Simple_Linear_Regression.ipynb

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        "<a href=\"https://colab.research.google.com/github/Rahman17309/ASSIGNMENT1/blob/main/Simple_Linear_Regression.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "cell_type": "markdown",
      "source": [
        "#1) Delivery_time -> Predict delivery time using sorting time"
      ],
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        "id": "5jh3FyQFsMZV"
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      "source": [
        "# import libraries\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "import statsmodels.formula.api as smf"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# import dataset\n",
        "df1=pd.read_csv('/content/delivery_time.csv')\n",
        "df1"
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      "execution_count": 30,
      "outputs": [
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          "data": {
            "text/plain": [
              "    Delivery Time  Sorting Time\n",
              "0           21.00            10\n",
              "1           13.50             4\n",
              "2           19.75             6\n",
              "3           24.00             9\n",
              "4           29.00            10\n",
              "5           15.35             6\n",
              "6           19.00             7\n",
              "7            9.50             3\n",
              "8           17.90            10\n",
              "9           18.75             9\n",
              "10          19.83             8\n",
              "11          10.75             4\n",
              "12          16.68             7\n",
              "13          11.50             3\n",
              "14          12.03             3\n",
              "15          14.88             4\n",
              "16          13.75             6\n",
              "17          18.11             7\n",
              "18           8.00             2\n",
              "19          17.83             7\n",
              "20          21.50             5"
            ],
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              "type": "dataframe",
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              "summary": "{\n  \"name\": \"df1\",\n  \"rows\": 21,\n  \"fields\": [\n    {\n      \"column\": \"Delivery Time\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5.074900890423285,\n        \"min\": 8.0,\n        \"max\": 29.0,\n        \"num_unique_values\": 21,\n        \"samples\": [\n          21.0,\n          18.11,\n          14.88\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sorting Time\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2,\n        \"min\": 2,\n        \"max\": 10,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          2,\n          4,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
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        "EDA and Data Visualization\n"
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        "df1.info()"
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      "execution_count": 31,
      "outputs": [
        {
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          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 21 entries, 0 to 20\n",
            "Data columns (total 2 columns):\n",
            " #   Column         Non-Null Count  Dtype  \n",
            "---  ------         --------------  -----  \n",
            " 0   Delivery Time  21 non-null     float64\n",
            " 1   Sorting Time   21 non-null     int64  \n",
            "dtypes: float64(1), int64(1)\n",
            "memory usage: 464.0 bytes\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import plotly.figure_factory as ff\n",
        "\n",
        "# Create a histogram\n",
        "hist_data = [df1['Delivery Time']]\n",
        "\n",
        "# Group labels\n",
        "group_labels = ['Delivery Time']\n",
        "\n",
        "fig = ff.create_distplot(hist_data, group_labels, show_hist=False, show_rug=True)\n",
        "\n",
        "fig.update_layout(\n",
        "    title=\"Delivery Time Distribution\",\n",
        "    xaxis_title=\"Delivery Time\",\n",
        "    yaxis_title=\"Density\"\n",
        ")\n",
        "fig.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "2fHsOfgntJ_D",
        "outputId": "942d7899-3819-4732-8447-17107b6a39d2"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
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    {
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      "source": [
        "# Create a histogram\n",
        "hist_data = [df1['Sorting Time']]\n",
        "\n",
        "# Group labels\n",
        "group_labels = ['Sorting Time']\n",
        "\n",
        "fig = ff.create_distplot(hist_data, group_labels, show_hist=False, show_rug=True)\n",
        "\n",
        "fig.update_layout(\n",
        "    title=\"Sorting Time Distribution\",\n",
        "    xaxis_title=\"Sorting Time\",\n",
        "    yaxis_title=\"Density\"\n",
        ")\n",
        "\n",
        "fig.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "o6Ds4scOt3GL",
        "outputId": "fede586b-5eeb-46f3-d597-c918116761c0"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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      "cell_type": "markdown",
      "source": [
        "Feature Engineering"
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      "metadata": {
        "id": "ux1qqUbMrTp8"
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    {
      "cell_type": "code",
      "source": [
        "# Renaming Columns\n",
        "df1=df1.rename({'Delivery Time':'delivery_time', 'Sorting Time':'sorting_time'},axis=1)\n",
        "df1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 708
        },
        "id": "d3nNnvLbp8y0",
        "outputId": "78395999-f803-4d1c-ad99-9c8d4bbbf908"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    delivery_time  sorting_time\n",
              "0           21.00            10\n",
              "1           13.50             4\n",
              "2           19.75             6\n",
              "3           24.00             9\n",
              "4           29.00            10\n",
              "5           15.35             6\n",
              "6           19.00             7\n",
              "7            9.50             3\n",
              "8           17.90            10\n",
              "9           18.75             9\n",
              "10          19.83             8\n",
              "11          10.75             4\n",
              "12          16.68             7\n",
              "13          11.50             3\n",
              "14          12.03             3\n",
              "15          14.88             4\n",
              "16          13.75             6\n",
              "17          18.11             7\n",
              "18           8.00             2\n",
