Multiple_Linear_Regression_50_Startups.ipynb

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      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt"
      ]
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      "cell_type": "code",
      "source": [
        "df =pd.read_csv(\"/content/50_Startups.csv\")"
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      "execution_count": 46,
      "outputs": []
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              "   R&D Spend  Administration  Marketing Spend       State     Profit\n",
              "0  165349.20       136897.80        471784.10    New York  192261.83\n",
              "1  162597.70       151377.59        443898.53  California  191792.06\n",
              "2  153441.51       101145.55        407934.54     Florida  191050.39\n",
              "3  144372.41       118671.85        383199.62    New York  182901.99\n",
              "4  142107.34        91391.77        366168.42     Florida  166187.94"
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              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 50,\n  \"fields\": [\n    {\n      \"column\": \"R&D Spend\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 45902.25648230753,\n        \"min\": 0.0,\n        \"max\": 165349.2,\n        \"num_unique_values\": 49,\n        \"samples\": [\n          91992.39,\n          1000.23,\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Administration\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 28017.802755488683,\n        \"min\": 51283.14,\n        \"max\": 182645.56,\n        \"num_unique_values\": 50,\n        \"samples\": [\n          135495.07,\n          82982.09,\n          115641.28\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Marketing Spend\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 122290.31072584528,\n        \"min\": 0.0,\n        \"max\": 471784.1,\n        \"num_unique_values\": 48,\n        \"samples\": [\n          353183.81,\n          172795.67,\n          134050.07\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"State\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"New York\",\n          \"California\",\n          \"Florida\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Profit\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 40306.18033765055,\n        \"min\": 14681.4,\n        \"max\": 192261.83,\n        \"num_unique_values\": 50,\n        \"samples\": [\n          134307.35,\n          81005.76,\n          99937.59\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
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          "metadata": {},
          "execution_count": 47
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    {
      "cell_type": "markdown",
      "source": [
        "Let's do some EDA & Feature selection"
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      "execution_count": 48,
      "outputs": [
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          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 50 entries, 0 to 49\n",
            "Data columns (total 5 columns):\n",
            " #   Column           Non-Null Count  Dtype  \n",
            "---  ------           --------------  -----  \n",
            " 0   R&D Spend        50 non-null     float64\n",
            " 1   Administration   50 non-null     float64\n",
            " 2   Marketing Spend  50 non-null     float64\n",
            " 3   State            50 non-null     object \n",
            " 4   Profit           50 non-null     float64\n",
            "dtypes: float64(4), object(1)\n",
            "memory usage: 2.1+ KB\n"
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        "df.shape"
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      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(50, 5)"
            ]
          },
          "metadata": {},
          "execution_count": 49
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        "df.isnull().sum()"
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      "execution_count": 50,
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "R&D Spend          0\n",
              "Administration     0\n",
              "Marketing Spend    0\n",
              "State              0\n",
              "Profit             0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
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      "source": [
        "df.corr()"
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        "id": "fhRgg94gIUgN",
        "outputId": "d37ddc78-5a9b-4db9-8b1c-f61a6855a44f"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-51-2f6f6606aa2c>:1: 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",
            "  df.corr()\n"
          ]
        },
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              "                 R&D Spend  Administration  Marketing Spend    Profit\n",
              "R&D Spend         1.000000        0.241955         0.724248  0.972900\n",
              "Administration    0.241955        1.000000        -0.032154  0.200717\n",
              "Marketing Spend   0.724248       -0.032154         1.000000  0.747766\n",
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              "      <td>0.724248</td>\n",
              "      <td>-0.032154</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.747766</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Profit</th>\n",
              "      <td>0.972900</td>\n",
              "      <td>0.200717</td>\n",
              "      <td>0.747766</td>\n",
              "      <td>1.000000</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-e6728c73-cf08-4aad-8359-108d55bd9a70')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "    </button>\n",
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              "  <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-e6728c73-cf08-4aad-8359-108d55bd9a70 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-e6728c73-cf08-4aad-8359-108d55bd9a70');\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-e8c5e1b4-c35a-421c-876d-31dcdcc436a0\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-e8c5e1b4-c35a-421c-876d-31dcdcc436a0')\"\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-e8c5e1b4-c35a-421c-876d-31dcdcc436a0 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\": \"df\",\n  \"rows\": 4,\n  \"fields\": [\n    {\n      \"column\": \"R&D Spend\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3512027732342562,\n        \"min\": 0.24195524508168925,\n        \"max\": 1.0,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.24195524508168925,\n          0.9729004656594831,\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Administration\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.44813433237964423,\n        \"min\": -0.03215387512538653,\n        \"max\": 1.0,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          1.0,\n          0.20071656826872125,\n          0.24195524508168925\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Marketing Spend\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.44590493173260376,\n        \"min\": -0.03215387512538653,\n        \"max\": 1.0,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          -0.03215387512538653,\n          0.7477657217414768,\n          0.7242481330538794\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Profit\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.37074552377091846,\n        \"min\": 0.20071656826872125,\n        \"max\": 1.0,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.20071656826872125,\n          1.0,\n          0.9729004656594831\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.heatmap(df.corr(),annot=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 611
