preprocessing_data_2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.preprocessing import OneHotEncoder, OrdinalEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>GAME_DATE_EST</th>\n",
       "      <th>GAME_ID</th>\n",
       "      <th>GAME_STATUS_TEXT</th>\n",
       "      <th>HOME_TEAM_ID</th>\n",
       "      <th>VISITOR_TEAM_ID</th>\n",
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       "      <th>FT_PCT_home</th>\n",
       "      <th>...</th>\n",
       "      <th>AST_home</th>\n",
       "      <th>REB_home</th>\n",
       "      <th>TEAM_ID_away</th>\n",
       "      <th>PTS_away</th>\n",
       "      <th>FG_PCT_away</th>\n",
       "      <th>FT_PCT_away</th>\n",
       "      <th>FG3_PCT_away</th>\n",
       "      <th>AST_away</th>\n",
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       "      <td>0.482</td>\n",
       "      <td>0.786</td>\n",
       "      <td>...</td>\n",
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       "      <td>37.0</td>\n",
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       "      <td>0.470</td>\n",
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       "      <td>0.433</td>\n",
       "      <td>20.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2022-12-21</td>\n",
       "      <td>22200467</td>\n",
       "      <td>Final</td>\n",
       "      <td>1610612755</td>\n",
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       "      <td>113.0</td>\n",
       "      <td>0.441</td>\n",
       "      <td>0.909</td>\n",
       "      <td>...</td>\n",
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       "      <td>49.0</td>\n",
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       "      <td>93.0</td>\n",
       "      <td>0.392</td>\n",
       "      <td>0.735</td>\n",
       "      <td>0.261</td>\n",
       "      <td>15.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2022-12-21</td>\n",
       "      <td>22200468</td>\n",
       "      <td>Final</td>\n",
       "      <td>1610612737</td>\n",
       "      <td>1610612741</td>\n",
       "      <td>2022</td>\n",
       "      <td>1610612737</td>\n",
       "      <td>108.0</td>\n",
       "      <td>0.429</td>\n",
       "      <td>1.000</td>\n",
       "      <td>...</td>\n",
       "      <td>22.0</td>\n",
       "      <td>47.0</td>\n",
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       "      <td>0.500</td>\n",
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       "      <td>0.292</td>\n",
       "      <td>20.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>0</td>\n",
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       "  </tbody>\n",
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       "<p>5 rows × 21 columns</p>\n",
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      ],
      "text/plain": [
       "  GAME_DATE_EST   GAME_ID GAME_STATUS_TEXT  HOME_TEAM_ID  VISITOR_TEAM_ID  \\\n",
       "0    2022-12-22  22200477            Final    1610612740       1610612759   \n",
       "1    2022-12-22  22200478            Final    1610612762       1610612764   \n",
       "2    2022-12-21  22200466            Final    1610612739       1610612749   \n",
       "3    2022-12-21  22200467            Final    1610612755       1610612765   \n",
       "4    2022-12-21  22200468            Final    1610612737       1610612741   \n",
       "\n",
       "   SEASON  TEAM_ID_home  PTS_home  FG_PCT_home  FT_PCT_home  ...  AST_home  \\\n",
       "0    2022    1610612740     126.0        0.484        0.926  ...      25.0   \n",
       "1    2022    1610612762     120.0        0.488        0.952  ...      16.0   \n",
       "2    2022    1610612739     114.0        0.482        0.786  ...      22.0   \n",
       "3    2022    1610612755     113.0        0.441        0.909  ...      27.0   \n",
       "4    2022    1610612737     108.0        0.429        1.000  ...      22.0   \n",
       "\n",
       "   REB_home  TEAM_ID_away  PTS_away  FG_PCT_away  FT_PCT_away  FG3_PCT_away  \\\n",
       "0      46.0    1610612759     117.0        0.478        0.815         0.321   \n",
       "1      40.0    1610612764     112.0        0.561        0.765         0.333   \n",
       "2      37.0    1610612749     106.0        0.470        0.682         0.433   \n",
       "3      49.0    1610612765      93.0        0.392        0.735         0.261   \n",
       "4      47.0    1610612741     110.0        0.500        0.773         0.292   \n",
       "\n",
       "   AST_away  REB_away  HOME_TEAM_WINS  \n",
