Project_Machine_Learning_wih_Ridge_and_Lasso.ipynb

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    {
      "cell_type": "markdown",
      "id": "1480f463",
      "metadata": {
        "id": "1480f463"
      },
      "source": [
        "## Import the libraries"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 126,
      "id": "f5504755",
      "metadata": {
        "id": "f5504755"
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      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import math\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "sns.set()\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.linear_model import RidgeCV\n",
        "from sklearn.linear_model import LassoCV\n",
        "from sklearn.linear_model import Lasso\n",
        "from sklearn.model_selection import RepeatedKFold\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import mean_squared_error"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "22487675",
      "metadata": {
        "id": "22487675"
      },
      "source": [
        "## Load the data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 127,
      "id": "3decc285",
      "metadata": {
        "colab": {
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        "id": "3decc285",
        "outputId": "52f8a1e5-c9e5-4395-e16d-5e94643ea766"
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          "data": {
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              "     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",
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              "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]"
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              "      border-bottom-color: var(--fill-color);\n",
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              "    20% {\n",
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              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
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              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
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              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 127
        }
      ],
      "source": [
        "data = pd.read_csv('Hitters.csv')\n",
        "df_hitters = data.copy()\n",
        "df_hitters"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Check the data types\n",
        "df_hitters.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RCOXnNvO2wYY",
        "outputId": "c619ec94-b346-4ddc-e1f1-47d2558b73d7"
      },
      "id": "RCOXnNvO2wYY",
      "execution_count": 128,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "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"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Check for duplicates\n",
        "df_hitters.duplicated().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uHRtt7-yB31C",
        "outputId": "4945efc0-4575-4776-bc5f-20587fc1196b"
      },
      "id": "uHRtt7-yB31C",
      "execution_count": 129,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 129
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 130,
      "id": "ee7db941",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ee7db941",
        "outputId": "3eb15ae4-5258-4c8b-f7d8-053bf26cd139"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The league types are: ['A' 'N']\n",
            "The divison types are: ['E' 'W']\n",
            "The new league options are: ['A' 'N']\n"
          ]
        }
      ],
      "source": [
        "# Categorical variables\n",
        "print('The league types are:', df_hitters['League'].unique())\n",
        "print('The divison types are:', df_hitters['Division'].unique())\n",
        "print('The new league options are:', df_hitters['NewLeague'].unique())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 131,
      "id": "de8c4d19",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "de8c4d19",
        "outputId": "ac59a3f8-c27c-4962-85a3-715b2adf6025"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "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  PutOuts  Assists  Errors  Salary  League_N  \\\n",
              "0       30    29      14      446       33      20     NaN         0   \n",
              "1      321   414     375      632       43      10   475.0         1   \n",
              "2      224   266     263      880       82      14   480.0         0   \n",
              "3      828   838     354      200       11       3   500.0         1   \n",
              "4       48    46      33      805       40       4    91.5         1   \n",
              "..     ...   ...     ...      ...      ...     ...     ...       ...   \n",
              "317    379   311     138      325        9       3   700.0         1   \n",
              "318    897   451     875      313      381      20   875.0         0   \n",
              "319    217    93     146       37      113       7   385.0         0   \n",
              "320    470   420     332     1314      131      12   960.0         0   \n",
              "321    775   357     249      408        4       3  1000.0         0   \n",
              "\n",
              "     Division_W  NewLeague_N  \n",
              "0             0            0  \n",
              "1             1            1  \n",
              "2             1            0  \n",
              "3             0            1  \n",
              "4             0            1  \n",
              "..          ...          ...  \n",
              "317           0            1  \n",
              "318           0            0  \n",
              "319           1            0  \n",
              "320           0            0  \n",
              "321           1            0  \n",