        },
        "id": "D4r0ThYNIUdV",
        "outputId": "de7c3899-fa9d-412e-8195-8e1588babe47"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-52-8df7bcac526d>:1: 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.corr(),annot=True)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 52
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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gEong7OwMKysr2Nra4v79+/j2228//xvwjqurK86fP6/Udu7cuY+eI5FIIJFIlNoyMxK/OAYq2bIzsxBz/T5c67rh6uG/AeT8o8GlrhuObTyY73ktBrZF66EdsaTXHDyKvK/02bndJ3HrVKRS24iNU3H+15M4s/MjQ3TaLjsbsmcPIK5QDdm3L+a0CQLETlWRdeHwR0/VqVob0NFB1rVTuT98l6iJylgjbf1sIO1t7j6kRFdXB1UqO+H85etoWs8bQM4/os9duY6u7Vp+9FyJnh6sLEojMysLR/86D79GuUdD9hw8htJmpmhYp0aRxK8RNGgIszCUyGQNAL755huMGzcOy5cvx9ixY9GxY0fMnDkTbdq0wcKFC+Ho6IiLFy8iKSkJRkZGWLduHdq1aweR6J+R4YSEBKSnp+PNmze4dOkSFixYgMTExI8mdjNmzEBqaipat24NBwcHvHr1CkuXLkVmZiaaN2+u6Kevr49evXrhhx9+QHJyMkaMGIFOnTrB2toaADBz5kyMGDECpqamaNmyJaRSKS5evIiXL1/mqnzlZ8SIEahXrx5++OEHtGvXDocOHcLBg/n/BVoSpaamIebJM8XXT5/F4/adezA1KQUb67IqjKzkOLp2H3ovHIqHkffwMCIaTfu2gZ6hRJFY9V44DK/ik7BnwRYAgN+gdvAf3RmhI5fgxZPnMHk3t02akg5pajpSXr1VWmwAANlZWUh+/hLx95+B8pd5dj8k7QdD9uy+YusOQVeCzCsnAAB67QdDnvwSmeHblM7T8fTNSfD+nYiJxJB0GgWRTQVItyyAIBIB7yp38rS3QLbmbCpa3Hp2/BpTFixHVWcnuDlXxKbdB5CWLkVAy8YAgMnzl6GsRWmM6tcNAHDt1l0kJCbB+StHJLxIwsqNOyGTyRHYuZ3SdWUyGfYcOo62zRtB518jLVpFg4YwC0OJTdZ0dHQwbNgwLFiwAIMHD4aRkRHOnDmDyZMnIzAwEM+fP0fVqlUxe/ZseHl5oXbt2hg1ahSWLl2quIazszMEQYCxsTGcnJzQokULBAUFKRKqvDRq1AjLly9Hz549ER8fD3Nzc3h6euLw4cNwdnZW9KtYsSI6dOiA1q1bIykpCV9//bXS1hz9+vWDoaEhvv/+e4wbNw5GRkZwc3PDqFGjCvw9qFOnDtasWYPg4GBMnz4dzZo1w9SpUzF79uzP+2ZqsOu376LP8AmKrxf8tBoA0K5VM8ydOkZVYZUoF/edgXFpE7Qd3RkmlmZ4cushlvaaq1h0ULqchWKuJwA07N4CuhJdDFqlPITz++Id2Lc4n2E4KpDsG+eQYWQCXd//g56xGWRxj5D+v/lASs7PQmRqAdkHPwsAEMrYQOzggrSN83JdTzAxh46LFwDAYPB3Sp+lrZ8F2cNbRfQkmq+lb10kvU7G8vU7kPjyFVy+csSqkMmweDcMGpuQCEH0z3w0aUYmflq3DU9iE2BooI8G3p6YN2EYTIyNlK577nIkYhMS0b6Vb3E+DqmYIJf/608uFbkZM2Zgz549iIiIUHUoBZKZeP/TnajYDPOa8OlOVCwW9S6x/97VOLp9J6o6BHpHz656kd8j7Y+ln+5UAAatRhTKdYoaf9MQERGRZuGcNSIiIiI1pmVz1krMPmuaZMaMGRozBEpERESqxcoaERERaRYOgxIRERGpMQ6DEhEREZG6YGWNiIiINAuHQYmIiIjUGIdBiYiIiEhdsLJGREREmoXDoERERERqTMuSNQ6DEhEREakxVtaIiIhIs8jlqo6gWDFZIyIiIs2iZcOgTNaIiIhIs2hZssY5a0RERERqjMkaERERaRa5rHCOL7B8+XI4OjpCX18ftWvXxoULFz7af/HixXB2doaBgQHs7OwwevRopKenf9Y9OQxKREREmkVFw6Dbt29HUFAQVq1ahdq1a2Px4sXw8/NDVFQUypYtm6v/li1bMHHiRISFhaFu3bq4c+cOevfuDUEQsGjRogLfl5U1IiIiogJYtGgR+vfvj8DAQFSpUgWrVq2CoaEhwsLC8ux/5swZ1KtXD926dYOjoyNatGiBrl27frIa929M1oiIiEizyOWFckilUiQnJysdUqk0z1tmZGTg0qVLaNasmaJNJBKhWbNmOHv2bJ7n1K1bF5cuXVIkZ/fv38eBAwfQunXrz3pcJmtERESkWWSyQjlCQkJgamqqdISEhOR5y8TERGRnZ8PKykqp3crKCnFxcXme061bN8yaNQv169eHrq4uvvrqKzRu3BiTJ0/+rMdlskZERERaadKkSXj9+rXSMWnSpEK7/vHjxzFv3jysWLECly9fxu7du7F//37Mnj37s67DBQZERESkWQppgYFEIoFEIilQXwsLC4jFYsTHxyu1x8fHw9raOs9zpk2bhh49eqBfv34AADc3N6SkpGDAgAGYMmUKRKKC1cxYWSMiIiLNooKtO/T09FCzZk2Eh4cr2mQyGcLDw+Hj45PnOampqbkSMrFYnPMIn/HKLFbWiIiIiAogKCgIvXr1gpeXF7y9vbF48WKkpKQgMDAQANCzZ0+UK1dOMe/N398fixYtgqenJ2rXro3o6GhMmzYN/v7+iqStIJisERERkUaRy1TzIvfOnTvj+fPnmD59OuLi4uDh4YGDBw8qFh3ExMQoVdKmTp0KQRAwdepUPH36FJaWlvD398fcuXM/676C/HPqcKSVMhPvqzoE+sAwrwmqDoHeWdSb/95VF7p9J6o6BHpHz656kd8jddXIQrmO4aAlhXKdosbfNERERKRZvvBVUZqKCwyIiIiI1Bgra0RERKRZVDRnTVWYrBEREZFmUdGL3FWFw6BEREREaoyVNSIiItIsWlZZY7JGREREmkXLdh3jMCgRERGRGmNljYiIiDQLh0GJiIiI1JiWbd3BYVAiIiIiNcbKGhEREWkWLXvdFJM1IiIi0ixaNgzKZI0+aZjXBFWHQB9YdvE7VYdA7xjYNlB1CPROx3Vpqg6B3tn+aE+R30OuZQsMOGeNiIiISI2xskZERESahcOgRERERGpMyxYYcBiUiIiISI2xskZERESahcOgRERERGqMq0GJiIiISF2wskZERESahcOgRERERGqMq0GJiIiISF2wskZERESahcOgREREROpL294NymSNiIiINIuWVdY4Z42IiIhIjbGyRkRERJpFyyprTNaIiIhIs3DrDiIiIiJSF6ysERERkWbhMCgRERGR+pJrWbLGYVAiIiIiNcbKGhEREWkWLausMVkjIiIizaJlbzDgMCgRERGRGmNljYiIiDQLh0GJiIiI1BiTNSIiIiL1JZdrV7LGOWtEREREaoyVNSIiItIsHAYlIiIiUmNalqxxGJSIiIhIjbGyRkRERBpF294NymSNiIiINIuWJWscBiUiIiJSY6ysERERkWbRrleDMlkjIiIizaJtc9Y4DEpERESkxlhZIyIiIs2iZZU1JmtERESkWThnjYiIiEh9cc6ampkxYwY8PDz+0zUePnwIQRAQERFRrPctTF/yDERERKT5VJKsnT17FmKxGG3atCmW+9nZ2SE2NhbVqlUr8Dljx45FeHh4gfoWdmLXu3dvBAQEKLV9yTOUdI17+GHuqeVYFrUZE/fMg2P1ivn2rd+lKcbumIVFV9dh0dV1GPW/aR/t321uf/z8cCea9mldFKFrpYsRkRg6Phi+bb9FtXqtEH7yjKpDKjFmBI/F40eX8eZ1NA79sQ0VK1b45DmDB/VC9J1zeJt8D2dO/Y5aXh5Kn69Y/h2ibp3Gm9fRiH16Dbt/CYOz81eKz93dq+B/m5bjwb2/8eZ1NCKvHcfwYX0L+9FKlBY9W+GnU6uxKWoH5uxZgK+qV8q3b5MuzTFj5zyEXvsfQq/9D1M3z/xof60jK6RDQ6gkWQsNDcXw4cNx8uRJPHv2rMjvJxaLYW1tDR2dgo/6Ghsbo0yZMoUaR2Zm5hef+yXPUJJ5fV0X/ze1F/Yv2Ym5bSbgyc1HGLFxCkqVMcmzf+U6VfH33lNY1HUmvuswBS9jX2Dkpqkwsyqdq6+HnzecPCvjZVxSUT+GVklLS4dzRSdMGTNE1aGUKOPGDsGwoX0wZNhE1K3vj5TUVBzYtxkSiSTfc775pi1++D4Ys+csQq3aLXH12k0c2L8Zlpb//M67fPka+vUPQjX3xmjdphsEQcAf+7dCJMr5a6OGpxsSEhLRq/dwuHs0Qcj8pZg7ZxKGDO5d1I+skXy+roeeU/vglyXbMPHrIDy69RCTNwXDpIxpnv2r+lTDmb1/YVaXaZjWfgJePEvElE0zYJ7H7yxtJJfJC+XQFMWerL19+xbbt2/H4MGD0aZNG6xfv17p8/nz58PKygqlSpVC3759kZ6ervT5+6rTvHnzYGVlBTMzM8yaNQtZWVkYN24cSpcujfLly2PdunWKc/49hHj8+HEIgoDw8HB4eXnB0NAQdevWRVRUlOKcf1fLjh8/Dm9vbxgZGcHMzAz16tXDo0ePsH79esycORNXr16FIAgQBEHxTIIgYOXKlWjbti2MjIwwd+5cZGdno2/fvqhQoQIMDAzg7OyMJUuWKN13w4YN+O233xTXO378eJ7DoCdOnIC3tzckEglsbGwwceJEZGVlKT5v3LgxRowYgfHjx6N06dKwtrbGjBkzvuwHp2aa9fsap7aF48zO44iNfoLNU1YjIy0DdTs1ybN/2KilOPG/w3hy8yHi7z3DxgmrIAgCXOopVyrNrEqjy4w+CB25BNkffC/pv2vgUwsjBvRCs0b1VB1KiTJieD/MC1mC338/jMjIW+gdOBK2tlZo184v33NGj+yPtaFbsGHjDty6dRdDhk5EamoaAnt3UfRZG7oZf506j0ePnuBKxHVMD14Ae/tycHS0AwCs37AdQWOCcfKvc3jwIAZbtuzG+g3b0T6A1ei8tOnXDuHbDuP4zj/x9O4TrJ28EhlpUvh2appn/59G/ojDm/7Ao5sP8OzeU6yasByCSIBbPfdijpzUQbEnazt27ICLiwucnZ3RvXt3hIWFQS6XKz6bMWMG5s2bh4sXL8LGxgYrVqzIdY0///wTz549w8mTJ7Fo0SIEBwfj66+/hrm5Oc6fP49BgwZh4MCBePLkyUdjmTJlChYuXIiLFy9CR0cHffr0ybNfVlYWAgIC0KhRI1y7dg1nz57FgAEDIAgCOnfujDFjxqBq1aqIjY1FbGwsOnfurDh3xowZaN++PSIjI9GnTx/IZDKUL18eO3fuxM2bNzF9+nRMnjwZO3bsAJAz/NqpUye0bNlScb26devmiunp06do3bo1atWqhatXr2LlypUIDQ3FnDlzlPpt2LABRkZGOH/+PBYsWIBZs2bhyJEjH/8hqTmxrg7sqznh1ulrija5XI7bp6/BqUblAl1Dz0APYl0dpLx6q2gTBAGBPw7H4dV7EXv34//fIVIHFSrYw8bGCuF/nlK0JSe/wYULV1Cnds08z9HV1UWNGu4I//MvRZtcLkf4n6dQp07e5xgaGqB3z864f/8RHj/OfzTE1LQUkl6++rKHKcHEujpwcvsKkaeUf2dFnrqKSjWcC3QNiYEedHTFePvB7yytpmXDoMU+phYaGoru3bsDAFq2bInXr1/jxIkTaNy4MRYvXoy+ffuib9+ceQ9z5szB0aNHc1XXSpcujaVLl0IkEsHZ2RkLFixAamoqJk+eDACYNGkS5s+fj1OnTqFLly7Iz9y5c9GoUSMAwMSJE9GmTRukp6dDX19fqV9ycjJev36Nr7/+Gl99lTNnw9XVVfG5sbExdHR0YG1tnese3bp1Q2BgoFLbzJkzFf9doUIFnD17Fjt27ECnTp1gbGwMAwMDSKXSPK/33ooVK2BnZ4dly5blVIhcXPDs2TNMmDAB06dPVwxVuLu7Izg4GABQqVIlLFu2DOHh4WjevHm+11Z3xualINYR403ia6X25OevYf1VuQJdo8PE7ngdn4RbpyMVbX6D20GWlY0/1x0o1HiJioq1VVkAQHz8c6X2+IREWFuXzfMcC4vS0NHRQUJ8olJ7QsJzuHwwJw0ABg3shfkhU2BsbITbUdFo2bprvtM5fOp4odM3bdG2Xc8vfZwSy+Td76zXia+U2l8nvobtV+ULdI1vJ/VCUvxLRJ6+WgQRah65BiVahaFYK2tRUVG4cOECunbtCgDQ0dFB586dERoaCgC4desWateurXSOj49PrutUrVpVkYwAgJWVFdzc3BRfi8VilClTBgkJCR+Nx939n3KyjY0NAOR5TunSpdG7d2/4+fnB398fS5YsQWxs7KceFwDg5eWVq2358uWoWbMmLC0tYWxsjNWrVyMmJqZA13vv1q1b8PHxgSAIirZ69erh7du3ShXFD58RyHnOj31fpFIpkpOTlY5sefZnxabu/AYHoJZ/Pawc+D2ypDl/8dhXc0KTwDZYP3a5iqMjyl/Xru3xKumO4tDVLdp/b2/Zuhte3n7wbdIBd+/ex9Ytq/KcC1e1qjN2/xKG2XN+xJGjJ4s0Jm3UbnAH1PWvj4UDQpAp/fK5z6S5ijVZCw0NRVZWFmxtbaGjowMdHR2sXLkSv/zyC16/fv3pC7yjq6ur9LUgCHm2yWQfT70/POd90pPfOevWrcPZs2dRt25dbN++HZUrV8a5c+c+GauRkZHS19u2bcPYsWPRt29fHD58GBEREQgMDERGRsYnr/UlPvf7EhISAlNTU6XjyuvbRRLbl3r78g2ys7JRykJ5Yq6JpSleP3/10XOb9/dHy8EBWNJjNp7e/idBruTtglJlTBByZiVWRG/DiuhtsChfFv83pRfmnmICR+rh998Po2atFooj8UXOIhgrK0ulflZlLRAXl/c/yhITk5CVlYWyVhZK7WXLWiLuXxW65OQ3iI5+gL9OnUenzgPg4lwRAQEtlfq4ulbC4YPbsTZ0M+aFLAHllvzud5aphZlSu6mFKV49f/nRc78e0A7tBnfE3O4zEHP7URFGqWFUOAy6fPlyODo6Ql9fH7Vr18aFCxc+2v/Vq1cYOnQobGxsIJFIULlyZRw48HkjOMWWrGVlZWHjxo1YuHAhIiIiFMfVq1dha2uLrVu3wtXVFefPn1c6ryAJUXHx9PTEpEmTcObMGVSrVg1btmwBAOjp6SE7u2DVp9OnT6Nu3boYMmQIPD09UbFiRdy7d0+pT0Gu5+rqirNnzyrm+72/dqlSpVC+fMHK6nmZNGkSXr9+rXR4mrp88fWKQnZmFmKu34dr3X+qqYIgwKWuG+5fvpPveS0GtkWb4f+Hpb3m4lHkfaXPzu0+idktx2JO63GK42VcEg6v3oulPecW2bMQfY63b1Nw795DxXHz5h3ExsajiW99RZ9SpYzh7e2Jc+cv5XmNzMxMXL58TekcQRDQxLc+zp3L+5z3fQRBgETvn8palSqVcfTwTmz6305Mm/5dITxhyZSdmYX7kfeUFgcIgoBq9dxx93JUvue1HdgeHYd3QkivmbgfeS/fftpILiuc43Nt374dQUFBCA4OxuXLl1G9enX4+fnlO2KVkZGB5s2b4+HDh9i1axeioqKwZs0alCtXsCk77xXbnLV9+/bh5cuX6Nu3L0xNlSsiHTt2RGhoKMaOHYvevXvDy8sL9erVw+bNm3Hjxg04OTkVV5h5evDgAVavXo22bdvC1tYWUVFRuHv3Lnr2zJmb4ejoiAcPHiAiIgLly5dHqVKl8l02X6lSJWzcuBGHDh1ChQoVsGnTJvz999+oUOGffZEcHR1x6NAhREVFoUyZMrm+XwAwZMgQLF68GMOHD8ewYcMQFRWF4OBgBAUFKQ0Rfy6JRJIrdrEg/uLrFZWja/eh98KheBh5Dw8jotG0bxvoGUpwZucxAEDvhcPwKj4JexbkJNR+g9rBf3RnhI5cghdPnsPE0gwAIE1JhzQ1HSmv3iotNgCA7KwsJD9/ifj7Rb+9jDZITU1DzJN/vpdPn8Xj9p17MDUpBZt85lfRpy39aS0mTxqBu9H38fDhY8ycMQ7PnsXjt98OKfocPrgde377AytWrgcA/LhkDdaF/ohLl6/h77+vYMTw/jAyMsD6DdsB5Cxc6PRNWxw5cgLPE1+gfDlbjB8/FGlp6fjjYM7+k1WrOuPIoR04fOQEfly8WlHdy87ORmIit735t/1rf8OQhSNx71o07l29i9Z9/CEx1MfxnTnfz6GLRiIp7gW2LvgfAKDtoPboFNQNS0cuQsKTBJi++52V/u53FqnGokWL0L9/f8Vc9FWrVmH//v0ICwvDxIkTc/UPCwtDUlISzpw5oxjpcnR0/Oz7FluyFhoaimbNmuWZeHTs2BELFiyAq6srpk2bhvHjxyM9PR0dO3bE4MGDcejQoTyuWHwMDQ1x+/ZtbNiwAS9evICNjQ2GDh2KgQMHKuLfvXs3fH198erVK6xbtw69e/fO81oDBw7ElStX0LlzZwiCgK5du2LIkCH4448/FH369++P48ePw8vLC2/fvsWxY8dy/XDLlSuHAwcOYNy4cahevTpKly6Nvn37YurUqUX1bVArF/edgXFpE7Qd3RkmlmZ4cushlvaaq1h0ULqchVLVsWH3FtCV6GLQqrFK1/l98Q7sW7yzWGPXVtdv30Wf4RMUXy/4aTUAoF2rZpg7dYyqwtJ43/+wAkZGhli1YgHMzExw+vTfaOPfHVKpVNHHyckBFhb/7M+1c+deWFqUxozpY2FtbYmrV2+gzdfdkZCQs+ggPV2K+vW8MWJ4P5ibmyI+PhF/nTqHBo3a4fnzFwCAjh3aoGxZC3T/tiO6f9tRce2HDx+jYuU6xfT0muPsvtMwKWOKTkFdYWZpjoc3HyCk50y8fvc7q4ytJWQf7PvVvHsr6Ep0MWbVBKXr7PxxG3Yt3lassaulQlpgIJVKlf6sAHkXLYCcKtmlS5cwadIkRZtIJEKzZs1w9uzZPK+/d+9e+Pj4YOjQofjtt99gaWmJbt26YcKECRCLC14IEeQf/o1GlIeBjt+oOgT6wLKLHG5SFwa2DVQdAr3T0aaWqkOgd7Y/2lPk93jevFGhXGd5PV+lHRoAIDg4OM89SZ89e4Zy5crhzJkzSosfx48fjxMnTuSaxgUALi4uePjwIb799lsMGTIE0dHRGDJkCEaMGKHYqaEguB0+ERERaZTC2rpj0qRJCAoKUmr72Ns/PpdMJkPZsmWxevVqiMVi1KxZE0+fPsX333/PZI2IiIjoU/Ib8syLhYUFxGIx4uPjldrj4+Pz3RfVxsYGurq6SkOerq6uiIuLQ0ZGBvT09Ap0b5W8G5SIiIjoS6liNaienh5q1qyJ8PBwRZtMJkN4eHiee8ICOfufRkdHK22ZdefOHdjY2BQ4UQOYrBEREZGmkQuFc3ymoKAgrFmzBhs2bMCtW7cwePBgpKSkKFaH9uzZU2kBwuDBg5GUlISRI0fizp072L9/P+bNm4ehQ4d+1n05DEpERERUAJ07d8bz588xffp0xMXFwcPDAwcPHoSVlRUAICYmRmn7LDs7Oxw6dAijR4+Gu7s7ypUrh5EjR2LChAn53SJPTNaIiIhIo6jy3aDDhg3DsGHD8vzs+PHjudp8fHz+8wb/TNaIiIhIo8hlnz+Eqck4Z42IiIhIjbGyRkRERBpFlcOgqsBkjYiIiDSK/AtWcmoyDoMSERERqTFW1oiIiEijcBiUiIiISI1p22pQJmtERESkUeRyVUdQvDhnjYiIiEiNsbJGREREGoXDoERERERqTNuSNQ6DEhEREakxVtaIiIhIo2jbAgMma0RERKRROAxKRERERGqDlTUiIiLSKNr2blAma0RERKRRtO11UxwGJSIiIlJjrKwRERGRRpFxGJSIiIhIfXHOGhEREZEa49YdRERERKQ2WFkjIiIijcI3GBARERGpMQ6DEhEREZHaYGWNiIiINAq37iAiIiJSY9q2dQeHQYmIiIjUGCtrREREpFG4GpSIiIhIjWnbnDUOgxIRERGpMVbWiIiISKNo2wIDJmtERESkUThnjYiIiEiNcc4aEREREakNVtbokxb15v9N1ImBbQNVh0DvpD37S9Uh0DuZ6+aoOgQqRpyzRkRERKTGOAxKRERERGqDlTUiIiLSKFq2GJTJGhEREWkWDoMSERERkdpgZY2IiIg0CleDEhEREakxmaoDKGYcBiUiIiJSY6ysERERkUaRg8OgRERERGpLpmV7dzBZIyIiIo0i07LKGuesEREREakxVtaIiIhIo3DOGhEREZEa49YdRERERKQ2WFkjIiIijcJhUCIiIiI1xmFQIiIiIlIbrKwRERGRRtG2yhqTNSIiItIo2jZnjcOgRERERGqMlTUiIiLSKDLtKqwxWSMiIiLNom3vBmWyRkRERBpFruoAihnnrBERERGpMSZrREREpFFkhXR8ieXLl8PR0RH6+vqoXbs2Lly4UKDztm3bBkEQEBAQ8Nn3ZLJGREREGkUmCIVyfK7t27cjKCgIwcHBuHz5MqpXrw4/Pz8kJCR89LyHDx9i7NixaNCgwRc9L5M1IiIiogJYtGgR+vfvj8DAQFSpUgWrVq2CoaEhwsLC8j0nOzsb3377LWbOnAknJ6cvui+TNSIiItIo8kI6pFIpkpOTlQ6pVJrnPTMyMnDp0iU0a9ZM0SYSidCsWTOcPXs231hnzZqFsmXLom/fvl/8vEzWiIiISKMU1py1kJAQmJqaKh0hISF53jMxMRHZ2dmwsrJSareyskJcXFye55w6dQqhoaFYs2bNf3pebt1BREREWmnSpEkICgpSapNIJIVy7Tdv3qBHjx5Ys2YNLCws/tO1mKwRERGRRimsNxhIJJICJ2cWFhYQi8WIj49Xao+Pj4e1tXWu/vfu3cPDhw/h7++vaJPJctag6ujoICoqCl999VWB7s1hUCIiItIoMgiFcnwOPT091KxZE+Hh4f/EIZMhPDwcPj4+ufq7uLggMjISERERiqNt27bw9fVFREQE7OzsCnxvVtaIiIiICiAoKAi9evWCl5cXvL29sXjxYqSkpCAwMBAA0LNnT5QrVw4hISHQ19dHtWrVlM43MzMDgFztn8JkjYiIiDSKql431blzZzx//hzTp09HXFwcPDw8cPDgQcWig5iYGIhEhT9oyWSNiIiINEphzVn7EsOGDcOwYcPy/Oz48eMfPXf9+vVfdE8ma0RERKRRvvRVUZqKCwyIiIiI1Bgra0RERKRRVDVnTVXUMllbv349Ro0ahVevXhXqdRs3bgwPDw8sXry4UK+raQRBwK+//oqAgABVh/Kf6NRqDt16/hCMTSGLi0HGH+she3ovz776vadB7FglV3vWnSuQblkAiMTQbdIJOpU8IJiXhVyahuz7kcg8ug3yNy+L+lE00ozgsejbpxvMzExw5sxFDB0+CdHRDz56zuBBvTAmaDCsrS1x7dpNjBw1DX9fjFB8vmL5d2japD5sba3w9m0qzp67iEmT5yIqKufn6u5eBePHDUW9ut6wsDDHw0dPsHr1Jvy0LLQoH7XEuRgRiXVbduHm7Wg8f5GEJSHT0LRhXVWHVeLoeDaBTq2WEIxMIUt4jMzwzZDF5f1nRNJ5PMT2Lrnas+9dhXT3EgCAXqs+0KlWX/nzB5GQ7vqx8INXc6qcs6YKnzUM2rt3bwiCgEGDBuX6bOjQoRAEAb179y6s2L7Y8ePHIQhCrmRv9+7dmD17dpHf/8GDB+jWrRtsbW2hr6+P8uXLo127drh9+3aR31tbiKvWgZ5fD2Qe/wVpP0+GLP4R9LtPBIxM8uyfvn0RUn8Y9M+xfBzksmxk3zyX00FXD2KbCsg4+SvSfp4M6fZFEJWxhaTr2GJ8Ks0xbuwQDBvaB0OGTUTd+v5ISU3FgX2bP7q55DfftMUP3wdj9pxFqFW7Ja5eu4kD+zfD0rKMos/ly9fQr38Qqrk3Rus23SAIAv7Yv1WxuqqGpxsSEhLRq/dwuHs0Qcj8pZg7ZxKGDO5d1I9coqSlpcO5ohOmjBmi6lBKLLFzLeg27ozMM3uRvnEm5M8fQ/JNEGBYKs/+0t+WI3XFKMWRFjYVclk2sqIuKvXLvh+p1E/6+8/F8TikYp9dWbOzs8O2bdvw448/wsDAAACQnp6OLVu2wN7e/j8HlJmZ+Z+vkZ/SpUsX2bXfy8zMRPPmzeHs7Izdu3fDxsYGT548wR9//FHolUJtpuvTBlmX/0RWxAkAQMa+UIgreULXszEyT+3NfUJailLZXKdaXSBTiqwb53MapGlI3zRP8bkcQMaBdTAYMBeCaRnIX78osmfRRCOG98O8kCX4/ffDAIDegSPx7EkE2rXzw44deXz/AYwe2R9rQ7dgw8YdAIAhQyeidaumCOzdBQu+Xw4AWBu6WdH/0aMnmB68AFcuHYWjox3u33+E9Ru2K13zwYMY1KldE+0DWmPFyvVF8KQlUwOfWmjgU0vVYZRoOl5+yLp2EtnXTwEAMg5vhL6TO3SqNUDWhQO5T0hPUfpS7FIbyMxA9p2/ldrl2ZlASnKRxa0puMDgE2rUqAE7Ozvs3r1b0bZ7927Y29vD09NTqe/BgwdRv359mJmZoUyZMvj6669x794/w1QPHz6EIAjYvn07GjVqBH19fWzevBn/9vz5c3h5eaF9+/aQSqWQyWQICQlBhQoVYGBggOrVq2PXrl2Ka/r6+gIAzM3Nlap9jRs3xqhRoxTXdXR0xLx589CnTx+UKlUK9vb2WL16tdK9z5w5Aw8PD+jr68PLywt79uyBIAiIiIjI8/tz48YN3Lt3DytWrECdOnXg4OCAevXqYc6cOahTp47Sc2/btg1169ZVbJx34sQJpWtdv34drVq1grGxMaysrNCjRw8kJiYqPm/cuDFGjBiB8ePHo3Tp0rC2tsaMGTOUrnH37l00bNgQ+vr6qFKlCo4cOZJn3BpFLIbItgKy71//p00uR/b96xCVr1SgS+h6NkbW9bNApjT/TvqGkMtlkKen/rd4S5gKFexhY2OF8D9PKdqSk9/gwoUrqFO7Zp7n6OrqokYNd4T/+ZeiTS6XI/zPU6hTJ+9zDA0N0LtnZ9y//wiPHz/LNx5T01JIevnqyx6GqCiIxBBZO0D26OYHjXLIHt2EyLZgrxfScWuA7NsXgMwMpXaxnQsMhiyGft950G3eA9A3KsTANUdhvchdU3zRatA+ffpg3bp1iq/DwsIUu/d+KCUlBUFBQbh48SLCw8MhEonQvn17xbux3ps4cSJGjhyJW7duwc/PT+mzx48fo0GDBqhWrRp27doFiUSCkJAQbNy4EatWrcKNGzcwevRodO/eHSdOnICdnR1++eUXAEBUVBRiY2OxZMmSfJ9l4cKF8PLywpUrVzBkyBAMHjwYUVFRAIDk5GT4+/vDzc0Nly9fxuzZszFhwoSPfm8sLS0hEomwa9cuZGdnf7TvuHHjMGbMGFy5cgU+Pj7w9/fHixc5FZxXr16hSZMm8PT0xMWLF3Hw4EHEx8ejU6dOStfYsGEDjIyMcP78eSxYsACzZs1SJGQymQwdOnSAnp4ezp8/j1WrVn0yfk0gGJpAEIkhf/taqV2e8hqCsdknzxeV+woiK3tkXT6WfycdXeg164rsyDOANO0/RlyyWFuVBQDExz9Xao9PSIS1ddk8z7GwKA0dHR0kxCcqtSckPIe1laVS26CBvfAq6Q6SX0XDr6UvWrbumm/F3aeOFzp90xZr1/7vSx+HqNAJBqVyfkelKlfA5KnJEIxMP3m+yLoCRJblkXXtpFJ79oPryDiwFuk7vkfmiZ0Ql3eG5P9GA4KWTeDSQl+UrHXv3h2nTp3Co0eP8OjRI5w+fRrdu3fP1a9jx47o0KEDKlasCA8PD4SFhSEyMhI3b95U6jdq1Ch06NABFSpUgI2NjaI9KioK9erVg5+fH9atWwexWAypVIp58+YhLCwMfn5+cHJyQu/evdG9e3f8/PPPEIvFiuHOsmXLwtraGqam+f/haN26NYYMGYKKFStiwoQJsLCwwLFjOX+Jb9myBYIgYM2aNahSpQpatWqFcePGffR7U65cOSxduhTTp0+Hubk5mjRpgtmzZ+P+/fu5+g4bNgwdO3aEq6srVq5cCVNTU4SG5kyUXrZsGTw9PTFv3jy4uLjA09MTYWFhOHbsGO7cuaO4hru7O4KDg1GpUiX07NkTXl5eiveWHT16FLdv38bGjRtRvXp1NGzYEPPmzcsVx4ekUimSk5OVDmnWx5NOTaPj2Riy+Jh8FyNAJIbkm5GAIEC6P6xYY1NHXbu2x6ukO4pDV7do1yVt2bobXt5+8G3SAXfv3sfWLavynAtXtaozdv8ShtlzfsSRoyfzuBKRZhK7N4Ds+eNcixGyb19A9r0IyBOfIjv6CqS7l0Bs4wSRXe6FCSWdXCicQ1N8UbJmaWmJNm3aYP369Vi3bh3atGkDCwuLXP3u3r2Lrl27wsnJCSYmJnB0dASQ8zqGD3l5eeU6Ny0tDQ0aNECHDh2wZMkSCO/+5RAdHY3U1FQ0b94cxsbGimPjxo1KQ6wF5e7urvhvQRBgbW2NhIQEADnJoru7O/T19RV9vL29P3nNoUOHIi4uDps3b4aPjw927tyJqlWr5hqC/PDFrzo6OvDy8sKtW7cAAFevXsWxY8eUntHFJecP5IfP+WH8AGBjY6OI/9atW7Czs4OtrW2e98xLSEgITE1NlY4fTt386DnFTZ6aDLksG4KxchIuGJlC/vbVx0/WlUCnWl1k5ldVe5eoCaYWSN84j1U1AL//fhg1a7VQHIkvkgAAVv+qiFmVtUBcXEKe10hMTEJWVhbKWin/nihb1hJx/6rQJSe/QXT0A/x16jw6dR4AF+eKCAhoqdTH1bUSDh/cjrWhmzEvJP/KOZEqyNPe5PyOMlRe8CQYmkCe8jqfs97R1YOOizeyrv318X4A5K+fQ576BiLzvCvaJZm2DYN+8T+R+/Tpo3jdwvLly/Ps4+/vDwcHB6xZswa2traQyWSoVq0aMjKUx+CNjHKPuUskEjRr1gz79u3DuHHjUK5cOQDA27dvAQD79+9XtH14zufS1dVV+loQhFzDtF+iVKlS8Pf3h7+/P+bMmQM/Pz/MmTMHzZs3L9D5b9++hb+/P7777rtcn31YfSzs+CdNmoSgoCCltqwF/b74ekUiOxuyZw8grlAN2bffrZQSBIidqiLrwuGPnqpTtTago4Osa6dyf/guUROVsUba+tlA2tsiCF7zvH2bgrdvlSc/x8bGo4lvfVy9egMAUKqUMby9PbFq9cY8r5GZmYnLl6+hiW997N17CEDO/1eb+NbHipXr8jznfR9BECDR++fPdpUqlXHk0A5s+t9OTJue+88HkcrJsiGLewSRgyuyo6+8axQgcnBF1uU/P3qquHItQKyLrJtnP3kbwdgcMDDKNSVEG2hSolUYvvgNBi1btkRGRgYyMzNzzTMDgBcvXiAqKgpTp05F06ZN4erqipcvC75flUgkwqZNm1CzZk34+vri2bOcCcZVqlSBRCJBTEwMKlasqHTY2dkBAPT09ADgk3PGPsXZ2RmRkZGQSv+ZhP73339/5Iy8CYIAFxcXpKQo/4V37tw5xX9nZWXh0qVLcHV1BZCzkOPGjRtwdHTM9Zx5Jbd5cXV1xePHjxEbG5vnPfMikUhgYmKidEh0xAV91GKTeXY/dGr6Qqd6QwgWttBr0weCrgSZV3IWaei1Hwzdpl1ynafj6ZuT4P07EROJIek0CiJbJ0h/WQZBJIJgbJpTvROr3/Or2tKf1mLypBH4+uvmqFbNBevXLcGzZ/H47bdDij6HD25X2lLjxyVr0K9vN/To8Q1cXCpi+bL5MDIyUKzwrFDBHhPGD0MNTzfY2dnCp44Xtm/7GWlp6fjjYM7QftWqzjh6eCeOHD2JHxevhpWVJaysLGFhUfQrvUuS1NQ03L5zD7fv5FTpnz6Lx+079xCbT2WUPl/WxUPQcW8EcdW6EErbQLdFDwi6EmS9Wx2q17ofdBt0zHWejnsDZN+9nGt1KHQl0G30DUQ2ThBMykBk7wpJ++GQv0xA9sPrua5DJcsXV9bEYrFiyE6cx19m5ubmKFOmDFavXg0bGxvExMRg4sSJn32PzZs3o2vXrmjSpAmOHz8Oa2trjB07FqNHj4ZMJkP9+vXx+vVrnD59GiYmJujVqxccHBwgCAL27duH1q1bw8DAAMbGxp/9jN26dcOUKVMwYMAATJw4ETExMfjhhx8AQDEs+28REREIDg5Gjx49UKVKFejp6eHEiRMICwvLNbl/+fLlqFSpElxdXfHjjz/i5cuX6NOnD4CcodQ1a9aga9euitWe0dHR2LZtG9auXZvn9/zfmjVrhsqVK6NXr174/vvvkZycjClTpnz290EdZd84hwwjE+j6/h/0jM0gi3uE9P/NB94NMYhMLSCTK+9xLZSxgdjBBWkbc8/bE0zMoeOSMxxvMFi5WpO2fhZkD28V0ZNopu9/WAEjI0OsWrEAZmYmOH36b7Tx7670DxsnJwelJGrnzr2wtCiNGdPHwtraElev3kCbr7sjISFn0UF6uhT163ljxPB+MDc3RXx8Iv46dQ4NGrXD8+c5C286dmiDsmUt0P3bjuj+7T9/0T18+BgVK9cppqfXfNdv30Wf4f/8PlrwU84q+HatmmHu1DGqCqtEyY76G5mGpaBbL0CxKa5014/Au0UHQqnSgFy5PiSYW0NcvjLSd/yQ+4JyGUSWdtCpWi9npfrbV5A9vIGMU78C2VnF8UhqhW8w+AwmJnlvQArkVMa2bduGESNGoFq1anB2dsbSpUvRuHHjzwtQRwdbt25F586dFQnb7NmzYWlpiZCQENy/fx9mZmaoUaMGJk+eDCBnkv/MmTMxceJEBAYGomfPnl/0pnsTExP8/vvvGDx4MDw8PODm5obp06ejW7duSvPYPlS+fHk4Ojpi5syZii063n89evRopb7z58/H/PnzERERgYoVK2Lv3r2KuX+2trY4ffo0JkyYgBYtWkAqlcLBwQEtW7ZUbBD6KSKRCL/++iv69u0Lb29vODo6YunSpWjZsuWnT9YAWRcO5zvsmb4+9+bH8hexSJnRNc/+8leJ+X5GeZsx8wfMmJnHXyrv5JU8rVi5Pt/90GJj4+HfrudH7zlr9iLMmr3os+Kk3LxruOP66T9UHUaJl3XlT2RdyXvYU7p9Qa42+cs4pH7fJ5+LZUK6i//ff0/b3mAgyOVybUtQ/5PNmzcjMDAQr1+/VmwK/LkePnyIChUq4MqVK/Dw8CjcAIsAkxj1YjqPKx/VRdqzT08Cp+KRuW6OqkOgdwzHFf0q+iX2uXeg+BIjYzRj2x+1fDeoOtm4cSOcnJxQrlw5XL16FRMmTECnTp2+OFEjIiKi/0bbFhgwWfuEuLg4TJ8+HXFxcbCxscE333yDuXPnqjosIiIircVkjZSMHz8e48ePL9RrOjo6gqPPREREVBBM1oiIiEijaFu5g8kaERERaRRtWw36xZviEhEREVHRY2WNiIiINAoXGBARERGpMc5ZIyIiIlJjMi1L1zhnjYiIiEiNsbJGREREGoVz1oiIiIjUmHYNgnIYlIiIiEitsbJGREREGoXDoERERERqjG8wICIiIiK1wcoaERERaRRt22eNyRoRERFpFO1K1TgMSkRERKTWWFkjIiIijcLVoERERERqjHPWiIiIiNSYdqVqnLNGREREpNZYWSMiIiKNwjlrRERERGpM2+ascRiUiIiISI2xskZEREQaRbvqakzWiIiISMNo25w1DoMSERERqTFW1oiIiEijyLVsIJTJGhEREWkUDoMSERERkdpgZY2IiIg0irbts8ZkjYiIiDSKdqVqTNaIiIhIw2hbZY1z1oiIiIjUGCtrREREpFG0bTUokzUiIiLSKNq2zxqHQYmIiIjUGCtrREREpFE4DEr0L7p9J6o6BPpAx3Vpqg6B3slcN0fVIdA7uoFTVR0CFSMOgxIRERGR2mBljYiIiDQKh0GJiIiI1JhMzmFQIiIiIlITTNaIiIhIo8gL6fgSy5cvh6OjI/T19VG7dm1cuHAh375r1qxBgwYNYG5uDnNzczRr1uyj/fPDZI2IiIg0igzyQjk+1/bt2xEUFITg4GBcvnwZ1atXh5+fHxISEvLsf/z4cXTt2hXHjh3D2bNnYWdnhxYtWuDp06efdV8ma0RERKRR5IX0v8+1aNEi9O/fH4GBgahSpQpWrVoFQ0NDhIWF5dl/8+bNGDJkCDw8PODi4oK1a9dCJpMhPDz8s+7LZI2IiIjoEzIyMnDp0iU0a9ZM0SYSidCsWTOcPXu2QNdITU1FZmYmSpcu/Vn35mpQIiIi0iiFtXWHVCqFVCpVapNIJJBIJLn6JiYmIjs7G1ZWVkrtVlZWuH37doHuN2HCBNja2iolfAXByhoRERFplMKasxYSEgJTU1OlIyQkpEhinj9/PrZt24Zff/0V+vr6n3UuK2tERESklSZNmoSgoCCltryqagBgYWEBsViM+Ph4pfb4+HhYW1t/9D4//PAD5s+fj6NHj8Ld3f2z42RljYiIiDRKYS0wkEgkMDExUTryS9b09PRQs2ZNpcUB7xcL+Pj45BvrggULMHv2bBw8eBBeXl5f9LysrBEREZFGUdXrpoKCgtCrVy94eXnB29sbixcvRkpKCgIDAwEAPXv2RLly5RRDqd999x2mT5+OLVu2wNHREXFxcQAAY2NjGBsbF/i+TNaIiIiICqBz5854/vw5pk+fjri4OHh4eODgwYOKRQcxMTEQif4ZtFy5ciUyMjLwf//3f0rXCQ4OxowZMwp8XyZrREREpFHkKnw36LBhwzBs2LA8Pzt+/LjS1w8fPiyUezJZIyIiIo3yJW8f0GRcYEBERESkxlhZIyIiIo2iqgUGqsJkjYiIiDTKl7zXU5MxWSMiIiKNwjlrRERERKQ2WFkjIiIijaLKrTtUgckaERERaRRtW2DAYVAiIiIiNcbKGhEREWkUrgYlIiIiUmNcDUpEREREaoOVNSIiItIoXA1KREREpMY4DEpEREREaoOVNSIiItIoXA1KREREpMZknLNGREREpL60K1XjnDUiIiIitcbKGhEREWkUbVsNymSNiIiINIq2JWscBiUiIiJSY6ysERERkUbhGwyIiIiI1BiHQUkjzZgxA1ZWVhAEAXv27EHv3r0REBCg6rCIiIjoP2JlrZj17t0bGzZsAADo6urC3t4ePXv2xOTJk6Gj82U/jlu3bmHmzJn49ddfUadOHZibm8PX11epTNy4cWN4eHhg8eLFhfEYKrf1t4NYv+N3JCa9gvNXDpg0rA/cXCrm2TczKwtrt+7B3sMnkJCYBEc7W4zu9y3qe3so+vh9OxTP4p/nOrdz2xaYOqJfUT1GidWiZyv4D2gPM0szPLr1EOuC1+De1bt59m3SpTkadvSFnbM9AOBB5D1sXfC/fPvTx+l4NoFOrZYQjEwhS3iMzPDNkMU9yLOvpPN4iO1dcrVn37sK6e4lAAC9Vn2gU62+8ucPIiHd9WPhB6+lLkZEYt2WXbh5OxrPXyRhScg0NG1YV9VhqTW+wYCKXMuWLbFu3TpIpVIcOHAAQ4cOha6uLiZNmqTULyMjA3p6ep+83r179wAA7dq1gyAIAACJRFL4gauJg8fO4PtVGzFtZH+4u1bCpl/2Y+DEufh93WKUMTfN1f+ndduw/+hfCA4aiAp25XDm4lWMmvE9Ni2ZA9dKFQAAW5eHQCaTKc65+yAGAybMgV9Dn2J7rpLC5+t66Dm1D9ZOWYm7EXfQuk9bTN4UjNG+Q5H84nWu/lV9quHM3r8Qdek2MqUZaDeoA6ZsmoExzYfjZXySCp5Ac4mda0G3cWdkHNkEWex96NZsDsk3QUgLnQykvsnVX/rbckAsVnwt6BtDv/dMZEVdVOqXfT8S0oOh/zRkZRXZM2ijtLR0OFd0Qvs2LTBq8hxVh6MRtG3OGodBVUAikcDa2hoODg4YPHgwmjVrhr179yqGLufOnQtbW1s4OzsDACIjI9GkSRMYGBigTJkyGDBgAN6+fQsgZ/jT398fACASiRTJ2ofDoL1798aJEyewZMkSCIIAQRDw8OHDYn/uwrLxl33o2Lop2rf0xVcO5TF9VH8YSPTw68Fjefbfd/Qv9OvWHg1r14CdrRU6t22BBt6e2LDrd0Wf0mYmsChtpjhOnr8MO1sreFWvUlyPVWK06dcO4dsO4/jOP/H07hOsnbwSGWlS+HZqmmf/n0b+iMOb/sCjmw/w7N5TrJqwHIJIgFs992KOXPPpePkh69pJZF8/BfmLZ8g4vBHyzAzoVGuQ9wnpKUBKsuIQO1YFMjOQfedvpW7y7EylfpCmFsPTaI8GPrUwYkAvNGtUT9WhkJpisqYGDAwMkJGRAQAIDw9HVFQUjhw5gn379iElJQV+fn4wNzfH33//jZ07d+Lo0aMYNmwYAGDs2LFYt24dACA2NhaxsbG5rr9kyRL4+Pigf//+ij52dnbF94CFKDMzCzfv3EedGm6KNpFIhDo13HD15p08z8nIyITkXxVKiUQPV65H5XuPfUf/QvuWvorklwpGrKsDJ7evEHnqmqJNLpcj8tRVVKrhXKBrSAz0oKMrxttXb4sqzJJJJIbI2gGyRzc/aJRD9ugmRLZfFegSOm4NkH37ApCZodQutnOBwZDF0O87D7rNewD6RoUYONHnk0FeKIem4DCoCsnlcoSHh+PQoUMYPnw4nj9/DiMjI6xdu1Yx/LlmzRqkp6dj48aNMDLK+QW5bNky+Pv747vvvoOVlRXMzMwAANbW1nnex9TUFHp6ejA0NMy3z3tSqRRSqVSpTZBmQCL59HBscXj5OhnZMhnKmJsptZcxN8ODx8/yPKeuV3Vs3LUPNd1cYWdrhXNXriP81AVkfzDs+aHw0xfw5m0K2rVoXMjRl3wm5qUg1hHjdeIrpfbXia9h+1X5Al3j20m9kBT/EpGnrxZBhCWXYFAKgkgMeWqyUrs8NRmi0jafPF9kXQEiy/LIOLhOqT37wXVk37kM2evnEJmVhW6DjhD932hIN88FtGwoitQHh0GpyO3btw/GxsbQ19dHq1at0LlzZ8yYMQMA4ObmpjRP7datW6hevboiUQOAevXqQSaTISoq78rQfxESEgJTU1OlY8Hy0E+fqMYmDg2EfTlrtO0zCjVadkPIT6Fo59cYonyqZr/+cQz1vT1Q1qJ0MUdK7QZ3QF3/+lg4IASZ0kxVh6NVxO4NIHv+ONdihOzbF5B9LwLyxKfIjr4C6e4lENs4QWSXe2ECUXFhZY2KnK+vL1auXAk9PT3Y2toqrQL9MClThUmTJiEoKEipTUgo/KTwS5mbmkAsEuHFy1dK7S9evspVbXuvtJkJls4aD2lGBl4lv0XZMub4ce1mlLexytX3WfxznLtyDT8Gjy2C6Eu+5JdvkJ2VDVMLM6V2UwtTvHr+8qPnfj2gHdoN7og5305HzO1HRRhlySRPewO5LBuCoYlSu2BoAnlK7oUdSnT1oOPijcxTez59n9fPIU99A5F5Wchibv2HiImooFhZUwEjIyNUrFgR9vb2n9yuw9XVFVevXkVKSoqi7fTp0xCJRIoFCAWhp6eH7OzsT/aTSCQwMTFROtRlCBQAdHV1UKWyE85fvq5ok8lkOHflOqpXqfzRcyV6erCyKI2s7Gwc/es8fOt65eqz5+AxlDYzRcM6NQo9dm2QnZmF+5H3lBYHCIKAavXccfdy/kl/24Ht0XF4J4T0mon7kfeKI9SSR5YNWdwjiBxcP2gUIHJwhezZx7+n4sq1ALEusm6e/eRtBGNzwMAI8refSACJipC8kP6nKZisqblvv/0W+vr66NWrF65fv45jx45h+PDh6NGjB6yscleG8uPo6Ijz58/j4cOHSExMVNqmQtP07Pg1fjkQjt8OH8f9R08we8lapKVLEdCyMQBg8vxlWLx2i6L/tVt3cfSv83j8LB6XIm9h8KR5kMnkCOzcTum6MpkMew4dR9vmjaDzwXYG9Hn2r/1NsXdauYrl0W/uIEgM9XF8ZzgAYOiikeg6vruif9tB7dFpTDesHL8MCU8SYGppBlNLM0gM9VX1CBor6+Ih6Lg3grhqXQilbaDbogcEXQmyrp8CAOi17gfdBh1znafj3gDZdy/nrA79kK4Euo2+gcjGCYJJGYjsXSFpPxzylwnIfng913Xoy6SmpuH2nXu4fScnqX76LB6379xDbFyCiiNTXzK5vFAOTcFhUDVnaGiIQ4cOYeTIkahVqxYMDQ3RsWNHLFq06LOuM3bsWPTq1QtVqlRBWloaHjx4AEdHx6IJuoi19K2LpNfJWL5+BxJfvoLLV45YFTIZFu+GQWMTEiGI/pmPJs3IxE/rtuFJbAIMDfTRwNsT8yYMg4mx8pDzucuRiE1IRPtWvsX5OCXO2X2nYVLGFJ2CusLM0hwPbz5ASM+ZeJ2YU4kpY2sJmeyfX5LNu7eCrkQXY1ZNULrOzh+3YdfibcUau6bLjvobmYaloFsvQLEprnTXj8C7RQdCqdKAXPkfaoK5NcTlKyN9xw+5LyiXQWRpB52q9QB9Q8jfvoLs4Q1knPoVyOZea4Xl+u276DP8n///L/hpNQCgXatmmDt1jKrCIjUiyLVtSQV9tozHXJWnTnrUD1Z1CPTOumFchKIudAOnqjoEekfXwqnI71HVqnahXOdG/PlCuU5RY2WNiIiINIomDWEWBs5ZIyIiIlJjrKwRERGRRtGklZyFgckaERERaRQOgxIRERGR2mBljYiIiDQKh0GJiIiI1Ji2DYMyWSMiIiKNom2VNc5ZIyIiIlJjrKwRERGRRpHLNff91l+CyRoRERFpFBmHQYmIiIhIXbCyRkRERBpFztWgREREROqLw6BEREREpDZYWSMiIiKNwmFQIiIiIjWmbW8w4DAoERERkRpjZY2IiIg0ira9borJGhEREWkUzlkjIiIiUmPcuoOIiIiI1AYra0RERKRROAxKREREpMa4dQcRERERqQ0ma0RERKRR5HJ5oRxfYvny5XB0dIS+vj5q166NCxcufLT/zp074eLiAn19fbi5ueHAgQOffU8ma0RERKRRZJAXyvG5tm/fjqCgIAQHB+Py5cuoXr06/Pz8kJCQkGf/M2fOoGvXrujbty+uXLmCgIAABAQE4Pr16591XyZrRERERAWwaNEi9O/fH4GBgahSpQpWrVoFQ0NDhIWF5dl/yZIlaNmyJcaNGwdXV1fMnj0bNWrUwLJlyz7rvkzWiIiISKMU1jCoVCpFcnKy0iGVSvO8Z0ZGBi5duoRmzZop2kQiEZo1a4azZ8/mec7Zs2eV+gOAn59fvv3zw2SNiIiINIpMLi+UIyQkBKampkpHSEhInvdMTExEdnY2rKyslNqtrKwQFxeX5zlxcXGf1T8/3LqDiIiItNKkSZMQFBSk1CaRSFQUTf6YrBEREZFGKawXuUskkgInZxYWFhCLxYiPj1dqj4+Ph7W1dZ7nWFtbf1b//HAYlIiIiDRKYQ2Dfg49PT3UrFkT4eHh/8QhkyE8PBw+Pj55nuPj46PUHwCOHDmSb//8sLJGREREGkVVr5sKCgpCr1694OXlBW9vbyxevBgpKSkIDAwEAPTs2RPlypVTzHsbOXIkGjVqhIULF6JNmzbYtm0bLl68iNWrV3/WfZmsERERERVA586d8fz5c0yfPh1xcXHw8PDAwYMHFYsIYmJiIBL9M2hZt25dbNmyBVOnTsXkyZNRqVIl7NmzB9WqVfus+wpybXsbKn22jMdXVR0CfaBH/WBVh0DvrBtWWtUh0Du6gVNVHQK9o2vhVOT3kOjbFcp1pOmPC+U6RY2VNSIiItIo2lZn4gIDIiIiIjXGyhoRERFpFG2rrDFZIyIiIo2iXakah0GJiIiI1BpXg5JWkEqlCAkJwaRJk9TyVSLahD8L9cGfhXrhz4Pyw2SNtEJycjJMTU3x+vVrmJiYqDocrcafhfrgz0K98OdB+eEwKBEREZEaY7JGREREpMaYrBERERGpMSZrpBUkEgmCg4M5aVcN8GehPvizUC/8eVB+uMCAiIiISI2xskZERESkxpisEREREakxJmtEREREaozJGhEREZEa44vciYhKsL179xa4b9u2bYswEiL6UlwNSiWGubk5BEEoUN+kpKQijoZIPYhEygMogiDgw1/7H/6Zyc7OLra4KEdMTAzs7Oxy/e6Sy+V4/Pgx7O3tVRQZqRNW1qjEWLx4seK/X7x4gTlz5sDPzw8+Pj4AgLNnz+LQoUOYNm2aiiLUXuHh4QgPD0dCQgJkMpnSZ2FhYSqKSjt8+P0+evQoJkyYgHnz5in9uZg6dSrmzZunqhC1WoUKFRAbG4uyZcsqtSclJaFChQpMoAkAK2tUQnXs2BG+vr4YNmyYUvuyZctw9OhR7NmzRzWBaaGZM2di1qxZ8PLygo2NTa4Kwq+//qqiyLRPtWrVsGrVKtSvX1+p/a+//sKAAQNw69YtFUWmvUQiEeLj42FpaanU/ujRI1SpUgUpKSkqiozUCZM1KpGMjY0RERGBihUrKrVHR0fDw8MDb9++VVFk2sfGxgYLFixAjx49VB2K1jMwMMDff/+NatWqKbVfu3YNtWvXRlpamooi0z5BQUEAgCVLlqB///4wNDRUfJadnY3z589DLBbj9OnTqgqR1AhXg1KJVKZMGfz222+52n/77TeUKVNGBRFpr4yMDNStW1fVYRCAWrVqISgoCPHx8Yq2+Ph4jBs3Dt7e3iqMTPtcuXIFV65cgVwuR2RkpOLrK1eu4Pbt26hevTrWr1+v6jBJTbCyRiXS+vXr0a9fP7Rq1Qq1a9cGAJw/fx4HDx7EmjVr0Lt3b9UGqEUmTJgAY2NjzhVUA9HR0Wjfvj3u3LkDOzs7AMDjx49RqVIl7NmzJ1clmopeYGAglixZAhMTE1WHQmqMyRqVWOfPn8fSpUsV83BcXV0xYsQIRfJGxWPkyJHYuHEj3N3d4e7uDl1dXaXPFy1apKLItJNcLseRI0dw+/ZtADl/Lpo1a1bgldREVPyYrBFRkfL19c33M0EQ8OeffxZjNESq16FDB6xfvx4mJibo0KHDR/vu3r27mKIidcatO6jEkslkiI6OznO7iIYNG6ooKu1z7NgxVYdAH+A2KqpnamqqqGSamJiwqkmfxGSNSqRz586hW7duePToEf5dPBYEgXsXqciTJ08AAOXLl1dxJNrpU9uoUPFo37499PX1AYCLCKhAOAxKJZKHhwcqV66MmTNn5vmXkqmpqYoi0z4ymQxz5szBwoULFVumlCpVCmPGjMGUKVNy7bBPRYfbqKgHsViMuLg4WFpaQiwW57kpLtGHWFmjEunu3bvYtWsXV7epgSlTpiA0NBTz589HvXr1AACnTp3CjBkzkJ6ejrlz56o4Qu3BbVTUg6WlJc6dOwd/f3/I5XJWOOmTWFmjEqlJkyYYP348WrZsqepQtJ6trS1WrVqV6yXhv/32G4YMGYKnT5+qKDLtw21U1MOMGTMwa9asAiVpnLJBACtrVEINHz4cY8aMQVxcHNzc3HJtF+Hu7q6iyLRPUlISXFxccrW7uLggKSlJBRFpr/T0dKxevRpHjx7lNioqNGPGDHTp0gXR0dFo27Yt1q1bBzMzM1WHRWqMlTUqkfKaByUIgmLIgf9aLT61a9dG7dq1sXTpUqX24cOH4++//8a5c+dUFJn24TYq6mfmzJkYN26c0uumiP6NyRqVSI8ePfro5w4ODsUUCZ04cQJt2rSBvb09fHx8AABnz57F48ePceDAATRo0EDFERKp3vPnzxEVFQUAcHZ2zvVid9JuTNaIqMg9e/YMy5cvV9o1f8iQIbC1tVVxZNopOjoa9+7dQ8OGDWFgYMBJ7iqUmpqKYcOGYdOmTYqKv1gsRs+ePfHTTz+x4kYAmKxRCbZp0yasWrUKDx48wNmzZ+Hg4IDFixejQoUKaNeunarDIyp2L168QKdOnXDs2DEIgoC7d+/CyckJffr0gbm5ORYuXKjqELXOwIEDcfToUSxbtkxptfSIESPQvHlzrFy5UsURkjrgBkdUIq1cuRJBQUFo3bo1Xr16pfgXq5mZGRYvXqza4LTAtWvXFLvjX7t27aMHFZ/Ro0dDV1cXMTExShWbzp074+DBgyqMTHv98ssvCA0NRatWrWBiYgITExO0bt0aa9aswa5du1QdHqkJrgalEumnn37CmjVrEBAQgPnz5yvavby8MHbsWBVGph08PDwQFxeHsmXLwsPDQ7G449+42KN4HT58GIcOHcr1BolKlSp9cp4nFY3U1FRYWVnlai9btixSU1NVEBGpIyZrVCI9ePAAnp6eudolEglSUlJUEJF2efDggWKC9IMHD1QcDb2XkpKS5xyopKQkSCQSFUREPj4+CA4OxsaNGxWvoEpLS8PMmTMVC3KImKxRiVShQgVERETkWvV58OBBuLq6qigq7fHh9/3Ro0eoW7cudHSUf91kZWXhzJkzXJlbjBo0aICNGzdi9uzZAHIqmzKZDAsWLPjoth5UdBYvXoyWLVuifPnyqF69OgDg6tWr0NfXx6FDh1QcHakLJmtUIgUFBWHo0KFIT0+HXC7HhQsXsHXrVoSEhGDt2rWqDk+r+Pr65vnuw9evX8PX15fDoMVowYIFaNq0KS5evIiMjAyMHz8eN27cQFJSEk6fPq3q8LSSm5sb7t69i82bNytWS3ft2hXffvstDAwMVBwdqQuuBqUSa/PmzZgxYwbu3bsHIOe1RzNnzkTfvn1VHJl2EYlEiI+Pz7Vv1J07d+Dl5YXk5GQVRaadXr9+jWXLluHq1at4+/YtatSogaFDh8LGxkbVoWmdzMxMuLi4YN++faz400cxWaMSLzU1FW/fvs1V2aGi1aFDBwA57wBt2bKl0pyo7OxsXLt2Dc7OzlyFSFqtXLlyOHr0KJM1+igOg1KJlpCQoNgVXBAE7gpejExNTQEAcrkcpUqVUhrS0dPTQ506ddC/f39Vhae1Xr58idDQUNy6dQsAUKVKFQQGBqJ06dIqjkw7DR06FN999x3Wrl2ba14n0XusrFGJ9ObNGwwZMgRbt25V7PclFovRuXNnLF++XJFIUNGbOXMmxo4dCyMjI1WHovVOnjwJf39/mJqawsvLCwBw6dIlvHr1Cr///jsaNmyo4gi1T/v27REeHg5jY2O4ubnl+nOye/duFUVG6oTJGpVInTt3xpUrV/DTTz8pvY9y5MiR8PDwwLZt21QcIVHxc3Nzg4+PD1auXAmxWAwgZ0h6yJAhOHPmDCIjI1UcofYJDAz86Ofr1q0rpkhInTFZoxLJyMgIhw4dQv369ZXa//rrL7Rs2ZJ7rRWzXbt2YceOHYiJiUFGRobSZ5cvX1ZRVNrHwMAAERERcHZ2VmqPioqCh4cH0tLSVBSZ9pHJZPj++++xd+9eZGRkoEmTJpgxYwZXgFKe+LopKpHKlCmT51CnqakpzM3NVRCR9lq6dCkCAwNhZWWFK1euwNvbG2XKlMH9+/fRqlUrVYenVWrUqKGYq/ahW7duKfb4ouIxd+5cTJ48GcbGxihXrhyWLl2KoUOHqjosUlOsrFGJtHr1auzcuRObNm2CtbU1ACAuLg69evVChw4dMHDgQBVHqD1cXFwQHByMrl27olSpUrh69SqcnJwwffp0JCUlYdmyZaoOUWts374d48ePx/Dhw1GnTh0AwLlz57B8+XLMnz9faUWiu7u7qsLUCpUqVcLYsWMVv4uOHj2KNm3aIC0tDSIR6yikjMkalUienp6Ijo6GVCqFvb09ACAmJgYSiQSVKlVS6sthuKJlaGiIW7duwcHBAWXLlsWRI0dQvXp13L17F3Xq1MGLFy9UHaLW+FQS8P4drnxna9GTSCSIjo6GnZ2dok1fXx/R0dG53t1KxHXCVCIFBASoOgR6x9raGklJSXBwcIC9vT3OnTuH6tWr48GDB3m+3J2KDt/Tqj6ysrIU7wJ9T1dXF5mZmSqKiNQZkzUqkYKDg1UdAr3TpEkT7N27F56enggMDMTo0aOxa9cuXLx4UbFxLhUPvodVfcjlcvTu3Vtps+j09HQMGjRIafsObt1BAIdBSQukp6dj+/btSElJQfPmzXMNg1LRkslkkMlkig0/t23bhjNnzqBSpUoYOHAg9PT0VBxhyXfnzh28evUK3t7eirbw8HDMmTMHKSkpCAgIwOTJk1UYofb51JYd73HrDgKYrFEJExQUhMzMTPz0008AgIyMDHh7e+PmzZswNDREVlYWDh8+jLp166o4Uu2QlZWFefPmoU+fPpyHo0Lt27eHm5sbZs2aBSBnOLRq1apo0KABXFxcEBYWhtmzZ2PUqFGqDZSI8sQlJ1SiHD58GM2bN1d8vXnzZsTExODu3bt4+fIlvvnmG8ydO1eFEWoXHR0dLFiwAFlZWaoORatdvHhRaZuUzZs3o3Llyjh06BCWLFmCxYsXY/369aoLkIg+iskalSgxMTGoUqWK4uvDhw/j//7v/+Dg4ABBEDBy5EhcuXJFhRFqn6ZNm+LEiROqDkOrJSYmKlU2jx07Bn9/f8XXjRs3xsOHD1UQGREVBBcYUIkiEomUVhieO3cO06ZNU3xtZmaGly9fqiI0rdWqVStMnDgRkZGRqFmzZq53H7Zt21ZFkWmP0qVLIzY2FnZ2dpDJZLh48SKCgoIUn2dkZHBlLpEaY7JGJYqrqyt+//13BAUF4caNG4iJiYGvr6/i80ePHsHKykqFEWqfIUOGAAAWLVqU6zPu51U8GjdujNmzZ2PFihXYuXMnZDIZGjdurPj85s2bcHR0VFl8RPRxTNaoRBk/fjy6dOmC/fv348aNG2jdujUqVKig+PzAgQNKK+Ko6MlkMlWHoPXmzp2L5s2bw8HBAWKxGEuXLlWqcG7atAlNmjRRYYRE9DFcDUolTnh4OPbt2wdra2sMHz4choaGis9mzpyJRo0aKVUVqGht3LgRnTt3VtpPCsgZetu2bRt69uyposi0S1ZWFm7cuAFLS0vY2toqfXb16lWUL18eZcqUUVF0RPQxTNaIqEiJxWLExsaibNmySu0vXrxA2bJlOQxKRPQJXA1KREXq/bsm/+3JkycwNTVVQURERJqFc9aIqEh4enpCEAQIgoCmTZsq3mAAANnZ2Xjw4AFatmypwgiJiDQDkzUiKhIBAQEAgIiICPj5+cHY2FjxmZ6eHhwdHdGxY0cVRUdEpDk4Z42IitSGDRvQpUuXXAsMiIioYJisUYmVmJiIhw8fQhAEODo6cqWbijx+/BiCICh20L9w4QK2bNmCKlWqYMCAASqOTrtcu3Ytz3ZBEKCvrw97e3sm1URqiMkalTg3btzA4MGDcfr0aaX2Ro0aYeXKlXB2dlZRZNqpQYMGGDBgAHr06IG4uDhUrlwZ1apVw927dzF8+HBMnz5d1SFqDZFIlOdij/d0dXXRuXNn/Pzzz9DX1y/GyIjoY7galEqUuLg4NGrUCM+fP8eiRYtw4MAB7N+/H99//z1iY2PRoEEDJCQkqDpMrXL9+nXFRsQ7duyAm5sbzpw5g82bN/Pl4cXs119/RaVKlbB69WpEREQgIiICq1evhrOzM7Zs2YLQ0FD8+eefmDp1qqpDJaIPsLJGJcqECRNw9OhRnD59OldlIC0tDfXr10eLFi0QEhKiogi1j7GxMa5fvw5HR0e0bdsW9erVw4QJExATEwNnZ2ekpaWpOkSt4e3tjdmzZ8PPz0+p/dChQ5g2bRouXLiAPXv2YMyYMbh3756KoiSif2NljUqUI0eOYMKECXkO4RgYGGDcuHE4dOiQCiLTXlWrVsWqVavw119/4ciRI4rtOp49e8Z5hMUsMjISDg4OudodHBwQGRkJAPDw8EBsbGxxh0ZEH8FkjUqU+/fvo0aNGvl+7uXlhfv37xdjRPTdd9/h559/RuPGjdG1a1dUr14dALB3716+p7WYubi4YP78+cjIyFC0ZWZmYv78+XBxcQEAPH36FFZWVqoKkYjywGFQKlHye7XRe/Hx8ShXrhyysrKKOTLtlp2djeTkZJibmyvaHj58CENDw3x/VlT4zpw5g7Zt20IkEsHd3R1ATrUtOzsb+/btQ506dbBp0ybExcVh3LhxKo6WiN5jskYlilgsxp07d2BpaZnn5/Hx8XBxceH7KElrvXnzBps3b8adO3cAAM7OzujWrRtKlSql4siIKD9M1qhE+dTWBO/fU8lkrWjVqFED4eHhMDc3V7x2Kj+XL18uxsiIiDQPXzdFJcqxY8dUHQIBaNeunWJz1fevnSL1cPfuXRw7dgwJCQmQyWRKn3HPOyL1xMoaEZGWWLNmDQYPHgwLCwtYW1srVTwFQWCVk0hNMVmjEm3RokVo2LAhvLy8AOQMg44ePRqLFy9WbWBaKCMjI89qjr29vYoi0j4ODg4YMmQIJkyYoOpQiOgzMFmjEs3d3R3R0dH46quvMGfOHISFheHEiRN49eqVqkPTGnfu3EHfvn1x5swZpXbOHyx+JiYmiIiIgJOTk6pDIaLPwDlrVKJdu3YN6enpWLJkCdq3bw9jY2P8/fffqg5LqwQGBkJHRwf79u2DjY3NRxcbUNH65ptvcPjwYQwaNEjVoRDRZ2CyRiXK2rVrYWlpiXbt2ina5HI59uzZA09PTyQlJeHSpUt8mXsxioiIwKVLlxSbrpLqVKxYEdOmTcO5c+fg5uYGXV1dpc9HjBihosiI6GM4DEoliqurK0JDQ1G3bl0AObuz+/v7IyUlBfv378eBAwewcOFCVteKUa1atfDjjz+ifv36qg5F61WoUCHfzwRB4Ns9iNQUkzUqUQwNDXH79m3Y29tDLpejW7duePHiBfbs2QNDQ0Pcu3cPbm5uSE1NVXWoWuPPP//E1KlTMW/evDyrOSYmJiqKjIhIM3AYlEoUKysr7Nu3D3369MGQIUMglUqxb98+6OnpAch5xVF+bzegotGsWTMAQNOmTZXaucCAiKhgmKxRiTJs2DAMGzYMo0aNgq2tLTw8PJCWlgY9PT08e/YMY8eOhZ+fn6rD1CrcqFi1goKCMHv2bBgZGSEoKOijfRctWlRMURHR52CyRiXKmDFj4O3tDZFIBA8PD7Rr1w62trawt7fHgwcPYG9vj3nz5qk6TK3SqFEjVYeg1a5cuYLMzEzFfxOR5uGcNSrR5HI5/vjjD0RGRqJcuXLo2LEjDAwMVB1WiXft2jVUq1YNIpEI165d+2hfd3f3YoqKiEgzMVkjrZOWlsaErYiJRCLExcWhbNmyEIlEEAQBef2q4Zy14tWnTx8sWbIEpUqVUmpPSUnB8OHDERYWpqLIiOhjmKyR1pBKpVi2bBm+//57xMXFqTqcEu3Ro0ewt7eHIAh49OjRR/s6ODgUU1QkFosRGxuLsmXLKrUnJibC2toaWVlZKoqMiD6Gc9aoRJFKpZgxYwaOHDkCPT09jB8/HgEBAVi3bh2mTJkCsViM0aNHqzrMEu/DBIzJmOolJydDLpdDLpfjzZs30NfXV3yWnZ2NAwcO5ErgiEh9MFmjEmX69On4+eef0axZM5w5cwbffPMNAgMDce7cOSxatAjffPMNxGKxqsPUOs+ePcOpU6fyfJE7d80vemZmZhAEAYIgoHLlyrk+FwQBM2fOVEFkRFQQTNaoRNm5cyc2btyItm3b4vr163B3d0dWVhauXr3Kd1KqyPr16zFw4EDo6emhTJkySj8HQRCYrBWDY8eOQS6Xo0mTJvjll19QunRpxWd6enpwcHCAra2tCiMkoo/hnDUqUfT09PDgwQOUK1cOAGBgYIALFy7Azc1NxZFpLzs7OwwaNAiTJk2CSCRSdTha7cO5hESkOfibk0qU7OxsxdsKAEBHRwfGxsYqjIhSU1PRpUsXJmpqwMHBAadOnUL37t1Rt25dPH36FACwadMmnDp1SsXREVF+OAxKJYpcLkfv3r0hkUgAAOnp6Rg0aBCMjIyU+u3evVsV4Wmlvn37YufOnZg4caKqQ9F6v/zyC3r06IFvv/0Wly9fhlQqBQC8fv0a8+bNw4EDB1QcIRHlhcOgVKIEBgYWqN+6deuKOBJ6Lzs7G19//TXS0tLyfJE7X3FUfDw9PTF69Gj07NkTpUqVwtWrV+Hk5IQrV66gVatW3NKGSE2xskYlCpMw9RMSEoJDhw7B2dkZAHItMKDiExUVhYYNG+ZqNzU1xatXr4o/ICIqECZrRFSkFi5ciLCwMPTu3VvVoWg9a2trREdHw9HRUan91KlTcHJyUk1QRPRJnPFLREVKIpGgXr16qg6DAPTv3x8jR47E+fPnIQgCnj17hs2bN2Ps2LEYPHiwqsMjonxwzhoRFamQkBDExsZi6dKlqg5F68nlcsybNw8hISFITU0FkJNMjx07FrNnz1ZxdESUHyZrRFSk2rdvjz///BNlypRB1apVcy0w4Mrc4pOZmQldXV1kZGQgOjoab9++RZUqVWBsbIzExERYWFioOkQiygPnrBFRkTIzM0OHDh1UHQYB6NKlC3bt2gU9PT1UqVJF0R4fH4+mTZvi+vXrKoyOiPLDyhoRkZaoVasW3N3dERoaqmiLjY1FkyZNULVqVezatUuF0RFRfrjAgIhISxw4cABnzpxBUFAQAODZs2do3Lgx3NzcsGPHDhVHR0T54TAoERU6T0/PAu+hdvny5SKOht6ztLTE4cOHUb9+fQDAvn37UKNGDWzevJmvAyNSY0zWiKjQBQQEKP47PT0dK1asQJUqVeDj4wMAOHfuHG7cuIEhQ4aoKELtZWdnhyNHjqBBgwZo3rw5Nm3axM2JidQc56wRUZHq168fbGxscm0NERwcjMePHyMsLExFkWkHc3PzPJOx1NRUSCQSiMViRVtSUlJxhkZEBcRkjYiKlKmpKS5evIhKlSoptd+9exdeXl54/fq1iiLTDhs2bChw3169ehVhJET0pTgMSkRFysDAAKdPn86VrJ0+fRr6+voqikp7vE/AsrKysGXLFvj5+cHKykrFURHR52CyRkRFatSoURg8eDAuX74Mb29vAMD58+cRGhqK6dOnqzg67aGjo4NBgwbh1q1bqg6FiD4TkzUiKlITJ06Ek5MTlixZgv/9738AgCpVqmDDhg1wdXVVcXTaxdvbG1euXIGDg4OqQyGiz8A5a0RUrJKTk7F161aEhobi0qVLyM7OVnVIWmPHjh2YNGkSRo8ejZo1a8LIyEjpc3d3dxVFRkQfw2SNiIrFyZMnERoail9++QW2trbo0KEDOnbsiFq1aqk6NK2R115qgiBALpdDEAQmzkRqisOgRFRk4uLisH79eoSGhiI5ORmdOnWCVCrFnj17lN5NScXjwYMHqg6BiL4AK2tEVCT8/f1x8uRJtGnTBt9++y1atmwJsVgMXV1dXL16lckaEVEBsbJGREXijz/+wIgRIzB48OBc23aQat28eRMxMTHIyMhQam/btq2KIiKij2GyRkRF4tSpUwgNDUXNmjXh6uqKHj16oEuXLqoOS6vdv38f7du3R2RkpGKuGgDFGw44Z41IPfHNvURUJOrUqYM1a9YgNjYWAwcOxLZt22BrawuZTIYjR47gzZs3qg5R64wcORIVKlRAQkICDA0NcePGDZw8eRJeXl44fvy4qsMjonxwzhoRFZuoqCiEhoZi06ZNePXqFZo3b469e/eqOiytYWFhgT///BPu7u4wNTXFhQsX4OzsjD///BNjxozBlStXVB0iEeWBlTUiKjbOzs5YsGABnjx5gq1bt6o6HK2TnZ2NUqVKAchJ3J49ewYAcHBwQFRUlCpDI6KP4Jw1Iip2YrEYAQEBCAgIUHUoWqVatWq4evUqKlSogNq1a2PBggXQ09PD6tWr4eTkpOrwiCgfHAYlItIShw4dQkpKCjp06IDo6Gh8/fXXuHPnDsqUKYPt27ejSZMmqg6RiPLAZI2ISIslJSXB3NxcsSKUiNQPkzUiIiIiNcY5a0REJVyfPn0K1C8sLKyIIyGiL8HKGhFRCScSieDg4ABPT0987Ff+r7/+WoxREVFBsbJGRFTCDR48GFu3bsWDBw8QGBiI7t27o3Tp0qoOi4gKiJU1IiItIJVKsXv3boSFheHMmTNo06YN+vbtixYtWnBxAZGaY7JGRKRlHj16hPXr12Pjxo3IysrCjRs3YGxsrOqwiCgffIMBEZGWEYlEihe58+XtROqPyRoRkRaQSqXYunUrmjdvjsqVKyMyMhLLli1DTEwMq2pEao4LDIiISrghQ4Zg27ZtsLOzQ58+fbB161ZYWFioOiwiKiDOWSMiKuFEIhHs7e3h6en50cUEu3fvLsaoiKigWFkjIirhevbsyRWfRBqMlTUiIiIiNcYFBkRERERqjMkaERERkRpjskZERESkxpisEREREakxJmtEREREaozJGhEREZEaY7JGREREpMaYrBERERGpMSZrRERERGrs/wFL9Mx66U1FgwAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's draw a scatterplot to better see the correlation between R&D Spend and Profit."