       "0      23.0      44.0               1  \n",
       "1      20.0      37.0               1  \n",
       "2      20.0      46.0               1  \n",
       "3      15.0      46.0               1  \n",
       "4      20.0      47.0               0  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('nba_games/games.csv')\n",
    "df.head()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Filtering to games only in the last 2 years because teams change and previous games would not represent current teams"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "      <td>1610612739</td>\n",
       "      <td>114.0</td>\n",
       "      <td>0.482</td>\n",
       "      <td>0.786</td>\n",
       "      <td>...</td>\n",
       "      <td>22.0</td>\n",
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       "      <td>1610612755</td>\n",
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       "      <td>0.441</td>\n",
       "      <td>0.909</td>\n",
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       "      <td>0.261</td>\n",
       "      <td>15.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2022-12-21</td>\n",
       "      <td>22200468</td>\n",
       "      <td>Final</td>\n",
       "      <td>1610612737</td>\n",
       "      <td>1610612741</td>\n",
       "      <td>2022</td>\n",
       "      <td>1610612737</td>\n",
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       "      <td>0.429</td>\n",
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       "  GAME_DATE_EST   GAME_ID GAME_STATUS_TEXT  HOME_TEAM_ID  VISITOR_TEAM_ID  \\\n",
       "0    2022-12-22  22200477            Final    1610612740       1610612759   \n",
       "1    2022-12-22  22200478            Final    1610612762       1610612764   \n",
       "2    2022-12-21  22200466            Final    1610612739       1610612749   \n",
       "3    2022-12-21  22200467            Final    1610612755       1610612765   \n",
       "4    2022-12-21  22200468            Final    1610612737       1610612741   \n",
       "\n",
       "   SEASON  TEAM_ID_home  PTS_home  FG_PCT_home  FT_PCT_home  ...  AST_home  \\\n",
       "0    2022    1610612740     126.0        0.484        0.926  ...      25.0   \n",
       "1    2022    1610612762     120.0        0.488        0.952  ...      16.0   \n",
       "2    2022    1610612739     114.0        0.482        0.786  ...      22.0   \n",
       "3    2022    1610612755     113.0        0.441        0.909  ...      27.0   \n",
       "4    2022    1610612737     108.0        0.429        1.000  ...      22.0   \n",
       "\n",
       "   REB_home  TEAM_ID_away  PTS_away  FG_PCT_away  FT_PCT_away  FG3_PCT_away  \\\n",
       "0      46.0    1610612759     117.0        0.478        0.815         0.321   \n",
       "1      40.0    1610612764     112.0        0.561        0.765         0.333   \n",
       "2      37.0    1610612749     106.0        0.470        0.682         0.433   \n",
       "3      49.0    1610612765      93.0        0.392        0.735         0.261   \n",
       "4      47.0    1610612741     110.0        0.500        0.773         0.292   \n",
       "\n",
       "   AST_away  REB_away  HOME_TEAM_WINS  \n",
       "0      23.0      44.0               1  \n",
       "1      20.0      37.0               1  \n",
       "2      20.0      46.0               1  \n",
       "3      15.0      46.0               1  \n",
       "4      20.0      47.0               0  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['GAME_DATE_EST'] = pd.to_datetime(df['GAME_DATE_EST'])\n",
    "df_filtered = df[df['GAME_DATE_EST'] > '2021-01-01']\n",
    "df_filtered.head()\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Filtering out columns we do not need and renaming the columns we need"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/j4/xht4swnx3v38mbt0w75bjqs40000gn/T/ipykernel_74650/3852254333.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_filtered.loc[idx, 'DAY_OF_YEAR'] = row['GAME_DATE_EST'].isocalendar().week\n"
     ]
    },
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1610612755</td>\n",
       "      <td>1610612765</td>\n",
       "      <td>1</td>\n",
       "      <td>51.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1610612737</td>\n",
       "      <td>1610612741</td>\n",
       "      <td>0</td>\n",
       "      <td>51.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   HOME_TEAM_ID  VISITOR_TEAM_ID  HOME_TEAM_WINS  DAY_OF_YEAR\n",