              "\n",
              "[322 rows x 20 columns]"
            ],
            "text/html": [
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              "    <tr>\n",
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              "      <td>497</td>\n",
              "      <td>127</td>\n",
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              "      <td>806</td>\n",
              "      <td>32</td>\n",
              "      <td>379</td>\n",
              "      <td>311</td>\n",
              "      <td>138</td>\n",
              "      <td>325</td>\n",
              "      <td>9</td>\n",
              "      <td>3</td>\n",
              "      <td>700.0</td>\n",
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              "      <td>39</td>\n",
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              "      <td>451</td>\n",
              "      <td>875</td>\n",
              "      <td>313</td>\n",
              "      <td>381</td>\n",
              "      <td>20</td>\n",
              "      <td>875.0</td>\n",
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              "      <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>37</td>\n",
              "      <td>113</td>\n",
              "      <td>7</td>\n",
              "      <td>385.0</td>\n",
              "      <td>0</td>\n",
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              "    <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",
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              "      <td>631</td>\n",
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              "      <td>77</td>\n",
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              "      <td>249</td>\n",
              "      <td>408</td>\n",
              "      <td>4</td>\n",
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              "      <td>1000.0</td>\n",
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              "<p>322 rows × 20 columns</p>\n",
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              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-48cb5a67-bfb2-4d92-8e8f-89ed3bcdf666 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-48cb5a67-bfb2-4d92-8e8f-89ed3bcdf666');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-66ade36e-7e9a-44f7-807d-cb92aab9b900\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-66ade36e-7e9a-44f7-807d-cb92aab9b900')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-66ade36e-7e9a-44f7-807d-cb92aab9b900 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 131
        }
      ],
      "source": [
        "# Encode the categorical variables\n",
        "df_hitters_num = pd.get_dummies(df_hitters, columns = ['League', 'Division', 'NewLeague'], drop_first=True)\n",
        "df_hitters_num"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 132,
      "id": "27f59a1e",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "27f59a1e",
        "outputId": "e298042d-ef2f-4175-e232-f545b14cc087"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "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"
            ]
          },
          "metadata": {},
          "execution_count": 132
        }
      ],
      "source": [
        "# Check if there are NaN values\n",
        "df_hitters.isnull().sum()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "'Salary' column has 59 null values. We will drop them at first and then use the original dataset after the machine learning process to predict their values."
      ],
      "metadata": {
        "id": "M46OrIeT3WKn"
      },
      "id": "M46OrIeT3WKn"
    },
    {
      "cell_type": "code",
      "execution_count": 133,
      "id": "5bc9f90b",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5bc9f90b",
        "outputId": "f08aaae8-7d19-48f3-9ed1-82379820b159"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "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",
              "PutOuts        0\n",
              "Assists        0\n",
              "Errors         0\n",
              "Salary         0\n",
              "League_N       0\n",
              "Division_W     0\n",
              "NewLeague_N    0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 133
        }
      ],
      "source": [
        "# Drop null values\n",
        "df_hitters_num_nonull = df_hitters_num.dropna()\n",
        "df_hitters_num_nonull.isnull().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 134,
      "id": "36e37db3",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 501
        },
        "id": "36e37db3",
        "outputId": "a9d96022-1896-47ea-e967-17c011c501f4"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x500 with 1 Axes>"
            ],
            "image/png": 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79lVCQkKN6+bMmaNRo0Zp6tSpkqTMzEzt2rVL8+fPV35+fgSrBAAgfIx6y7ouxcXF2rt3r7Kzs/2Wjxw5Uhs2bFBFRUWUKgMAoH6MDOScnBz17t1bw4YN0xNPPCG32y1JKioqkiSlpKT4je/evbsqKytVXFwc8VoBAAgHo96yTkxM1JQpU3TRRRfJZrNpzZo1mj17tg4dOqQZM2aopKREkuR0Ov3u573tXR+qmJiaX584HHa/n7XxrrfZbLLZbPWq5Vy8c9dVT7QE2i98jZ4Fh34Fh34FJ1r9MiqQBw0apEGDBvluX3LJJWrRooWeeeYZTZo0qUG3bbfb1KFDm3OOcTpbBTSXw2FXTIwjHGXVOn8w9USL6fWZiJ4Fh34Fh34FJ9L9MiqQa5Kdna2nnnpK27dvV3x8vCSptLRUiYmJvjEul0uSfOtD4fFYcrnKalzncNjldLaSy1Uut9tT6xzecW63R1VV7pBrqYu3hrrqiZZA+4Wv0bPg0K/g0K/ghNKvunboAmF8IH9TamqqpDPHkr2/e2/HxsYqKSmpXvNXVZ278WeCtu4/jmVZsiyrXrXUNX8w9USL6fWZiJ4Fh34Fh34FJ9L9Mv6AQkFBgRwOh/r06aOkpCQlJyersLCw2pisrCzFxcVFqUoAAOrHqD3k8ePHKyMjQ7169ZIkrV69Wn//+981btw431vUU6ZM0Z133qlu3bopIyNDBQUF2rZtmxYvXhzN0gEAqBejAjklJUXLli3TwYMH5fF4lJycrHvuuUdjx471jcnJyVF5ebny8/O1cOFCpaSkaN68eerfv38UKwcAoH6MCuT77rsvoHF5eXnKy8tr4GoAAIgc448hAwDQHIQcyOPGjdOGDRtqXf/ee+9p3LhxoU4PAECzEnIgb9q0Sf/9739rXX/06FFt3rw51OkBAGhW6vWW9blOD/n