{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "RcqBh1HEjvGX"
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.read_csv(\"https://raw.githubusercontent.com/kirenz/datasets/master/Hitters.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "mbzK2GHBCUeA"
   },
   "source": [
    "Data frame called “Hitters” with 20 variables and 322 observations of major league players\n",
    "\n",
    "We want to predict a baseball player’s salary on the basis of various statistics associated with performance in the previous year."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "FzH_hgP4_B-9"
   },
   "source": [
    "A data frame with 322 observations of major league players on the following 20 variables.\n",
    "- AtBat Number of times at bat in 1986\n",
    "- Hits Number of hits in 1986\n",
    "- HmRun Number of home runs in 1986\n",
    "- Runs Number of runs in 1986\n",
    "- RBI Number of runs batted in in 1986\n",
    "- Walks Number of walks in 1986\n",
    "- Years Number of years in the major leagues\n",
    "- CAtBat Number of times at bat during his career\n",
    "- CHits Number of hits during his career\n",
    "- CHmRun Number of home runs during his career\n",
    "- CRuns Number of runs during his career\n",
    "- CRBI Number of runs batted in during his career\n",
    "- CWalks Number of walks during his career\n",
    "- League A factor with levels A and N indicating player’s league at the end of 1986\n",
    "- Division A factor with levels E and W indicating player’s division at the end of 1986\n",
    "- PutOuts Number of put outs in 1986\n",
    "- Assists Number of assists in 1986\n",
    "- Errors Number of errors in 1986\n",
    "- Salary 1987 annual salary on opening day in thousands of dollars\n",
    "- NewLeague A factor with levels A and N indicating player’s league at the beginning of 1987"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 424
    },
    "id": "76Sa_jrQpNzc",
    "outputId": "9ec35b84-7d85-4e76-bcec-353ce6b517cb"
   },
   "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>AtBat</th>\n",
       "      <th>Hits</th>\n",
       "      <th>HmRun</th>\n",
       "      <th>Runs</th>\n",
       "      <th>RBI</th>\n",
       "      <th>Walks</th>\n",
       "      <th>Years</th>\n",
       "      <th>CAtBat</th>\n",
       "      <th>CHits</th>\n",
       "      <th>CHmRun</th>\n",
       "      <th>CRuns</th>\n",
       "      <th>CRBI</th>\n",
       "      <th>CWalks</th>\n",
       "      <th>League</th>\n",
       "      <th>Division</th>\n",
       "      <th>PutOuts</th>\n",
       "      <th>Assists</th>\n",
       "      <th>Errors</th>\n",
       "      <th>Salary</th>\n",
       "      <th>NewLeague</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>293</td>\n",
       "      <td>66</td>\n",
       "      <td>1</td>\n",
       "      <td>30</td>\n",
       "      <td>29</td>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "      <td>293</td>\n",
       "      <td>66</td>\n",
       "      <td>1</td>\n",
       "      <td>30</td>\n",
       "      <td>29</td>\n",
       "      <td>14</td>\n",
       "      <td>A</td>\n",
       "      <td>E</td>\n",
       "      <td>446</td>\n",
       "      <td>33</td>\n",
       "      <td>20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>315</td>\n",
       "      <td>81</td>\n",
       "      <td>7</td>\n",
       "      <td>24</td>\n",
       "      <td>38</td>\n",
       "      <td>39</td>\n",
       "      <td>14</td>\n",
       "      <td>3449</td>\n",
       "      <td>835</td>\n",
       "      <td>69</td>\n",
       "      <td>321</td>\n",
       "      <td>414</td>\n",
       "      <td>375</td>\n",
       "      <td>N</td>\n",
       "      <td>W</td>\n",
       "      <td>632</td>\n",
       "      <td>43</td>\n",
       "      <td>10</td>\n",
       "      <td>475.0</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>479</td>\n",
       "      <td>130</td>\n",
       "      <td>18</td>\n",
       "      <td>66</td>\n",
       "      <td>72</td>\n",
       "      <td>76</td>\n",
       "      <td>3</td>\n",
       "      <td>1624</td>\n",
       "      <td>457</td>\n",
       "      <td>63</td>\n",
       "      <td>224</td>\n",
       "      <td>266</td>\n",
       "      <td>263</td>\n",
       "      <td>A</td>\n",
       "      <td>W</td>\n",
       "      <td>880</td>\n",
       "      <td>82</td>\n",
       "      <td>14</td>\n",
       "      <td>480.0</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>496</td>\n",
       "      <td>141</td>\n",
       "      <td>20</td>\n",
       "      <td>65</td>\n",
       "      <td>78</td>\n",
       "      <td>37</td>\n",
       "      <td>11</td>\n",
       "      <td>5628</td>\n",
       "      <td>1575</td>\n",
       "      <td>225</td>\n",
       "      <td>828</td>\n",
       "      <td>838</td>\n",
       "      <td>354</td>\n",
       "      <td>N</td>\n",
       "      <td>E</td>\n",
       "      <td>200</td>\n",
       "      <td>11</td>\n",
       "      <td>3</td>\n",
       "      <td>500.0</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>321</td>\n",
       "      <td>87</td>\n",
       "      <td>10</td>\n",
       "      <td>39</td>\n",
       "      <td>42</td>\n",
       "      <td>30</td>\n",
       "      <td>2</td>\n",
       "      <td>396</td>\n",
       "      <td>101</td>\n",
       "      <td>12</td>\n",
       "      <td>48</td>\n",
       "      <td>46</td>\n",
       "      <td>33</td>\n",
       "      <td>N</td>\n",
       "      <td>E</td>\n",
       "      <td>805</td>\n",
       "      <td>40</td>\n",
       "      <td>4</td>\n",
       "      <td>91.5</td>\n",
       "      <td>N</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>317</th>\n",
       "      <td>497</td>\n",
       "      <td>127</td>\n",
       "      <td>7</td>\n",
       "      <td>65</td>\n",
       "      <td>48</td>\n",
       "      <td>37</td>\n",
       "      <td>5</td>\n",
       "      <td>2703</td>\n",
       "      <td>806</td>\n",
       "      <td>32</td>\n",
       "      <td>379</td>\n",
       "      <td>311</td>\n",
       "      <td>138</td>\n",
       "      <td>N</td>\n",
       "      <td>E</td>\n",
       "      <td>325</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "      <td>700.0</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>318</th>\n",
       "      <td>492</td>\n",
       "      <td>136</td>\n",
       "      <td>5</td>\n",
       "      <td>76</td>\n",
       "      <td>50</td>\n",
       "      <td>94</td>\n",
       "      <td>12</td>\n",
       "      <td>5511</td>\n",
       "      <td>1511</td>\n",
