nba_notebook (2).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data import and visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sklearn\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "import warnings\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.experimental import enable_iterative_imputer\n",
    "from sklearn.impute import IterativeImputer\n",
    "from sklearn.impute import KNNImputer\n",
    "from imblearn.over_sampling import SMOTE\n",
    "import imblearn\n",
    "%matplotlib inline\n",
    "warnings.simplefilter(action='ignore', category=FutureWarning)\n",
    "plt.style.use(\"Solarize_Light2\")\n",
    "\n",
    "\n",
    "#common imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "scrolled": true
   },
   "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>league</th>\n",
       "      <th>games</th>\n",
       "      <th>minutes</th>\n",
       "      <th>pts</th>\n",
       "      <th>offReb</th>\n",
       "      <th>defReb</th>\n",
       "      <th>reb</th>\n",
       "      <th>asts</th>\n",
       "      <th>stl</th>\n",
       "      <th>blk</th>\n",
       "      <th>...</th>\n",
       "      <th>ftm</th>\n",
       "      <th>tpa</th>\n",
       "      <th>tpm</th>\n",
       "      <th>totalSeasons</th>\n",
       "      <th>Position</th>\n",
       "      <th>firstSeason</th>\n",
       "      <th>lastSeason</th>\n",
       "      <th>careerEnded</th>\n",
       "      <th>yrsRetired2004</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>408</td>\n",
       "      <td>9825.0</td>\n",
       "      <td>5232.0</td>\n",
       "      <td>476.0</td>\n",
       "      <td>824.0</td>\n",
       "      <td>1300.0</td>\n",
       "      <td>866.0</td>\n",
       "      <td>265.0</td>\n",
       "      <td>175.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1125.0</td>\n",
       "      <td>153.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>1982</td>\n",
       "      <td>1990</td>\n",
       "      <td>1.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>51</td>\n",
       "      <td>NaN</td>\n",
       "      <td>587.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>158.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>143.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1949</td>\n",
       "      <td>1949</td>\n",
       "      <td>1.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>265</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1712.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>72.0</td>\n",
       "      <td>311.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>402.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1946</td>\n",
       "      <td>1950</td>\n",
       "      <td>1.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>133</td>\n",
       "      <td>2587.0</td>\n",
       "      <td>992.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>198.0</td>\n",
       "      <td>253.0</td>\n",
       "      <td>291.0</td>\n",
       "      <td>123.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>...</td>\n",
       "      <td>102.0</td>\n",
       "      <td>349.0</td>\n",
       "      <td>134.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2003</td>\n",
       "      <td>2004</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>28.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2004</td>\n",
       "      <td>2004</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   league  games  minutes     pts  offReb  defReb     reb   asts    stl  \\\n",
       "0       1    408   9825.0  5232.0   476.0   824.0  1300.0  866.0  265.0   \n",
       "1       1     51      NaN   587.0     NaN     NaN     NaN  158.0    NaN   \n",
       "2       1    265      NaN  1712.0     NaN     NaN    72.0  311.0    NaN   \n",
       "3       1    133   2587.0   992.0    55.0   198.0   253.0  291.0  123.0   \n",
       "4       1      8     28.0     7.0     NaN     2.0     2.0    NaN    NaN   \n",
       "\n",
       "     blk  ...     ftm    tpa    tpm  totalSeasons  Position  firstSeason  \\\n",
       "0  175.0  ...  1125.0  153.0   45.0             9         1         1982   \n",
       "1    NaN  ...   143.0    NaN    NaN             1         1         1949   \n",
       "2    NaN  ...   402.0    NaN    NaN             5         1         1946   \n",
       "3   14.0  ...   102.0  349.0  134.0             2         1         2003   \n",
       "4    NaN  ...     3.0    NaN    NaN             1         1         2004   \n",
       "\n",
       "   lastSeason  careerEnded  yrsRetired2004  class  \n",
       "0        1990          1.0            14.0      0  \n",
       "1        1949          1.0            55.0      0  \n",
       "2        1950          1.0            54.0      0  \n",
       "3        2004          NaN             NaN      0  \n",
       "4        2004          NaN             NaN      0  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=pd.read_csv(\"C:/Users/frame/OneDrive\\Desktop\\CSV-Hall-of-fame-train.csv\") #substitute this with path of the dataset on your device\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1.0000e+00 4.0800e+02 9.8250e+03 ... 1.0000e+00 1.4000e+01 0.0000e+00]\n",
      " [1.0000e+00 5.1000e+01 9.3500e+02 ... 1.0000e+00 5.5000e+01 0.0000e+00]\n",
      " [1.0000e+00 2.6500e+02 1.0000e+00 ... 1.0000e+00 5.4000e+01 0.0000e+00]\n",
      " ...\n",
      " [1.0000e+00 8.1400e+02 1.9241e+04 ... 1.0000e+00 3.0000e+01 0.0000e+00]\n",
      " [1.0000e+00 9.8000e+01 1.2200e+03 ... 1.0000e+00 2.0000e+00 0.0000e+00]\n",
      " [1.0000e+00 9.9000e+01 1.3010e+03 ... 1.0000e+00 4.8000e+01 0.0000e+00]]\n"
     ]
    }
   ],
   "source": [
    "imp = KNNImputer(n_neighbors=2, weights=\"uniform\")\n",
    "print(imp.fit_transform(data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>league</th>\n",
       "      <th>games</th>\n",
       "      <th>minutes</th>\n",
       "      <th>pts</th>\n",
       "      <th>offReb</th>\n",
       "      <th>defReb</th>\n",
       "      <th>reb</th>\n",
       "      <th>asts</th>\n",
       "      <th>stl</th>\n",
       "      <th>blk</th>\n",
       "      <th>...</th>\n",
       "      <th>ftm</th>\n",
       "      <th>tpa</th>\n",
       "      <th>tpm</th>\n",
       "      <th>totalSeasons</th>\n",
       "      <th>Position</th>\n",
       "      <th>firstSeason</th>\n",
       "      <th>lastSeason</th>\n",
       "      <th>careerEnded</th>\n",
       "      <th>yrsRetired2004</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>408.0</td>\n",
       "      <td>9825.0</td>\n",
       "      <td>5232.0</td>\n",
       "      <td>476.0</td>\n",
       "      <td>824.0</td>\n",
       "      <td>1300.0</td>\n",
       "      <td>866.0</td>\n",
       "      <td>265.0</td>\n",
       "      <td>175.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1125.0</td>\n",
       "      <td>153.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1982.0</td>\n",
       "      <td>1990.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>935.0</td>\n",
       "      <td>587.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>149.0</td>\n",
       "      <td>188.5</td>\n",
       "      <td>158.0</td>\n",
       "      <td>77.5</td>\n",
       "      <td>13.0</td>\n",
       "      <td>...</td>\n",
       "      <td>143.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1949.0</td>\n",
       "      <td>1949.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>265.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1712.0</td>\n",
       "      <td>175.0</td>\n",
       "      <td>311.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>311.0</td>\n",
       "      <td>264.0</td>\n",
       "      <td>32.5</td>\n",
       "      <td>...</td>\n",
       "      <td>402.0</td>\n",
       "      <td>361.0</td>\n",
       "      <td>103.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1946.0</td>\n",
       "      <td>1950.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>133.0</td>\n",
       "      <td>2587.0</td>\n",
       "      <td>992.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>198.0</td>\n",
       "      <td>253.0</td>\n",
       "      <td>291.0</td>\n",
       "      <td>123.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>...</td>\n",
       "      <td>102.0</td>\n",
       "      <td>349.0</td>\n",
       "      <td>134.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2003.0</td>\n",
       "      <td>2004.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>49.5</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2004.0</td>\n",
       "      <td>2004.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.5</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   league  games  minutes     pts  offReb  defReb     reb   asts    stl  \\\n",
       "0     1.0  408.0   9825.0  5232.0   476.0   824.0  1300.0  866.0  265.0   \n",
       "1     1.0   51.0    935.0   587.0    77.0   149.0   188.5  158.0   77.5   \n",
       "2     1.0  265.0      1.0  1712.0   175.0   311.0    72.0  311.0  264.0   \n",
       "3     1.0  133.0   2587.0   992.0    55.0   198.0   253.0  291.0  123.0   \n",
       "4     1.0    8.0     28.0     7.0     1.5     2.0     2.0    2.0    1.0   \n",
       "\n",
       "     blk  ...     ftm    tpa    tpm  totalSeasons  Position  firstSeason  \\\n",
       "0  175.0  ...  1125.0  153.0   45.0           9.0       1.0       1982.0   \n",
       "1   13.0  ...   143.0   13.0    2.5           1.0       1.0       1949.0   \n",
       "2   32.5  ...   402.0  361.0  103.0           5.0       1.0       1946.0   \n",
       "3   14.0  ...   102.0  349.0  134.0           2.0       1.0       2003.0   \n",
       "4    2.0  ...     3.0    3.0    2.0           1.0       1.0       2004.0   \n",
       "\n",
       "   lastSeason  careerEnded  yrsRetired2004  class  \n",
       "0      1990.0          1.0            14.0    0.0  \n",
       "1      1949.0          1.0            55.0    0.0  \n",
       "2      1950.0          1.0            54.0    0.0  \n",
       "3      2004.0          1.0            49.5    0.0  \n",
       "4      2004.0          1.0             7.5    0.0  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "idf = pd.DataFrame(imp.fit_transform(data))\n",
    "idf.columns=data.columns\n",
    "idf.index=data.index\n",
    "idf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 495 entries, 0 to 494\n",
      "Data columns (total 25 columns):\n",
      " #   Column          Non-Null Count  Dtype  \n",
      "---  ------          --------------  -----  \n",
      " 0   league          495 non-null    float64\n",
      " 1   games           495 non-null    float64\n",