555+rTZv6f1EaAIDmIKgPdb3yyit65ZVXfLcXLFigv//979XGlZaWaufOnRo8eHD9KwQAoBkIKpDLy8t17Ngx3+2TJ0/Kbq++k926dWtde+21+sUvflH/CgEAaAaCCuQxY8ZozJgxkqShQ4fq3nvv1bBhwxqkMAAAmpOQv4e8Zs2acNYBAECzVu8Tg5w4cUJffPGFXC5XjRdU+P73v1/fTQAA0OSFHMhHjx7VrFmz9MYbb8jtrn6pQcuyZLPZtH379noVCABAcxByIM+YMUNr167V2LFjNWDAADmdznDWBQBAsxJyIK9fv1433HCD7rrrrnDWAwBAsxTyiUFatmypLl26hLMWAACarZADOTc3V6tWrQpnLQAANFshv2U9YsQIbd68WePHj9c111yj888/Xw6Ho9q4vn371qtAAACag5AD2XuCEEl69913q63nU9YNz+Fo+KtnejyWPJ7qX2cDAIRXyIH88MMPh7MOBKFd61h5PJaczlYNvi23x6Pjx8oIZQBoYCEH8lVXXRXOOhCEli1iZLfb9MLrO3Toy5MNtp3zElprzOW9ZbfbCGQAaGD1PlMXoufw0TIdOHIi2mUAAMIg5ED+zW9+U+cYm82mhx56KNRNAADQbIQcyBs3bqy2zOPx6MiRI3K73UpISFCrVg1/jBMAgKYg7Fd7qqys1EsvvaRnnnlGTz31VMiFAQDQnIT9ezOxsbG6/vrrNXDgQD344IPhnh4AgCapwb7IesEFF2jz5s0NNT0AAE1KgwXyu+++yzFkAAACFPIx5Hnz5tW4vLS0VJs3b9ann36qiRMnhlwYAADNSdgDOT4+XklJSZo5c6Z++tOfhlwYAADNSciBvGPHjnDWAQBAs9bwVycAAAB1qvepMzdt2qS33npLX3zxhSSpc+fOGjJkiC6++OJ6FwcAQHMRciBXVFToV7/6lVatWiXLsuR0OiVJLpdLf/3rX/XDH/5Qjz32mGJjY8NWLAAATVXIb1nPnz9fb775pm688Ub985//1KZNm7Rp0yatX79eN910k9544w3Nnz8/nLUCANBkhRzIr732mq666irddddd+ta3vuVb3rFjR/3617/WlVdeqX/84x9hKRIAgKYu5EA+cuSI+vXrV+v6fv366ciRI6FODwBAsxJyIJ9//vnatGlTres3b96s888/P9TpdfLkSQ0ePFi9evXSv/71L791S5Ys0YgRI5SWlqbc3FytXbs25O0AAGCCkAP5yiuv1MqVKzVjxgwVFRXJ7XbL4/GoqKhI999/vwoLC3XVVVeFXNjjjz8ut9tdbfmKFSs0ffp0ZWdnKz8/X+np6Zo8ebI++uijkLcFAEC0hfwp60mTJqm4uFh///vftWTJEtntZ7Ld4/HIsixdddVVmjRpUkhz79mzR88//7zuvvtu3X///X7r5syZo1GjRmnq1KmSpMzMTO3atUvz589Xfn5+qA8HAICoCjmQHQ6HHnnkEf385z/X22+/rQMHDkiSunTposGDB+uCCy4IuahZs2bp2muvVUpKit/y4uJi7d27V7/+9a/9lo8cOVK///3vVVFRobi4uJC3CwBAtAQVyKdPn9bvfvc7fec739HYsWMlnbnM4tnh++yzz+rFF1/UvffeG/T3kAsLC7Vr1y7NnTtXn3zyid+6oqIiSaoW1N27d1dlZaWKi4vVvXv3oLYHAIAJggrkl156Sa+88ooKCgrOOW7IkCF69NFH1bNnT40ZMybg+cvLy/XII4/o9ttvV9u2bautLykpkSTfSUi8vLe960MVE1PzIXWHw+73szbe9TabTTabrV61nItvbpsisp26HvfZAu0XvkbPgkO/gkO/ghOtfgUVyCtXrtSPfvQjJSUlnXNct27ddPnll2vFihVBBfKCBQvUsWNH/fjHPw6mrLCw223q0KHNOcc4nYFd39nhsCsmxhGOsmqe/6vj9Q57A2/nqydjoI/7bKHerzmjZ8GhX8GhX8GJdL+CCuRdu3bpiiuuCGhs//79g/o60oEDB/TUU09p/vz5Ki0tlSSVlZX5fp48eVLx8fGSzlxzOTEx0Xdfl8slSb71ofB4LLlcZTWuczjscjpbyeUql9vtqXUO7zi326OqquqfEA8Xt8fj+9mg2/nqsdb1uM8WaL/wNXoWHPoVHPoVnFD6VdcOXSCCCuTKysqAjwnHxsaqoqIi4Ln379+vyspKTZw4sdq6cePG6aKLLtJjjz0m6cyx5NTUVN/6oqIixcbG1rnnXpeqqnM3/kzQ1v3HsSxLlmXVq5a65j/ziyKynUAf99lCvV9zRs+CQ7+CQ7+CE+l+BRXI5513nnbv3h3Q2N27d+u8884LeO7evXvr2Wef9Vu2fft2Pfzww5o5c6bS0tKUlJSk5ORkFRYWavjw4b5xBQUFysrK4hPWAIBGK6hA/sEPfqBXX31Vt9xyizp27FjruC+//FKvvvqqRowYEfDcTqdTGRkZNa7r27ev+vbtK0maMmWK7rzzTnXr1k0ZGRkqKCjQtm3btHjx4mAeCgAARgnqI2QTJkzQ6dOndcMNN2jr1q01jtm6dat+/vOf6/Tp07r55pvDUuQ35eTk6MEHH9Ty5cs1fvx4bdmyRfPmzVP//v3Dvi0AACIlqD3kpKQkzZ49W3fccYeuvfZaJSUlqWfPnmrTpo1Onjyp3bt3a9++fWrZsqX++Mc/qlu3bvUqLiMjQzt37qy2PC8vT3l5efWaGwAAkwR9pq4hQ4boH//4h/Lz8/XWW29p1apVvnXnnXee8vLyNGHChHp/wAoAgOYkpFNndu3aVTNnzpQknThxQidPnlSbNm1qPJkHAACoW8jnsvZq27YtQQwAQD1xHjUAAAxAIAMAYAACGQAAA9T7GDIQLna7TXZ7w129ysvjseTxNNwpRwEgFAQyjGC329S+Q2vflawaktvj0fFjZYQyAKMQyDCC3W6Tw27X84XbdfhozVfdCofzElprzOW9ZbfbCGQARiGQYZTDR8t04MiJaJcBABHHh7oAADAAe8iok8MR3Os27/hg7hfsNgCgqSGQUat2rWPl8VhyOluFdP9Q7wcAzRGBjFq1bBEju92mF17foUNfngz4fjabTQ6HXW63R5YV2AeneiUnKPsHKbLZGv5rTwBgIgIZdQr2g1Y2m00xMQ5VVbkDDuTEDuxNA2jeOHAHAIABCGQAAAxAIAMAYAACGQAAAxDIAAAYgEAGAMAABDIAAAYgkAEAMACBDACAAQhkAAAMQCADAGAAAhkAAAMQyAAAGIBABgDAAAQyAAAGIJABADAAgQwAgAEIZAAADEAgAwBgAKMCed26dbr++uuVmZmpCy+8UMOGDdPDDz+s0tJSv3Fr1qxRbm6u0tLSNGLECC1btixKFQMAEB4x0S7gm44fP65+/fpp7Nixat++vXbv3q25c+dq9+7deuqppyRJ77//viZPnqyf/OQnuueee/Tee+/p3nvvVZs2bXT55ZdH+REAABAaowJ59OjRfrczMjIUFxen6dOn69ChQ+rUqZMWLFigfv366YEHHpAkZWZmqri4WHPmzCGQAQCNllFvWdekffv2kqTKykpVVFRo48aN1YJ35MiR2rNnj/bv3x+FCgEAqD8jA9ntduv06dP65JNPNH/+fA0dOlRdu3bVvn37VFlZqdTUVL/x3bt3lyQVFRVFo1wAAOrNqLesvS677DIdOnRIkjRo0CA99thjkqSSkhJJktPp9Bvvve1dH6qYmJpfnzgcdr+ftfGut9lsstls9arlXHxz22Tmdmxf/7QpsPtF+jHV9beMtECfYziDfgWHfgUnWv0yMpAXLlyo8vJy/fvf/9aCBQs0adIk/fWvf23QbdrtNnXo0OacY5zOVgHN5XDYFRPjCEdZNc9vt/t+mrydGEfg94nYY/rqf7BA/5aRZmpdpqJfwaFfwYl0v4wM5AsuuECS1L9/f6WlpWn06NF688031aNHD0mq9jUol8slSYqPjw95mx6PJZerrMZ1DoddTmcruVzlcrs9tc7hHed2e1RV5Q65lrq4PR7fTyO3YzsTxlVut2Q18LaC5P371fW3jLRAn2M4g34Fh34FJ5R+1bVDFwgjA/mbevXqpdjYWO3bt09Dhw5VbGysioqKNGjQIN8Y77Hjs48tB6uq6tyNPxO0df9xLMuSZQWYRCHwzW3JyO343qYO4n6RfkyB/i0jzdS6TEW/gkO/ghPpfhl/QGHr1q2qrKxU165dFRcXp4yMDL3++ut+YwoKCtS9e3d17do1SlUCAFA/Ru0hT548WRdeeKF69eqlli1baseOHXryySfVq1cvDR8+XJJ06623aty4cfrtb3+r7Oxsbdy4UcuXL9ef/vSnKFcPAEDojArkfv36qaCgQAsXLpRlWerSpYvy8vI0fvx4xcXFSZIGDBiguXPnavbs2Vq6dKk6d+6sWbNmKTs7O8rVAwAQOqMCeeLEiZo4cWKd44YNG6Zhw4ZFoCIAACLD+GPIAAA0BwQyAAAGIJABADAAgQwAgAEIZAAADEAgAwBgAAIZAAADEMgAABiAQAYAwAAEMgAABiCQAQAwAIEMAIABCGQAAAxAIAMAYAACGQAAAxDIAAAYgEAGAMAABDIAAAYgkAEAMACBDACAAQhkAAAMQCADAGAAAhkAAAMQyAAAGIBABgDAADHRLgCIBoej4V+LejyWPB6rwbcDoGkgkNGstGsdK4/HktPZqsG35fZ4dPxYGaEMICAEMpqVli1iZLfb9MLrO3Toy5MNtp3zElprzOW9ZbfbCGQAASGQ0SwdPlqmA0dORLsMAPDhQ10AABiAQAYAwAAEMgAABiCQAQAwAIEMAIABCGQAAAxgVCCvXLlSt956qwYPHqz09HSNHj1aS5culWX5f49zyZIlGjFihNLS0pSbm6u1a9dGqWIAAMLDqEB++umn1apVK02bNk0LFizQ4MGDNX36dM2fP983ZsWKFZo+fbqys7OVn5+v9PR0TZ48WR999FH0CgcAoJ6MOjHIggULlJCQ4LudlZWl48eP669//av+3//7f7Lb7ZozZ45GjRqlqVOnSpIyMzO1a9cuzZ8/X/n5+VGqHACA+jFqD/mbYezVu3dvnThxQmVlZSouLtbevXuVnZ3tN2bkyJHasGGDKioqIlUqAABhZVQg1+SDDz5Qp06d1LZtWxUVFUmSUlJS/MZ0795dlZWVKi4ujkaJAADUm1FvWZ/t/fffV0FBge6++25JUklJiSTJ6XT6jfPe9q4PVUxMza9PvJfqq+uSfd71NptNNputXrWci29um8zcju3rnzYFdj/jH1OI2wn0Mo+BPsdwBv0KDv0KTrT6ZWwgHzx4ULfffrsyMjI0bty4Bt+e3W5Thw5tzjkm0Ev2ORx2xcQ4wlFWzfPb7b6fJm8nxhH4fRrLYwp4O1/9jxzsZR4jcVnIpoR+BYd+BSfS/TIykF0ulyZMmKD27dtr7ty5sn/1j2h8fLwkqbS0VImJiX7jv7k+FB6PJZerrMZ1DoddTmcruVzlcrs9tc7hHed2e1RV5Q65lrq4PR7fTyO3YzsTxlVutxTglQeNf0zBbuer50ldzxmvQJ9jOIN+BYd+BSeUftW1QxcI4wL51KlTuuWWW1RaWqqXXnpJ7dq1861LTU2VJBUVFfl+996OjY1VUlJSvbZdVXXuxp8J2rr/OJZlVfvudDj55rZk5HZ8b1MHcT/TH1Oo2wn0OeMV7Pjmjn4Fh34FJ9L9MuqAQlVVlaZOnaqioiItWrRInTp18luflJSk5ORkFRYW+i0vKChQVlaW4uLiIlkuAABhY9Qe8syZM7V27VpNmzZNJ06c8DvZR58+fRQXF6cpU6