       "      <td>39</td>\n",
       "      <td>897</td>\n",
       "      <td>451</td>\n",
       "      <td>875</td>\n",
       "      <td>A</td>\n",
       "      <td>E</td>\n",
       "      <td>313</td>\n",
       "      <td>381</td>\n",
       "      <td>20</td>\n",
       "      <td>875.0</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>319</th>\n",
       "      <td>475</td>\n",
       "      <td>126</td>\n",
       "      <td>3</td>\n",
       "      <td>61</td>\n",
       "      <td>43</td>\n",
       "      <td>52</td>\n",
       "      <td>6</td>\n",
       "      <td>1700</td>\n",
       "      <td>433</td>\n",
       "      <td>7</td>\n",
       "      <td>217</td>\n",
       "      <td>93</td>\n",
       "      <td>146</td>\n",
       "      <td>A</td>\n",
       "      <td>W</td>\n",
       "      <td>37</td>\n",
       "      <td>113</td>\n",
       "      <td>7</td>\n",
       "      <td>385.0</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>320</th>\n",
       "      <td>573</td>\n",
       "      <td>144</td>\n",
       "      <td>9</td>\n",
       "      <td>85</td>\n",
       "      <td>60</td>\n",
       "      <td>78</td>\n",
       "      <td>8</td>\n",
       "      <td>3198</td>\n",
       "      <td>857</td>\n",
       "      <td>97</td>\n",
       "      <td>470</td>\n",
       "      <td>420</td>\n",
       "      <td>332</td>\n",
       "      <td>A</td>\n",
       "      <td>E</td>\n",
       "      <td>1314</td>\n",
       "      <td>131</td>\n",
       "      <td>12</td>\n",
       "      <td>960.0</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>321</th>\n",
       "      <td>631</td>\n",
       "      <td>170</td>\n",
       "      <td>9</td>\n",
       "      <td>77</td>\n",
       "      <td>44</td>\n",
       "      <td>31</td>\n",
       "      <td>11</td>\n",
       "      <td>4908</td>\n",
       "      <td>1457</td>\n",
       "      <td>30</td>\n",
       "      <td>775</td>\n",
       "      <td>357</td>\n",
       "      <td>249</td>\n",
       "      <td>A</td>\n",
       "      <td>W</td>\n",
       "      <td>408</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>322 rows × 20 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     AtBat  Hits  HmRun  Runs  RBI  Walks  Years  CAtBat  CHits  CHmRun  \\\n",
       "0      293    66      1    30   29     14      1     293     66       1   \n",
       "1      315    81      7    24   38     39     14    3449    835      69   \n",
       "2      479   130     18    66   72     76      3    1624    457      63   \n",
       "3      496   141     20    65   78     37     11    5628   1575     225   \n",
       "4      321    87     10    39   42     30      2     396    101      12   \n",
       "..     ...   ...    ...   ...  ...    ...    ...     ...    ...     ...   \n",
       "317    497   127      7    65   48     37      5    2703    806      32   \n",
       "318    492   136      5    76   50     94     12    5511   1511      39   \n",
       "319    475   126      3    61   43     52      6    1700    433       7   \n",
       "320    573   144      9    85   60     78      8    3198    857      97   \n",
       "321    631   170      9    77   44     31     11    4908   1457      30   \n",
       "\n",
       "     CRuns  CRBI  CWalks League Division  PutOuts  Assists  Errors  Salary  \\\n",
       "0       30    29      14      A        E      446       33      20     NaN   \n",
       "1      321   414     375      N        W      632       43      10   475.0   \n",
       "2      224   266     263      A        W      880       82      14   480.0   \n",
       "3      828   838     354      N        E      200       11       3   500.0   \n",
       "4       48    46      33      N        E      805       40       4    91.5   \n",
       "..     ...   ...     ...    ...      ...      ...      ...     ...     ...   \n",
       "317    379   311     138      N        E      325        9       3   700.0   \n",
       "318    897   451     875      A        E      313      381      20   875.0   \n",
       "319    217    93     146      A        W       37      113       7   385.0   \n",
       "320    470   420     332      A        E     1314      131      12   960.0   \n",
       "321    775   357     249      A        W      408        4       3  1000.0   \n",
       "\n",
       "    NewLeague  \n",
       "0           A  \n",
       "1           N  \n",
       "2           A  \n",
       "3           N  \n",
       "4           N  \n",
       "..        ...  \n",
       "317         N  \n",
       "318         A  \n",
       "319         A  \n",
       "320         A  \n",
       "321         A  \n",
       "\n",
       "[322 rows x 20 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "qwa2XRUJpVuS",
    "outputId": "78a35cbe-cfb0-402a-d065-d8f95ea3e67b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 322 entries, 0 to 321\n",
      "Data columns (total 20 columns):\n",
      " #   Column     Non-Null Count  Dtype  \n",
      "---  ------     --------------  -----  \n",
      " 0   AtBat      322 non-null    int64  \n",
      " 1   Hits       322 non-null    int64  \n",
      " 2   HmRun      322 non-null    int64  \n",
      " 3   Runs       322 non-null    int64  \n",
      " 4   RBI        322 non-null    int64  \n",
      " 5   Walks      322 non-null    int64  \n",
      " 6   Years      322 non-null    int64  \n",
      " 7   CAtBat     322 non-null    int64  \n",
      " 8   CHits      322 non-null    int64  \n",
      " 9   CHmRun     322 non-null    int64  \n",
      " 10  CRuns      322 non-null    int64  \n",
      " 11  CRBI       322 non-null    int64  \n",
      " 12  CWalks     322 non-null    int64  \n",
      " 13  League     322 non-null    object \n",
      " 14  Division   322 non-null    object \n",
      " 15  PutOuts    322 non-null    int64  \n",
      " 16  Assists    322 non-null    int64  \n",
      " 17  Errors     322 non-null    int64  \n",
      " 18  Salary     263 non-null    float64\n",
      " 19  NewLeague  322 non-null    object \n",
      "dtypes: float64(1), int64(16), object(3)\n",
      "memory usage: 50.4+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 320
    },
    "id": "AANCReIW_HY3",
    "outputId": "7d9994af-961e-4331-a092-5e63550f09e8"
   },
   "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>AtBat</th>\n",
       "      <th>Hits</th>\n",
       "      <th>HmRun</th>\n",
       "      <th>Runs</th>\n",
       "      <th>RBI</th>\n",
       "      <th>Walks</th>\n",
       "      <th>Years</th>\n",
       "      <th>CAtBat</th>\n",