      " 2   minutes         495 non-null    float64\n",
      " 3   pts             495 non-null    float64\n",
      " 4   offReb          495 non-null    float64\n",
      " 5   defReb          495 non-null    float64\n",
      " 6   reb             495 non-null    float64\n",
      " 7   asts            495 non-null    float64\n",
      " 8   stl             495 non-null    float64\n",
      " 9   blk             495 non-null    float64\n",
      " 10  turnover        495 non-null    float64\n",
      " 11  pf              495 non-null    float64\n",
      " 12  fga             495 non-null    float64\n",
      " 13  fgm             495 non-null    float64\n",
      " 14  fta             495 non-null    float64\n",
      " 15  ftm             495 non-null    float64\n",
      " 16  tpa             495 non-null    float64\n",
      " 17  tpm             495 non-null    float64\n",
      " 18  totalSeasons    495 non-null    float64\n",
      " 19  Position        495 non-null    float64\n",
      " 20  firstSeason     495 non-null    float64\n",
      " 21  lastSeason      495 non-null    float64\n",
      " 22  careerEnded     495 non-null    float64\n",
      " 23  yrsRetired2004  495 non-null    float64\n",
      " 24  class           495 non-null    float64\n",
      "dtypes: float64(25)\n",
      "memory usage: 96.8 KB\n"
     ]
    }
   ],
   "source": [
    "data = idf\n",
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>league</th>\n",
       "      <th>games</th>\n",
       "      <th>minutes</th>\n",
       "      <th>pts</th>\n",
       "      <th>offReb</th>\n",
       "      <th>defReb</th>\n",
       "      <th>reb</th>\n",
       "      <th>asts</th>\n",
       "      <th>stl</th>\n",
       "      <th>blk</th>\n",
       "      <th>...</th>\n",
       "      <th>ftm</th>\n",
       "      <th>tpa</th>\n",
       "      <th>tpm</th>\n",
       "      <th>totalSeasons</th>\n",
       "      <th>Position</th>\n",
       "      <th>firstSeason</th>\n",
       "      <th>lastSeason</th>\n",
       "      <th>careerEnded</th>\n",
       "      <th>yrsRetired2004</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>495.000000</td>\n",
       "      <td>495.000000</td>\n",
       "      <td>495.000000</td>\n",
       "      <td>495.000000</td>\n",
       "      <td>495.000000</td>\n",
       "      <td>495.000000</td>\n",
       "      <td>495.000000</td>\n",
       "      <td>495.000000</td>\n",
       "      <td>495.000000</td>\n",
       "      <td>495.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>495.000000</td>\n",
       "      <td>495.000000</td>\n",
       "      <td>495.000000</td>\n",
       "      <td>495.000000</td>\n",
       "      <td>495.000000</td>\n",
       "      <td>495.000000</td>\n",
       "      <td>495.000000</td>\n",
       "      <td>495.0</td>\n",
       "      <td>495.000000</td>\n",
       "      <td>495.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.860606</td>\n",
       "      <td>279.462626</td>\n",
       "      <td>6946.015152</td>\n",
       "      <td>3249.532323</td>\n",
       "      <td>335.208081</td>\n",
       "      <td>716.220202</td>\n",
       "      <td>1388.336364</td>\n",
       "      <td>731.658586</td>\n",
       "      <td>211.879798</td>\n",
       "      <td>117.340404</td>\n",
       "      <td>...</td>\n",
       "      <td>713.523232</td>\n",
       "      <td>213.818182</td>\n",
       "      <td>79.621212</td>\n",
       "      <td>5.357576</td>\n",
       "      <td>1.957576</td>\n",
       "      <td>1974.947475</td>\n",
       "      <td>1979.305051</td>\n",
       "      <td>1.0</td>\n",
       "      <td>27.418182</td>\n",
       "      <td>0.090909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.346708</td>\n",
       "      <td>303.704033</td>\n",
       "      <td>9687.577828</td>\n",
       "      <td>5068.871384</td>\n",
       "      <td>636.867177</td>\n",
       "      <td>1207.201084</td>\n",
       "      <td>2579.085462</td>\n",
       "      <td>1293.805720</td>\n",
       "      <td>308.812715</td>\n",
       "      <td>244.338148</td>\n",
       "      <td>...</td>\n",
       "      <td>1185.164303</td>\n",
       "      <td>508.567204</td>\n",
       "      <td>185.759818</td>\n",
       "      <td>4.461647</td>\n",
       "      <td>0.915575</td>\n",
       "      <td>16.935973</td>\n",
       "      <td>17.555634</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16.109974</td>\n",
       "      <td>0.287771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1946.000000</td>\n",
       "      <td>1946.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>46.000000</td>\n",
       "      <td>402.000000</td>\n",
       "      <td>143.000000</td>\n",
       "      <td>26.500000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>72.250000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>4.500000</td>\n",
       "      <td>...</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1963.000000</td>\n",
       "      <td>1968.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>151.000000</td>\n",
       "      <td>2222.000000</td>\n",
       "      <td>907.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>246.000000</td>\n",
       "      <td>395.000000</td>\n",
       "      <td>190.000000</td>\n",
       "      <td>73.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>199.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1974.000000</td>\n",
       "      <td>1979.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>439.000000</td>\n",
       "      <td>10102.500000</td>\n",
       "      <td>4115.000000</td>\n",
       "      <td>367.000000</td>\n",
       "      <td>864.500000</td>\n",
       "      <td>1632.000000</td>\n",
       "      <td>881.500000</td>\n",
       "      <td>296.500000</td>\n",
       "      <td>107.250000</td>\n",
       "      <td>...</td>\n",
       "      <td>798.000000</td>\n",
       "      <td>140.000000</td>\n",
       "      <td>54.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1989.000000</td>\n",
       "      <td>1995.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1611.000000</td>\n",
       "      <td>47859.000000</td>\n",
       "      <td>31419.000000</td>\n",
       "      <td>6731.000000</td>\n",
       "      <td>10117.000000</td>\n",
       "      <td>23924.000000</td>\n",
       "      <td>9887.000000</td>\n",
       "      <td>2207.000000</td>\n",
       "      <td>2361.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>8531.000000</td>\n",
       "      <td>3434.000000</td>\n",
       "      <td>1301.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2004.000000</td>\n",
       "      <td>2004.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>58.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           league        games       minutes           pts       offReb  \\\n",
       "count  495.000000   495.000000    495.000000    495.000000   495.000000   \n",
       "mean     0.860606   279.462626   6946.015152   3249.532323   335.208081   \n",
       "std      0.346708   303.704033   9687.577828   5068.871384   636.867177   \n",
       "min      0.000000     1.000000      1.000000      1.000000     1.000000   \n",
       "25%      1.000000    46.000000    402.000000    143.000000    26.500000   \n",
       "50%      1.000000   151.000000   2222.000000    907.000000    99.000000   \n",
       "75%      1.000000   439.000000  10102.500000   4115.000000   367.000000   \n",
       "max      1.000000  1611.000000  47859.000000  31419.000000  6731.000000   \n",
       "\n",
       "             defReb           reb         asts          stl          blk  ...  \\\n",
       "count    495.000000    495.000000   495.000000   495.000000   495.000000  ...   \n",
       "mean     716.220202   1388.336364   731.658586   211.879798   117.340404  ...   \n",
       "std     1207.201084   2579.085462  1293.805720   308.812715   244.338148  ...   \n",
       "min        1.000000      1.000000     1.000000     1.000000     1.000000  ...   \n",
       "25%       48.000000     72.250000    29.000000    14.000000     4.500000  ...   \n",
       "50%      246.000000    395.000000   190.000000    73.000000    23.000000  ...   \n",
       "75%      864.500000   1632.000000   881.500000   296.500000   107.250000  ...   \n",
       "max    10117.000000  23924.000000  9887.000000  2207.000000  2361.000000  ...   \n",
       "\n",
       "               ftm          tpa          tpm  totalSeasons    Position  \\\n",
       "count   495.000000   495.000000   495.000000    495.000000  495.000000   \n",
       "mean    713.523232   213.818182    79.621212      5.357576    1.957576   \n",
       "std    1185.164303   508.567204   185.759818      4.461647    0.915575   \n",
       "min       1.000000     1.000000     1.000000      1.000000    1.000000   \n",
       "25%      29.000000     5.000000     2.000000      1.000000    1.000000   \n",
       "50%     199.000000    18.000000     9.000000      4.000000    2.000000   \n",
       "75%     798.000000   140.000000    54.000000      9.000000    3.000000   \n",
       "max    8531.000000  3434.000000  1301.000000     21.000000    3.000000   \n",
       "\n",
       "       firstSeason   lastSeason  careerEnded  yrsRetired2004       class  \n",
       "count   495.000000   495.000000        495.0      495.000000  495.000000  \n",
       "mean   1974.947475  1979.305051          1.0       27.418182    0.090909  \n",
       "std      16.935973    17.555634          0.0       16.109974    0.287771  \n",
       "min    1946.000000  1946.000000          1.0        1.000000    0.000000  \n",
       "25%    1963.000000  1968.000000          1.0       14.000000    0.000000  \n",
       "50%    1974.000000  1979.000000          1.0       27.000000    0.000000  \n",
       "75%    1989.000000  1995.000000          1.0       37.000000    0.000000  \n",
       "max    2004.000000  2004.000000          1.0       58.000000    1.000000  \n",
       "\n",
       "[8 rows x 25 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x=\"class\", data=data)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Targer class is heavily unbalanced."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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>league</th>\n",
       "      <th>games</th>\n",
       "      <th>minutes</th>\n",
       "      <th>pts</th>\n",
       "      <th>offReb</th>\n",
       "      <th>defReb</th>\n",
       "      <th>reb</th>\n",
       "      <th>asts</th>\n",
       "      <th>stl</th>\n",
       "      <th>blk</th>\n",
       "      <th>...</th>\n",
       "      <th>ftm</th>\n",