bozjvvVLdu3ZSRkaGCggJt27ZNixcvjl7hAADUk1GBvH79eknSI488Um3d6tWr1bVrV+Xk5Ki8vFz5+flauHChUlJSNG/ePPXv3z/S5QIAEDZGBfKaNWsCGpeXl6e8vLwGrgYAgMgx6hgyAADNFYEMAIABCGQAAAxAIAMAYAACGQAAAxDIAAAYgEAGAMAABDIAAAYgkAEAMACBDACAAQhkAAAMQCADAGAAAhkAAAMQyAAAGIBABgDAAAQyAAAGIJABADAAgQwAgAEIZAAADEAgAwBgAAIZAAADEMgAABiAQAYAwAAEMgAABiCQAQAwQEy0CwCaMocjsNe83nGBjv8mj8eSx2MFfT8AZiGQgQbQrnWsPB5LTmeroO4X7HhJcns8On6sjFAGGjkCGWgALVvEyG636YXXd+jQlyfrHG+z2eRw2OV2e2RZgQfreQmtNeby3rLbbQQy0MgRyEADOny0TAeOnKhznM1mU0yMQ1VV7qACGUDTwYe6AAAwAIEMAIABCGQAAAxAIAMAYAACGQAAAxgVyJ9//rlmzJih0aNHq0+fPsrJyalx3JIlSzRixAilpaUpNzdXa9eujXClAACEl1GBvHv3bq1bt07/8z//o+7du9c4ZsWKFZo+fbqys7OVn5+v9PR0TZ48WR999FFkiwUAIIyM+h7y0KFDNXz4cEnStGnT9PHHH1cbM2fOHI0aNUpTp06VJGVmZmrXrl2aP3++8vPzI1kuAABhY9Qest1+7nKKi4u1d+9eZWdn+y0fOXKkNmzYoIqKioYsDwCABmNUINelqKhIkpSSkuK3vHv37qqsrFRxcXE0ygIAoN6Mesu6LiUlJZIkp9Ppt9x727s+VDExNb8+CfRKPN71NptNNputXrWci29um8zcju3rnzYFdj/jH1NDbyeEnn1zO6FcJaoxq8/VsZoj+hWcaPWrUQVyQ7LbberQoc05xwR6JR6Hw66YGEc4yqp5/q/e2nfYzd5OjCPw+zSWx9TQ2wmmZ9LX/2CEcpWopqC5Pu5Q0a/gRLpfjSqQ4+PjJUmlpaVKTEz0LXe5XH7rQ+HxWHK5ympc53DY5XS2kstVLrfbU+sc3nFut0dVVe6Qa6mL2+Px/TRyO7YzwVLldksBXifB+MfU0NsJoWeSfM/Hup6bTU2g/0/iDPoVnFD6VdcOXSAaVSCnpqZKOnMs2fu793ZsbKySkpLqNX9V1bkbfyZo6/7jWJbVoFfs8c1tycjt+N5yDeJ+pj+mht5OKD375nYCfW42Nc31cYeKfgUn0v1qVAcUkpKSlJycrMLCQr/lBQUFysrKUlxcXJQqAwCgfozaQy4vL9e6deskSQcOHNCJEyd84XvxxRcrISFBU6ZM0Z133qlu3bopIyNDBQUF2rZtmxYvXhzN0gEAqBejAvnLL7/Ubbfd5rfMe/vZZ59VRkaGcnJyVF5ervz8fC1cuFApKSmaN2+e+vfvH42SAQAIC6MCuWvXrtq5c2ed4/Ly8pSXlxeBigAAiIxGdQwZAICmyqg9ZAChicQJDDweSx5Pw30yHWjuCGSgEWvXOlYejxWRExi4PR4dP1ZGKAMNhEAGGrGWLWJkt9v0wus7dOjLkw22nfMSWmvM5b1lt9sIZKCBEMhAE3D4aJkOHDkR7TIA1AMf6gIAwAAEMgAABiCQAQAwAIEMAIABCGQAAAxAIAMAYAACGQAAAxDIAAAYgEAGAMAABDIAAAYgkAEAMADnsgYQMC7zGDy73Sa73dbg22lqfWuOCGQAdeIyj6Gx221q36G1HPaGfyHTlPrWXBHIAOrEZR5DY7fb5LDb9Xzhdh0+WtZg22lqfWuuCGQAAeMyj6GhbwgEH+oCAMAABDIAAAYgkAEAMACBDACAAQhkAAAMwKesARinrhOQeNfX50QlnEgDpiGQARgj2BOQ1OdEJZxIA6YhkAEYI9ATkNhsNjkcdrndHllW8IHKiTRgIgIZgHHqOpGGzWZTTIxDVVXukAIZMBEf6gIAwADsIQMAjNMcr5JFIAMAjNJcr5JFIAMAjNJcr5JFIAMAjNTcrpLVKD/UtWfPHt14441KT0/XwIED9fvf/14VFRXRLgsAgJA1uj3kkpIS3XDDDUpOTtbcuXN16NAhPfLIIzp16pRmzJgR7fIANCL1OdOXCfMHur1wnNkskhpLneHW6AL5xRdf1MmTJzVv3jy1b99ekuR2uzVz5kzdcsst6tSpU3QLBGC8YM8IZrpAH084Hq/HY0Xk08/NUaML5LfffltZWVm+MJak7Oxs3X///Vq/fr2uvvrq6BUHoFEI9Ixg9dUrOUHZP0iRzdawAVbX46nvmc28vI+nqfTNNI0ukIuKivTjH//Yb5nT6VRiYqKKioqiVBWAxqihPzSU2CGye+C1PZ5wndnM+3iaWt9MYbMa2Xnn+vbtq9tuu00TJ070W56Tk6P+/fvrwQcfDGley6r9y+E2m2S32+XxeHSubnnHnSirkLsBP0IfG2NX65axTWY7kdwW22E7bIfteDnsNrVtHVft3/ZA/833mysMx70b3R5yQznzls653x6xB/gl9bat48JRUrPbTiS3xXbYDtthO161/dse6L/5YasjolsLA6fTqdLS0mrLS0pKFB8fH4WKAACov0YXyKmpqdWOFZeWlurIkSNKTU2NUlUAANRPowvkwYMH691335XL5fItKywslN1u18CBA6NYGQAAoWt0H+oqKSnRqFGjlJKSoltuucV3YpArrriCE4MAABqtRhfI0plTZz744IP68MMP1aZNG40ePVq333674uIi9+EjAADCqVEGMgAATU2jO4YMAEBTRCADAGAAAhkAAAMQyAAAGIBABgDAAAQyAAAGIJDPYc+ePbrxxhuVnp6ugQMH6ve//70qKiqiXVbEvfzyy+rVq1e1//7whz/4jVuyZIlGjBihtLQ05ebmau3atdXmKi0t1T333KOLL75Y/fv31y9/+UsdPnw4Ug+lQXz++eeaMWOGRo8erT59+ignJ6fGceHsz5YtW3TNNdeoX79+uuyyy7Rw4cJ6XVYvkgLp19ixY2t8zu3Zs8dvXHPo18qVK3Xrrbdq8ODBSk9P1+jRo7V06dJq9fP8OiOQfpn6/OJqT7UoKSnRDTfcoOTkZM2dO9d3RrBTp0412zOCLVq0SO3atfPd7tSpk+/3FStWaPr06Zo0aZIyMzNVUFCgyZMn67nnnlN6erpv3NSpU/Xvf/9bv/3tb9WiRQvNnj1bEyZM0LJlyxQT0zifjrt379a6det00UUXfXW5tur/I4azP59//rnGjx+vgQMHaurUqdq5c6f+8Ic/yOFwaPz48ZF62CELpF+S9N3vfld3332337KuXbv63W4O/Xr66afVpUsXTZs2TR06dNC7776r6dOn6+DBg5o8ebIknl/fFEi/JEOfXxZq9Je//MVKT0+3jh075lv24osvWr1797YOHjwYvcKiYNmyZVbPnj2tL7/8stYxP/rRj6w77rjDb9k111xj3Xzzzb7bW7ZssXr27Gm98847vmV79uyxevXqZa1YsSL8hUeI2+32/X733Xdbo0aNqjYmnP2ZPn26ddlll1mnT5/2LXvsscesAQMG+C0zVSD9uv76662JEyeec57m0q+a/r+77777rO9+97u+XvL8+log/TL1+cVb1rV4++23lZWVpfbt2/uWZWdny+PxaP369dErzEDFxcXau3evsrOz/ZaPHDlSGzZs8L3N//bbb8vpdPpdBCQ1NVW9e/fW22+/HdGaw6mua6aGuz9vv/22hg0b5neq2JEjR8rlcunDDz8Mx0NqUOG6xmxz6VdCQkK1Zb1799aJEydUVlbG8+ssdfUrUNHoF4Fci6KiomqXc3Q6nUpMTKx2+cfmIicnR71799awYcP0xBNPyO12S5KvHykpKX7ju3fvrsrKShUXF/vGpaSkyGaz+Y2r6ZKaTUk4+1NWVqb//Oc/1Z6bqampstlsTaqPmzZtUnp6utLS0nT99ddr8+bNfuubc78++OADderUSW3btuX5FYBv9svLxOdX4zxoFwEul0tOp7Pa8vj4eJWUlEShouhJTEzUlClTdNFFF8lms2nNmjWaPXu2Dh06pBkzZvj6cXa/vLe9610ul98xaK/4+Hh9/PHHDfwooiec/SktLa1xrri4OLVq1arJPDe///3va/To0UpOTtbhw4f15JNP6sYbb9Tf/vY39e/fX1Lz7df777+vgoIC3/FPnl/ndna/JHOfXwQy6jRo0CANGjTId/uSSy5RixYt9Mwzz2jSpElRrAxN1S9/+Uu/20OGDFFOTo4ef/xx5efnR6mq6Dt48KBuv/12ZWRkaNy4cdEux3i19cvU5xdvWdfC6XT6Xv18U0lJieLj46NQkVmys7Pldru1fft2Xz/O7pfL5ZIk33qn06kTJ05Um6up9zSc/fG+Yj97roqKCpWXlzfZPrZu3VqXXnqpPvnkE9+y5tYvl8ulCRMmqH379po7d67vWDzPr5rV1q+amPL8IpBrUdNxzdLSUh05cqTa8YLmztuPs/tVVFSk2NhYJSUl+cZ99tln1b7m8tlnnzXpnoazP61bt9a3v/3tanN579eU+3i25tSvU6dO6ZZbblFpaWm1rx/y/KruXP0KVDT6RSDXYvDgwXr33Xd9rzIlqbCwUHa73e9Td81VQUGBHA6H+vTpo6SkJCUnJ6uwsLDamKysLN+nDwcPHqySkhJt2LDBN+azzz7Tp59+qsGDB0e0/kgKd38GDx6s1atXq7Ky0m8up9PpO/7V1JSVlemtt95SWlqab1lz6VdVVZWmTp2qoqIiLVq0yO/7/xLPr7PV1a+aGPP8CupLUs3I8ePHrYEDB1rXX3+99c4771hLly61BgwYYM2cOTPapUXcTTfdZD3xxBPWW2+9Zb311lvW9OnTrV69elm/+93vfGNee+01q1evXtaf//xn67333rNmzJhh9enTx9qyZUu1uS699FKroKDAWr16tZWTk2Pl5uZalZWVkX5YYVNWVmatXLnSWrlypXX99ddbl156qe+29zuR4ezP3r17rfT0dGvKlCnWu+++az399NNW3759rUWLFkX0cYeqrn5t3rzZuuWWW6ylS5daGzZssF599VXryiuvtPr27Wtt3brVb67m0K/77rvP6tmzp/XUU09ZH374od9/3u+58vz6Wl39Mvn5ZbOsRnI+tCjYs2ePHnzwQX344Ydq06aNRo8erdtvv93v+2bNwaxZs/TOO+/o4MGD8ng8Sk5OVl5ensaOHev3lYAlS5YoPz9fX3zxhVJSUnTHHXfosssu85urtLRUDz/8sN58801VVVXpkksu0X333RfQq1hT7d+/X8OGDatx3bPPPquMjAxJ4e3Pli1b9Mgjj2j79u1KSEjQz372M02YMKHaVzRMVFe/zj//fD3wwAPauXOnjh8/rlatWql///6aPHmy+vXr5ze+OfRr6NChOnDgQI3rVq9e7Tu7FM+vM+rql9vtNvb5RSADAGAAjiEDAGAAAhkAAAMQyAAAGIBABgDAAAQyAAAGIJABADAAgQwAgAEIZAAADEAgA6hm7NixGjt2bLTLAJoVrocMNBE7d+7U/Pnz9a9//Uv//e9/1b59e/Xo0UNDhw4lXIFGgEAGmoAtW7Zo3Lhx6ty5s/Ly8pSYmKj//Oc/2rp1q5599lkCGWgECGSgCfjLX/6idu3aaenSpXI6nX7rvvzyyyhVdUZVVZU8Hk+zuygLECyOIQNNwL59+9SjR49qYSxJHTt29P2+bNkyjRs3TllZWbrwwgs1cuRIPf/883XOX1FRoT//+c+6+uqr9b3vfU/p6ekaM2aM3nvvPb9x+/fvV69evfTkk0/q6aef1vDhw5WWlqZt27YpPT1ds2bNqjb3wYMH1bt3bz3xxBMhPHKg6WAPGWgCunTpog8//FC7du1Sz549ax33wgsv6Dvf+Y6GDh2qmJgYrV27VjNnzpRlWfrZz35W6/1OnDihJUuWKCcnR3l5eTp58qSWLl2qm2++WUuWLFHv3r39xr/88ss6ffq0fvrTnyouLk6dO3fW8OHDtXLlSv3mN7+Rw+HwjV2+fLksy9IVV1xR/0YAjRiXXwSagPXr12vChAmSpH79+ul73/uesrKylJGRodjYWN+4U6dOqWXLln73HT9+vD7//HOtWrXKt8x7zPlvf/ubJMntdsvtdvu97exyuZSdna1LL71UDz30kKSvr3Xctm1bvfnmm0pISPCN/+c//6nx48crPz9fgwcP9i3Pzc1VfHy8b1tAc8Vb1kATMHDgQL344osaOnSoduzYoUWLFmn8+PEaPHiwVq9e7Rv3zTAuLS3V0aNHdfHFF6u4uFilpaW1zu9wOHxh7PF4dPz4cVVVVenCCy/Up59+Wm38j370I78wlqQf/OAHOu+88/Taa6/5lu3atUs7d+5Ubm5uyI8daCp4yxpoIvr166d58+apoqJCO3bs0KpVq/T000/rtttu0//93/+pR48e+uCDDzR37lx99NFHKi8v97t/aWmp2rVrV+v8r7zyip566il99tlnqqys9C3v2rVrtbE1LbPb7briiiv0wgsvqLy8XK1atdJrr72mFi1a6PLLL6/HIweaBgIZaGLi4uLUr18/9evXT8nJyfrNb36jwsJC5ebm6uc//7lSU1M1bdo0ffvb31ZsbKzWrVunp59+Wh6Pp9Y5X331VU2bNk3Dhw/X+PHj1bFjRzkcDj3xxBMqLi6uNv7st8W9rrzySj355JNatWqVcnJytHz5cg0ZMuScLwSA5oJABpqwCy+8UJJ0+PBhrVmzRhUVFVqwYIE6d+7sG7Nx48Y653n99deVlJSkefPmyWaz+ZbPmTMnqHp69uypPn366LXXXtP555+vL774Qvfdd19QcwBNFceQgSbgvffeU02fz1y3bp0kKTU11ffJ5m+OKy0t1bJly+qcv6b7bt26VR999FHQtY4ePVrr16/XM888o/bt2/t9wAtozthDBpqAWbNmqby8XD/84Q+VmpqqyspKbdmyRStXrlSXLl109dVX67///a9iY2M1adIkXXvttTp58qSWLFmijh076siRI+ecf8iQIXrjjTf0i1/8QkOGDNH+/fv14osvqkePHiorKwuq1pycHD366KN68803dd111/l9ChxozghkoAm46667VFhYqHXr1umll15SZWWlOnfurDFjxujWW2+V0+mU0+nUnDlzNHv2bP3v//6vvvWtb+m6665TQkKC7rnnnnPO7w30l156Sf/85z/Vo0cPPfrooyosLNSmTZuCqvVb3/qWBg4cqHXr1mn06NH1edhAk8L3kAFE3C9+8Qvt2rVLb775ZrRLAYzBMWQAEXX48GH2joEa8JY1gIgoLi7Wli1btHTpUsXExOiaa66JdkmAUdhDBhARmzdv1l133aX9+/frkUceUWJiYrRLAozCMWQAAAzAHjIAAAYgkAEAMACBDACAAQhkAAAMQCADAGAAAhkAAAMQyAAAGIBABgDAAAQyAAAG+P+coBTMrhkUPwAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Check distribution\n",
        "sns.displot(df_hitters_num_nonull['Salary'])\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 135,
      "id": "84290119",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "84290119",
        "outputId": "c5092c35-3b0d-455f-ccab-045fda90d186"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Division_W    -0.192514\n",
              "League_N      -0.014282\n",
              "Errors        -0.005401\n",
              "NewLeague_N   -0.002834\n",
              "Assists        0.025436\n",
              "PutOuts        0.300480\n",
              "HmRun          0.343028\n",
              "AtBat          0.394771\n",
              "Years          0.400657\n",
              "Runs           0.419859\n",
              "Hits           0.438675\n",
              "Walks          0.443867\n",
              "RBI            0.449457\n",
              "CWalks         0.489822\n",
              "CHmRun         0.524931\n",
              "CAtBat         0.526135\n",
              "CHits          0.548910\n",
              "CRuns          0.562678\n",
              "CRBI           0.566966\n",
              "Salary         1.000000\n",
              "Name: Salary, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 135
        }
      ],
      "source": [
        "# Check the correlation between the dependent and independent variables\n",
        "correlation = df_hitters_num_nonull.corr()\n",
        "correlation['Salary'].sort_values(ascending=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 136,
      "id": "66a00120",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 870
        },
        "id": "66a00120",
        "outputId": "50e0bb12-c2ac-42b0-fa31-d70ccdf3ce1a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-136-2106f104d560>:3: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
            "  sns.heatmap(df_hitters.corr(),\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1100x900 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Check for multicolinearity\n",
        "plt.figure(figsize=(11,9))\n",
        "sns.heatmap(df_hitters.corr(),\n",
        "            vmin = -1,\n",
        "            vmax = 1,\n",
        "            cmap =\"GnBu\",\n",
        "            annot=True)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "threshold = 0.4\n",
        "selected_features = correlation['Salary'][abs(correlation['Salary']) > threshold].index\n",
        "# Exclude 'Salary' from the selected features\n",
        "selected_features = selected_features[selected_features != 'Salary']\n",
        "print(selected_features)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PcIx7KVJ6qcZ",
        "outputId": "52783916-9d46-4751-aec5-d061adba2536"
      },
      "id": "PcIx7KVJ6qcZ",
      "execution_count": 137,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Index(['Hits', 'Runs', 'RBI', 'Walks', 'Years', 'CAtBat', 'CHits', 'CHmRun',\n",
            "       'CRuns', 'CRBI', 'CWalks'],\n",
            "      dtype='object')\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "id": "3c7526ba",
      "metadata": {
        "id": "3c7526ba"
      },
      "source": [
        "## Declare the dependent and independent variables"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Split the data into independent (X) and dependent (y) variables\n",
        "X = df_hitters_num_nonull[selected_features]\n",
        "y = df_hitters_num_nonull['Salary']"
      ],
      "metadata": {
        "id": "pwOyOjyY66ME"
      },
      "id": "pwOyOjyY66ME",
      "execution_count": 138,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "id": "da4e75c6",
      "metadata": {
        "id": "da4e75c6"
      },
      "source": [
        "## Split the data into training and testing parts"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 139,
      "id": "613f1942",
      "metadata": {
        "id": "613f1942"
      },
      "outputs": [],
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "To address multicolinearity we will use Lasso for Feature Selection."
