project.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "002b9283",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.metrics import r2_score\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3a6793fb",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('NBA_dataset_new.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "328de05d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 9898 entries, 0 to 9897\n",
      "Data columns (total 54 columns):\n",
      " #   Column              Non-Null Count  Dtype  \n",
      "---  ------              --------------  -----  \n",
      " 0   Unnamed: 0          9898 non-null   int64  \n",
      " 1   Player              9898 non-null   object \n",
      " 2   G                   9898 non-null   float64\n",
      " 3   GS                  9898 non-null   float64\n",
      " 4   MP                  9898 non-null   float64\n",
      " 5   FG                  9898 non-null   float64\n",
      " 6   FGA                 9898 non-null   float64\n",
      " 7   3P                  9898 non-null   float64\n",
      " 8   3PA                 9898 non-null   float64\n",
      " 9   2P                  9898 non-null   float64\n",
      " 10  2PA                 9898 non-null   float64\n",
      " 11  FT                  9898 non-null   float64\n",
      " 12  FTA                 9898 non-null   float64\n",
      " 13  ORB                 9898 non-null   float64\n",
      " 14  DRB                 9898 non-null   float64\n",
      " 15  TRB                 9898 non-null   float64\n",
      " 16  AST                 9898 non-null   float64\n",
      " 17  STL                 9898 non-null   float64\n",
      " 18  BLK                 9898 non-null   float64\n",
      " 19  TOV                 9898 non-null   float64\n",
      " 20  PF                  9898 non-null   float64\n",
      " 21  PTS                 9898 non-null   float64\n",
      " 22  Age                 9898 non-null   float64\n",
      " 23  PER                 9897 non-null   float64\n",
      " 24  TS%                 9888 non-null   float64\n",
      " 25  3PAr                9885 non-null   float64\n",
      " 26  FTr                 9885 non-null   float64\n",
      " 27  ORB%                9897 non-null   float64\n",
      " 28  DRB%                9897 non-null   float64\n",
      " 29  TRB%                9897 non-null   float64\n",
      " 30  AST%                9897 non-null   float64\n",
      " 31  STL%                9897 non-null   float64\n",
      " 32  BLK%                9897 non-null   float64\n",
      " 33  TOV%                9890 non-null   float64\n",
      " 34  USG%                9897 non-null   float64\n",
      " 35  OWS                 9898 non-null   float64\n",
      " 36  DWS                 9898 non-null   float64\n",
      " 37  WS                  9898 non-null   float64\n",
      " 38  WS/48               9897 non-null   float64\n",
      " 39  OBPM                9898 non-null   float64\n",
      " 40  DBPM                9898 non-null   float64\n",
      " 41  BPM                 9898 non-null   float64\n",
      " 42  VORP                9898 non-null   float64\n",
      " 43  FG%                 9885 non-null   float64\n",
      " 44  3P%                 8571 non-null   float64\n",
      " 45  2P%                 9881 non-null   float64\n",
      " 46  eFG%                9885 non-null   float64\n",
      " 47  FT%                 9775 non-null   float64\n",
      " 48  Pos                 9898 non-null   object \n",
      " 49  height              9898 non-null   float64\n",
      " 50  weight              9898 non-null   float64\n",
      " 51  seasonStartYear     9898 non-null   int64  \n",
      " 52  salary              9898 non-null   int64  \n",
      " 53  inflationAdjSalary  9898 non-null   int64  \n",
      "dtypes: float64(48), int64(4), object(2)\n",
      "memory usage: 4.1+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f6d78ebe",
   "metadata": {},
   "outputs": [],
   "source": [
    "def drop_missing_values(df, col):\n",
    "    df.dropna(subset=[col], inplace=True)\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c2b0813f",
   "metadata": {},
   "outputs": [],
   "source": [
    "for column in df.columns:\n",
    "    drop_missing_values(df, column)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0248567f",
   "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>Unnamed: 0</th>\n",
       "      <th>Player</th>\n",
       "      <th>G</th>\n",
       "      <th>GS</th>\n",
       "      <th>MP</th>\n",
       "      <th>FG</th>\n",
       "      <th>FGA</th>\n",
       "      <th>3P</th>\n",
       "      <th>3PA</th>\n",
       "      <th>2P</th>\n",
       "      <th>...</th>\n",
       "      <th>3P%</th>\n",
       "      <th>2P%</th>\n",
       "      <th>eFG%</th>\n",
       "      <th>FT%</th>\n",
       "      <th>Pos</th>\n",
       "      <th>height</th>\n",
       "      <th>weight</th>\n",
       "      <th>seasonStartYear</th>\n",
       "      <th>salary</th>\n",
       "      <th>inflationAdjSalary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>A.C. Green</td>\n",
       "      <td>82.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>2709.0</td>\n",
       "      <td>385.0</td>\n",
       "      <td>806.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>372.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.283</td>\n",
       "      <td>0.489</td>\n",
       "      <td>0.486</td>\n",
       "      <td>0.751</td>\n",
       "      <td>PF</td>\n",
       "      <td>203.0</td>\n",
       "      <td>106.0</td>\n",
       "      <td>1990</td>\n",
       "      <td>1750000</td>\n",
       "      <td>3991872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>A.C. Green</td>\n",
       "      <td>82.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>2164.0</td>\n",
       "      <td>258.0</td>\n",
       "      <td>542.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>247.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.200</td>\n",
       "      <td>0.507</td>\n",
       "      <td>0.486</td>\n",
       "      <td>0.738</td>\n",
       "      <td>PF</td>\n",
       "      <td>203.0</td>\n",
       "      <td>106.0</td>\n",
       "      <td>1991</td>\n",
       "      <td>1750000</td>\n",
       "      <td>3812825</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>A.C. Green</td>\n",
       "      <td>82.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>2902.0</td>\n",
       "      <td>382.0</td>\n",
       "      <td>803.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>370.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.214</td>\n",
       "      <td>0.495</td>\n",
       "      <td>0.483</td>\n",
       "      <td>0.744</td>\n",
       "      <td>PF</td>\n",
       "      <td>203.0</td>\n",
       "      <td>106.0</td>\n",
       "      <td>1992</td>\n",
       "      <td>1750000</td>\n",
       "      <td>3698603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>A.C. Green</td>\n",
       "      <td>82.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>2819.0</td>\n",
       "      <td>379.0</td>\n",
       "      <td>706.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>363.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.348</td>\n",
       "      <td>0.550</td>\n",
       "      <td>0.548</td>\n",
       "      <td>0.739</td>\n",
       "      <td>PF</td>\n",
       "      <td>203.0</td>\n",
       "      <td>106.0</td>\n",
       "      <td>1993</td>\n",
       "      <td>1885000</td>\n",
       "      <td>3868048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>A.C. Green</td>\n",
       "      <td>82.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>2825.0</td>\n",
       "      <td>465.0</td>\n",
       "      <td>926.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>457.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.229</td>\n",
       "      <td>0.513</td>\n",
       "      <td>0.506</td>\n",
       "      <td>0.735</td>\n",
       "      <td>PF</td>\n",
       "      <td>203.0</td>\n",
       "      <td>106.0</td>\n",
       "      <td>1994</td>\n",
       "      <td>6472600</td>\n",
       "      <td>12958801</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 54 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0      Player     G    GS      MP     FG    FGA    3P   3PA  \\\n",
       "0           0  A.C. Green  82.0  82.0  2709.0  385.0  806.0  13.0  46.0   \n",
       "1           1  A.C. Green  82.0  21.0  2164.0  258.0  542.0  11.0  55.0   \n",
       "2           2  A.C. Green  82.0  53.0  2902.0  382.0  803.0  12.0  56.0   \n",
       "3           3  A.C. Green  82.0  55.0  2819.0  379.0  706.0  16.0  46.0   \n",
       "4           4  A.C. Green  82.0  55.0  2825.0  465.0  926.0   8.0  35.0   \n",
       "\n",
       "      2P  ...    3P%    2P%   eFG%    FT%  Pos  height  weight  \\\n",
       "0  372.0  ...  0.283  0.489  0.486  0.751   PF   203.0   106.0   \n",
       "1  247.0  ...  0.200  0.507  0.486  0.738   PF   203.0   106.0   \n",
       "2  370.0  ...  0.214  0.495  0.483  0.744   PF   203.0   106.0   \n",
       "3  363.0  ...  0.348  0.550  0.548  0.739   PF   203.0   106.0   \n",
       "4  457.0  ...  0.229  0.513  0.506  0.735   PF   203.0   106.0   \n",
       "\n",
       "   seasonStartYear   salary  inflationAdjSalary  \n",
       "0             1990  1750000             3991872  \n",