              "19          17.83             7\n",
              "20          21.50             5"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-94044bae-6c82-4e3d-aaae-2b9397fa3f6c\" class=\"colab-df-container\">\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>delivery_time</th>\n",
              "      <th>sorting_time</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>21.00</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>13.50</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>19.75</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>24.00</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>29.00</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>15.35</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>19.00</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>9.50</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>17.90</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>18.75</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>19.83</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>10.75</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>16.68</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>11.50</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>12.03</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>14.88</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>13.75</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>18.11</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>8.00</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>17.83</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>21.50</td>\n",
              "      <td>5</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-94044bae-6c82-4e3d-aaae-2b9397fa3f6c')\"\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-94044bae-6c82-4e3d-aaae-2b9397fa3f6c 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-94044bae-6c82-4e3d-aaae-2b9397fa3f6c');\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-0051755b-47ad-4461-9243-6c9452a85ca1\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-0051755b-47ad-4461-9243-6c9452a85ca1')\"\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",
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              "    </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-0051755b-47ad-4461-9243-6c9452a85ca1 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_4b53410c-9b6d-41e6-8860-4ffdcd847373\">\n",
              "    <style>\n",
              "      .colab-df-generate {\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-generate: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",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate: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",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df1')\"\n",
              "            title=\"Generate code using this dataframe.\"\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",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_4b53410c-9b6d-41e6-8860-4ffdcd847373 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df1');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df1",
              "summary": "{\n  \"name\": \"df1\",\n  \"rows\": 21,\n  \"fields\": [\n    {\n      \"column\": \"delivery_time\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5.074900890423285,\n        \"min\": 8.0,\n        \"max\": 29.0,\n        \"num_unique_values\": 21,\n        \"samples\": [\n          21.0,\n          18.11,\n          14.88\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sorting_time\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2,\n        \"min\": 2,\n        \"max\": 10,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          2,\n          4,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Correlation Analysis"
      ],
      "metadata": {
        "id": "KqZ04su5rfaU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df1.corr()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "5G2JJQsVp8v1",
        "outputId": "cbdd31ad-9a52-44be-d8b5-02073f7e2704"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               delivery_time  sorting_time\n",
              "delivery_time       1.000000      0.825997\n",
              "sorting_time        0.825997      1.000000"
            ],
            "text/html": [
              "\n",
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              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>delivery_time</th>\n",
              "      <th>sorting_time</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>delivery_time</th>\n",
              "      <td>1.000000</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>sorting_time</th>\n",
              "      <td>0.825997</td>\n",
              "      <td>1.000000</td>\n",
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              "    <div class=\"colab-df-buttons\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
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              "    .colab-df-container {\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "\n",
              "    .colab-df-buttons div {\n",
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              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "\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",
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              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-ac162362-46da-4a03-ba64-111cc3ddef62 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-ac162362-46da-4a03-ba64-111cc3ddef62');\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",
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              "\n",
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              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-de1424c2-353d-4018-b2b0-fd4c37dfd8f7')\"\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
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              "      --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",
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              "    display: none;\n",
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              "    height: 32px;\n",
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              "    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",
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              "      border-top-color: var(--fill-color);\n",
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              "      border-top-color: var(--fill-color);\n",
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              "    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",