       "0    1610612740       1610612759               1         51.0\n",
       "1    1610612762       1610612764               1         51.0\n",
       "2    1610612739       1610612749               1         51.0\n",
       "3    1610612755       1610612765               1         51.0\n",
       "4    1610612737       1610612741               0         51.0"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "for idx, row in df_filtered.iterrows():\n",
    "    # df_filtered.loc[idx, 'DAY_OF_YEAR'] = row['GAME_DATE_EST'].timetuple().tm_yday\n",
    "    df_filtered.loc[idx, 'DAY_OF_YEAR'] = row['GAME_DATE_EST'].isocalendar().week\n",
    "\n",
    "df_data = df_filtered[['HOME_TEAM_ID', 'VISITOR_TEAM_ID', 'HOME_TEAM_WINS', 'DAY_OF_YEAR']]\n",
    "# df_data.rename(columns={'team_abbreviation_home':'home', \"away\":\"away\", \"game_date\":\"date\", \"wl_home\":\"wl_home\"}, inplace=True)\n",
    "df_data.head()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Checking for missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>HOME_TEAM_ID</th>\n",
       "      <th>VISITOR_TEAM_ID</th>\n",
       "      <th>HOME_TEAM_WINS</th>\n",
       "      <th>DAY_OF_YEAR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [HOME_TEAM_ID, VISITOR_TEAM_ID, HOME_TEAM_WINS, DAY_OF_YEAR]\n",
       "Index: []"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# df_data.at[62312,'wl_home']='L'\n",
    "# df_data.at[62313,'wl_home']='W'\n",
    "# df_data.at[62314,'wl_home']='W'\n",
    "# df_data.at[62315,'wl_home']='L'\n",
    "# df_data.at[62316,'wl_home']='L'\n",
    "# df_data.at[62317,'wl_home']='W'\n",
    "df_data[df_data.isnull().any(axis=1)]\n",
    "\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Look for dupicate data, found none"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dups = df_data.duplicated()\n",
    "dups.any()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Baseline frequency"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    0.558678\n",
       "0    0.441322\n",
       "Name: HOME_TEAM_WINS, dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_data.HOME_TEAM_WINS.value_counts(normalize=True)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Save processed data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_data.to_csv(\"cleaned.csv\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.compose import make_column_transformer\n",
    "from sklearn.preprocessing import OneHotEncoder, OrdinalEncoder\n",
    "ohe = OneHotEncoder(sparse=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "features = ['HOME_TEAM_ID', 'VISITOR_TEAM_ID']\n",
    "X = df_data[features]\n",
    "y = df_data.HOME_TEAM_WINS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/joshhuang/opt/anaconda3/lib/python3.9/site-packages/sklearn/preprocessing/_encoders.py:808: FutureWarning: `sparse` was renamed to `sparse_output` in version 1.2 and will be removed in 1.4. `sparse_output` is ignored unless you leave `sparse` to its default value.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[0., 0., 0., 1., 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.,\n",
       "        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.],\n",
       "       [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., 1., 0., 0., 0., 0., 0., 0.,\n",
       "        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., 1., 0., 0.],\n",
       "       [0., 0., 1., 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.,\n",
       "        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0.,\n",
       "        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.,\n",
       "        0., 0., 1., 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.,\n",
       "        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0.],\n",
       "       [1., 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.,\n",
       "        0., 0., 1., 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.]])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ct = make_column_transformer(\n",
    "       (ohe, features),\n",
    "       remainder = 'passthrough'\n",
    ")\n",
    "a = ct.fit_transform(X)\n",
    "a[:5, : ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>HOME_TEAM_ID</th>\n",
       "      <th>VISITOR_TEAM_ID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1610612740</td>\n",
       "      <td>1610612759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1610612762</td>\n",
       "      <td>1610612764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1610612739</td>\n",