       "1             1991  1750000             3812825  \n",
       "2             1992  1750000             3698603  \n",
       "3             1993  1885000             3868048  \n",
       "4             1994  6472600            12958801  \n",
       "\n",
       "[5 rows x 54 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "52cb4d65",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8516, 54)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b0a429c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(df.columns[0], axis=1, inplace=True)\n",
    "df.drop(columns='salary', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a66b07b4",
   "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>Player</th>\n",
       "      <th>G</th>\n",
       "      <th>GS</th>\n",
       "      <th>MP</th>\n",
       "      <th>FG</th>\n",
       "      <th>FGA</th>\n",
       "      <th>3P</th>\n",
       "      <th>3PA</th>\n",
       "      <th>2P</th>\n",
       "      <th>2PA</th>\n",
       "      <th>...</th>\n",
       "      <th>3P%</th>\n",
       "      <th>2P%</th>\n",
       "      <th>eFG%</th>\n",
       "      <th>FT%</th>\n",
       "      <th>Pos</th>\n",
       "      <th>height</th>\n",
       "      <th>weight</th>\n",
       "      <th>seasonStartYear</th>\n",
       "      <th>inflationAdjSalary</th>\n",
       "      <th>Pos_code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A.C. Green</td>\n",
       "      <td>82.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>2709.0</td>\n",
       "      <td>385.0</td>\n",
       "      <td>806.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>372.0</td>\n",
       "      <td>760.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.283</td>\n",
       "      <td>0.489</td>\n",
       "      <td>0.486</td>\n",
       "      <td>0.751</td>\n",
       "      <td>PF</td>\n",
       "      <td>203.0</td>\n",
       "      <td>106.0</td>\n",
       "      <td>1990</td>\n",
       "      <td>3991872</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A.C. Green</td>\n",
       "      <td>82.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>2164.0</td>\n",
       "      <td>258.0</td>\n",
       "      <td>542.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>247.0</td>\n",
       "      <td>487.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.200</td>\n",
       "      <td>0.507</td>\n",
       "      <td>0.486</td>\n",
       "      <td>0.738</td>\n",
       "      <td>PF</td>\n",
       "      <td>203.0</td>\n",
       "      <td>106.0</td>\n",
       "      <td>1991</td>\n",
       "      <td>3812825</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A.C. Green</td>\n",
       "      <td>82.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>2902.0</td>\n",
       "      <td>382.0</td>\n",
       "      <td>803.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>370.0</td>\n",
       "      <td>747.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.214</td>\n",
       "      <td>0.495</td>\n",
       "      <td>0.483</td>\n",
       "      <td>0.744</td>\n",
       "      <td>PF</td>\n",
       "      <td>203.0</td>\n",
       "      <td>106.0</td>\n",
       "      <td>1992</td>\n",
       "      <td>3698603</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A.C. Green</td>\n",
       "      <td>82.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>2819.0</td>\n",
       "      <td>379.0</td>\n",
       "      <td>706.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>363.0</td>\n",
       "      <td>660.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.348</td>\n",
       "      <td>0.550</td>\n",
       "      <td>0.548</td>\n",
       "      <td>0.739</td>\n",
       "      <td>PF</td>\n",
       "      <td>203.0</td>\n",
       "      <td>106.0</td>\n",
       "      <td>1993</td>\n",
       "      <td>3868048</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A.C. Green</td>\n",
       "      <td>82.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>2825.0</td>\n",
       "      <td>465.0</td>\n",
       "      <td>926.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>457.0</td>\n",
       "      <td>891.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.229</td>\n",
       "      <td>0.513</td>\n",
       "      <td>0.506</td>\n",
       "      <td>0.735</td>\n",
       "      <td>PF</td>\n",
       "      <td>203.0</td>\n",
       "      <td>106.0</td>\n",
       "      <td>1994</td>\n",
       "      <td>12958801</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 53 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Player     G    GS      MP     FG    FGA    3P   3PA     2P    2PA  \\\n",