              "      } 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-de1424c2-353d-4018-b2b0-fd4c37dfd8f7 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df1\",\n  \"rows\": 2,\n  \"fields\": [\n    {\n      \"column\": \"delivery_time\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.12303851683651315,\n        \"min\": 0.8259972607955327,\n        \"max\": 1.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.8259972607955327,\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sorting_time\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.12303851683651315,\n        \"min\": 0.8259972607955327,\n        \"max\": 1.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1.0,\n          0.8259972607955327\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import plotly.graph_objects as go\n",
        "\n",
        "# Create a scatter plot with regression line\n",
        "fig = go.Figure()\n",
        "\n",
        "fig.add_trace(go.Scatter(\n",
        "    x=df1['sorting_time'],\n",
        "    y=df1['delivery_time'],\n",
        "    mode='markers',\n",
        "    marker=dict(color='blue'),\n",
        "    name='Data'\n",
        "))\n",
        "\n",
        "# Fit a linear regression line\n",
        "m, b = np.polyfit(df1['sorting_time'], df1['delivery_time'], 1)\n",
        "x_fit = np.linspace(df1['sorting_time'].min(), df1['sorting_time'].max(), 100)\n",
        "y_fit = m * x_fit + b\n",
        "\n",
        "fig.add_trace(go.Scatter(\n",
        "    x=x_fit,\n",
        "    y=y_fit,\n",
        "    mode='lines',\n",
        "    marker=dict(color='red'),\n",
        "    name='Regression Line'\n",
        "))\n",
        "fig.update_layout(\n",
        "    title=\"Sorting Time vs Delivery Time\",\n",
        "    xaxis_title=\"Sorting Time\",\n",
        "    yaxis_title=\"Delivery Time\"\n",
        ")\n",
        "fig.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "58QacLI6u-oa",
        "outputId": "bdabfa3c-777c-4bb0-d9ce-09441194abab"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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Time vs Delivery Time\"},\"xaxis\":{\"title\":{\"text\":\"Sorting Time\"}},\"yaxis\":{\"title\":{\"text\":\"Delivery Time\"}}},                        {\"responsive\": true}                    ).then(function(){\n",
              "                            \n",
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              "</body>\n",
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            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Model Building"
      ],
      "metadata": {
        "id": "PmYqMNdArr9B"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model=smf.ols(\"delivery_time~sorting_time\",data=df1).fit()"
      ],
      "metadata": {
        "id": "fNsO5YPkrqPH"
      },
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Model Testing"
      ],
      "metadata": {
        "id": "zD_RQ-Acry5b"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Finding Coefficient parameters\n",
        "model.params"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IxuYBqBdrqK0",
        "outputId": "9d70e8ec-c17e-4752-d95f-cb2892e4d9a8"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Intercept       6.582734\n",
              "sorting_time    1.649020\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Finding tvalues and pvalues\n",
        "model.tvalues , model.pvalues"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c3uP3xeirqFz",
        "outputId": "ecf7ac06-fd33-4cba-872c-d7167b06103c"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(Intercept       3.823349\n",
              " sorting_time    6.387447\n",
              " dtype: float64,\n",
              " Intercept       0.001147\n",
              " sorting_time    0.000004\n",
              " dtype: float64)"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Finding Rsquared Values\n",
        "model.rsquared , model.rsquared_adj"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "J3Ox_K14rqDN",
        "outputId": "20885dd8-b337-40bb-a237-d49387b03230"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0.6822714748417231, 0.6655489208860244)"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Model Predictions"
      ],
      "metadata": {
        "id": "nZFvyJ5br86M"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Manual prediction for say sorting time 5\n",
        "delivery_time = (6.582734) + (1.649020)*(5)\n",
        "delivery_time"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MwvY4lD5rp_-",
        "outputId": "92b3bb24-ec97-47d1-b33c-3b62822e626b"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "14.827834"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Automatic Prediction for say sorting time 5, 8\n",
        "new_data=pd.Series([5,8])\n",
        "new_data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eLxo0qY2sBB3",
        "outputId": "d1657b07-4080-452f-b235-5c191f676bbe"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    5\n",
              "1    8\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_pred=pd.DataFrame(new_data,columns=['sorting_time'])\n",
        "data_pred"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 125
        },
        "id": "HskvTHWisA_C",
        "outputId": "66605c26-5f6e-4bad-e1bd-66351bfd2c90"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   sorting_time\n",
              "0             5\n",
              "1             8"
            ],
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
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              "\n",
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              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "data_pred",
              "summary": "{\n  \"name\": \"data_pred\",\n  \"rows\": 2,\n  \"fields\": [\n    {\n      \"column\": \"sorting_time\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2,\n        \"min\": 5,\n        \"max\": 8,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          8,\n          5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.predict(data_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qm_uEumLsA7L",
        "outputId": "91f4a8b7-5d48-4505-bb23-88b49fbae00a"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    14.827833\n",
              "1    19.774893\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#2) Salary_hike -> Build a prediction model for Salary_hike\n"
      ],
      "metadata": {
        "id": "yN6pvyC8vbEC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# import dataset\n",
        "df2=pd.read_csv('/content/Salary_Data.csv')\n",
        "df2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 990
        },
        "id": "mQEPUaFRsA2R",
        "outputId": "7ce4983a-049a-4bd5-8962-04f85be38f39"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    YearsExperience    Salary\n",
              "0               1.1   39343.0\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df2",