       "      <th>CHits</th>\n",
       "      <th>CHmRun</th>\n",
       "      <th>CRuns</th>\n",
       "      <th>CRBI</th>\n",
       "      <th>CWalks</th>\n",
       "      <th>PutOuts</th>\n",
       "      <th>Assists</th>\n",
       "      <th>Errors</th>\n",
       "      <th>Salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>322.000000</td>\n",
       "      <td>322.000000</td>\n",
       "      <td>322.000000</td>\n",
       "      <td>322.000000</td>\n",
       "      <td>322.000000</td>\n",
       "      <td>322.000000</td>\n",
       "      <td>322.000000</td>\n",
       "      <td>322.00000</td>\n",
       "      <td>322.000000</td>\n",
       "      <td>322.000000</td>\n",
       "      <td>322.000000</td>\n",
       "      <td>322.000000</td>\n",
       "      <td>322.000000</td>\n",
       "      <td>322.000000</td>\n",
       "      <td>322.000000</td>\n",
       "      <td>322.000000</td>\n",
       "      <td>263.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>380.928571</td>\n",
       "      <td>101.024845</td>\n",
       "      <td>10.770186</td>\n",
       "      <td>50.909938</td>\n",
       "      <td>48.027950</td>\n",
       "      <td>38.742236</td>\n",
       "      <td>7.444099</td>\n",
       "      <td>2648.68323</td>\n",
       "      <td>717.571429</td>\n",
       "      <td>69.490683</td>\n",
       "      <td>358.795031</td>\n",
       "      <td>330.118012</td>\n",
       "      <td>260.239130</td>\n",
       "      <td>288.937888</td>\n",
       "      <td>106.913043</td>\n",
       "      <td>8.040373</td>\n",
       "      <td>535.925882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>153.404981</td>\n",
       "      <td>46.454741</td>\n",
       "      <td>8.709037</td>\n",
       "      <td>26.024095</td>\n",
       "      <td>26.166895</td>\n",
       "      <td>21.639327</td>\n",
       "      <td>4.926087</td>\n",
       "      <td>2324.20587</td>\n",
       "      <td>654.472627</td>\n",
       "      <td>86.266061</td>\n",
       "      <td>334.105886</td>\n",
       "      <td>333.219617</td>\n",
       "      <td>267.058085</td>\n",
       "      <td>280.704614</td>\n",
       "      <td>136.854876</td>\n",
       "      <td>6.368359</td>\n",
       "      <td>451.118681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>16.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>19.00000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>67.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>255.250000</td>\n",
       "      <td>64.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>30.250000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>816.75000</td>\n",
       "      <td>209.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>100.250000</td>\n",
       "      <td>88.750000</td>\n",
       "      <td>67.250000</td>\n",
       "      <td>109.250000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>190.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>379.500000</td>\n",
       "      <td>96.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>1928.00000</td>\n",
       "      <td>508.000000</td>\n",
       "      <td>37.500000</td>\n",
       "      <td>247.000000</td>\n",
       "      <td>220.500000</td>\n",
       "      <td>170.500000</td>\n",
       "      <td>212.000000</td>\n",
       "      <td>39.500000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>425.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>512.000000</td>\n",
       "      <td>137.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>69.000000</td>\n",
       "      <td>64.750000</td>\n",
       "      <td>53.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>3924.25000</td>\n",
       "      <td>1059.250000</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>526.250000</td>\n",
       "      <td>426.250000</td>\n",
       "      <td>339.250000</td>\n",
       "      <td>325.000000</td>\n",
       "      <td>166.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>750.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>687.000000</td>\n",
       "      <td>238.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>130.000000</td>\n",
       "      <td>121.000000</td>\n",
       "      <td>105.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>14053.00000</td>\n",
       "      <td>4256.000000</td>\n",
       "      <td>548.000000</td>\n",
       "      <td>2165.000000</td>\n",
       "      <td>1659.000000</td>\n",
       "      <td>1566.000000</td>\n",
       "      <td>1378.000000</td>\n",
       "      <td>492.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>2460.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            AtBat        Hits       HmRun        Runs         RBI       Walks  \\\n",
       "count  322.000000  322.000000  322.000000  322.000000  322.000000  322.000000   \n",
       "mean   380.928571  101.024845   10.770186   50.909938   48.027950   38.742236   \n",
       "std    153.404981   46.454741    8.709037   26.024095   26.166895   21.639327   \n",
       "min     16.000000    1.000000    0.000000    0.000000    0.000000    0.000000   \n",
       "25%    255.250000   64.000000    4.000000   30.250000   28.000000   22.000000   \n",
       "50%    379.500000   96.000000    8.000000   48.000000   44.000000   35.000000   \n",
       "75%    512.000000  137.000000   16.000000   69.000000   64.750000   53.000000   \n",
       "max    687.000000  238.000000   40.000000  130.000000  121.000000  105.000000   \n",
       "\n",
       "            Years       CAtBat        CHits      CHmRun        CRuns  \\\n",
       "count  322.000000    322.00000   322.000000  322.000000   322.000000   \n",
       "mean     7.444099   2648.68323   717.571429   69.490683   358.795031   \n",
       "std      4.926087   2324.20587   654.472627   86.266061   334.105886   \n",
       "min      1.000000     19.00000     4.000000    0.000000     1.000000   \n",
       "25%      4.000000    816.75000   209.000000   14.000000   100.250000   \n",
       "50%      6.000000   1928.00000   508.000000   37.500000   247.000000   \n",
       "75%     11.000000   3924.25000  1059.250000   90.000000   526.250000   \n",
       "max     24.000000  14053.00000  4256.000000  548.000000  2165.000000   \n",
       "\n",
       "              CRBI       CWalks      PutOuts     Assists      Errors  \\\n",
       "count   322.000000   322.000000   322.000000  322.000000  322.000000   \n",
       "mean    330.118012   260.239130   288.937888  106.913043    8.040373   \n",
       "std     333.219617   267.058085   280.704614  136.854876    6.368359   \n",