       "0  A.C. Green  82.0  82.0  2709.0  385.0  806.0  13.0  46.0  372.0  760.0   \n",
       "1  A.C. Green  82.0  21.0  2164.0  258.0  542.0  11.0  55.0  247.0  487.0   \n",
       "2  A.C. Green  82.0  53.0  2902.0  382.0  803.0  12.0  56.0  370.0  747.0   \n",
       "3  A.C. Green  82.0  55.0  2819.0  379.0  706.0  16.0  46.0  363.0  660.0   \n",
       "4  A.C. Green  82.0  55.0  2825.0  465.0  926.0   8.0  35.0  457.0  891.0   \n",
       "\n",
       "   ...    3P%    2P%   eFG%    FT%  Pos  height  weight  seasonStartYear  \\\n",
       "0  ...  0.283  0.489  0.486  0.751   PF   203.0   106.0             1990   \n",
       "1  ...  0.200  0.507  0.486  0.738   PF   203.0   106.0             1991   \n",
       "2  ...  0.214  0.495  0.483  0.744   PF   203.0   106.0             1992   \n",
       "3  ...  0.348  0.550  0.548  0.739   PF   203.0   106.0             1993   \n",
       "4  ...  0.229  0.513  0.506  0.735   PF   203.0   106.0             1994   \n",
       "\n",
       "   inflationAdjSalary  Pos_code  \n",
       "0             3991872         2  \n",
       "1             3812825         2  \n",
       "2             3698603         2  \n",
       "3             3868048         2  \n",
       "4            12958801         2  \n",
       "\n",
       "[5 rows x 53 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Pos'] = df['Pos'].astype('category')\n",
    "df['Pos_code'] = df['Pos'].cat.codes\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c43b1652",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_numeric = df.select_dtypes(include='number')\n",
    "corr = df_numeric.corr()\n",
    "inflationAdjSalary_corr = corr['inflationAdjSalary'].sort_values(ascending=False)\n",
    "# inflationAdjSalary_corr[:11]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "9464834c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1200x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(12,10))\n",
    "features = inflationAdjSalary_corr.index[:11]\n",
    "\n",
    "df_select = df[features]\n",
    "\n",
    "corr = df_select.corr()\n",
    "\n",
    "# Generate a heatmap of the correlation matrix\n",
    "sns.heatmap(corr, annot=True, cmap='coolwarm', ax=ax)\n",
    "plt.title('Correlation Heatmap')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "4a872524",
   "metadata": {},
   "outputs": [],
   "source": [
    "def remove_outliers(df, column_name):\n",
    "    \"\"\"\n",
    "    Remove outliers from a given column in a pandas DataFrame.\n",
    "\n",
    "    Parameters:\n",
    "    df (pandas.DataFrame): the input DataFrame\n",
    "    column_name (str): the name of the column to remove outliers from\n",
    "\n",
    "    Returns:\n",
    "    pandas.DataFrame: the DataFrame with outliers removed\n",
    "    \"\"\"\n",
    "    # Calculate the first and third quartiles\n",
    "    q1 = df[column_name].quantile(0.25)\n",
    "    q3 = df[column_name].quantile(0.75)\n",
    "\n",
    "    # Calculate the interquartile range\n",
    "    iqr = q3 - q1\n",
    "\n",
    "    # Define the outlier range\n",
    "    outlier_range = (q1 - 1.5 * iqr, q3 + 1.5 * iqr)\n",
    "\n",
    "    # Remove outliers from the specified column\n",
    "    df = df[(df[column_name] >= outlier_range[0]) & (df[column_name] <= outlier_range[1])]\n",
    "\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "d1734903",
   "metadata": {},
   "outputs": [],
   "source": [
    "for column in features:\n",
    "    df = remove_outliers(df, column)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "bf2a16d1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6409, 53)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b830a5f2",
   "metadata": {},
   "source": [
    "# Train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "50786dff",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.model_selection import GridSearchCV, train_test_split\n",
    "from sklearn.metrics import mean_squared_error, r2_score, mean_absolute_error\n",
    "from sklearn.linear_model import Lasso, Ridge\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.preprocessing import StandardScaler, MinMaxScaler\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "import numpy as np\n",
    "\n",
    "def mae(y_true, y_pred):\n",
    "    mae_score = mean_absolute_error(y_true, y_pred)\n",
    "    return mae_score\n",
    "\n",
    "def rmse(y_true, y_pred):\n",
    "    rmse_score = mean_squared_error(y_true, y_pred, squared=False)\n",
    "    return rmse_score\n",
    "\n",
    "def r2(y_true, y_pred):\n",
    "    r2 = r2_score(y_true, y_pred)\n",
    "    return r2\n",
    "\n",
    "def plot_curves(model):\n",
    "    train_errors = []\n",