              "summary": "{\n  \"name\": \"df2\",\n  \"rows\": 30,\n  \"fields\": [\n    {\n      \"column\": \"YearsExperience\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.8378881576627184,\n        \"min\": 1.1,\n        \"max\": 10.5,\n        \"num_unique_values\": 28,\n        \"samples\": [\n          3.9,\n          9.6,\n          3.7\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Salary\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 27414.4297845823,\n        \"min\": 37731.0,\n        \"max\": 122391.0,\n        \"num_unique_values\": 30,\n        \"samples\": [\n          112635.0,\n          67938.0,\n          113812.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "EDA and Data Visualization"
      ],
      "metadata": {
        "id": "mZUVFsfjw7j5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df2.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RvA297SNvyZS",
        "outputId": "8575a3bb-500a-4535-89d2-2fd734545a03"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 30 entries, 0 to 29\n",
            "Data columns (total 2 columns):\n",
            " #   Column           Non-Null Count  Dtype  \n",
            "---  ------           --------------  -----  \n",
            " 0   YearsExperience  30 non-null     float64\n",
            " 1   Salary           30 non-null     float64\n",
            "dtypes: float64(2)\n",
            "memory usage: 608.0 bytes\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Create a histogram\n",
        "hist_data = [df2['YearsExperience']]\n",
        "\n",
        "# Group labels\n",
        "group_labels = ['YearsExperience']\n",
        "\n",
        "# Create figure\n",
        "fig = ff.create_distplot(hist_data, group_labels, show_hist=False, show_rug=True)\n",
        "\n",
        "# Update layout\n",
        "fig.update_layout(\n",
        "    title=\"Years of Experience Distribution\",\n",
        "    xaxis_title=\"Years of Experience\",\n",
        "    yaxis_title=\"Density\"\n",
        ")\n",
        "\n",
        "fig.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "sgRihruKvyUs",
        "outputId": "9b59a30a-9cda-4f47-d4cd-6a7d40dc5163"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
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        "sns.distplot(df2['Salary'])"
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          "text": [
            "<ipython-input-51-122a0bd7de0e>:1: UserWarning:\n",
            "\n",
            "\n",
            "\n",
            "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
            "\n",
            "Please adapt your code to use either `displot` (a figure-level function with\n",
            "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "\n",
            "For a guide to updating your code to use the new functions, please see\n",
            "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
            "\n",
            "\n"
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            "text/plain": [
              "<Axes: xlabel='Salary', ylabel='Density'>"
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          },
          "metadata": {},
          "execution_count": 51
        },
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          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
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            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Correlation Analysis"
      ],
      "metadata": {
        "id": "ftzOE1r4w-yR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df2.corr()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "HRodpUjtw38N",
        "outputId": "8492c5fe-0beb-42fa-bf3d-59d93aa123c3"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                 YearsExperience    Salary\n",
              "YearsExperience         1.000000  0.978242\n",
              "Salary                  0.978242  1.000000"
            ],
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              "        const element = document.querySelector('#df-33740749-cad4-4aab-b1ec-ae1af507af0c');\n",
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              "        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",
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              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "      --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",
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              "  [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",
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              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
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              "  .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",
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              "    border-bottom-color: var(--fill-color);\n",
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              "  }\n",
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              "      border-left-color: var(--fill-color);\n",
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              "      border-top-color: var(--fill-color);\n",
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              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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              "      border-bottom-color: var(--fill-color);\n",
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              "      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",
              "      } 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",
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              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
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              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df2\",\n  \"rows\": 2,\n  \"fields\": [\n    {\n      \"column\": \"YearsExperience\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.015385499114241795,\n        \"min\": 0.97824161848876,\n        \"max\": 1.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.97824161848876,\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Salary\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.015385499114241795,\n        \"min\": 0.97824161848876,\n        \"max\": 1.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1.0,\n          0.97824161848876\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Create a scatter plot with regression line\n",
        "fig = go.Figure()\n",
        "\n",
        "fig.add_trace(go.Scatter(\n",
        "    x=df2['YearsExperience'],\n",
        "    y=df2['Salary'],\n",
        "    mode='markers',\n",
        "    marker=dict(color='blue'),\n",
        "    name='Data'\n",
        "))\n",
        "\n",
        "# Fit a linear regression line\n",
        "m, b = np.polyfit(df2['YearsExperience'], df2['Salary'], 1)\n",
        "x_fit = np.linspace(df2['YearsExperience'].min(), df2['YearsExperience'].max(), 100)\n",
        "y_fit = m * x_fit + b\n",
        "\n",
        "fig.add_trace(go.Scatter(\n",