      ],
      "metadata": {
        "id": "n-nNneIEF0Am"
      },
      "id": "n-nNneIEF0Am"
    },
    {
      "cell_type": "code",
      "source": [
        "# Instantiate the Lasso model with a specific alpha\n",
        "lasso_model = Lasso(alpha=0.01, random_state=9, max_iter=10000)\n",
        "\n",
        "# Fit the Lasso model to the training data\n",
        "lasso_model.fit(X_train, y_train)\n",
        "\n",
        "# Get the coefficients of the features (feature importances)\n",
        "lasso_coef = lasso_model.coef_\n",
        "\n",
        "# Create a mask to select columns with non-zero coefficients\n",
        "selected_columns_mask = lasso_coef > 0\n",
        "\n",
        "# Subset the training and test features using iloc with the mask\n",
        "X_train_selected = X_train.iloc[:, selected_columns_mask]\n",
        "X_test_selected = X_test.iloc[:, selected_columns_mask]\n",
        "\n",
        "# Print the selected feature names\n",
        "selected_feature_names = X.columns[selected_columns_mask]\n",
        "print(\"Selected Features:\", selected_feature_names)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3UrQJ9axEZik",
        "outputId": "284bb6de-97d0-45c3-da9b-d2e6915c47d8"
      },
      "id": "3UrQJ9axEZik",
      "execution_count": 140,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Selected Features: Index(['Hits', 'Walks', 'Years', 'CHits', 'CRuns', 'CRBI'], dtype='object')\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Standardize the training and test data\n",
        "scaler = StandardScaler()\n",
        "X_train_selected = scaler.fit_transform(X_train_selected)\n",
        "X_test_selected = scaler.transform(X_test_selected)"
      ],
      "metadata": {
        "id": "4HXIy0LMGAqc"
      },
      "id": "4HXIy0LMGAqc",
      "execution_count": 141,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "id": "7195abbf",
      "metadata": {
        "id": "7195abbf"
      },
      "source": [
        "## Perform linear regression"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 142,
      "id": "19e3a995",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "19e3a995",
        "outputId": "b7bcfaf6-6c1b-4935-8f98-b2b9868e49a5"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ],
            "text/html": [
              "<style>#sk-container-id-5 {color: black;background-color: white;}#sk-container-id-5 pre{padding: 0;}#sk-container-id-5 div.sk-toggleable {background-color: white;}#sk-container-id-5 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-5 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-5 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-5 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-5 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-5 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-5 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-5 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-5 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-5 div.sk-item {position: relative;z-index: 1;}#sk-container-id-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-5 div.sk-item::before, #sk-container-id-5 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-5 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-5 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-5 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-5 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-5 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-5 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-5 div.sk-label-container {text-align: center;}#sk-container-id-5 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-5 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 142
        }
      ],
      "source": [
        "lin_reg = LinearRegression()\n",
        "lin_reg.fit(X_train_selected, y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 143,
      "id": "5b14e8d1",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5b14e8d1",
        "outputId": "41145ee7-ee41-4a69-eb1c-47775c184d1f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Linear Regression coefficients are:  [130.82141631  54.09287697 -55.07749016  25.12713861  62.92942332\n",
            " 188.09657312]\n",
            "Linear Regression y-intercept is:  543.6646238095238\n"
          ]
        }
      ],
      "source": [
        "# We get the coefficient and the intercept of the linear regression\n",
        "print(\"Linear Regression coefficients are: \",lin_reg.coef_)\n",
        "print(\"Linear Regression y-intercept is: \",lin_reg.intercept_)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 144,
      "id": "43ac19b3",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "43ac19b3",
        "outputId": "7af51293-58ac-4bdd-bb26-ee9116155946"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 592.80555319,  642.0678755 ,  903.04218995,  373.03892465,\n",
              "        554.38213186,  288.44028044,  594.85940555,  503.02466995,\n",
              "        891.17444044,  831.25253776,  711.61282109, 1105.0983867 ,\n",
              "        465.92453872,  456.04586268,  399.60487131,  846.88163363,\n",
              "        813.55242745,  149.71002519, 1053.21228695,  443.86586602,\n",
              "        469.59673145,  819.16410747,  588.42940258,  437.6749356 ,\n",
              "        729.06950137,  155.60887932,  914.19763576,  281.20306218,\n",
              "        357.34993257,   62.58938656,  136.11517485,  -98.89334824,\n",
              "        356.31581328,  510.51044693,  201.03747704,  671.38178229,\n",
              "       1382.30885019,  355.32676563,  -24.93394385,  197.61392617,\n",
              "         96.98595329,  337.08641875,  614.9509702 ,  436.78318453,\n",
              "       1003.5311736 ,  731.42683405,  396.91056319,  427.83810381,\n",
              "        370.21827388,  728.28649733,  662.27081401,  229.0019999 ,\n",
              "        759.69567628])"
            ]
          },
          "metadata": {},
          "execution_count": 144
        }
      ],
      "source": [
        "lin_reg_y_pred = lin_reg.predict(X_test_selected)\n",
        "lin_reg_y_pred"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 145,
      "id": "047235a2",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "047235a2",
        "outputId": "647b9a38-af48-4c4a-b2e6-05839cdcb5f1"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Predicted    Actual\n",
              "148   592.805553   850.000\n",
              "154   642.067876   277.500\n",
              "318   903.042190   875.000\n",
              "279   373.038925   150.000\n",
              "88    554.382132   260.000\n",
              "307   288.440280   277.500\n",
              "221   594.859406   210.000\n",
              "268   503.024670   140.000\n",
              "10    891.174440   517.143\n",
              "132   831.252538   325.000\n",
              "28    711.612821   625.000\n",
              "143  1105.098387   773.333\n",
              "61    465.924539   275.000\n",
              "276   456.045863   155.000\n",
              "241   399.604871   300.000\n",
              "267   846.881634   740.000\n",
              "108   813.552427  1175.000\n",
              "7     149.710025   100.000\n",
              "313  1053.212287  1600.000\n",
              "89    443.865866   475.000\n",
              "181   469.596731   737.500\n",
              "187   819.164107   365.000\n",
              "23    588.429403   600.000\n",
              "312   437.674936   550.000\n",
              "141   729.069501   250.000\n",
              "176   155.608879    86.500\n",
              "107   914.197636   750.000\n",
              "90    281.203062   431.500\n",
              "245   357.349933   400.000\n",
              "17     62.589387   175.000\n",
              "37    136.115175    90.000\n",
              "217   -98.893348  2127.333\n",
              "285   356.315813   341.667\n",
              "215   510.510447   175.000\n",
              "45    201.037477   305.000\n",
              "170   671.381782   825.000\n",
              "96   1382.308850  1861.460\n",
              "294   355.326766   185.000\n",
              "261   -24.933944   190.000\n",
              "11    197.613926   512.500\n",
              "151    96.985953    95.000\n",
              "60    337.086419    90.000\n",
              "127   614.950970   600.000\n",
              "99    436.783185   490.000\n",
              "29   1003.531174   900.000\n",
              "207   731.426834   700.000\n",
              "256   396.910563   400.000\n",
              "21    427.838104   115.000\n",
              "116   370.218274   155.000\n",
              "182   728.286497   625.000\n",
              "192   662.270814   525.000\n",
              "242   229.002000   250.000\n",
              "147   759.695676   775.000"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-2af11861-2224-4339-b0ea-f7888f37471b\" class=\"colab-df-container\">\n",
              "    <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",
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              "\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>Predicted</th>\n",
              "      <th>Actual</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>148</th>\n",
              "      <td>592.805553</td>\n",
              "      <td>850.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>154</th>\n",
              "      <td>642.067876</td>\n",
              "      <td>277.500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>318</th>\n",
              "      <td>903.042190</td>\n",
              "      <td>875.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>279</th>\n",
              "      <td>373.038925</td>\n",
              "      <td>150.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>88</th>\n",
              "      <td>554.382132</td>\n",
              "      <td>260.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>307</th>\n",
              "      <td>288.440280</td>\n",
              "      <td>277.500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>221</th>\n",
              "      <td>594.859406</td>\n",
              "      <td>210.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>268</th>\n",
              "      <td>503.024670</td>\n",
              "      <td>140.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>891.174440</td>\n",
              "      <td>517.143</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>132</th>\n",
              "      <td>831.252538</td>\n",
              "      <td>325.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>711.612821</td>\n",
              "      <td>625.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>143</th>\n",
              "      <td>1105.098387</td>\n",
              "      <td>773.333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>61</th>\n",
              "      <td>465.924539</td>\n",
              "      <td>275.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>276</th>\n",
              "      <td>456.045863</td>\n",
              "      <td>155.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>241</th>\n",
              "      <td>399.604871</td>\n",
              "      <td>300.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>267</th>\n",
              "      <td>846.881634</td>\n",