       "      <th>tpa</th>\n",
       "      <th>tpm</th>\n",
       "      <th>totalSeasons</th>\n",
       "      <th>Position</th>\n",
       "      <th>firstSeason</th>\n",
       "      <th>lastSeason</th>\n",
       "      <th>careerEnded</th>\n",
       "      <th>yrsRetired2004</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>408.000000</td>\n",
       "      <td>9825.000000</td>\n",
       "      <td>5232.000000</td>\n",
       "      <td>476.000000</td>\n",
       "      <td>824.000000</td>\n",
       "      <td>1300.000000</td>\n",
       "      <td>866.000000</td>\n",
       "      <td>265.000000</td>\n",
       "      <td>175.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1125.000000</td>\n",
       "      <td>153.000000</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1982.000000</td>\n",
       "      <td>1990.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>51.000000</td>\n",
       "      <td>935.000000</td>\n",
       "      <td>587.000000</td>\n",
       "      <td>77.000000</td>\n",
       "      <td>149.000000</td>\n",
       "      <td>188.500000</td>\n",
       "      <td>158.000000</td>\n",
       "      <td>77.500000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>143.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1949.000000</td>\n",
       "      <td>1949.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>265.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1712.000000</td>\n",
       "      <td>175.000000</td>\n",
       "      <td>311.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>311.000000</td>\n",
       "      <td>264.000000</td>\n",
       "      <td>32.500000</td>\n",
       "      <td>...</td>\n",
       "      <td>402.000000</td>\n",
       "      <td>361.000000</td>\n",
       "      <td>103.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1946.000000</td>\n",
       "      <td>1950.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>54.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>133.000000</td>\n",
       "      <td>2587.000000</td>\n",
       "      <td>992.000000</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>198.000000</td>\n",
       "      <td>253.000000</td>\n",
       "      <td>291.000000</td>\n",
       "      <td>123.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>102.000000</td>\n",
       "      <td>349.000000</td>\n",
       "      <td>134.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2003.000000</td>\n",
       "      <td>2004.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>49.500000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2004.000000</td>\n",
       "      <td>2004.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.500000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>895</th>\n",
       "      <td>0.0</td>\n",
       "      <td>105.717580</td>\n",
       "      <td>3458.833173</td>\n",
       "      <td>2094.630373</td>\n",
       "      <td>139.533800</td>\n",
       "      <td>229.577947</td>\n",
       "      <td>920.486009</td>\n",
       "      <td>551.002366</td>\n",
       "      <td>177.784391</td>\n",
       "      <td>18.896778</td>\n",
       "      <td>...</td>\n",
       "      <td>518.175799</td>\n",
       "      <td>32.696098</td>\n",
       "      <td>33.929001</td>\n",
       "      <td>12.402148</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1960.793555</td>\n",
       "      <td>1972.195704</td>\n",
       "      <td>1.0</td>\n",
       "      <td>31.804296</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>896</th>\n",
       "      <td>1.0</td>\n",
       "      <td>896.579875</td>\n",
       "      <td>32335.064663</td>\n",
       "      <td>14146.108673</td>\n",
       "      <td>281.907007</td>\n",
       "      <td>914.934486</td>\n",
       "      <td>10793.012061</td>\n",
       "      <td>2515.912699</td>\n",
       "      <td>81.063289</td>\n",
       "      <td>163.629839</td>\n",
       "      <td>...</td>\n",
       "      <td>2799.581816</td>\n",
       "      <td>66.662803</td>\n",
       "      <td>59.848647</td>\n",
       "      <td>12.484941</td>\n",
       "      <td>2.757529</td>\n",
       "      <td>1962.242471</td>\n",
       "      <td>1973.727412</td>\n",
       "      <td>1.0</td>\n",
       "      <td>30.272588</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>897</th>\n",
       "      <td>1.0</td>\n",
       "      <td>924.538486</td>\n",
       "      <td>30022.570331</td>\n",
       "      <td>17093.006156</td>\n",
       "      <td>317.729785</td>\n",
       "      <td>890.136080</td>\n",
       "      <td>4250.205970</td>\n",
       "      <td>6050.073501</td>\n",
       "      <td>476.653784</td>\n",
       "      <td>109.307892</td>\n",
       "      <td>...</td>\n",
       "      <td>4359.603147</td>\n",
       "      <td>96.983268</td>\n",
       "      <td>68.000000</td>\n",
       "      <td>18.115297</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1954.577135</td>\n",
       "      <td>1971.692432</td>\n",
       "      <td>1.0</td>\n",
       "      <td>32.307568</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>898</th>\n",
       "      <td>1.0</td>\n",
       "      <td>545.783236</td>\n",
       "      <td>16515.650953</td>\n",
       "      <td>8287.706204</td>\n",
       "      <td>648.352771</td>\n",
       "      <td>1444.944518</td>\n",
       "      <td>3759.343584</td>\n",
       "      <td>1705.653138</td>\n",
       "      <td>357.185249</td>\n",
       "      <td>154.904694</td>\n",
       "      <td>...</td>\n",
       "      <td>2309.998577</td>\n",
       "      <td>63.958256</td>\n",
       "      <td>19.313328</td>\n",
       "      <td>9.377284</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1962.095306</td>\n",
       "      <td>1970.472590</td>\n",
       "      <td>1.0</td>\n",
       "      <td>33.527410</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>899</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1190.852382</td>\n",
       "      <td>38052.442550</td>\n",
       "      <td>25544.396646</td>\n",
       "      <td>2774.728395</td>\n",
       "      <td>3766.061502</td>\n",
       "      <td>6540.789896</td>\n",
       "      <td>4386.606305</td>\n",
       "      <td>1089.881165</td>\n",
       "      <td>830.711883</td>\n",
       "      <td>...</td>\n",
       "      <td>4285.449974</td>\n",
       "      <td>133.579418</td>\n",
       "      <td>34.237599</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>2.959858</td>\n",
       "      <td>1976.140498</td>\n",
       "      <td>1990.140498</td>\n",
       "      <td>1.0</td>\n",
       "      <td>13.859502</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>900 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     league        games       minutes           pts       offReb  \\\n",
       "0       1.0   408.000000   9825.000000   5232.000000   476.000000   \n",
       "1       1.0    51.000000    935.000000    587.000000    77.000000   \n",
       "2       1.0   265.000000      1.000000   1712.000000   175.000000   \n",
       "3       1.0   133.000000   2587.000000    992.000000    55.000000   \n",
       "4       1.0     8.000000     28.000000      7.000000     1.500000   \n",
       "..      ...          ...           ...           ...          ...   \n",
       "895     0.0   105.717580   3458.833173   2094.630373   139.533800   \n",
       "896     1.0   896.579875  32335.064663  14146.108673   281.907007   \n",
       "897     1.0   924.538486  30022.570331  17093.006156   317.729785   \n",
       "898     1.0   545.783236  16515.650953   8287.706204   648.352771   \n",
       "899     1.0  1190.852382  38052.442550  25544.396646  2774.728395   \n",
       "\n",
       "          defReb           reb         asts          stl         blk  ...  \\\n",
       "0     824.000000   1300.000000   866.000000   265.000000  175.000000  ...   \n",
       "1     149.000000    188.500000   158.000000    77.500000   13.000000  ...   \n",
       "2     311.000000     72.000000   311.000000   264.000000   32.500000  ...   \n",
       "3     198.000000    253.000000   291.000000   123.000000   14.000000  ...   \n",
       "4       2.000000      2.000000     2.000000     1.000000    2.000000  ...   \n",
       "..           ...           ...          ...          ...         ...  ...   \n",
       "895   229.577947    920.486009   551.002366   177.784391   18.896778  ...   \n",
       "896   914.934486  10793.012061  2515.912699    81.063289  163.629839  ...   \n",
       "897   890.136080   4250.205970  6050.073501   476.653784  109.307892  ...   \n",
       "898  1444.944518   3759.343584  1705.653138   357.185249  154.904694  ...   \n",
       "899  3766.061502   6540.789896  4386.606305  1089.881165  830.711883  ...   \n",
       "\n",
       "             ftm         tpa         tpm  totalSeasons  Position  firstSeason  \\\n",
       "0    1125.000000  153.000000   45.000000      9.000000  1.000000  1982.000000   \n",
       "1     143.000000   13.000000    2.500000      1.000000  1.000000  1949.000000   \n",
       "2     402.000000  361.000000  103.000000      5.000000  1.000000  1946.000000   \n",
       "3     102.000000  349.000000  134.000000      2.000000  1.000000  2003.000000   \n",
       "4       3.000000    3.000000    2.000000      1.000000  1.000000  2004.000000   \n",
       "..           ...         ...         ...           ...       ...          ...   \n",
       "895   518.175799   32.696098   33.929001     12.402148  3.000000  1960.793555   \n",
       "896  2799.581816   66.662803   59.848647     12.484941  2.757529  1962.242471   \n",
       "897  4359.603147   96.983268   68.000000     18.115297  1.000000  1954.577135   \n",
       "898  2309.998577   63.958256   19.313328      9.377284  3.000000  1962.095306   \n",
       "899  4285.449974  133.579418   34.237599     15.000000  2.959858  1976.140498   \n",
       "\n",
       "      lastSeason  careerEnded  yrsRetired2004  class  \n",
       "0    1990.000000          1.0       14.000000    0.0  \n",
       "1    1949.000000          1.0       55.000000    0.0  \n",
       "2    1950.000000          1.0       54.000000    0.0  \n",
       "3    2004.000000          1.0       49.500000    0.0  \n",
       "4    2004.000000          1.0        7.500000    0.0  \n",
       "..           ...          ...             ...    ...  \n",
       "895  1972.195704          1.0       31.804296    1.0  \n",
       "896  1973.727412          1.0       30.272588    1.0  \n",
       "897  1971.692432          1.0       32.307568    1.0  \n",
       "898  1970.472590          1.0       33.527410    1.0  \n",