        "    x=x_fit,\n",
        "    y=y_fit,\n",
        "    mode='lines',\n",
        "    marker=dict(color='red'),\n",
        "    name='Regression Line'\n",
        "))\n",
        "\n",
        "# Update layout\n",
        "fig.update_layout(\n",
        "    title=\"Years of Experience vs Salary\",\n",
        "    xaxis_title=\"Years of Experience\",\n",
        "    yaxis_title=\"Salary\"\n",
        ")\n",
        "\n",
        "fig.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "lnC2MXKnw34k",
        "outputId": "92a0145a-550f-494f-b946-5b9ee7981ea0"
      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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of Experience vs Salary\"},\"xaxis\":{\"title\":{\"text\":\"Years of Experience\"}},\"yaxis\":{\"title\":{\"text\":\"Salary\"}}},                        {\"responsive\": true}                    ).then(function(){\n",
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    {
      "cell_type": "markdown",
      "source": [
        "Model Building"
      ],
      "metadata": {
        "id": "3gXoYQ9zxclM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model2=smf.ols(\"Salary~YearsExperience\",data=df2).fit()"
      ],
      "metadata": {
        "id": "gjgZ3-dEw31R"
      },
      "execution_count": 57,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Model Testing"
      ],
      "metadata": {
        "id": "bQ9cXm-nxrrh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Finding Cefficient Parameters\n",
        "model2.params"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jy7oKj0-w3yB",
        "outputId": "c51967e5-b90e-4792-b7c1-1013b676ba74"
      },
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Intercept          25792.200199\n",
              "YearsExperience     9449.962321\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Finding Pvalues and tvalues\n",
        "model2.tvalues, model2.pvalues"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ACRfuOuVxveW",
        "outputId": "e60ca37c-9a64-4758-a5c5-054fc52e8f16"
      },
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(Intercept          11.346940\n",
              " YearsExperience    24.950094\n",
              " dtype: float64,\n",
              " Intercept          5.511950e-12\n",
              " YearsExperience    1.143068e-20\n",
              " dtype: float64)"
            ]
          },
          "metadata": {},
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Finding Rsquared values\n",
        "model2.rsquared , model2.rsquared_adj"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "h2z3INlfxva6",
        "outputId": "ee60dd6e-b993-4926-b3f4-7251c5e315a1"
      },
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0.9569566641435086, 0.9554194021486339)"
            ]
          },
          "metadata": {},
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Model Predictions"
      ],
      "metadata": {
        "id": "Fx7VoV-IyDQR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Manual prediction for say 3 Years Experience\n",
        "Salary = (25792.200199) + (9449.962321)*(3)\n",
        "Salary"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d-lUAVPhxvYa",
        "outputId": "ec6738d3-e7b6-48eb-c2c7-186ed43c985b"
      },
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "54142.087162"
            ]
          },
          "metadata": {},
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Automatic Prediction for say 3 & 5 Years Experience\n",
        "new_data=pd.Series([3,5])\n",
        "new_data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QfBD0IoTyGNS",
        "outputId": "92730fd3-5533-4fc6-b116-6c3c9d6b51e2"
      },
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    3\n",
              "1    5\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 62
        }
      ]
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      "cell_type": "code",
      "source": [
        "data_pred=pd.DataFrame(new_data,columns=['YearsExperience'])\n",
        "data_pred"
      ],
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        "colab": {
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          "height": 125
        },
        "id": "mNNiqamnyGJN",
        "outputId": "90d70e69-467f-43db-c56d-216556f0f34b"
      },
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "execute_result",
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              "   YearsExperience\n",
              "0                3\n",
              "1                5"
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              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "    </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",
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              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "\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-f38de36e-9750-4b73-95d3-a231130dc0a7 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-f38de36e-9750-4b73-95d3-a231130dc0a7');\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",
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              "\n",
              "\n",
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              "            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",
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              "    </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-166cf555-e5f1-4f31-8bee-e6dae3ed62ad button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_5c998067-58a3-4a5f-84a6-282f9a9af90c\">\n",
              "    <style>\n",
              "      .colab-df-generate {\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-generate: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",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate: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",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('data_pred')\"\n",
              "            title=\"Generate code using this dataframe.\"\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",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_5c998067-58a3-4a5f-84a6-282f9a9af90c button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('data_pred');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "data_pred",
              "summary": "{\n  \"name\": \"data_pred\",\n  \"rows\": 2,\n  \"fields\": [\n    {\n      \"column\": \"YearsExperience\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 3,\n        \"max\": 5,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          5,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model2.predict(data_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "N5LUY5QGyGFD",
        "outputId": "564be5d7-14ce-48e2-9d7a-46e5d8459013"
      },
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    54142.087163\n",
              "1    73042.011806\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 64
        }
      ]
    }
  ]
}