       "min       0.000000     0.000000     0.000000    0.000000    0.000000   \n",
       "25%      88.750000    67.250000   109.250000    7.000000    3.000000   \n",
       "50%     220.500000   170.500000   212.000000   39.500000    6.000000   \n",
       "75%     426.250000   339.250000   325.000000  166.000000   11.000000   \n",
       "max    1659.000000  1566.000000  1378.000000  492.000000   32.000000   \n",
       "\n",
       "            Salary  \n",
       "count   263.000000  \n",
       "mean    535.925882  \n",
       "std     451.118681  \n",
       "min      67.500000  \n",
       "25%     190.000000  \n",
       "50%     425.000000  \n",
       "75%     750.000000  \n",
       "max    2460.000000  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "1M9C2NDVpY9f",
    "outputId": "fac8ac8f-7fff-417a-b5b9-19260407e84d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AtBat         0\n",
      "Hits          0\n",
      "HmRun         0\n",
      "Runs          0\n",
      "RBI           0\n",
      "Walks         0\n",
      "Years         0\n",
      "CAtBat        0\n",
      "CHits         0\n",
      "CHmRun        0\n",
      "CRuns         0\n",
      "CRBI          0\n",
      "CWalks        0\n",
      "League        0\n",
      "Division      0\n",
      "PutOuts       0\n",
      "Assists       0\n",
      "Errors        0\n",
      "Salary       59\n",
      "NewLeague     0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(df.isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "08xUiroOpcBg"
   },
   "outputs": [],
   "source": [
    "# drop missing cases\n",
    "df = df.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "lCyrOVNApedI"
   },
   "outputs": [],
   "source": [
    "dummies = pd.get_dummies(df[['League', 'Division','NewLeague']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "nQY7hd8PphJo",
    "outputId": "f3f522e8-d044-472d-ca9c-538df73968b3"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 263 entries, 1 to 321\n",
      "Data columns (total 6 columns):\n",
      " #   Column       Non-Null Count  Dtype\n",
      "---  ------       --------------  -----\n",
      " 0   League_A     263 non-null    bool \n",
      " 1   League_N     263 non-null    bool \n",
      " 2   Division_E   263 non-null    bool \n",
      " 3   Division_W   263 non-null    bool \n",
      " 4   NewLeague_A  263 non-null    bool \n",
      " 5   NewLeague_N  263 non-null    bool \n",
      "dtypes: bool(6)\n",
      "memory usage: 3.6 KB\n"
     ]
    }
   ],
   "source": [
    "dummies.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "B7G4lBs3pmLY",
    "outputId": "4e3964ba-c065-4750-f4d8-0cf893ffe7c4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   League_A  League_N  Division_E  Division_W  NewLeague_A  NewLeague_N\n",
      "1     False      True       False        True        False         True\n",
      "2      True     False       False        True         True        False\n",
      "3     False      True        True       False        False         True\n",
      "4     False      True        True       False        False         True\n",
      "5      True     False       False        True         True        False\n"
     ]
    }
   ],
   "source": [
    "print(dummies.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "id": "CTlRS474pott"
   },
   "outputs": [],
   "source": [
    "y = df['Salary']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "id": "8UmwTAHcprrq"
   },
   "outputs": [],
   "source": [
    "X_numerical = df.drop(['Salary', 'League', 'Division', 'NewLeague'], axis=1).astype('float64')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "sOACqH5VpuZ2",
    "outputId": "c11db38f-e1b6-4bdd-b69e-3ef22b4abe36"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['AtBat', 'Hits', 'HmRun', 'Runs', 'RBI', 'Walks', 'Years', 'CAtBat',\n",
       "       'CHits', 'CHmRun', 'CRuns', 'CRBI', 'CWalks', 'PutOuts', 'Assists',\n",
       "       'Errors'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list_numerical = X_numerical.columns\n",
    "list_numerical"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "LW5DgH3ZpxSd",
    "outputId": "fb64a024-de92-4a35-8b7d-b66492bfac5f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 263 entries, 1 to 321\n",
      "Data columns (total 19 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   AtBat        263 non-null    float64\n",
      " 1   Hits         263 non-null    float64\n",
      " 2   HmRun        263 non-null    float64\n",
      " 3   Runs         263 non-null    float64\n",
      " 4   RBI          263 non-null    float64\n",
      " 5   Walks        263 non-null    float64\n",
      " 6   Years        263 non-null    float64\n",
      " 7   CAtBat       263 non-null    float64\n",
      " 8   CHits        263 non-null    float64\n",
      " 9   CHmRun       263 non-null    float64\n",
      " 10  CRuns        263 non-null    float64\n",
      " 11  CRBI         263 non-null    float64\n",
      " 12  CWalks       263 non-null    float64\n",
      " 13  PutOuts      263 non-null    float64\n",
      " 14  Assists      263 non-null    float64\n",
      " 15  Errors       263 non-null    float64\n",
      " 16  League_N     263 non-null    bool   \n",
      " 17  Division_W   263 non-null    bool   \n",
      " 18  NewLeague_N  263 non-null    bool   \n",
      "dtypes: bool(3), float64(16)\n",
      "memory usage: 35.7 KB\n"
     ]
    }
   ],
   "source": [
    "# Create all features\n",
    "X = pd.concat([X_numerical, dummies[['League_N', 'Division_W', 'NewLeague_N']]], axis=1)\n",
    "X.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "id": "82pBGm_Zp0j8"
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "4-ct1-Vxp2sX",
    "outputId": "5416954e-1c53-4be5-9de7-c27416f06152"
   },
   "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>AtBat</th>\n",
       "      <th>Hits</th>\n",
       "      <th>HmRun</th>\n",
       "      <th>Runs</th>\n",
       "      <th>RBI</th>\n",
       "      <th>Walks</th>\n",
       "      <th>Years</th>\n",
       "      <th>CAtBat</th>\n",
       "      <th>CHits</th>\n",
       "      <th>CHmRun</th>\n",
       "      <th>CRuns</th>\n",
       "      <th>CRBI</th>\n",
       "      <th>CWalks</th>\n",
       "      <th>PutOuts</th>\n",
       "      <th>Assists</th>\n",
       "      <th>Errors</th>\n",
       "      <th>League_N</th>\n",
       "      <th>Division_W</th>\n",
       "      <th>NewLeague_N</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>260</th>\n",
       "      <td>496.0</td>\n",
       "      <td>119.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3358.0</td>\n",
       "      <td>882.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>365.0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>165.0</td>\n",
       "      <td>155.0</td>\n",
       "      <td>371.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>317.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>317.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>122.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>343.0</td>\n",
       "      <td>103.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>4338.0</td>\n",
       "      <td>1193.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>581.0</td>\n",
       "      <td>421.0</td>\n",
       "      <td>325.0</td>\n",
       "      <td>211.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>314.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1457.0</td>\n",
       "      <td>405.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>156.0</td>\n",
       "      <td>159.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>533.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>495.0</td>\n",
       "      <td>151.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>5624.0</td>\n",
       "      <td>1679.0</td>\n",
       "      <td>275.0</td>\n",
       "      <td>884.0</td>\n",
       "      <td>1015.0</td>\n",
       "      <td>709.0</td>\n",
       "      <td>1045.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     AtBat   Hits  HmRun  Runs   RBI  Walks  Years  CAtBat   CHits  CHmRun  \\\n",
       "260  496.0  119.0    8.0  57.0  33.0   21.0    7.0  3358.0   882.0    36.0   \n",
       "92   317.0   78.0    7.0  35.0  35.0   32.0    1.0   317.0    78.0     7.0   \n",
       "137  343.0  103.0    6.0  48.0  36.0   40.0   15.0  4338.0  1193.0    70.0   \n",
       "90   314.0   83.0   13.0  39.0  46.0   16.0    5.0  1457.0   405.0    28.0   \n",
       "100  495.0  151.0   17.0  61.0  84.0   78.0   10.0  5624.0  1679.0   275.0   \n",
       "\n",
       "     CRuns    CRBI  CWalks  PutOuts  Assists  Errors  League_N  Division_W  \\\n",
       "260  365.0   280.0   165.0    155.0    371.0    29.0      True        True   \n",
       "92    35.0    35.0    32.0     45.0    122.0    26.0     False       False   \n",
       "137  581.0   421.0   325.0    211.0     56.0    13.0     False       False   \n",
       "90   156.0   159.0    76.0    533.0     40.0     4.0     False        True   \n",
       "100  884.0  1015.0   709.0   1045.0     88.0    13.0     False       False   \n",
       "\n",
       "     NewLeague_N  \n",
       "260         True  \n",
       "92         False  \n",
       "137        False  \n",
       "90         False  \n",
       "100        False  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "id": "pa7qR4wIp5-D"
   },
   "outputs": [],
   "source": [
    "\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "scaler = StandardScaler().fit(X_train[list_numerical])\n",
    "\n",
    "X_train[list_numerical] = scaler.transform(X_train[list_numerical])\n",
    "\n",
    "X_test[list_numerical] = scaler.transform(X_test[list_numerical])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 424
    },
    "id": "2GSApakCp8y1",
    "outputId": "eb220133-210b-4340-bae0-3ed51c3fa588"
   },
   "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>AtBat</th>\n",
       "      <th>Hits</th>\n",
       "      <th>HmRun</th>\n",
       "      <th>Runs</th>\n",
       "      <th>RBI</th>\n",
       "      <th>Walks</th>\n",
       "      <th>Years</th>\n",
       "      <th>CAtBat</th>\n",
       "      <th>CHits</th>\n",
       "      <th>CHmRun</th>\n",
       "      <th>CRuns</th>\n",
       "      <th>CRBI</th>\n",
       "      <th>CWalks</th>\n",
       "      <th>PutOuts</th>\n",
       "      <th>Assists</th>\n",
       "      <th>Errors</th>\n",
       "      <th>League_N</th>\n",
       "      <th>Division_W</th>\n",
       "      <th>NewLeague_N</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>260</th>\n",
       "      <td>0.644577</td>\n",
       "      <td>0.257439</td>\n",
       "      <td>-0.456963</td>\n",
       "      <td>0.101010</td>\n",
       "      <td>-0.763917</td>\n",
       "      <td>-0.975959</td>\n",
       "      <td>-0.070553</td>\n",
       "      <td>0.298535</td>\n",
       "      <td>0.239063</td>\n",
       "      <td>-0.407836</td>\n",
       "      <td>0.011298</td>\n",
       "      <td>-0.163736</td>\n",
       "      <td>-0.361084</td>\n",
       "      <td>-0.482387</td>\n",
       "      <td>1.746229</td>\n",
       "      <td>3.022233</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>-0.592807</td>\n",
       "      <td>-0.671359</td>\n",
       "      <td>-0.572936</td>\n",
       "      <td>-0.778318</td>\n",
       "      <td>-0.685806</td>\n",
       "      <td>-0.458312</td>\n",
       "      <td>-1.306911</td>\n",
       "      <td>-1.001403</td>\n",
       "      <td>-0.969702</td>\n",
       "      <td>-0.746705</td>\n",
       "      <td>-0.957639</td>\n",
       "      <td>-0.898919</td>\n",
       "      <td>-0.844319</td>\n",
       "      <td>-0.851547</td>\n",
       "      <td>0.022276</td>\n",
       "      <td>2.574735</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>-0.413075</td>\n",
       "      <td>-0.105019</td>\n",
       "      <td>-0.688910</td>\n",
       "      <td>-0.258715</td>\n",
       "      <td>-0.646751</td>\n",
       "      <td>-0.081841</td>\n",
       "      <td>1.577925</td>\n",
       "      <td>0.717456</td>\n",
       "      <td>0.706633</td>\n",
       "      <td>-0.010542</td>\n",
       "      <td>0.645511</td>\n",
       "      <td>0.259369</td>\n",
       "      <td>0.220252</td>\n",
       "      <td>-0.294452</td>\n",
       "      <td>-0.434676</td>\n",
       "      <td>0.635577</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>-0.613545</td>\n",
       "      <td>-0.558091</td>\n",
       "      <td>0.122907</td>\n",
       "      <td>-0.618440</td>\n",
       "      <td>-0.256196</td>\n",
       "      <td>-1.211253</td>\n",
       "      <td>-0.482672</td>\n",
       "      <td>-0.514087</td>\n",
       "      <td>-0.478077</td>\n",
       "      <td>-0.501317</td>\n",
       "      <td>-0.602362</td>\n",
       "      <td>-0.526826</td>\n",
       "      <td>-0.684451</td>\n",
       "      <td>0.786178</td>\n",
       "      <td>-0.545452</td>\n",
       "      <td>-0.706917</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>0.637665</td>\n",
       "      <td>0.982354</td>\n",
       "      <td>0.586803</td>\n",
       "      <td>0.260888</td>\n",
       "      <td>1.227914</td>\n",