       "      <td>1610612749</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1610612755</td>\n",
       "      <td>1610612765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1610612737</td>\n",
       "      <td>1610612741</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   HOME_TEAM_ID  VISITOR_TEAM_ID\n",
       "0    1610612740       1610612759\n",
       "1    1610612762       1610612764\n",
       "2    1610612739       1610612749\n",
       "3    1610612755       1610612765\n",
       "4    1610612737       1610612741"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head(5)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cross Validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.pipeline import make_pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "features = ['GAME_ID',\n",
    "            'HOME_TEAM_ID',\n",
    "            'HOME_P1',\n",
    "            'HOME_P2',\n",
    "            'HOME_P3',\n",
    "            'HOME_P4',\n",
    "            'HOME_P5',\n",
    "            'HOME_P6',\n",
    "            'HOME_P7',\n",
    "            'HOME_P8',\n",
    "            'HOME_P9',\n",
    "            'VISITOR_TEAM_ID',\n",
    "            'VISITOR_P1',\n",
    "            'VISITOR_P2',\n",
    "            'VISITOR_P3',\n",
    "            'VISITOR_P4',\n",
    "            'VISITOR_P5',\n",
    "            'VISITOR_P6',\n",
    "            'VISITOR_P7',\n",
    "            'VISITOR_P8',\n",
    "            'VISITOR_P9',\n",
    "            'DAY',\n",
    "            'WEEK',\n",
    "            'MONTH',\n",
    "            'HOME_TEAM_WINS']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/joshhuang/opt/anaconda3/lib/python3.9/site-packages/sklearn/preprocessing/_encoders.py:808: FutureWarning: `sparse` was renamed to `sparse_output` in version 1.2 and will be removed in 1.4. `sparse_output` is ignored unless you leave `sparse` to its default value.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['GAME_ID', 'HOME_TEAM_ID', 'HOME_P1', 'HOME_P2', 'HOME_P3', 'HOME_P4', 'HOME_P5', 'HOME_P6', 'HOME_P7', 'HOME_P8', 'HOME_P9', 'VISITOR_TEAM_ID', 'VISITOR_P1', 'VISITOR_P2', 'VISITOR_P3', 'VISITOR_P4', 'VISITOR_P5', 'VISITOR_P6', 'VISITOR_P7', 'VISITOR_P8', 'VISITOR_P9', 'DAY', 'WEEK', 'MONTH']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/joshhuang/opt/anaconda3/lib/python3.9/site-packages/sklearn/preprocessing/_encoders.py:808: FutureWarning: `sparse` was renamed to `sparse_output` in version 1.2 and will be removed in 1.4. `sparse_output` is ignored unless you leave `sparse` to its default value.\n",
      "  warnings.warn(\n",
      "/Users/joshhuang/opt/anaconda3/lib/python3.9/site-packages/sklearn/preprocessing/_encoders.py:808: FutureWarning: `sparse` was renamed to `sparse_output` in version 1.2 and will be removed in 1.4. `sparse_output` is ignored unless you leave `sparse` to its default value.\n",
      "  warnings.warn(\n",
      "/Users/joshhuang/opt/anaconda3/lib/python3.9/site-packages/sklearn/preprocessing/_encoders.py:808: FutureWarning: `sparse` was renamed to `sparse_output` in version 1.2 and will be removed in 1.4. `sparse_output` is ignored unless you leave `sparse` to its default value.\n",
      "  warnings.warn(\n",
      "/Users/joshhuang/opt/anaconda3/lib/python3.9/site-packages/sklearn/preprocessing/_encoders.py:808: FutureWarning: `sparse` was renamed to `sparse_output` in version 1.2 and will be removed in 1.4. `sparse_output` is ignored unless you leave `sparse` to its default value.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.5375319650292539"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "import pandas as pd\n",
    "\n",
    "df_data = pd.read_csv(\"processed.csv\")\n",
    "\n",
    "features = df_data.columns.to_list()\n",
    "features.remove('Unnamed: 0')\n",
    "features.remove('HOME_TEAM_WINS')\n",
    "print(features)\n",
    "\n",
    "ct = make_column_transformer(\n",
    "       (OneHotEncoder(handle_unknown='ignore', sparse=False), features),\n",
    "       remainder = 'passthrough'\n",
    ")\n",
    "\n",
    "lgr = LogisticRegression()\n",
    "\n",
    "p1 = make_pipeline(ct, lgr)\n",
    "\n",
    "X = df_data[features]\n",
    "y = df_data.HOME_TEAM_WINS\n",
    "\n",
    "\n",
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "cross_val_score( p1, X , y, cv=5, scoring='accuracy').mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  }
 ],
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