       "899  1990.140498          1.0       13.859502    1.0  \n",
       "\n",
       "[900 rows x 25 columns]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sm = SMOTE(random_state=42)\n",
    "sm.fit_resample(data, data['class'])\n",
    "pd.DataFrame(sm.fit_resample(data, data['class'])[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = pd.DataFrame(sm.fit_resample(data, data['class'])[0])\n",
    "sns.countplot(x=\"class\", data=data)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.to_csv(r'C:/Users/frame/OneDrive/Desktop/Weka projects/NBA_data_fixed.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,10), dpi=80)\n",
    "sns.boxplot(data=data, orient=\"h\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Boxplot graph is useful to show outliers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 25 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.hist(bins=50,figsize=(15,15), color='#58A4B0', )\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data is definetly not standardized."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "class             1.000000\n",
       "ftm               0.784146\n",
       "fta               0.772113\n",
       "pf                0.764651\n",
       "fga               0.764621\n",
       "pts               0.750183\n",
       "fgm               0.735406\n",
       "minutes           0.718829\n",
       "totalSeasons      0.716189\n",
       "games             0.706401\n",
       "asts              0.633031\n",
       "reb               0.610943\n",
       "turnover          0.589696\n",
       "stl               0.514398\n",
       "defReb            0.498579\n",
       "offReb            0.444082\n",
       "blk               0.431398\n",
       "tpm               0.247852\n",
       "tpa               0.228429\n",
       "yrsRetired2004    0.151676\n",
       "league            0.149410\n",
       "Position          0.033719\n",
       "lastSeason       -0.237358\n",
       "firstSeason      -0.466003\n",
       "careerEnded            NaN\n",
       "Name: class, dtype: float64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_matrix = data.corr()\n",
    "corr_matrix[\"class\"].sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 910x910 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(13,13), dpi=70)\n",
    "sns.heatmap(corr_matrix, cmap=\"viridis\", annot=False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I start dividing the sample data into features and targets:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "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>league</th>\n",
       "      <th>games</th>\n",
       "      <th>minutes</th>\n",
       "      <th>pts</th>\n",
       "      <th>offReb</th>\n",
       "      <th>defReb</th>\n",
       "      <th>reb</th>\n",
       "      <th>asts</th>\n",
       "      <th>stl</th>\n",
       "      <th>blk</th>\n",
       "      <th>...</th>\n",
       "      <th>fta</th>\n",
       "      <th>ftm</th>\n",
       "      <th>tpa</th>\n",
       "      <th>tpm</th>\n",
       "      <th>totalSeasons</th>\n",
       "      <th>Position</th>\n",
       "      <th>firstSeason</th>\n",
       "      <th>lastSeason</th>\n",
       "      <th>careerEnded</th>\n",
       "      <th>yrsRetired2004</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>408.0</td>\n",
       "      <td>9825.0</td>\n",
       "      <td>5232.0</td>\n",
       "      <td>476.0</td>\n",
       "      <td>824.0</td>\n",
       "      <td>1300.0</td>\n",
       "      <td>866.0</td>\n",
       "      <td>265.0</td>\n",
       "      <td>175.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1495.0</td>\n",
       "      <td>1125.0</td>\n",
       "      <td>153.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1982.0</td>\n",
       "      <td>1990.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>935.0</td>\n",
       "      <td>587.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>149.0</td>\n",
       "      <td>188.5</td>\n",
       "      <td>158.0</td>\n",
       "      <td>77.5</td>\n",
       "      <td>13.0</td>\n",
       "      <td>...</td>\n",
       "      <td>181.0</td>\n",
       "      <td>143.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1949.0</td>\n",
       "      <td>1949.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>55.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>265.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1712.0</td>\n",
       "      <td>175.0</td>\n",
       "      <td>311.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>311.0</td>\n",
       "      <td>264.0</td>\n",
       "      <td>32.5</td>\n",
       "      <td>...</td>\n",
       "      <td>620.0</td>\n",
       "      <td>402.0</td>\n",
       "      <td>361.0</td>\n",
       "      <td>103.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1946.0</td>\n",
       "      <td>1950.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>54.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>133.0</td>\n",
       "      <td>2587.0</td>\n",
       "      <td>992.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>198.0</td>\n",
       "      <td>253.0</td>\n",
       "      <td>291.0</td>\n",
       "      <td>123.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>...</td>\n",
       "      <td>130.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>349.0</td>\n",
       "      <td>134.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2003.0</td>\n",
       "      <td>2004.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>49.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2004.0</td>\n",
       "      <td>2004.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   league  games  minutes     pts  offReb  defReb     reb   asts    stl  \\\n",
       "0     1.0  408.0   9825.0  5232.0   476.0   824.0  1300.0  866.0  265.0   \n",
       "1     1.0   51.0    935.0   587.0    77.0   149.0   188.5  158.0   77.5   \n",
       "2     1.0  265.0      1.0  1712.0   175.0   311.0    72.0  311.0  264.0   \n",
       "3     1.0  133.0   2587.0   992.0    55.0   198.0   253.0  291.0  123.0   \n",
       "4     1.0    8.0     28.0     7.0     1.5     2.0     2.0    2.0    1.0   \n",
       "\n",
       "     blk  ...     fta     ftm    tpa    tpm  totalSeasons  Position  \\\n",
       "0  175.0  ...  1495.0  1125.0  153.0   45.0           9.0       1.0   \n",
       "1   13.0  ...   181.0   143.0   13.0    2.5           1.0       1.0   \n",
       "2   32.5  ...   620.0   402.0  361.0  103.0           5.0       1.0   \n",
       "3   14.0  ...   130.0   102.0  349.0  134.0           2.0       1.0   \n",
       "4    2.0  ...     5.0     3.0    3.0    2.0           1.0       1.0   \n",
       "\n",
       "   firstSeason  lastSeason  careerEnded  yrsRetired2004  \n",
       "0       1982.0      1990.0          1.0            14.0  \n",
       "1       1949.0      1949.0          1.0            55.0  \n",
       "2       1946.0      1950.0          1.0            54.0  \n",
       "3       2003.0      2004.0          1.0            49.5  \n",
       "4       2004.0      2004.0          1.0             7.5  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = data.iloc[:, :-1]\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.0\n",
       "1    0.0\n",
       "2    0.0\n",
       "3    0.0\n",
       "4    0.0\n",
       "Name: class, dtype: float64"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "targets = data.iloc[:,-1]\n",
    "targets.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data preparation\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option('mode.chained_assignment',None) #to avoid display of chained assignment warning\n",
    "training_data, test_data = train_test_split(data, test_size=0.20)\n",
    "\n",
    "training_targets = training_data.iloc[:, -1] \n",
    "test_targets =  test_data.iloc[:, -1]\n",
    "\n",
    "training_data.drop(columns=[\"class\"], inplace=True)\n",
    "test_data.drop(columns=[\"class\"], inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I split the data set into training (80%) and test data (20%) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "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>league</th>\n",
       "      <th>games</th>\n",
       "      <th>minutes</th>\n",
       "      <th>pts</th>\n",
       "      <th>offReb</th>\n",
       "      <th>defReb</th>\n",
       "      <th>reb</th>\n",
       "      <th>asts</th>\n",
       "      <th>stl</th>\n",
       "      <th>blk</th>\n",
       "      <th>...</th>\n",
       "      <th>fta</th>\n",
       "      <th>ftm</th>\n",
       "      <th>tpa</th>\n",
       "      <th>tpm</th>\n",
       "      <th>totalSeasons</th>\n",
       "      <th>Position</th>\n",
       "      <th>firstSeason</th>\n",
       "      <th>lastSeason</th>\n",
       "      <th>careerEnded</th>\n",
       "      <th>yrsRetired2004</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>929.584588</td>\n",
       "      <td>566.171160</td>\n",
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       "      <td>1961.000000</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>1989.000000</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>1946.000000</td>\n",
       "      <td>1955.000000</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
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       "      <th>509</th>\n",
       "      <td>1.000000</td>\n",
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       "      <td>1366.532600</td>\n",
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       "      <td>5687.373285</td>\n",
       "      <td>2463.498026</td>\n",
       "      <td>356.763477</td>\n",
       "      <td>758.308941</td>\n",
       "      <td>...</td>\n",
       "      <td>3112.802855</td>\n",
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       "      <td>10.116653</td>\n",
       "      <td>1.294449</td>\n",
       "      <td>1953.822204</td>\n",
       "      <td>1962.938858</td>\n",
       "      <td>1.0</td>\n",
       "      <td>41.061142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>360</th>\n",
       "      <td>1.000000</td>\n",
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       "      <td>13353.000000</td>\n",
       "      <td>5545.000000</td>\n",
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       "      <td>455.000000</td>\n",
       "      <td>163.000000</td>\n",
       "      <td>...</td>\n",
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       "      <td>1987.000000</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>...</td>\n",
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       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1978.000000</td>\n",
       "      <td>1978.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>26.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>476.000000</td>\n",
       "      <td>9189.000000</td>\n",
       "      <td>3823.000000</td>\n",
       "      <td>134.000000</td>\n",
       "      <td>543.000000</td>\n",
       "      <td>677.000000</td>\n",
       "      <td>1661.000000</td>\n",
       "      <td>353.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>868.000000</td>\n",
       "      <td>712.000000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1978.000000</td>\n",
       "      <td>1985.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>19.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>249.000000</td>\n",
       "      <td>4147.000000</td>\n",
       "      <td>1448.000000</td>\n",
       "      <td>188.000000</td>\n",
       "      <td>416.000000</td>\n",
       "      <td>604.000000</td>\n",
       "      <td>252.000000</td>\n",
       "      <td>161.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>455.000000</td>\n",
       "      <td>300.000000</td>\n",
       "      <td>147.000000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1998.000000</td>\n",
       "      <td>2001.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>720 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       league       games       minutes          pts       offReb  \\\n",
       "321  1.000000    3.000000     13.000000     2.000000     1.500000   \n",
       "629  0.899222  465.449430  15845.909175  9324.922026  1164.201375   \n",
       "233  1.000000   58.000000   1050.000000   320.000000    37.500000   \n",
       "454  1.000000    3.000000     10.000000     4.000000     1.500000   \n",
       "173  1.000000  598.000000   7036.000000  5859.000000   366.000000   \n",
       "..        ...         ...           ...          ...          ...   \n",
       "509  1.000000  644.410302  21283.186738  8504.654016  1366.532600   \n",
       "360  1.000000  569.000000  13353.000000  5545.000000   903.000000   \n",
       "485  1.000000   12.000000     97.000000    28.000000     9.000000   \n",
       "122  1.000000  476.000000   9189.000000  3823.000000   134.000000   \n",
       "129  1.000000  249.000000   4147.000000  1448.000000   188.000000   \n",
       "\n",
       "          defReb          reb         asts         stl         blk  ...  \\\n",
       "321     1.000000     1.000000     1.000000    1.500000    1.500000  ...   \n",
       "629  2419.675412  4290.947713   929.584588  566.171160  211.877331  ...   \n",
       "233    84.500000   178.000000   114.000000   43.000000    9.000000  ...   \n",
       "454     2.000000     2.000000     1.500000    2.000000    1.000000  ...   \n",
       "173   539.000000  2294.000000   940.000000  256.000000   96.500000  ...   \n",
       "..           ...          ...          ...         ...         ...  ...   \n",
       "509  2873.739779  5687.373285  2463.498026  356.763477  758.308941  ...   \n",
       "360  1214.000000  2117.000000   853.000000  455.000000  163.000000  ...   \n",
       "485     8.000000    17.000000     4.000000    3.000000    3.000000  ...   \n",
       "122   543.000000   677.000000  1661.000000  353.000000   40.000000  ...   \n",
       "129   416.000000   604.000000   252.000000  161.000000   62.000000  ...   \n",
       "\n",
       "             fta          ftm         tpa        tpm  totalSeasons  Position  \\\n",
       "321     2.000000     1.000000    1.000000   2.000000      1.000000  3.000000   \n",
       "629  3400.541704  2650.293830   44.148001  11.116519      7.907002  3.000000   \n",
       "233   137.000000   106.000000   45.000000  12.500000      1.000000  1.000000   \n",
       "454     6.000000     4.000000    1.000000   2.000000      1.000000  3.000000   \n",
       "173  2211.000000  1499.000000  268.500000  86.000000     10.000000  2.000000   \n",
       "..           ...          ...         ...        ...           ...       ...   \n",
       "509  3112.802855  2377.017667   71.983907  16.808527     10.116653  1.294449   \n",
       "360  1642.000000  1292.000000   36.000000   3.000000      8.000000  3.000000   \n",
       "485    12.000000    10.000000    2.500000   1.000000      1.000000  3.000000   \n",
       "122   868.000000   712.000000   48.000000   3.000000      8.000000  1.000000   \n",
       "129   455.000000   300.000000  147.000000  48.000000      4.000000  1.000000   \n",
       "\n",
       "     firstSeason   lastSeason  careerEnded  yrsRetired2004  \n",
       "321  2000.000000  2000.000000          1.0        4.000000  \n",
       "629  1954.814004  1961.721006          1.0       42.278994  \n",
       "233  1961.000000  1961.000000          1.0       43.000000  \n",
       "454  1989.000000  1989.000000          1.0       15.000000  \n",
       "173  1946.000000  1955.000000          1.0       49.000000  \n",
       "..           ...          ...          ...             ...  \n",
       "509  1953.822204  1962.938858          1.0       41.061142  \n",
       "360  1980.000000  1987.000000          1.0       17.000000  \n",
       "485  1978.000000  1978.000000          1.0       26.000000  \n",
       "122  1978.000000  1985.000000          1.0       19.000000  \n",
       "129  1998.000000  2001.000000          1.0        3.000000  \n",
       "\n",
       "[720 rows x 24 columns]"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "training_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Standardization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>StandardScaler()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "StandardScaler()"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaler= StandardScaler()\n",
    "scaler.fit(training_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
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       "      <td>-1.003284</td>\n",
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       "      <td>-0.883876</td>\n",
       "      <td>-0.670984</td>\n",
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       "      <td>-0.979438</td>\n",
       "      <td>-0.991550</td>\n",
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       "      <td>-1.440941</td>\n",
       "      <td>-1.158236</td>\n",
       "      <td>-0.523684</td>\n",
       "      <td>-0.993549</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>-1.044569</td>\n",
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       "      <td>-0.991661</td>\n",
       "      <td>-0.694990</td>\n",
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       "      <td>1.230623</td>\n",
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       "      <td>-0.963087</td>\n",
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       "      <td>-0.269665</td>\n",
       "      <td>-0.412726</td>\n",
       "      <td>-0.323918</td>\n",
       "      <td>-0.408424</td>\n",
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       "      <td>0.357575</td>\n",
       "      <td>-0.806538</td>\n",
       "      <td>-0.981847</td>\n",
       "      <td>-0.869335</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>-0.459861</td>\n",
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       "      <td>-0.919838</td>\n",
       "      <td>-0.989032</td>\n",
       "      <td>-0.688989</td>\n",
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       "      <td>-1.032899</td>\n",
       "      <td>-1.046386</td>\n",
       "      <td>-0.583278</td>\n",
       "      <td>-0.609200</td>\n",
       "      <td>-1.440941</td>\n",
       "      <td>1.230623</td>\n",
       "      <td>0.561438</td>\n",
       "      <td>0.095561</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.204379</td>\n",
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       "    <tr>\n",
       "      <th>718</th>\n",
       "      <td>0.341544</td>\n",
       "      <td>-0.068403</td>\n",
       "      <td>-0.431131</td>\n",
       "      <td>-0.545632</td>\n",
       "      <td>-0.589071</td>\n",
       "      <td>-0.514352</td>\n",
       "      <td>-0.670520</td>\n",
       "      <td>-0.022098</td>\n",
       "      <td>-0.068913</td>\n",
       "      <td>-0.577963</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.666794</td>\n",
       "      <td>-0.645399</td>\n",
       "      <td>-0.499469</td>\n",
       "      <td>-0.598864</td>\n",
       "      <td>-0.059995</td>\n",
       "      <td>-1.158236</td>\n",
       "      <td>0.561438</td>\n",
       "      <td>0.544019</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.687193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>719</th>\n",
       "      <td>0.341544</td>\n",
       "      <td>-0.678443</td>\n",
       "      <td>-0.803178</td>\n",
       "      <td>-0.855916</td>\n",
       "      <td>-0.526972</td>\n",
       "      <td>-0.593817</td>\n",
       "      <td>-0.688617</td>\n",
       "      <td>-0.785475</td>\n",
       "      <td>-0.573664</td>\n",
       "      <td>-0.511948</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.843431</td>\n",
       "      <td>-0.880736</td>\n",
       "      <td>-0.317116</td>\n",
       "      <td>-0.366301</td>\n",
       "      <td>-0.849107</td>\n",
       "      <td>-1.158236</td>\n",
       "      <td>1.838052</td>\n",
       "      <td>1.569063</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.790769</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>720 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       league     games   minutes       pts    offReb    defReb       reb  \\\n",
       "0    0.341544 -1.339543 -1.108225 -1.044830 -0.741444 -0.853487 -0.838101   \n",
       "1   -0.016856 -0.096757  0.060079  0.173172  0.595645  0.659904  0.225379   \n",
       "2    0.341544 -1.191736 -1.031705 -1.003284 -0.700044 -0.801240 -0.794222   \n",
       "3    0.341544 -1.339543 -1.108446 -1.044569 -0.741444 -0.852861 -0.837853   \n",
       "4    0.341544  0.259460 -0.590000 -0.279636 -0.322274 -0.516854 -0.269665   \n",
       "..        ...       ...       ...       ...       ...       ...       ...   \n",
       "715  0.341544  0.384183  0.461294  0.066008  0.828323  0.944017  0.571553   \n",
       "716  0.341544  0.181525 -0.123872 -0.320659  0.295267 -0.094500 -0.313543   \n",
       "717  0.341544 -1.315357 -1.102026 -1.041433 -0.732819 -0.849107 -0.834134   \n",
       "718  0.341544 -0.068403 -0.431131 -0.545632 -0.589071 -0.514352 -0.670520   \n",
       "719  0.341544 -0.678443 -0.803178 -0.855916 -0.526972 -0.593817 -0.688617   \n",
       "\n",