    "    val_errors = []\n",
    "    batch_size = 100\n",
    "    train_sizes = []\n",
    "    for i in range(batch_size, len(X_train)+1, batch_size):\n",
    "        train_sizes.append(i)\n",
    "        lr.fit(X_train[:i], y_train[:i])\n",
    "        y_train_pred = lr.predict(X_train[:i])\n",
    "        y_val_pred = lr.predict(X_val)\n",
    "\n",
    "        train_error = mae(y_train[:i], y_train_pred)\n",
    "        val_error = mae(y_val, y_val_pred)\n",
    "        train_errors.append(train_error)\n",
    "        val_errors.append(val_error)\n",
    "    plt.plot(train_sizes, train_errors)\n",
    "    plt.plot(train_sizes, val_errors)\n",
    "    plt.show()\n",
    "\n",
    "def train(model, X_train, y_train):\n",
    "    model.fit(X_train, y_train)\n",
    "    return model\n",
    "\n",
    "def evaluate(model, X_val, y_val):\n",
    "    y_pred = model.predict(X_val)\n",
    "    print(\"mae: \", mae(y_val, y_pred))\n",
    "    print(\"r2: \", r2(y_val, y_pred))\n",
    "    print(\"rmse: \", rmse(y_val, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "948d3b46",
   "metadata": {},
   "outputs": [],
   "source": [
    "new_df = df_numeric[features]\n",
    "# new_df = df_numeric.dropna().reset_index(drop=True)\n",
    "X = new_df.loc[:, ~new_df.columns.isin(['inflationAdjSalary'])]\n",
    "\n",
    "y = new_df.loc[:, new_df.columns.isin(['inflationAdjSalary'])]\n",
    "\n",
    "scaler = MinMaxScaler()\n",
    "X_normalized = scaler.fit_transform(X)\n",
    "X_train, X_val, y_train, y_val = train_test_split(X_normalized, y, test_size=0.3, random_state=42)\n",
    "\n",
    "# scaler = StandardScaler()\n",
    "# X_scaled = scaler.fit_transform(X)\n",
    "# X_train, X_val, y_train, y_val = train_test_split(X_scaled, y, test_size=0.3, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "d2665659",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mae:  3612229.9497144967\n",
      "r2:  0.3528507467340527\n",
      "rmse:  5084494.844252467\n",
      "mae:  3799337.202817964\n",
      "r2:  0.35225639902517203\n",
      "rmse:  5350605.339270767\n"
     ]
    }
   ],
   "source": [
    "lr = LinearRegression()\n",
    "lr = train(lr, X_train, y_train)\n",
    "evaluate(lr, X_train, y_train)\n",
    "evaluate(lr, X_val, y_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "b0394939",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mae:  3616443.1288263677\n",
      "r2:  0.3473595819446532\n",
      "rmse:  5106020.653698751\n",
      "mae:  3795398.936682947\n",
      "r2:  0.35219715953403163\n",
      "rmse:  5350850.003896072\n"
     ]
    }
   ],
   "source": [
    "ridge = Ridge()\n",
    "ridge = train(ridge, X_train, y_train)\n",
    "evaluate(ridge, X_train, y_train)\n",
    "evaluate(ridge, X_val, y_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "61816298",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mae:  3612269.6134585585\n",
      "r2:  0.35285010262217054\n",
      "rmse:  5084497.374567315\n",
      "mae:  3799305.561440033\n",
      "r2:  0.35222513550171175\n",
      "rmse:  5350734.461933583\n"
     ]
    }
   ],
   "source": [
    "lasso = Lasso()\n",
    "lasso = train(lasso, X_train, y_train)\n",
    "evaluate(lasso, X_train, y_train)\n",
    "evaluate(lasso, X_val, y_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "966db2e4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mae:  1305798.6155812782\n",
      "r2:  0.9124047182654256\n",
      "rmse:  1870621.583521221\n",
      "mae:  3759001.641499021\n",
      "r2:  0.3523954489854758\n",
      "rmse:  5350031.006007142\n"
     ]
    }
   ],
   "source": [
    "rf = RandomForestRegressor()\n",
    "rf = train(rf, X_train, y_train)\n",
    "evaluate(rf, X_train, y_train)\n",
    "evaluate(rf, X_val, y_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "bffcb1b1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mae:  3298967.5871415366\n",
      "r2:  0.4390419321915586\n",
      "rmse:  4733809.570563506\n",
      "mae:  3539954.0956195677\n",
      "r2:  0.40711886622725013\n",
      "rmse:  5119000.456947705\n"
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "poly = PolynomialFeatures(degree=2) \n",
    "X_train_poly = poly.fit_transform(X_train)\n",
    "X_val_poly = poly.fit_transform(X_val)\n",
    "\n",
    "lr = LinearRegression()\n",
    "lr = train(lr, X_train_poly, y_train)\n",
    "evaluate(lr, X_train_poly, y_train)\n",
    "evaluate(lr, X_val_poly, y_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f340560d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "925f4c4e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a3a47105",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}