h5\")\n",
    "print(f\"{loaded_model.score(X_test_M3E, y_test_M3E)}\")\n",
    "\n",
    "loaded_model = joblib.load(\"models/west_log_WL.h5\")\n",
    "print(f\"{loaded_model.score(X_test_M3W, y_test_M3W)}\")\n",
    "loaded_model = joblib.load(\"models/west_rfc_WL.h5\")\n",
    "print(f\"{loaded_model.score(X_test_M3W, y_test_M3W)}\")\n",
    "loaded_model = joblib.load(\"models/west_svm_WL.h5\")\n",
    "print(f\"{loaded_model.score(X_test_M3W, y_test_M3W)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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