              "      <td>740.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>108</th>\n",
              "      <td>813.552427</td>\n",
              "      <td>1175.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>149.710025</td>\n",
              "      <td>100.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>313</th>\n",
              "      <td>1053.212287</td>\n",
              "      <td>1600.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>89</th>\n",
              "      <td>443.865866</td>\n",
              "      <td>475.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>181</th>\n",
              "      <td>469.596731</td>\n",
              "      <td>737.500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>187</th>\n",
              "      <td>819.164107</td>\n",
              "      <td>365.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>588.429403</td>\n",
              "      <td>600.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>312</th>\n",
              "      <td>437.674936</td>\n",
              "      <td>550.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>141</th>\n",
              "      <td>729.069501</td>\n",
              "      <td>250.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>176</th>\n",
              "      <td>155.608879</td>\n",
              "      <td>86.500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>107</th>\n",
              "      <td>914.197636</td>\n",
              "      <td>750.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>90</th>\n",
              "      <td>281.203062</td>\n",
              "      <td>431.500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>245</th>\n",
              "      <td>357.349933</td>\n",
              "      <td>400.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>62.589387</td>\n",
              "      <td>175.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>136.115175</td>\n",
              "      <td>90.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>217</th>\n",
              "      <td>-98.893348</td>\n",
              "      <td>2127.333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>285</th>\n",
              "      <td>356.315813</td>\n",
              "      <td>341.667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>215</th>\n",
              "      <td>510.510447</td>\n",
              "      <td>175.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45</th>\n",
              "      <td>201.037477</td>\n",
              "      <td>305.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>170</th>\n",
              "      <td>671.381782</td>\n",
              "      <td>825.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>96</th>\n",
              "      <td>1382.308850</td>\n",
              "      <td>1861.460</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>294</th>\n",
              "      <td>355.326766</td>\n",
              "      <td>185.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>261</th>\n",
              "      <td>-24.933944</td>\n",
              "      <td>190.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>197.613926</td>\n",
              "      <td>512.500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>151</th>\n",
              "      <td>96.985953</td>\n",
              "      <td>95.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>60</th>\n",
              "      <td>337.086419</td>\n",
              "      <td>90.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>127</th>\n",
              "      <td>614.950970</td>\n",
              "      <td>600.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>99</th>\n",
              "      <td>436.783185</td>\n",
              "      <td>490.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>1003.531174</td>\n",
              "      <td>900.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>207</th>\n",
              "      <td>731.426834</td>\n",
              "      <td>700.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>256</th>\n",
              "      <td>396.910563</td>\n",
              "      <td>400.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>427.838104</td>\n",
              "      <td>115.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>116</th>\n",
              "      <td>370.218274</td>\n",
              "      <td>155.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>182</th>\n",
              "      <td>728.286497</td>\n",
              "      <td>625.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>192</th>\n",
              "      <td>662.270814</td>\n",
              "      <td>525.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>242</th>\n",
              "      <td>229.002000</td>\n",
              "      <td>250.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>147</th>\n",
              "      <td>759.695676</td>\n",
              "      <td>775.000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2af11861-2224-4339-b0ea-f7888f37471b')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-2af11861-2224-4339-b0ea-f7888f37471b button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-2af11861-2224-4339-b0ea-f7888f37471b');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-0df42b48-6c56-4cb9-bde7-f2559f14bf35\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-0df42b48-6c56-4cb9-bde7-f2559f14bf35')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-0df42b48-6c56-4cb9-bde7-f2559f14bf35 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 145
        }
      ],
      "source": [
        "# Put the predictions in a dataframe\n",
        "lin_comp = pd.DataFrame({'Predicted': lin_reg_y_pred, 'Actual': y_test})\n",
        "lin_comp"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 146,
      "id": "d834ff2d",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d834ff2d",
        "outputId": "56ac0c0e-4060-43e2-889b-428900480f0a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Linear Regression Model RMSE is:  389.9721058818014\n",
            "Linear Regression Model Training Score:  0.5041956952898656\n",
            "Linear Regression Model Testing Score:  0.15919416466465997\n"
          ]
        }
      ],
      "source": [
        "print(\"Linear Regression Model RMSE is: \", math.sqrt(mean_squared_error(y_test, lin_reg_y_pred)))\n",
        "print(\"Linear Regression Model Training Score: \",lin_reg.score(X_train_selected, y_train))\n",
        "print(\"Linear Regression Model Testing Score: \",lin_reg.score(X_test_selected, y_test))"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "fd6300bd",
      "metadata": {
        "id": "fd6300bd"
      },
      "source": [
        "## Perform ridge regression"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 147,
      "id": "959049b7",
      "metadata": {
        "id": "959049b7"
      },
      "outputs": [],
      "source": [
        "cv = RepeatedKFold(n_splits=5, n_repeats=3, random_state=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 148,
      "id": "9c2c29bc",
      "metadata": {
        "id": "9c2c29bc"
      },
      "outputs": [],
      "source": [
        "# Initializing the ridge regressor\n",
        "ridge = RidgeCV(alphas=np.arange(0.1, 10, 0.1), cv=cv, scoring='neg_mean_absolute_error')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 149,
      "id": "f4a62f89",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f4a62f89",
        "outputId": "d6959a55-1418-4f42-c14f-23678b00f6fd"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Ridge tuning parameter: 9.9\n",
            "Ridge model coefficients: [125.90461257  61.17874086 -31.65113805  50.34716504  59.37918739\n",
            " 139.60990442]\n",
            "Ridge model intercept: 543.6646238095238\n"
          ]
        }
      ],
      "source": [
        "# Fitting the ridge regressor\n",
        "ridge.fit(X_train_selected,y_train)\n",
        "ridge_reg_y_pred = ridge.predict(X_test_selected)\n",
        "\n",
        "print(\"Ridge tuning parameter:\", (ridge.alpha_))\n",
        "print (\"Ridge model coefficients:\", (ridge.coef_))\n",
        "print (\"Ridge model intercept:\", (ridge.intercept_))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 150,
      "id": "2f0b7fea",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2f0b7fea",
        "outputId": "aa4f27b7-7dfe-42fa-9f1d-e020eeb528b9"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Ridge Regression Model RMSE is:  389.43475864177606\n",
            "Ridge Regression Model Training Score:  0.5026910374262492\n",
            "Ridge Regression Model Testing Score:  0.16150968115751663\n"
          ]
        }
      ],
      "source": [
        "print(\"Ridge Regression Model RMSE is: \", math.sqrt(mean_squared_error(y_test, ridge_reg_y_pred)))\n",
        "print(\"Ridge Regression Model Training Score: \",ridge.score(X_train_selected, y_train))\n",
        "print(\"Ridge Regression Model Testing Score: \",ridge.score(X_test_selected, y_test))"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "f7d5daf7",
      "metadata": {
        "id": "f7d5daf7"
      },
      "source": [
        "## Perform lasso regression"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 151,
      "id": "0494e21f",
      "metadata": {
        "id": "0494e21f"
      },
      "outputs": [],
      "source": [
        "# Initializing the lasso regressor\n",
        "lasso = LassoCV(alphas=np.arange(0.1, 10.0, 0.1), cv=cv, tol = 1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 152,
      "id": "952ab278",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "952ab278",
        "outputId": "cd3f4453-d918-455c-b89d-a5cff3d41fac"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Lasso tuning parameter: 9.2\n",
            "Lasso model coefficients: [159.38864687  74.25814214 139.16523699  53.14271949   7.6867278\n",
            "  22.13561532]\n",
            "Lassso model intercept: 543.6646238095238\n"
          ]
        }
      ],
      "source": [
        "# Fitting the lasso regressor\n",
        "lasso.fit(X_train_selected, y_train)\n",
        "lasso_reg_y_pred = lasso.predict(X_test_selected)\n",
        "\n",
        "print(\"Lasso tuning parameter:\", (lasso.alpha_))\n",
        "print (\"Lasso model coefficients:\", (lasso.coef_))\n",
        "print (\"Lassso model intercept:\", (lasso.intercept_))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 153,
      "id": "e738580b",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e738580b",
        "outputId": "c46fd9ab-6b69-468f-c5ea-304622568f8c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Lasso Regression Model RMSE is:  418.66166208279225\n",
            "Lasso Regression Model Training Score:  0.4683722317390858\n",
            "Lasso Regression Model Testing Score:  0.0309302965248891\n"
          ]
        }
      ],
      "source": [
        "print(\"Lasso Regression Model RMSE is: \", math.sqrt(mean_squared_error(y_test, lasso_reg_y_pred)))\n",
        "print(\"Lasso Regression Model Training Score: \",lasso.score(X_train_selected, y_train))\n",
        "print(\"Lasso Regression Model Testing Score: \",lasso.score(X_test_selected, y_test))"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "a17e742f",
      "metadata": {
        "id": "a17e742f"
      },
      "source": [
        "## Compare the score"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 154,