       "         asts       stl       blk  ...       fta       ftm       tpa  \\\n",
       "0   -0.921464 -0.992975 -0.693490  ... -1.037176 -1.051527 -0.586041   \n",
       "1   -0.418369  0.491494 -0.062212  ...  0.416358  0.461769 -0.506564   \n",
       "2   -0.860242 -0.883876 -0.670984  ... -0.979438 -0.991550 -0.504995   \n",
       "3   -0.921193 -0.991661 -0.694990  ... -1.035466 -1.049814 -0.586041   \n",
       "4   -0.412726 -0.323918 -0.408424  ... -0.092401 -0.195859 -0.093319   \n",
       "..        ...       ...       ...  ...       ...       ...       ...   \n",
       "715  0.412685 -0.059020  1.577460  ...  0.293294  0.305672 -0.455292   \n",
       "716 -0.459861  0.199235 -0.208878  ... -0.335759 -0.314098 -0.521572   \n",
       "717 -0.919838 -0.989032 -0.688989  ... -1.032899 -1.046386 -0.583278   \n",
       "718 -0.022098 -0.068913 -0.577963  ... -0.666794 -0.645399 -0.499469   \n",
       "719 -0.785475 -0.573664 -0.511948  ... -0.843431 -0.880736 -0.317116   \n",
       "\n",
       "          tpm  totalSeasons  Position  firstSeason  lastSeason  careerEnded  \\\n",
       "0   -0.604032     -1.440941  1.230623     1.965713    1.504998          0.0   \n",
       "1   -0.556917     -0.078341  1.230623    -0.918540   -0.947357          0.0   \n",
       "2   -0.549767     -1.440941 -1.158236    -0.523684   -0.993549          0.0   \n",
       "3   -0.604032     -1.440941  1.230623     1.263576    0.800280          0.0   \n",
       "4   -0.169914      0.334561  0.036193    -1.481144   -1.377940          0.0   \n",
       "..        ...           ...       ...          ...         ...          ...   \n",
       "715 -0.527500      0.357575 -0.806538    -0.981847   -0.869335          0.0   \n",
       "716 -0.598864     -0.059995  1.230623     0.689099    0.672149          0.0   \n",
       "717 -0.609200     -1.440941  1.230623     0.561438    0.095561          0.0   \n",
       "718 -0.598864     -0.059995 -1.158236     0.561438    0.544019          0.0   \n",
       "719 -0.366301     -0.849107 -1.158236     1.838052    1.569063          0.0   \n",
       "\n",
       "     yrsRetired2004  \n",
       "0         -1.721796  \n",
       "1          0.918440  \n",
       "2          0.968170  \n",
       "3         -0.963087  \n",
       "4          1.382011  \n",
       "..              ...  \n",
       "715        0.834440  \n",
       "716       -0.825140  \n",
       "717       -0.204379  \n",
       "718       -0.687193  \n",
       "719       -1.790769  \n",
       "\n",
       "[720 rows x 24 columns]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "training_scaled_data= pd.DataFrame(scaler.transform(training_data))\n",
    "training_scaled_data.columns = list(df.columns)\n",
    "\n",
    "test_scaled_data= pd.DataFrame(scaler.transform(test_data))\n",
    "test_scaled_data.columns = list(df.columns)\n",
    "\n",
    "df_st = np.concatenate((training_scaled_data.copy(), test_scaled_data.copy()), axis = 0) #to store all the standadized data for later\n",
    "\n",
    "training_scaled_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I decided to convert my data after scaling into Pandas DataFrame to visualize it better."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "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>league</th>\n",
       "      <th>games</th>\n",
       "      <th>minutes</th>\n",
       "      <th>pts</th>\n",
       "      <th>offReb</th>\n",
       "      <th>defReb</th>\n",
       "      <th>reb</th>\n",
       "      <th>asts</th>\n",
       "      <th>stl</th>\n",
       "      <th>blk</th>\n",
       "      <th>...</th>\n",
       "      <th>fta</th>\n",
       "      <th>ftm</th>\n",
       "      <th>tpa</th>\n",
       "      <th>tpm</th>\n",
       "      <th>totalSeasons</th>\n",
       "      <th>Position</th>\n",
       "      <th>firstSeason</th>\n",
       "      <th>lastSeason</th>\n",
       "      <th>careerEnded</th>\n",
       "      <th>yrsRetired2004</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>7.200000e+02</td>\n",
       "      <td>7.200000e+02</td>\n",
       "      <td>7.200000e+02</td>\n",
       "      <td>7.200000e+02</td>\n",
       "      <td>7.200000e+02</td>\n",
       "      <td>7.200000e+02</td>\n",
       "      <td>7.200000e+02</td>\n",
       "      <td>7.200000e+02</td>\n",
       "      <td>7.200000e+02</td>\n",
       "      <td>7.200000e+02</td>\n",
       "      <td>...</td>\n",
       "      <td>7.200000e+02</td>\n",
       "      <td>7.200000e+02</td>\n",
       "      <td>7.200000e+02</td>\n",
       "      <td>7.200000e+02</td>\n",
       "      <td>7.200000e+02</td>\n",
       "      <td>7.200000e+02</td>\n",
       "      <td>7.200000e+02</td>\n",
       "      <td>7.200000e+02</td>\n",
       "      <td>720.0</td>\n",
       "      <td>7.200000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.384638e-16</td>\n",
       "      <td>-6.630499e-17</td>\n",
       "      <td>1.819532e-17</td>\n",
       "      <td>1.942890e-16</td>\n",
       "      <td>3.330669e-17</td>\n",
       "      <td>-3.037694e-17</td>\n",
       "      <td>-7.771561e-17</td>\n",
       "      <td>-7.324388e-17</td>\n",
       "      <td>-1.771731e-16</td>\n",
       "      <td>2.390063e-17</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.158767e-18</td>\n",
       "      <td>-7.894919e-17</td>\n",
       "      <td>-4.417762e-17</td>\n",
       "      <td>-7.501715e-17</td>\n",
       "      <td>1.017704e-17</td>\n",
       "      <td>-1.258253e-16</td>\n",
       "      <td>8.403772e-16</td>\n",
       "      <td>8.258517e-15</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.609823e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.000695e+00</td>\n",
       "      <td>1.000695e+00</td>\n",
       "      <td>1.000695e+00</td>\n",
       "      <td>1.000695e+00</td>\n",
       "      <td>1.000695e+00</td>\n",
       "      <td>1.000695e+00</td>\n",
       "      <td>1.000695e+00</td>\n",
       "      <td>1.000695e+00</td>\n",
       "      <td>1.000695e+00</td>\n",
       "      <td>1.000695e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000695e+00</td>\n",
       "      <td>1.000695e+00</td>\n",
       "      <td>1.000695e+00</td>\n",
       "      <td>1.000695e+00</td>\n",
       "      <td>1.000695e+00</td>\n",
       "      <td>1.000695e+00</td>\n",
       "      <td>1.000695e+00</td>\n",
       "      <td>1.000695e+00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000695e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-3.214786e+00</td>\n",
       "      <td>-1.344918e+00</td>\n",
       "      <td>-1.109110e+00</td>\n",
       "      <td>-1.044960e+00</td>\n",
       "      <td>-7.420187e-01</td>\n",
       "      <td>-8.534866e-01</td>\n",
       "      <td>-8.381007e-01</td>\n",
       "      <td>-9.214636e-01</td>\n",
       "      <td>-9.942897e-01</td>\n",
       "      <td>-6.949900e-01</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.037604e+00</td>\n",
       "      <td>-1.051527e+00</td>\n",
       "      <td>-5.860406e-01</td>\n",
       "      <td>-6.091997e-01</td>\n",
       "      <td>-1.440941e+00</td>\n",
       "      <td>-1.158236e+00</td>\n",
       "      <td>-1.481144e+00</td>\n",
       "      <td>-1.954528e+00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.928716e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3.415440e-01</td>\n",
       "      <td>-9.995871e-01</td>\n",
       "      <td>-9.794434e-01</td>\n",
       "      <td>-9.468127e-01</td>\n",
       "      <td>-6.618073e-01</td>\n",
       "      <td>-7.543812e-01</td>\n",
       "      <td>-7.661152e-01</td>\n",
       "      <td>-8.349132e-01</td>\n",
       "      <td>-8.451401e-01</td>\n",
       "      <td>-6.484793e-01</td>\n",
       "      <td>...</td>\n",
       "      <td>-9.461845e-01</td>\n",
       "      <td>-9.679881e-01</td>\n",
       "      <td>-5.659784e-01</td>\n",
       "      <td>-5.881215e-01</td>\n",
       "      <td>-8.491067e-01</td>\n",
       "      <td>-1.158236e+00</td>\n",
       "      <td>-8.527914e-01</td>\n",
       "      <td>-8.180501e-01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-7.956969e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.415440e-01</td>\n",
       "      <td>1.479227e-02</td>\n",
       "      <td>-1.115417e-01</td>\n",
       "      <td>-1.499948e-01</td>\n",
       "      <td>-3.349242e-01</td>\n",
       "      <td>-2.947770e-01</td>\n",
       "      <td>-2.686735e-01</td>\n",
       "      <td>-3.210993e-01</td>\n",
       "      <td>-2.433164e-01</td>\n",
       "      <td>-4.061693e-01</td>\n",
       "      <td>...</td>\n",
       "      <td>-9.091200e-02</td>\n",
       "      <td>-7.407485e-02</td>\n",
       "      <td>-4.618494e-01</td>\n",
       "      <td>-4.528658e-01</td>\n",
       "      <td>1.372835e-01</td>\n",
       "      <td>3.619338e-02</td>\n",
       "      <td>-8.188313e-02</td>\n",
       "      <td>-7.204580e-02</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.479584e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.415440e-01</td>\n",
       "      <td>8.699790e-01</td>\n",
       "      <td>9.885418e-01</td>\n",
       "      <td>7.908766e-01</td>\n",
       "      <td>3.094162e-01</td>\n",
       "      <td>3.978875e-01</td>\n",
       "      <td>3.594434e-01</td>\n",
       "      <td>5.028556e-01</td>\n",
       "      <td>4.718269e-01</td>\n",
       "      <td>2.914872e-01</td>\n",
       "      <td>...</td>\n",
       "      <td>6.109145e-01</td>\n",
       "      <td>6.386145e-01</td>\n",
       "      <td>6.194701e-02</td>\n",
       "      <td>-1.474883e-02</td>\n",
       "      <td>8.214705e-01</td>\n",
       "      <td>1.230623e+00</td>\n",
       "      <td>6.890995e-01</td>\n",
       "      <td>8.002797e-01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.302232e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3.415440e-01</td>\n",
       "      <td>2.981795e+00</td>\n",
       "      <td>2.422312e+00</td>\n",
       "      <td>3.059674e+00</td>\n",
       "      <td>6.997377e+00</td>\n",
       "      <td>5.476200e+00</td>\n",
       "      <td>5.092420e+00</td>\n",
       "      <td>3.865107e+00</td>\n",
       "      <td>4.805085e+00</td>\n",
       "      <td>6.386641e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>4.035269e+00</td>\n",
       "      <td>3.820872e+00</td>\n",
       "      <td>5.737371e+00</td>\n",
       "      <td>6.109280e+00</td>\n",
       "      <td>2.504620e+00</td>\n",
       "      <td>1.230623e+00</td>\n",
       "      <td>2.221036e+00</td>\n",