       "      <td>1.706394</td>\n",
       "      <td>0.547626</td>\n",
       "      <td>1.267183</td>\n",
       "      <td>1.437305</td>\n",
       "      <td>2.384908</td>\n",
       "      <td>1.535171</td>\n",
       "      <td>2.041811</td>\n",
       "      <td>1.615457</td>\n",
       "      <td>2.504446</td>\n",
       "      <td>-0.213124</td>\n",
       "      <td>0.635577</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>274</th>\n",
       "      <td>0.824309</td>\n",
       "      <td>0.733164</td>\n",
       "      <td>0.470829</td>\n",
       "      <td>0.740521</td>\n",
       "      <td>0.954525</td>\n",
       "      <td>0.859335</td>\n",
       "      <td>-0.688732</td>\n",
       "      <td>-0.824858</td>\n",
       "      <td>-0.808834</td>\n",
       "      <td>-0.571428</td>\n",
       "      <td>-0.787341</td>\n",
       "      <td>-0.685866</td>\n",
       "      <td>-0.648118</td>\n",
       "      <td>3.427344</td>\n",
       "      <td>0.326910</td>\n",
       "      <td>1.232241</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>0.423369</td>\n",
       "      <td>0.461321</td>\n",
       "      <td>1.862516</td>\n",
       "      <td>0.500704</td>\n",
       "      <td>1.618469</td>\n",
       "      <td>0.482865</td>\n",
       "      <td>1.165805</td>\n",
       "      <td>1.354814</td>\n",
       "      <td>1.246368</td>\n",
       "      <td>1.625375</td>\n",
       "      <td>1.112362</td>\n",
       "      <td>1.516681</td>\n",
       "      <td>0.681687</td>\n",
       "      <td>-1.002566</td>\n",
       "      <td>-0.822392</td>\n",
       "      <td>-1.303581</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>159</th>\n",
       "      <td>1.474109</td>\n",
       "      <td>1.254197</td>\n",
       "      <td>1.746542</td>\n",
       "      <td>1.140215</td>\n",
       "      <td>2.126191</td>\n",
       "      <td>-0.458312</td>\n",
       "      <td>-0.894792</td>\n",
       "      <td>-0.522636</td>\n",
       "      <td>-0.520174</td>\n",
       "      <td>-0.068968</td>\n",
       "      <td>-0.528958</td>\n",
       "      <td>-0.322776</td>\n",
       "      <td>-0.662651</td>\n",
       "      <td>-0.633407</td>\n",
       "      <td>1.310048</td>\n",
       "      <td>0.933909</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>-1.470728</td>\n",
       "      <td>-1.396275</td>\n",
       "      <td>-1.152806</td>\n",
       "      <td>-1.217982</td>\n",
       "      <td>-1.740306</td>\n",
       "      <td>-1.258312</td>\n",
       "      <td>-0.482672</td>\n",
       "      <td>-0.932153</td>\n",
       "      <td>-0.933620</td>\n",
       "      <td>-0.770075</td>\n",
       "      <td>-0.869554</td>\n",
       "      <td>-0.934928</td>\n",
       "      <td>-0.818885</td>\n",
       "      <td>-0.660255</td>\n",
       "      <td>0.403069</td>\n",
       "      <td>1.083075</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>162</th>\n",
       "      <td>-1.643547</td>\n",
       "      <td>-1.554850</td>\n",
       "      <td>-1.152806</td>\n",
       "      <td>-1.657646</td>\n",
       "      <td>-1.701250</td>\n",
       "      <td>-1.211253</td>\n",
       "      <td>-0.894792</td>\n",
       "      <td>-1.053127</td>\n",
       "      <td>-1.020819</td>\n",
       "      <td>-0.805130</td>\n",
       "      <td>-1.007554</td>\n",
       "      <td>-0.973938</td>\n",
       "      <td>-0.895185</td>\n",
       "      <td>0.111623</td>\n",
       "      <td>-0.690846</td>\n",
       "      <td>-1.005249</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>184 rows × 19 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        AtBat      Hits     HmRun      Runs       RBI     Walks     Years  \\\n",
       "260  0.644577  0.257439 -0.456963  0.101010 -0.763917 -0.975959 -0.070553   \n",
       "92  -0.592807 -0.671359 -0.572936 -0.778318 -0.685806 -0.458312 -1.306911   \n",
       "137 -0.413075 -0.105019 -0.688910 -0.258715 -0.646751 -0.081841  1.577925   \n",
       "90  -0.613545 -0.558091  0.122907 -0.618440 -0.256196 -1.211253 -0.482672   \n",
       "100  0.637665  0.982354  0.586803  0.260888  1.227914  1.706394  0.547626   \n",
       "..        ...       ...       ...       ...       ...       ...       ...   \n",
       "274  0.824309  0.733164  0.470829  0.740521  0.954525  0.859335 -0.688732   \n",
       "196  0.423369  0.461321  1.862516  0.500704  1.618469  0.482865  1.165805   \n",
       "159  1.474109  1.254197  1.746542  1.140215  2.126191 -0.458312 -0.894792   \n",
       "17  -1.470728 -1.396275 -1.152806 -1.217982 -1.740306 -1.258312 -0.482672   \n",
       "162 -1.643547 -1.554850 -1.152806 -1.657646 -1.701250 -1.211253 -0.894792   \n",
       "\n",
       "       CAtBat     CHits    CHmRun     CRuns      CRBI    CWalks   PutOuts  \\\n",
       "260  0.298535  0.239063 -0.407836  0.011298 -0.163736 -0.361084 -0.482387   \n",
       "92  -1.001403 -0.969702 -0.746705 -0.957639 -0.898919 -0.844319 -0.851547   \n",
       "137  0.717456  0.706633 -0.010542  0.645511  0.259369  0.220252 -0.294452   \n",
       "90  -0.514087 -0.478077 -0.501317 -0.602362 -0.526826 -0.684451  0.786178   \n",
       "100  1.267183  1.437305  2.384908  1.535171  2.041811  1.615457  2.504446   \n",
       "..        ...       ...       ...       ...       ...       ...       ...   \n",
       "274 -0.824858 -0.808834 -0.571428 -0.787341 -0.685866 -0.648118  3.427344   \n",
       "196  1.354814  1.246368  1.625375  1.112362  1.516681  0.681687 -1.002566   \n",
       "159 -0.522636 -0.520174 -0.068968 -0.528958 -0.322776 -0.662651 -0.633407   \n",
       "17  -0.932153 -0.933620 -0.770075 -0.869554 -0.934928 -0.818885 -0.660255   \n",
       "162 -1.053127 -1.020819 -0.805130 -1.007554 -0.973938 -0.895185  0.111623   \n",
       "\n",
       "      Assists    Errors  League_N  Division_W  NewLeague_N  \n",
       "260  1.746229  3.022233      True        True         True  \n",
       "92   0.022276  2.574735     False       False        False  \n",
       "137 -0.434676  0.635577     False       False        False  \n",
       "90  -0.545452 -0.706917     False        True        False  \n",
       "100 -0.213124  0.635577     False       False        False  \n",
       "..        ...       ...       ...         ...          ...  \n",
       "274  0.326910  1.232241      True       False         True  \n",
       "196 -0.822392 -1.303581     False        True        False  \n",
       "159  1.310048  0.933909     False        True        False  \n",
       "17   0.403069  1.083075     False        True        False  \n",