      "id": "80e21a6b",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "80e21a6b",
        "outputId": "ed51e8c4-b84d-4d4b-a661-aa6424000032"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Linear Regression Model Training Score:  0.5041956952898656\n",
            "Linear Regression Model Testing Score:  0.15919416466465997\n",
            "\n",
            "Ridge Regression Model Training Score:  0.5026910374262492\n",
            "Ridge Regression Model Testing Score:  0.16150968115751663\n",
            "\n",
            "Lasso Regression Model Training Score:  0.4683722317390858\n",
            "Lasso Regression Model Testing Score:  0.0309302965248891\n"
          ]
        }
      ],
      "source": [
        "print(\"Linear Regression Model Training Score: \",lin_reg.score(X_train_selected, y_train))\n",
        "print(\"Linear Regression Model Testing Score: \",lin_reg.score(X_test_selected, y_test))\n",
        "print(\"\\nRidge Regression Model Training Score: \",ridge.score(X_train_selected, y_train))\n",
        "print(\"Ridge Regression Model Testing Score: \",ridge.score(X_test_selected, y_test))\n",
        "print(\"\\nLasso Regression Model Training Score: \",lasso.score(X_train_selected, y_train))\n",
        "print(\"Lasso Regression Model Testing Score: \",lasso.score(X_test_selected, y_test))"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "30763c39",
      "metadata": {
        "id": "30763c39"
      },
      "source": [
        "## Root mean squared error"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 155,
      "id": "214fff75",
      "metadata": {
        "scrolled": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "214fff75",
        "outputId": "c05e6956-8610-445e-f99e-85c6b72771ef"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Linear Regression Model RMSE is:  389.9721058818014\n",
            "Ridge Regression Model RMSE is:  389.43475864177606\n",
            "Lasso Regression Model RMSE is:  418.66166208279225\n"
          ]
        }
      ],
      "source": [
        "print(\"Linear Regression Model RMSE is: \", math.sqrt(mean_squared_error(y_test, lin_reg_y_pred)))\n",
        "print(\"Ridge Regression Model RMSE is: \", math.sqrt(mean_squared_error(y_test, ridge_reg_y_pred)))\n",
        "print(\"Lasso Regression Model RMSE is: \", math.sqrt(mean_squared_error(y_test, lasso_reg_y_pred)))"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "c8bba971",
      "metadata": {
        "id": "c8bba971"
      },
      "source": [
        "## Replacing the missing values in the DataFrame"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 156,
      "id": "94fe1e2c",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "94fe1e2c",
        "outputId": "381d1282-fc7d-4fa9-ecad-75597f76346f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    AtBat  Hits  HmRun  Runs  RBI  Walks  Years  CAtBat  CHits  CHmRun  CRuns  \\\n",
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          },
          "metadata": {},
          "execution_count": 156
        }
      ],
      "source": [
        "df_hitters_nan = df_hitters_num[df_hitters_num['Salary'].isnull()]\n",
        "df_hitters_nan.head()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Select the features for prediction\n",
        "X_predict = df_hitters_nan[selected_feature_names]\n",
        "\n",
        "# Standardize the data using the same scaler\n",
        "X_predict = scaler.transform(X_predict)\n",
        "\n",
        "# Use the best model (Linear Regression) to predict missing 'Salary' values\n",
        "predicted_salary = lin_reg.predict(X_predict)\n",
        "\n",
        "# Assign the predicted values to the 'Salary' column in the DataFrame\n",
        "df_hitters_nan.loc[:, 'Salary'] = predicted_salary"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wBEqt0SE9whw",
        "outputId": "433bd09c-560f-4b4b-e66d-4c4273004baa"
      },
      "id": "wBEqt0SE9whw",
      "execution_count": 157,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-157-309e714b7506>:11: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df_hitters_nan.loc[:, 'Salary'] = predicted_salary\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Concatenate the DataFrame with predicted 'Salary' values with the original data\n",
        "df_hitters_filled = pd.concat([df_hitters_num_nonull, df_hitters_nan])\n",
        "\n",
        "# Check if all NaN values in the 'Salary' column are filled\n",
        "print(\"Number of NaN values in 'Salary' column:\", df_hitters_filled['Salary'].isnull().sum())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GbSO7Zzc-FgI",
        "outputId": "76e659d0-b3a9-4bb5-fbf3-e3fdd4266c23"
      },
      "id": "GbSO7Zzc-FgI",
      "execution_count": 158,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of NaN values in 'Salary' column: 0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_hitters_filled"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "hGwbdpNIAiKv",
        "outputId": "24c3b622-876e-433b-fa76-a74623c3c027"
      },
      "id": "hGwbdpNIAiKv",
      "execution_count": 159,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     AtBat  Hits  HmRun  Runs  RBI  Walks  Years  CAtBat  CHits  CHmRun  \\\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",
              "5      594   169      4    74   51     35     11    4408   1133      19   \n",
              "..     ...   ...    ...   ...  ...    ...    ...     ...    ...     ...   \n",
              "292    289    63      7    36   41     44     17    7402   1954     195   \n",
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              "305    580   194      9    91   62     78      8    3372   1028      48   \n",
              "316    221    53      2    21   23     22      8    1063    283      15   \n",
              "\n",
              "     CRuns  CRBI  CWalks  PutOuts  Assists  Errors       Salary  League_N  \\\n",
              "1      321   414     375      632       43      10   475.000000         1   \n",
              "2      224   266     263      880       82      14   480.000000         0   \n",
              "3      828   838     354      200       11       3   500.000000         1   \n",
              "4       48    46      33      805       40       4    91.500000         1   \n",
              "5      501   336     194      282      421      25   750.000000         0   \n",
              "..     ...   ...     ...      ...      ...     ...          ...       ...   \n",
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              "305    604   314     469      270       13       6   931.459220         1   \n",
              "316    107   124     106      325       58       6   138.517275         1   \n",
              "\n",
              "     Division_W  NewLeague_N  \n",
              "1             1            1  \n",
              "2             1            0  \n",
              "3             0            1  \n",
              "4             0            1  \n",
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              "305           0            1  \n",
              "316           0            1  \n",
              "\n",
              "[322 rows x 20 columns]"
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              "      <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",
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              "      <td>937</td>\n",
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              "      <td>88</td>\n",
              "      <td>95</td>\n",
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              "      <td>37</td>\n",
              "      <td>98</td>\n",
              "      <td>9</td>\n",
              "      <td>118.029956</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>302</th>\n",
              "      <td>200</td>\n",
              "      <td>51</td>\n",
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              "      <td>14</td>\n",
              "      <td>29</td>\n",
              "      <td>25</td>\n",
              "      <td>23</td>\n",
              "      <td>9778</td>\n",
              "      <td>2732</td>\n",
              "      <td>379</td>\n",
              "      <td>1272</td>\n",
              "      <td>1652</td>\n",
              "      <td>925</td>\n",
              "      <td>398</td>\n",
              "      <td>29</td>\n",
              "      <td>7</td>\n",
              "      <td>1141.556524</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>305</th>\n",
              "      <td>580</td>\n",
              "      <td>194</td>\n",
              "      <td>9</td>\n",
              "      <td>91</td>\n",
              "      <td>62</td>\n",
              "      <td>78</td>\n",
              "      <td>8</td>\n",
              "      <td>3372</td>\n",
              "      <td>1028</td>\n",
              "      <td>48</td>\n",
              "      <td>604</td>\n",
              "      <td>314</td>\n",
              "      <td>469</td>\n",
              "      <td>270</td>\n",
              "      <td>13</td>\n",
              "      <td>6</td>\n",
              "      <td>931.459220</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>316</th>\n",
              "      <td>221</td>\n",
              "      <td>53</td>\n",
              "      <td>2</td>\n",
              "      <td>21</td>\n",
              "      <td>23</td>\n",
              "      <td>22</td>\n",
              "      <td>8</td>\n",
              "      <td>1063</td>\n",
              "      <td>283</td>\n",
              "      <td>15</td>\n",
              "      <td>107</td>\n",
              "      <td>124</td>\n",
              "      <td>106</td>\n",
              "      <td>325</td>\n",
              "      <td>58</td>\n",
              "      <td>6</td>\n",
              "      <td>138.517275</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>322 rows × 20 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
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              "    .colab-df-convert:hover {\n",
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              "        document.querySelector('#df-01b764b2-7726-4a7a-b5f3-87d17494a5c1 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-01b764b2-7726-4a7a-b5f3-87d17494a5c1');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
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              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "        element.appendChild(docLink);\n",
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              "\n",
              "\n",
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              "\n",
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              "    border-radius: 50%;\n",
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              "    display: none;\n",
              "    fill: var(--fill-color);\n",
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              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
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              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-471e8328-6378-45b9-b063-4457aedd2074 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 159
        }
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "language": "python",
      "name": "python3"
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