       "      <td>1.761259e+00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.002772e+00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             league         games       minutes           pts        offReb  \\\n",
       "count  7.200000e+02  7.200000e+02  7.200000e+02  7.200000e+02  7.200000e+02   \n",
       "mean   3.384638e-16 -6.630499e-17  1.819532e-17  1.942890e-16  3.330669e-17   \n",
       "std    1.000695e+00  1.000695e+00  1.000695e+00  1.000695e+00  1.000695e+00   \n",
       "min   -3.214786e+00 -1.344918e+00 -1.109110e+00 -1.044960e+00 -7.420187e-01   \n",
       "25%    3.415440e-01 -9.995871e-01 -9.794434e-01 -9.468127e-01 -6.618073e-01   \n",
       "50%    3.415440e-01  1.479227e-02 -1.115417e-01 -1.499948e-01 -3.349242e-01   \n",
       "75%    3.415440e-01  8.699790e-01  9.885418e-01  7.908766e-01  3.094162e-01   \n",
       "max    3.415440e-01  2.981795e+00  2.422312e+00  3.059674e+00  6.997377e+00   \n",
       "\n",
       "             defReb           reb          asts           stl           blk  \\\n",
       "count  7.200000e+02  7.200000e+02  7.200000e+02  7.200000e+02  7.200000e+02   \n",
       "mean  -3.037694e-17 -7.771561e-17 -7.324388e-17 -1.771731e-16  2.390063e-17   \n",
       "std    1.000695e+00  1.000695e+00  1.000695e+00  1.000695e+00  1.000695e+00   \n",
       "min   -8.534866e-01 -8.381007e-01 -9.214636e-01 -9.942897e-01 -6.949900e-01   \n",
       "25%   -7.543812e-01 -7.661152e-01 -8.349132e-01 -8.451401e-01 -6.484793e-01   \n",
       "50%   -2.947770e-01 -2.686735e-01 -3.210993e-01 -2.433164e-01 -4.061693e-01   \n",
       "75%    3.978875e-01  3.594434e-01  5.028556e-01  4.718269e-01  2.914872e-01   \n",
       "max    5.476200e+00  5.092420e+00  3.865107e+00  4.805085e+00  6.386641e+00   \n",
       "\n",
       "       ...           fta           ftm           tpa           tpm  \\\n",
       "count  ...  7.200000e+02  7.200000e+02  7.200000e+02  7.200000e+02   \n",
       "mean   ... -2.158767e-18 -7.894919e-17 -4.417762e-17 -7.501715e-17   \n",
       "std    ...  1.000695e+00  1.000695e+00  1.000695e+00  1.000695e+00   \n",
       "min    ... -1.037604e+00 -1.051527e+00 -5.860406e-01 -6.091997e-01   \n",
       "25%    ... -9.461845e-01 -9.679881e-01 -5.659784e-01 -5.881215e-01   \n",
       "50%    ... -9.091200e-02 -7.407485e-02 -4.618494e-01 -4.528658e-01   \n",
       "75%    ...  6.109145e-01  6.386145e-01  6.194701e-02 -1.474883e-02   \n",
       "max    ...  4.035269e+00  3.820872e+00  5.737371e+00  6.109280e+00   \n",
       "\n",
       "       totalSeasons      Position   firstSeason    lastSeason  careerEnded  \\\n",
       "count  7.200000e+02  7.200000e+02  7.200000e+02  7.200000e+02        720.0   \n",
       "mean   1.017704e-17 -1.258253e-16  8.403772e-16  8.258517e-15          0.0   \n",
       "std    1.000695e+00  1.000695e+00  1.000695e+00  1.000695e+00          0.0   \n",
       "min   -1.440941e+00 -1.158236e+00 -1.481144e+00 -1.954528e+00          0.0   \n",
       "25%   -8.491067e-01 -1.158236e+00 -8.527914e-01 -8.180501e-01          0.0   \n",
       "50%    1.372835e-01  3.619338e-02 -8.188313e-02 -7.204580e-02          0.0   \n",
       "75%    8.214705e-01  1.230623e+00  6.890995e-01  8.002797e-01          0.0   \n",
       "max    2.504620e+00  1.230623e+00  2.221036e+00  1.761259e+00          0.0   \n",
       "\n",
       "       yrsRetired2004  \n",
       "count    7.200000e+02  \n",
       "mean     1.609823e-16  \n",
       "std      1.000695e+00  \n",
       "min     -1.928716e+00  \n",
       "25%     -7.956969e-01  \n",
       "50%      1.479584e-02  \n",
       "75%      8.302232e-01  \n",
       "max      2.002772e+00  \n",
       "\n",
       "[8 rows x 24 columns]"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "training_scaled_data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Performing PCA on data that is not standardized could lead to scaling problems, therefore I standardize the data using StandardScaler before applying PCA."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# PCA (Explained Variance Method)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.decomposition import PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "pca = PCA(0.95)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Instead of exhaustively trying every possible number of principal components and evaluating the performance, to choose the best number of principal components for PCA, I decided to input in pca a number between 0 and 1, by doing this \n",
    "PCA selects the minimum number of components such that the amount of explained variance is greater than the percentage specified by n_components."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>PCA(n_components=0.95)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">PCA</label><div class=\"sk-toggleable__content\"><pre>PCA(n_components=0.95)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "PCA(n_components=0.95)"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca.fit(training_scaled_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_pca = pca.transform(training_scaled_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(720, 24)"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "training_scaled_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.57970259, 0.12702931, 0.08455243, 0.05680404, 0.04080484,\n",
       "       0.03754472, 0.02187625, 0.01299859])"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca.explained_variance_ratio_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9613127660886585"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(pca.explained_variance_ratio_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(720, 8)"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_pca.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this case the algorithm has chosen 29 principal components so that the sum of the explained variance is greater than 0.95."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "training_data_pca = pca.transform(training_data)\n",
    "test_data_pca = pca.transform(test_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, I apply the mapping transform also to the test set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "training_features_pca = pd.DataFrame(training_data_pca)\n",
    "test_features_pca = pd.DataFrame(test_data_pca)\n",
    "\n",
    "df_pca = np.concatenate((training_data_pca.copy(), test_data_pca.copy()), axis = 0)\n",
    "\n",
    "training_features_pca.columns = list(df.columns[0:x_pca.shape[1]])\n",
    "test_features_pca.columns = list(df.columns[0:x_pca.shape[1]])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I also convert all the data into Pandas DataFrame."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Random Forest Classifier on PCA data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "import timeit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "training_targets = training_targets.astype(int)\n",
    "test_target = test_targets.astype(int)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This will allow me to make further considerations about performance based on speed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'criterion': 'gini', 'max_depth': 100, 'n_estimators': 200}"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "start = timeit.default_timer() #to keep track of running time\n",
    "param_grid={\n",
    "        'n_estimators' : [100, 200, 500],\n",
    "        'criterion': ['gini'],\n",
    "        'max_depth': [10, 50, 100],\n",
    "        \n",
    "    }\n",
    "\n",
    "grid_pca = GridSearchCV(RandomForestClassifier(), param_grid, cv=12, scoring='roc_auc', refit='True', verbose=0)\n",
    "grid_pca.fit(training_features_pca, training_targets)\n",
    "\n",
    "best_n_estimators_pca = grid_pca.best_params_['n_estimators']\n",
    "grid_pca.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "log_reg_pca = RandomForestClassifier(n_estimators=best_n_estimators_pca, criterion=\"gini\", random_state=42, max_depth=grid_pca.best_params_['max_depth']) \n",
    "log_reg_pca.fit(training_features_pca, training_targets)\n",
    "\n",
    "stop = timeit.default_timer()\n",
    "Time_pca= (stop-start)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And performed RandomForestClassifier with the best parameters obtained."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2160x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn import tree\n",
    "len(log_reg_pca.estimators_)\n",
    "plt.figure(figsize=(30,10))\n",
    "_ = tree.plot_tree(log_reg_pca.estimators_[0], filled=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Random Forest Classifier (without PCA)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'criterion': 'gini', 'max_depth': 100, 'n_estimators': 200}"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "start_1 = timeit.default_timer()\n",
    "\n",
    "param_grid={\n",
    "        'n_estimators' : [100, 200, 500],\n",
    "        'criterion': ['gini'],\n",
    "        'max_depth': [10, 50, 100],\n",
    "        \n",
    "    }\n",
    "\n",
    "\n",
    "grid = GridSearchCV(RandomForestClassifier(), param_grid, cv=12, scoring='roc_auc', refit='True', verbose=0)\n",
    "grid.fit(training_scaled_data, training_targets)\n",
    "\n",
    "best_n_estimators_not_pca = grid.best_params_['n_estimators']\n",
    "grid.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "log_reg = RandomForestClassifier(n_estimators=best_n_estimators_pca, criterion=\"gini\", random_state=42, max_depth=grid_pca.best_params_['max_depth'])\n",
    "log_reg.fit(training_scaled_data, training_targets)\n",
    "\n",
    "stop_1 = timeit.default_timer()\n",
    "Time_not_pca= (stop_1-start_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2160x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn import tree\n",
    "len(log_reg_pca.estimators_)\n",
    "plt.figure(figsize=(30,10))\n",
    "_ = tree.plot_tree(log_reg_pca.estimators_[0], filled=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Performance metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import mean_squared_error, accuracy_score, classification_report, confusion_matrix\n",
    "from sklearn.model_selection import cross_val_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions_pca = log_reg_pca.predict(test_features_pca)\n",
    "predictions_not_pca = log_reg.predict(test_scaled_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.03888888888888889"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_squared_error_pca= mean_squared_error(test_targets, predictions_pca)\n",
    "mean_squared_error_pca"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 0, 1, 1, 1, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1,\n",