       "162 -0.690846 -1.005249     False        True         True  \n",
       "\n",
       "[184 rows x 19 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 81
    },
    "id": "zU4A-f7_p_ho",
    "outputId": "719670d9-fdc1-4814-e167-0018df873b3e"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-2 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-2 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-2 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-2 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Lasso(alpha=1)</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 fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;Lasso<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.Lasso.html\">?<span>Documentation for Lasso</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>Lasso(alpha=1)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "Lasso(alpha=1)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import Lasso\n",
    "\n",
    "reg = Lasso(alpha=1)\n",
    "reg.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "eAW1NYJoqDX3",
    "outputId": "ea7314f4-cdff-4e4d-ba4a-b55b186f077a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R squared training set 60.43\n",
      "R squared test set 33.01\n"
     ]
    }
   ],
   "source": [
    "print('R squared training set', round(reg.score(X_train, y_train)*100, 2))\n",
    "print('R squared test set', round(reg.score(X_test, y_test)*100, 2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "rH-BG-7GqF0o",
    "outputId": "043c34fb-deac-4228-e4d7-0181bdc02e94"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE training set 80571.73\n",
      "MSE test set 134426.33\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "# Training data\n",
    "pred_train = reg.predict(X_train)\n",
    "mse_train = mean_squared_error(y_train, pred_train)\n",
    "print('MSE training set', round(mse_train, 2))\n",
    "\n",
    "# Test data\n",
    "pred = reg.predict(X_test)\n",
    "mse_test =mean_squared_error(y_test, pred)\n",
    "print('MSE test set', round(mse_test, 2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 476
    },
    "id": "IbZ_tni5qJof",
    "outputId": "f346a19a-23cf-4216-f7bf-83a4959594fd"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "alphas = np.linspace(0.01,500,100)\n",
    "lasso = Lasso(max_iter=10000)\n",
    "coefs = []\n",
    "\n",
    "for a in alphas:\n",
    "    lasso.set_params(alpha=a)\n",
    "    lasso.fit(X_train, y_train)\n",
    "    coefs.append(lasso.coef_)\n",
    "\n",
    "ax = plt.gca()\n",
    "\n",
    "ax.plot(alphas, coefs)\n",
    "ax.set_xscale('log')\n",
    "plt.axis('tight')\n",
    "plt.xlabel('alpha')\n",
    "plt.ylabel('Standardized Coefficients')\n",
    "plt.title('Lasso coefficients as a function of alpha');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 81
    },
    "id": "6cQgBrJSqN2F",
    "outputId": "f795eaab-8973-4405-f1a6-d83f991566aa"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-3 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-3 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-3 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-3 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-3 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-3 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-3 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-3 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-3 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-3 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LassoCV(cv=5, max_iter=10000, random_state=0)</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 fitted 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 fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;LassoCV<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.LassoCV.html\">?<span>Documentation for LassoCV</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LassoCV(cv=5, max_iter=10000, random_state=0)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "LassoCV(cv=5, max_iter=10000, random_state=0)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LassoCV\n",
    "\n",
    "# Lasso with 5 fold cross-validation\n",
    "model = LassoCV(cv=5, random_state=0, max_iter=10000)\n",
    "\n",
    "# Fit model\n",
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "pVGC_8SrqQyh",
    "outputId": "005b2b45-559c-40aa-e82b-7b59b3017eb0"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.3441244939374593"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.alpha_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 81
    },
    "id": "b9-S9TPuqTq-",
    "outputId": "2cf57a5e-b558-4a62-e19e-fd85189cbfdd"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-4 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-4 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-4 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-4 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-4 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-4 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-4 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-4 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-4 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-4 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-4 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Lasso(alpha=2.3441244939374593)</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 fitted 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 fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;Lasso<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.Lasso.html\">?<span>Documentation for Lasso</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>Lasso(alpha=2.3441244939374593)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "Lasso(alpha=2.3441244939374593)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Set best alpha\n",
    "lasso_best = Lasso(alpha=model.alpha_)\n",
    "lasso_best.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "xxov_yRIqWMs",
    "outputId": "a5fdc604-9bc6-45b9-8462-be7210369915"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(-176.45309657050498, 'AtBat'), (271.23333276345323, 'Hits'), (-13.049492223041677, 'HmRun'), (-48.97878412496759, 'Runs'), (-13.83696437015553, 'RBI'), (140.12896436568295, 'Walks'), (-10.616534012348882, 'Years'), (-0.0, 'CAtBat'), (0.0, 'CHits'), (78.65781330867388, 'CHmRun'), (355.66188056426347, 'CRuns'), (60.50548334806944, 'CRBI'), (-262.7512352402544, 'CWalks'), (65.61587416521267, 'PutOuts'), (-0.14505342495227297, 'Assists'), (-1.2293157493169835, 'Errors'), (99.66112742179898, 'League_N'), (-116.86405569164934, 'Division_W'), (-69.87497671182551, 'NewLeague_N')]\n"
     ]
    }
   ],
   "source": [
    "print(list(zip(lasso_best.coef_, X)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 645
    },
    "id": "I65MlKax_YRf",
    "outputId": "78875bd6-6789-4105-a2b3-843fbfd19868"
   },
   "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>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-176.453097</td>\n",
       "      <td>AtBat</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>271.233333</td>\n",
       "      <td>Hits</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-13.049492</td>\n",
       "      <td>HmRun</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-48.978784</td>\n",
       "      <td>Runs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-13.836964</td>\n",
       "      <td>RBI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>140.128964</td>\n",
       "      <td>Walks</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-10.616534</td>\n",
       "      <td>Years</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>-0.000000</td>\n",
       "      <td>CAtBat</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>CHits</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>78.657813</td>\n",
       "      <td>CHmRun</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>355.661881</td>\n",
       "      <td>CRuns</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>60.505483</td>\n",
       "      <td>CRBI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>-262.751235</td>\n",
       "      <td>CWalks</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>65.615874</td>\n",
       "      <td>PutOuts</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>-0.145053</td>\n",
       "      <td>Assists</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>-1.229316</td>\n",
       "      <td>Errors</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>99.661127</td>\n",
       "      <td>League_N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>-116.864056</td>\n",
       "      <td>Division_W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>-69.874977</td>\n",
       "      <td>NewLeague_N</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             0            1\n",
       "0  -176.453097        AtBat\n",
       "1   271.233333         Hits\n",
       "2   -13.049492        HmRun\n",
       "3   -48.978784         Runs\n",
       "4   -13.836964          RBI\n",
       "5   140.128964        Walks\n",
       "6   -10.616534        Years\n",
       "7    -0.000000       CAtBat\n",
       "8     0.000000        CHits\n",
       "9    78.657813       CHmRun\n",
       "10  355.661881        CRuns\n",
       "11   60.505483         CRBI\n",
       "12 -262.751235       CWalks\n",
       "13   65.615874      PutOuts\n",
       "14   -0.145053      Assists\n",
       "15   -1.229316       Errors\n",
       "16   99.661127     League_N\n",
       "17 -116.864056   Division_W\n",
       "18  -69.874977  NewLeague_N"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(list(zip(lasso_best.coef_, X)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "GX-W2vgEqZo2",
    "outputId": "25375559-dd75-4cbc-e2a8-12d0decb82ec"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R squared training set 59.18\n"
     ]
    }
   ],
   "source": [
    "print('R squared training set', round(lasso_best.score(X_train, y_train)*100, 2))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "A4DLhPnkqcW5",
    "outputId": "c0986990-4699-496d-eba6-d56f03706e78"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "129468.59746481"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_squared_error(y_test, lasso_best.predict(X_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 476
    },
    "id": "HILTw_wpqeuO",
    "outputId": "4c9dad70-e091-4721-8496-e3cd3ffaa1e2"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.semilogx(model.alphas_, model.mse_path_, \":\")\n",
    "plt.plot(\n",
    "    model.alphas_ ,\n",
    "    model.mse_path_.mean(axis=-1),\n",
    "    \"k\",\n",
    "    label=\"Average across the folds\",\n",
    "    linewidth=2,\n",
    ")\n",
    "plt.axvline(\n",
    "    model.alpha_, linestyle=\"--\", color=\"k\", label=\"alpha: CV estimate\"\n",
    ")\n",
    "\n",
    "plt.legend()\n",
    "plt.xlabel(\"alphas\")\n",
    "plt.ylabel(\"Mean square error\")\n",
    "plt.title(\"Mean square error on each fold\")\n",
    "plt.axis(\"tight\")\n",
    "\n",
    "ymin, ymax = 50000, 250000\n",
    "plt.ylim(ymin, ymax);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "tC6I9Qe4x8i3",
    "outputId": "fd33b24d-15be-447a-9193-2764db9949e8"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Ridge Regression:\n",
      "MSE: 142408.39136111396\n",
      "MSE: 79210.90860181357\n"
     ]
    }
   ],
   "source": [
    "# Ridge Regression\n",
    "from sklearn.linear_model import Ridge, ElasticNet\n",
    "ridge_model = Ridge(alpha=0.1)  # Adjust alpha for regularization strength\n",
    "ridge_model.fit(X_train, y_train)\n",
    "ridge_y_pred_test = ridge_model.predict(X_test)\n",
    "ridge_y_pred_train = ridge_model.predict(X_train)\n",
    "ridge_mse_test = mean_squared_error(y_test, ridge_y_pred_test)\n",
    "ridge_mse_train = mean_squared_error(y_train, ridge_y_pred_train)\n",
    "#ridge_r2 = r2_score(y_test, ridge_y_pred_)\n",
    "print(\"\\nRidge Regression:\")\n",
    "print(f\"MSE: {ridge_mse_test}\")\n",
    "print(f\"MSE: {ridge_mse_train}\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "IYx1zAjWymjM",
    "outputId": "7ad46dfe-ef51-4ad2-c02c-3ed39d481251"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Elastic Net Regression:\n",
      "MSE: 128123.64676256798\n",
      "MSE: 88255.34545236964\n"
     ]
    }
   ],
   "source": [
    "# Elastic Net Regression\n",
    "elastic_net_model = ElasticNet(alpha=0.1, l1_ratio=0.5)  # Adjust alpha and l1_ratio\n",
    "elastic_net_model.fit(X_train, y_train)\n",
    "elastic_y_pred_train = elastic_net_model.predict(X_train)\n",
    "elastic_net_y_pred = elastic_net_model.predict(X_test)\n",
    "elastic_net_mse = mean_squared_error(y_test, elastic_net_y_pred)\n",
    "#elastic_net_r2 = r2_score(y_test, elastic_net_y_pred)\n",
    "elastic_mse_test = mean_squared_error(y_test, elastic_net_y_pred)\n",
    "elastic_mse_train = mean_squared_error(y_train, elastic_y_pred_train)\n",
    "print(\"\\nElastic Net Regression:\")\n",
    "print(f\"MSE: {elastic_mse_test}\")\n",
    "print(f\"MSE: {elastic_mse_train}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.12.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}