       "       1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 0, 1, 0, 0, 0,\n",
       "       0, 1, 0, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 0, 1, 0,\n",
       "       0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 1, 1, 0, 1, 0,\n",
       "       1, 1, 0, 1, 1, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 1, 1, 0,\n",
       "       0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 0, 1,\n",
       "       0, 1, 0, 1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1,\n",
       "       0, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1,\n",
       "       1, 1, 0, 0])"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions_pca"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9611111111111111"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_pca = accuracy_score(test_targets, predictions_pca)\n",
    "accuracy_pca"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(15,6))\n",
    "\n",
    "ax1 = fig.add_subplot(121)\n",
    "clf_report = classification_report(test_targets, predictions_pca, output_dict=True)\n",
    "sns.heatmap(pd.DataFrame(clf_report).iloc[:-1, :].T, linewidths=2, annot=True)\n",
    "\n",
    "ax2 = fig.add_subplot(122)\n",
    "cm = confusion_matrix(test_targets, predictions_pca)\n",
    "sns.heatmap(cm, annot=True, linewidths=3, square = True, cmap = 'Blues_r')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here I show the classification report including differnt perfomance measures such as recall and f-1 score and the confusion matrix that shows if the model is mislabeling one class as another one."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cross_validation_accuracy = 0.9680555555555556\n"
     ]
    }
   ],
   "source": [
    "cross_val_score_train_pca =  cross_val_score(log_reg_pca, training_data_pca, training_targets, cv=10).mean()\n",
    "print(f'cross_validation_accuracy = {cross_val_score_train_pca}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cross_validation_roc_auc = 0.9963343402926738\n"
     ]
    }
   ],
   "source": [
    "cross_val_score_train_pca =  cross_val_score(log_reg_pca, training_data_pca, training_targets, scoring='roc_auc', cv=10).mean()\n",
    "print(f'cross_validation_roc_auc = {cross_val_score_train_pca}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then I show the same performance measures relative to the LogisticRegression without PCA data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.022222222222222223"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_squared_error_not_pca= mean_squared_error(test_targets, predictions_not_pca)\n",
    "mean_squared_error_not_pca"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9777777777777777"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_not_pca = accuracy_score(test_targets, predictions_not_pca)\n",
    "accuracy_not_pca"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(15,6))\n",
    "\n",
    "ax1 = fig.add_subplot(121)\n",
    "clf_report = classification_report(test_targets, predictions_not_pca, output_dict=True)\n",
    "sns.heatmap(pd.DataFrame(clf_report).iloc[:-1, :].T, linewidths=2, annot=True)\n",
    "\n",
    "ax2 = fig.add_subplot(122)\n",
    "cm = confusion_matrix(test_targets, predictions_not_pca)\n",
    "sns.heatmap(cm, annot=True, linewidths=3, square = True, cmap = 'Blues_r')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cross_validation_accuracy = 0.975\n"
     ]
    }
   ],
   "source": [
    "cross_val_score_train =  cross_val_score(log_reg, training_scaled_data, training_targets, cv=10).mean()\n",
    "print(f'cross_validation_accuracy = {cross_val_score_train}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cross_validation_roc_auc = 0.9983794508794508\n"
     ]
    }
   ],
   "source": [
    "cross_val_score_train =  cross_val_score(log_reg, training_scaled_data, training_targets,scoring='roc_auc', cv=10).mean()\n",
    "print(f'cross_validation_roc_auc = {cross_val_score_train}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The accuracy in both cases is pretty high."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pipeline strategy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this section I decided to implement a different strategy in order to find the optimal vallue for the model and the optimal number of principal components:\n",
    "\n",
    "I use a pipeline to optimize my parameters, including the number of principal components, using GridSearchCV, so that I can also check my results by cross validation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn import linear_model, decomposition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "start_3 = timeit.default_timer()\n",
    "\n",
    "sc = StandardScaler() #scaler object\n",
    "\n",
    "pca = decomposition.PCA() #PCA object\n",
    "\n",
    "RandomForest = sklearn.ensemble.RandomForestClassifier #RandomForestClassifier object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "pipe= Pipeline(steps=[('sc', sc),\n",
    "                     ('pca', pca),\n",
    "                     ('classifier', RandomForestClassifier(random_state = 42))])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here I implemented the pipeline using StandardScaler and PCA:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "hyperparameters = { \n",
    "                    'classifier__n_estimators': [20, 30, 50],\n",
    "                    'classifier__max_depth': [2, 4],\n",
    "                    'classifier__min_samples_leaf': [2, 4]\n",
    "                  }"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These are the parameters to optimize and by using scoring=None in GridSearchCV, the strategy to evaluate performance will be the roc_auc."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = GridSearchCV(pipe, hyperparameters, cv=3)\n",
    "\n",
    "clf.fit(df, targets)\n",
    "\n",
    "stop_3 = timeit.default_timer()\n",
    "Time_pipeline= (stop_3-start_3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best classifier__n_estimators: 30\n",
      "Best classifier__max_depth: 4\n",
      "Best classifier__min_samples_leaf: 2\n",
      "\n",
      "RandomForestClassifier(max_depth=4, min_samples_leaf=2, n_estimators=30,\n",
      "                       random_state=42)\n"
     ]
    }
   ],
   "source": [
    "print('Best classifier__n_estimators:', clf.best_estimator_.get_params()['classifier__n_estimators'])\n",
    "print('Best classifier__max_depth:', clf.best_estimator_.get_params()['classifier__max_depth'])\n",
    "print('Best classifier__min_samples_leaf:', clf.best_estimator_.get_params()['classifier__min_samples_leaf'])\n",
    "print(); print(clf.best_estimator_.get_params()['classifier'])\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "CV_result: [0.9837037  0.99432099 0.99679012 0.99111111 0.97987654]\n",
      "\n",
      "Mean of CV_Result: 0.9891604938271603\n",
      "\n",
      "Standard Deviation of CV_Result: 0.006397111068748994\n"
     ]
    }
   ],
   "source": [
    "CV_Result = cross_val_score(clf, df, targets, scoring='roc_auc',cv=5, n_jobs=-1)\n",
    "print(); print('CV_result:', CV_Result)\n",
    "print(); print('Mean of CV_Result:',CV_Result.mean())\n",
    "print(); print('Standard Deviation of CV_Result:',CV_Result.std())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I also use cross validation to check the Cross Validation score of our RandomForestClassifier."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Alternative models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this section I try to choose an alternative model, based on mean_squared_error, so I randomly split my data again and I perform PCA."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option('mode.chained_assignment',None) #to avoid display of chained assignment warning\n",
    "training_data, test_data = train_test_split(data, test_size=0.20)\n",
    "\n",
    "training_targets = training_data.iloc[:, 0] \n",
    "test_targets =  test_data.iloc[:, 0]\n",
    "\n",
    "training_data.drop(columns=[\"class\"], inplace=True)\n",
    "test_data.drop(columns=[\"class\"], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "training_targets = training_targets.astype(int)\n",
    "test_target = test_targets.astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      ],
      "text/plain": [
       "StandardScaler()"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaler= StandardScaler()\n",
    "scaler.fit(training_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "training_scaled_data= pd.DataFrame(scaler.transform(training_data))\n",
    "test_scaled_data= pd.DataFrame(scaler.transform(test_data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Support Vector Machine Regressor:0.00941414906122437\n",
      "Gradient Boosting Regressor:0.007972805791106687\n",
      "RandomForestRegressor:0.007973504673506477\n",
      "RandomForestClassifier:0.007973504673506477\n",
      "AdaBoost Classifier:0.007973504673506477\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.svm import SVR\n",
    "from sklearn.ensemble import GradientBoostingRegressor\n",
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "\n",
    "\n",
    "models=dict()\n",
    "\n",
    "models[\"Support Vector Machine Regressor\"]=SVR()\n",
    "models[\"Gradient Boosting Regressor\"]=GradientBoostingRegressor()\n",
    "models[\"RandomForestRegressor\"]=RandomForestRegressor()\n",
    "models[\"RandomForestClassifier\"]=RandomForestClassifier()\n",
    "models[\"AdaBoost Classifier\"]=AdaBoostClassifier()\n",
    "\n",
    "for model in models:\n",
    "    model_inuse=models[model].fit(training_scaled_data,training_targets)\n",
    "    prediction=model_inuse.predict(test_scaled_data)\n",
    "    print(f\"{model}:{mean_squared_error(test_targets,prediction)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Random Forest Classifier seems to achieve the lowest MSE."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conclusion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running time without PCA:161.70059130000004\n"
     ]
    }
   ],
   "source": [
    "print(f'Running time without PCA:{Time_not_pca}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running time with method of explained_variance:141.68254290000004\n"
     ]
    }
   ],
   "source": [
    "print(f'Running time with method of explained_variance:{Time_pca}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running time with pipeline method:7.790022000000022\n"
     ]
    }
   ],
   "source": [
    "print(f'Running time with pipeline method:{Time_pipeline}')"
   ]
  }
 ],
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