      ],
      "metadata": {
        "id": "_dwJkmktIvq2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.scatterplot(x = \"R&D Spend\" ,y = \"Profit\", data = df, color = \"orange\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 470
        },
        "id": "Zsa5ydBbIUZ9",
        "outputId": "15eaadcc-d52e-4936-bac2-de6c080ea329"
      },
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='R&D Spend', ylabel='Profit'>"
            ]
          },
          "metadata": {},
          "execution_count": 53
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.regplot(data = df, x = \"R&D Spend\" ,y = \"Profit\", ci = None, color = \"purple\");"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 453
        },
        "id": "mcGpruqiIUVP",
        "outputId": "7a38fe38-90a2-4c18-ff7b-c9342eb55084"
      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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xQUlJCby9XX83biIiajnO/ngWm2duxrkD5xTlfl39MObdMega37WZetb86vv77TI7Gebn52PTpk2YOnVqrbrly5fD398f/fv3x8qVK2Gz2eS61NRUDB06VA6oACAuLg5ZWVkoKiqS28TGxiqOGRcXh9TUVABAdnY28vLyFG18fHwQFRUltyEiInJFZefL8J+p/8EnUZ8oAiqthxajlo/CE7880aoDqoZwmYnq//jHP+Dl5YV77rlHUf7UU0/h1ltvhZ+fH/bu3YtFixYhNzcXb731FgAgLy8PERHKrLFBQUFyXZs2bZCXlyeXXd4mLy9Pbnf55+pqUxez2Qyz2Sy/NxqNDblkIiKiRmO32XHg/QPY+dJOmEvMirpe9/bCnSvvhHd7PlVpCJcJqv7+97/j/vvvh5ubm6J87ty58p/79OkDnU6HadOmYdmyZdDrmzdfxrJly7B06dJm7QMREdGVTn1/CltmbcH5X84rygN7BSL+vXiEDw9vno65OJd4/PfDDz8gKysLjz766HXbRkVFwWaz4dSpUwCA4OBg5OfnK9rUvA8ODr5mm8vrL/9cXW3qsmjRIpSUlMiv33///br9JyIiaizGM0b8v8n/D/8Y/g9FQKX30WPMqjGYlj6NAdVNcImg6tNPP8WAAQPQt2/f67Y9dOgQVCoVAgMDAQDR0dHYvXs3rFar3CYpKQmRkZFo06aN3Obyye81baKjowEAERERCA4OVrQxGo3Yv3+/3KYuer0e3t7eihcREVFTs5ltSHk9Bau7r0bml5mKun6P9MOs32YhalYUVBqXCAucVrM+/istLcXx48fl99nZ2Th06BD8/PzQoUMHANXBy4YNG/Dmm2/W+nxqair279+PESNGwMvLC6mpqZgzZw4eeOABOWC67777sHTpUkydOhULFixAZmYm3n33Xbz99tvycZ5++mkMGzYMb775JsaNG4cvv/wSBw8elNMuSJKE2bNn49VXX0XXrl0RERGBF198EaGhoddcrUhERNTcjm89jsSnEnHxt4uK8tDbQhG/Oh7to9o3U89aINGMdu7cKQDUek2ZMkVu8+GHHwqDwSCKi4trfT4tLU1ERUUJHx8f4ebmJnr06CFee+01UVlZqWj3888/i5iYGKHX60W7du3E8uXLax3r66+/Ft26dRM6nU7ccsstYtOmTYp6u90uXnzxRREUFCT0er0YNWqUyMrKatD1lpSUCACipKSkQZ8jIiJqqMKTheLLCV+KJViieL3u/7pI+zhN2Kvszd1Fl1Hf32+nyVPVGjBPFRERNTZrhRV7Xt+DPa/vga3yUoohSSXhtiduw4iXR8DgZ2jGHrqe+v5+u8zqPyIiIro6IQSObjyKbXO3ofhUsaKuQ0wHxK+OR3Dfqy+uopvHoIqIiMjFFWQVIPGpRJzYdkJR7hniiTtX3Ine9/fmPn1NgEEVERGRizKbzNj9ym7se2cf7Fa7XK7SqDB4zmAMfXEo9F7Nm7OxNWFQRURE5GKEEMj8Vya2PbMNpbmlirpOsZ0Q/148AroHNFPvWi8GVURERC4kPyMfm2duRs4POYpynw4+GP3WaPS4pwcf9TUTBlVEREQuoKKoArsW78KBNQcg7JcW7qv1atwx/w7cMf8OFGQV4MS2E3APcEdI/xBIKgZXTYlBFRERkRMTdoH0delIXpiM8oJyRV23u7oh7u04lJwuwVf3fIWCrALYLXaodCoERAYgZmEMIkZGNFPPWx8GVURERE7q7IGz2DxjM84dOKco9+vqhzHvjkHX+K7I3pGNhGkJMJvMMPgboNFrYDPbkJ+Rj4RpCRj/4XgGVk2EQRUREZGTKbtQhuTnkpH+aXr1XiP/o3XXYuiLQzF4zmBo9BoIu0DK8hSYTWZ4tfOS51JpDVpo2mlgOmtCyvIUhA8P56PAJsCgioiIyEnYbXYcXHsQO1/cicriSkVdr3t74c6Vd8K7/aWM3rnpuSjIKoDB31BrcrokSTD4GVCQVYDc9FyEDghtkmtozRhUEREROYHTu09j88zNOP/LeUV521vaYuzqsQgfHl7rM+UF5bBb7NDo6/4517hpUFlUWWsuFjUOBlVERETNyHjWiKRnk5D5r0xFud5bj+EvD8fAJwdCrVXX+Vn3AHeodCrYzDZoDdpa9bZKG1Q6FdwD3Bul76TEoIqIiKgZVFmqkPp2Kna/shvWMquirt/D/TBq2Sh4Bnle8xgh/UMQEBmA/Ix8aNppFI8AhRCoKKxAUJ8ghPQPaZRrICUGVURERE3s+NbjSHwqERd/u6goDxkQgrGrx6L94Pb1Oo6kkhCzMAYJ0xJgOmuCwc8AjZsGtkobKgoroPfWI2ZhDCepNxEGVURERE2kKLsI2+Zuw9GNRxXlBn8DRi0bhf6P9IdKrWrQMSNGRmD8h+ORsjwFBVkFqCyqhEqnQlCfIOapamIMqoiIiBqZtcKKPSv2YM/yPbBV2uRySSVhwPQBGPnKSBj8DDd8/IiREQgfHo7c9FyUF5Qzo3ozYVBFRETUSIQQyPpPFrbO2YriU8WKurA7wjB29VgE9wt2yLkklcS0Cc2MQRUREVEjKMgqQOLTiTix9YSi3DPYE3euvBO97+/NjY9bGAZVREREDmQpteD7V77Hvrf3wW61y+UqjQpRs6Mw7MVh0Hvrm7GH1FgYVBERETmAEAKZX2Yi6ZkkmM6ZFHURoyIQ/1482vZo20y9o6bAoIqIiOgm5WfkY8usLTi9+7Si3DvMG3FvxaHHxB581NcKMKgiIiK6QZXFldj50k4cWHMAwn5p52O1Xo3bn70dQxYNgda9dqZzapkYVBERETWQsAscWn8I2xduR/kF5b563e7qhri34+DX2a+ZekfNhUEVERFRA5w9cBZbZm7B2R/PKsr9uvhhzLtj0HVsVwDVgRfzRrUuDKqIiIjqoexCGZKfS0b6p+nApSd90LprMeSFIYieGw2NvvpnNXtHtpzh3G6xQ6VTISAyQJHhnEFXy8OgioiI6BrsNjsOrj2InS/uRGVxpaLulr/cgjvfuBM+YT4QdoFzaedwcvtJHFh9ADaLDe4B7tDoNbCZbcjPyEfCtASM/3A8AFw36CLXIwkhxPWbkSMYjUb4+PigpKQE3t7ezd0dIiK6jtM/nMaWmVuQn5GvKG97S1vEvxePiBHVAZA8MnW0AGX5ZbDb7NB6aOEZ7CnnpBJCwHTWBO/23jCXmGE2mWHwN8hBV8XFCui99Bj/4XgGVk6mvr/fHKkiIiK6gumcCUnzk/DL578oyvXeegxfOhwDZwyEWqsGUB1QJUxLgNlkhtZdC7uwQ6VRwVZhQ0lOCXw6+EDvrYckSXBr44bzmeeh89DBJ9xHTrOgNWihaaeB6awJKctTED48nI8CXRCDKiIiov+pslRh37v7sPvl3bCUWhR1faf0RezyWHgGe8plwi6QsjwFZpMZXu28YDFZIAkJkkaCpJZgt9pRmlcKnZeuOoASgN1qh9ZTWytvlSRJMPgZUJBVgNz0XO7j54IYVBEREQE4kXQCW2ZtwcWsi4rykFtDEL86HmHRYbU+k5uei4KsAhj8DZAkCSqNClABENUbHKs0KtgqbbCV26D10MJabgVQPTJVF42bBpVFlSgvKK+znpwbgyoiImrVik8XY9vcbTjyzRFFucHPgFHLRqH/1P5QqVV1fra8oBx2i11e9ad111bPkaqwQaVTARKqR6dsdgghYCmzQKVTQVLX/WjPVln9OfcAd4deIzUNBlVERNQqWSus2LtyL1KWpcBWaZPLJZWEAdMGYMQrI+Duf+3gxj3AHSqdCjazTR598grxQvHpYtgt9up5UVJ1UGU6a4LBz4A2EW1gPGOEpp1G8QhQCIGKwgoE9QlCSP+QxrloalQMqoiIqMWqKxcUJCDrv1nYOnsrik8VK9qH3R6G+NXx9Q5qQvqHICAyAPkZ+XKQpPPSwbejL4znjLCWW6HWqFFVVYWgPkGIWRgDAEiYliAHWRo3DWyVNlQUVkDvrUfMwhhOUndRDKqIiKhFqisBp3d7b9itdpw7cE7R1iPIA3euuBN9HuzToI2PJZWEmIUxtYIkSS1B56GDoY0BA2cMRKfYTorknuM/HC/3rbKoEiqdSg66mE7BdTFPVRNinioioqZxeZoDg78BKo0KprMmVFysULRTaVQY9NQgDF88XM4ndaPna2gyT2ZUdx3MU0VERK3S5WkOPEM9YS42w3jGCLvVrmgXMSoC8avi0bZn25s+Z8TICIQPD29QkCSpJKZNaGEYVBERUYtSk+ZA66FF0bGiWvmmVBoV9L56jFo+CgHdA3Au7ZxDRosYJFHda0SbyO7du3HXXXchNDQUkiRh48aNivq//vWvkCRJ8RozZoyiTWFhIe6//354e3vD19cXU6dORWlpqaJNRkYGhgwZAjc3N4SFhWHFihW1+rJhwwZ0794dbm5u6N27NzZv3qyoF0LgpZdeQkhICAwGA2JjY3Hs2DHH3AgiInKY4lPFqCioQPHJYmVAJQGewZ5oe0tbqDVqZCdn47Mxn+Gre77Cf/76H3x1z1f4bMxnyN6R3XydJ5fWrEFVWVkZ+vbtizVr1ly1zZgxY5Cbmyu//vWvfynq77//fvz6669ISkpCQkICdu/ejccff1yuNxqNGD16NDp27Ii0tDSsXLkSS5YswUcffSS32bt3LyZPnoypU6ciPT0dEyZMwIQJE5CZmSm3WbFiBVatWoW1a9di//798PDwQFxcHCorlZtrEhFR8xB2gfR16Uh4PEFOsllD76NH255t4dXOC1WWKtjtdhxYcwD5GfnQeergGeIJnadO3vSYgRXdCKeZqC5JEr799ltMmDBBLvvrX/+K4uLiWiNYNY4cOYKePXviwIEDuO222wAAiYmJGDt2LM6cOYPQ0FB88MEHeP7555GXlwedTgcAWLhwITZu3IijR48CACZNmoSysjIkJCTIxx48eDD69euHtWvXQgiB0NBQzJs3D8888wwAoKSkBEFBQVi/fj3uvffeel0jJ6oTETWOcwfPYfPMzTi7/6yiXK1XwzvMG24+bgCqnzoYzxghbAIqrQpe7bxq5YoynTUhqE8QHkh8gBPHCUD9f7+bdaSqPnbt2oXAwEBERkbiiSeewMWLl7YPSE1Nha+vrxxQAUBsbCxUKhX2798vtxk6dKgcUAFAXFwcsrKyUFRUJLeJjY1VnDcuLg6pqakAgOzsbOTl5Sna+Pj4ICoqSm5TF7PZDKPRqHgREZHjlBeU47vHv8PHgz5WBFRqvRoGPwP0PnqotWoIu4C13ArTWRM0eg0kjSRvLXO5K/ffI2oIpw6qxowZg3/+859ITk7G66+/ju+//x7x8fGoqqoCAOTl5SEwMFDxGY1GAz8/P+Tl5cltgoKCFG1q3l+vzeX1l3+urjZ1WbZsGXx8fORXWFjtfaOIiKjh7FV2HHj/AN7r9h5++vgn4LJnLj3/3BOzfpuFP2/4M4L7BsNSZkFpbiksZRYE9QnCwBkDoVKp5K1lrqRx08BusXP/PWowp179d/ljtd69e6NPnz7o3Lkzdu3ahVGjRjVjz+pn0aJFmDt3rvzeaDQysCIiukk5KTnYMmsL8g4p/1HbtmdbjFk1Bp1GdQIA+HTwqTPNQW56Lg68f0CxtczluP8e3SinDqqu1KlTJwQEBOD48eMYNWoUgoODcf78eUUbm82GwsJCBAcHAwCCg4ORn5+vaFPz/nptLq+vKQsJCVG06dev31X7q9frodffeDI5IqLm4KxJKU25Jmyfvx0Zn2UoyvXeegxbMgyDZg6CWqtW1NWV5qCurWVqcP89uhlO/fjvSmfOnMHFixflwCY6OhrFxcVIS0uT2+zYsQN2ux1RUVFym927d8NqvbQSJCkpCZGRkWjTpo3cJjk5WXGupKQkREdHAwAiIiIQHBysaGM0GrF//365DRFRS5C9w/nSDFRZqrD3jb1Y3W11rYCq75S+mJk1E9FzomsFVFdTs7WM3ksP01kTrOVWxZwr7r9HN6pZV/+Vlpbi+PHjAID+/fvjrbfewogRI+Dn5wc/Pz8sXboUEydORHBwME6cOIH58+fDZDLhl19+kUeA4uPjkZ+fj7Vr18JqteLhhx/Gbbfdhi+++AJA9Sq9yMhIjB49GgsWLEBmZiYeeeQRvP3223Lqhb1792LYsGFYvnw5xo0bhy+//BKvvfYafvrpJ/Tq1QsA8Prrr2P58uX4xz/+gYiICLz44ovIyMjA4cOH4ebmVq/r5eo/InJmV27totFrYDPbUHGxAnovPcZ/OL7J96U7kXQCiU8louBogaI85NYQxK+OR1j0jU+puJGtZah1qu/vd7MGVbt27cKIESNqlU+ZMgUffPABJkyYgPT0dBQXFyM0NBSjR4/GK6+8opgwXlhYiJkzZ+K7776DSqXCxIkTsWrVKnh6esptMjIyMGPGDBw4cAABAQGYNWsWFixYoDjnhg0b8MILL+DUqVPo2rUrVqxYgbFjx8r1QggsXrwYH330EYqLixETE4P3338f3bp1q/f1MqgiImcl7AKfjfkM+Rn5TpFmoPh0MbbN24Yj/++IotzgZ8DI10bi1kdvhUp98w9bnPVRJzkXlwiqWhsGVUTkrM4eOIt//eFfUOvU0LnroHFXzjWyllthKbNg0jeTGnUrFlulDXtW7kHKshTYKmyXKiRgwLQBGPnqSLj7cwI5NS1uqExERPWSvSMb257dhvLz5dWjNFJ1WgHPYE/ovaunWmjcNKgsqmy0NANCCPz23W/YOmcrik4WKeraR7fH2NVjEXIrJ46Tc2NQRUTUitXMo6oorABUgKSu3mfVVmFDSU4JfDr4QO+tb9Q0AxePXUTi04k4vuW4otwj0AOxK2LR98G+fCRHLoFBFRFRKyXsAinLU2A2meET7oOqY1WwVdgg6SSotCrYrXaU5pVC66ltlDQDljILfvjbD0h9MxVVliq5XFJLiHoqCsMWD5O3lyFyBQyqiIhaqdz0XBRkFcjbtXiFeKH4dHH1SjiNCpL6fyNWp0vg7u/usDQDQgj8+vWvSHomCcYzyu27IkZGYMyqMQi8JfAqnyZyXgyqiIhc1M2uXCsvKIfdYpe3a9F56eDb0RemXBNsZhtElQAE4NvRF6PfGO2QNAPnM89jy1NbcGrnKUW5d5g34t6KQ4+JPWrtx0fkKhhUERG5IEfkWHIPcIdKp1Js16Lz0sHfy19e7VdlqcL4D8ej3cB2N9XfypJK7FqyCz++92N1sPY/ap0atz97O2IWxUDnobvGEYicH4MqIiIXc7UknfkZ+UiYllDvJJ3X2q5FY9DI86huJoWCsAv8/M+fsX3BdpSdL1PUdR3XFWPeGQO/Ln43fHwiZ8KgiojIhVw+ufzyJJ1agxaadhqYzpqQsjwF4cPDr/sosGa7loRpCTCdNcHgZ4DGTQNbpQ0VhRU3vV3LubRz2DJzC87sO6Mob9O5Dca8Mwbdxtc/eTKRK2BQRUTkQq6cXH45SZJg8DOgIKsAuem59RphihgZgfEfjpcfJVYWVUKlUyGoT9ANb9dSXlCO5OeT8dPHPwGXpZfWGDQY8vwQ3D7vdmjc+PNDLQ+/1URELuTKyeVXupEknREjIxA+PPymt2uxV9mR9mEadrywA5VFlYq6nn/qidFvjoZPB58GHZPIlTCoIiJyIXVNLr/cjSbplFTSTc2dytmTgy0ztyDvUJ6iPKBHAOLfi0enUZ1u+NhEroJBFRGRC7nW5HIhRKMk6bwWU64J2xdsR8b/ZSjKdV46DF8yHINmDYJaq26SvhA1NwZVREQupLEnl9dXlbUK+1ftx/dLv4fFZFHU9X2oL0YtHwWvEK9G7QORs2FQRUTkYhpjcnlDnNx+Elue2oKCIwWK8uB+wYhfHY8Od3Ro1PMTOSsGVURELshRk8sbovh0MbbN24Yj/++IotytjRtG/m0kBjw+ACq1qtHOT+TsGFQREbmom51cXl+2Shv2vrEXP7z2A2wVtss6AAx4fABGvjqywRPjiVoiBlVERFQnIQR+S/gNW2dvRdHJIkVd+8HtEb86vkmCOiJXwaCKiIhquXjsIrbO3opjm48pyj0CPRC7IhZ9H+zb6JPhiVwNgyoiIhcg7KJJ5k9Zyiz44bUfkPpGKqosVXK5pJYwaNYgDF8yHG4+bg4/L1FLwKCKiMjJZe/Illf62S12qHQqBEQGOHSlnxAChzccxrZ522A8Y1TUhY8IR/x78Qi8JdAh5yJqqRhUERE5sewd2UiYlgCzyQyDvwEavQY2sw35GflImJaA8R+Ov+nA6vyv57Fl1hac2nlKUe7d3huj3xyNnn/uWWufQSKqjUEVEZGTEnaBlOUpMJvM8GrnJQc2WoMWmnYamM6akLI8BeHDw2/oUWBlSSV2LdmFH9/7EaLq0s7Hap0a0c9EY8hzQ6Dz0DnseppaUz0yJarBoIqIyEnlpueiIKsABn9DrZEiSZJg8DOgIKsAuem5DVqFJ+wCP//fz9g+fzvKzpcp6rrEd8GYd8fAv6u/Q66huTTFI1OiKzGoIiJyUuUF5bBb7NDo6/6rWuOmQWVRJcoLyut9zHNp57Bl5hac2XdGUd6mUxvEvR2Hbnd1c/lHfU3xyJSoLgyqiIiclHuAO1Q6FWxmG7QGba16W6UNKp2qXok3yy+WY8fzO5D2URpw6UkfNAYNhjw3BLc/czs0bq7/k9DYj0yJrsX1/w8iImqhQvqHICAyAPkZ+dC00yhGkIQQqCisQFCfIIT0D7nqMexVdvz08U/Y8fwOVBRWKOp6TOyB0W+Ohm9H38a6hCbXWI9MieqDQRURkZOSVBJiFsYgYVoCTGdNMPgZoHHTwFZpQ0VhBfTeesQsjLnqiMvve3/H5pmbkZeepygP6B6A+Pfi0Sm2U1Ncxk1p6GTzxnhkSlRfDKqIiJxYxMgIjP9wvDzpurKoEiqdCkF9gq466bo0rxTbF2zHz//8WVGu89Jh2OJhiJoVBbVO3VSXcMNuZLK5Ix+ZEjUUgyoiIicXMTIC4cPDrztiU2Wtwo+rf8SuxbtgMVkUdX0e7IPY12PhFeLVlF2/YTc62dwRj0yJbhSDKiIiFyCppGvOATqZfBKJTyXiwuELivLgfsGIXx2PDnd0aOwuOszNTDa/2UemRDeDQRURkQsrySnBtme24fCGw4pytzZuGPm3kRjw+ACo1Kpm6t2NudnJ5jfyyJTIERhUERFdxlWycNsqbdj75l6kvJYCa7n1UoUE3ProrRj12iiXnTfkiMnm9X1kSuRIDKqIiP7HVbJw/7bpNyQ+nYiiE0WK8nZR7TB29ViE3ubaqQIcNdn8eo9MiRyNQRUREVwjC3fh8UIkzk7EsU3HFOUegR6IfT0WfR/q2yJGYjjZnFwVgyoiavWcPQu3pcyClGUp2LtyL6osVXK5pJYwaOYgDF8yHHpvfYt51MXJ5uSqGFQRUavnrFm4hRA4/O/D2DZvG4y/GxV14cPDEf9ePAJ7BbrMY8uG4GRzckXNuiRk9+7duOuuuxAaGgpJkrBx40a5zmq1YsGCBejduzc8PDwQGhqKhx56COfOnVMcIzw8HJIkKV7Lly9XtMnIyMCQIUPg5uaGsLAwrFixolZfNmzYgO7du8PNzQ29e/fG5s2bFfVCCLz00ksICQmBwWBAbGwsjh07Vus4ROR66jMx2m6xN2kW7guHL+D/Yv8P//7LvxUBlVc7L0z8ciIe2vGQHFAlTEtAfkY+dJ46eIZ4Quepkx9bZu/IbrI+O1rEyAg8kPgAJn0zCX9c/0dM+mYSHkh8gAEVOa1mDarKysrQt29frFmzplZdeXk5fvrpJ7z44ov46aef8M033yArKwt/+MMfarV9+eWXkZubK79mzZol1xmNRowePRodO3ZEWloaVq5ciSVLluCjjz6S2+zduxeTJ0/G1KlTkZ6ejgkTJmDChAnIzMyU26xYsQKrVq3C2rVrsX//fnh4eCAuLg6VlZUOvitE1NQunxhdl6bMwl1ZUomtc7dibd+1ioBIpVWh38P9EL86Hn5d/ABR+7Gl1qCFpJKgNWjh1c4LZpMZKctTIOziGmd0bjWTzbvEdUHogFA+8iOnJgkhnOL/NkmS8O2332LChAlXbXPgwAEMGjQIp0+fRocO1YnswsPDMXv2bMyePbvOz3zwwQd4/vnnkZeXB51OBwBYuHAhNm7ciKNHjwIAJk2ahLKyMiQkJMifGzx4MPr164e1a9dCCIHQ0FDMmzcPzzzzDACgpKQEQUFBWL9+Pe699956XaPRaISPjw9KSkrg7e1dr88QUeMTdoHPxnyG/Ix8xZwqoHqU2nTWhKA+QXgg8YFG+1EXdoGMzzKQND8JZfllirp2g9pB0kgwnjEqHu91v6c7UpalQOepq3OVnLXcCkuZBZO+mcRVcEQ3ob6/3y6VEa6kpASSJMHX11dRvnz5cvj7+6N///5YuXIlbLZL/9pMTU3F0KFD5YAKAOLi4pCVlYWioiK5TWxsrOKYcXFxSE1NBQBkZ2cjLy9P0cbHxwdRUVFym7qYzWYYjUbFi4icT83EaL2XHqazJljLrRB2AWu5FaazJodNjBZ2gXNp53B863GcSzsnjyDl/pSLdUPWYeOUjYqAyjfCFyNeGYHyi+UoOlFU6/HeD6/8AIvJ4lSPLYlaM5eZqF5ZWYkFCxZg8uTJiijxqaeewq233go/Pz/s3bsXixYtQm5uLt566y0AQF5eHiIilM/fg4KC5Lo2bdogLy9PLru8TV5entzu8s/V1aYuy5Ytw9KlS2/wiomoKTX2xOi6JpO3iWgDvbcevyX8Blz2zEBj0CBmUQxun3c7vpzwJSylljpXJZacLoG13AprpRU6d12tc3LzYKKm5RJBldVqxV/+8hcIIfDBBx8o6ubOnSv/uU+fPtDpdJg2bRqWLVsGvV7f1F1VWLRokaJ/RqMRYWFhzdgjIrqWxsrCfWUOLLVOjdK8UpzefVoRTAFAj4k9MPrN0fDt6ItzaeeuuSrRI9ADljILyvLLoA3XMp8TUTNz+qCqJqA6ffo0duzYcd25SFFRUbDZbDh16hQiIyMRHByM/Px8RZua98HBwfJ/62pzeX1NWUhIiKJNv379rtoXvV7f7IEdETWMo7NwXzmZ3FpmxcXfLsJWrpwUH9A9AGNWjUHnOzvLZfVZlahz10GtVzOfE5ETcOo5VTUB1bFjx7B9+3b4+/tf9zOHDh2CSqVCYGAgACA6Ohq7d++G1Xppb6ykpCRERkaiTZs2cpvk5GTFcZKSkhAdHQ0AiIiIQHBwsKKN0WjE/v375TZERHWpyYGl99Gj5FQJLmYpAypJJUHvq8cf1v1BEVAB9VuVqPXUYthLwxDUJwiWMgtKc0thKbMgqE8Qxq9t/izwRK1Js45UlZaW4vjx4/L77OxsHDp0CH5+fggJCcGf/vQn/PTTT0hISEBVVZU8f8nPzw86nQ6pqanYv38/RowYAS8vL6SmpmLOnDl44IEH5IDpvvvuw9KlSzF16lQsWLAAmZmZePfdd/H222/L53366acxbNgwvPnmmxg3bhy+/PJLHDx4UE67IEkSZs+ejVdffRVdu3ZFREQEXnzxRYSGhl5ztSIRUWleKSqLKmEts9ZKbeDm5wavUC9UFFTAXGKu9dn6btcy4PEBGPD4gBaTUZ3IVTVrSoVdu3ZhxIgRtcqnTJmCJUuW1JpgXmPnzp0YPnw4fvrpJzz55JM4evQozGYzIiIi8OCDD2Lu3LmKx24ZGRmYMWMGDhw4gICAAMyaNQsLFixQHHPDhg144YUXcOrUKXTt2hUrVqzA2LFj5XohBBYvXoyPPvoIxcXFiImJwfvvv49u3brV+3qZUoGodcnekY3/PvZfFJ8sVpRrDBr4hPlA56W7btoDxXysOh7vcTSKqPHV9/fbafJUtQYMqohah5LfS7Bt3jYc3nBYUS6pJXiFesG9rTskSap3DqyWuA0NkSup7++3009UJyJyFTazDalvpuKHv/0Aa/mleZyQAL2XHmq9GjpPHSAAa4W13pPJG2tVIhE5FoMqImq1hF04LFA5tvkYEp9OROHxQkV5u0HtEL86HhaT5aZyYDl6VSIROR6DKiJqlRz1SK3wRCG2ztmK3777TVHu3tYdsa/Hot+UfnKgxtEmopaNc6qaEOdUETmHK5NxavQa2Mw2VFysgN5Lj/EfXn/yt7Xcih+W/YC9K/eiylwll0tqCQOfHIgRL4+Am69bY18KETUBzqkiIqrDlck4r9z6xXTWhJTlKQgfHl7nKJIQAkf+3xFsm7cNJTklirqOwzoi/r14BPUOqvU5Imr5GFQRUatSk4zzalu/GPwMKMgqQG56bq05TBcOX8CWp7YgOzlbUe4V6oXRb47GLZNuqXVMImo9GFQRUatSn61fKosqUV5QLpeZjWZ8//L32P/ufthtdrlcpVUhel40hj4/tHpVHxG1agyqiKhVuXzrF61BW6veVmmDSqeCe4A7hBDI+CwD2+dvR2leqaJdlzFdMObdMfDvdv3ts4iodWBQRUStSn23fgGAdUPW4fc9vys+7xvuizHvjkG3u7rxUR8RKTCoIqJWRVJJiFkYg4RpCTCdNdXa+kXroYXGoMEngz5R7NWncdMgZlEMbn/29jpHuIiImFKhCTGlApHzuDJPlaSVoPfUw3TWBLNRublxj3t6YPSbo+Eb7ts8nSWiZsWUCkRE13D51i+nd5/GTx//hIIjBYo2/pH+iF8Vj86jOzdTL4nIlTCoIqJWq+xCGQ6sPoBD6w8pynWeOgxbPAxRT0VBrVM3T+eIyOUwqCKiVqfKWoUDaw5g1+JdtR719b6/N+5ccSe8Qr2aqXdE5KoYVBFRq5K9MxtbZm3BhV8vKMqD+gQhfnU8Og7pCGEXOJd2jnv0EVGDMKgiolah5PcSJD2ThF+//lVR7ubrhhGvjsBt026DSqNy2EbLRNT6MKgiohbNZrYh9c1U7H5lN2yVtksVEtB/an+Mem0UPNp6ALj6Rsv5GflImJZQr42Wiaj1YlBFRC3Wsc3H8N3j38F01qQo13vrMWrZKAx8cqBcdrMbLRMRqW7kQzk5OagrvZUQAjk5OTfdKSKim1F4ohD/+sO/8MW4LxQBlUqjgmeIJ9Ruaux7ex+yd1zaGLkhGy0TEdXlhkaqIiIikJubi8DAQEV5YWEhIiIiUFVV5ZDOEREJu0Buem69Jo1by634YdkP2LtyL6rMyr+H3Nu6wyvUCyqNCkKIWiNPZefLYC2zQq1VA3ZA467cwqaujZaJiC53Q0GVEKLOPa9KS0vh5uZ2050iIgJqZz2/2qRxIQSOfHME2+ZuQ0lOieIYGncNfDv6Qut+aWuZK0eezCVmfL/0e1QWV6KyuBKSSoLGTQPPYE/ovfUAlBstExHVpUFB1dy5cwFU/4X04osvwt390l8uVVVV2L9/P/r16+fQDhJRy3a1kaj6Thq/cOQCEp9KxMntJxXHNfgbYLfZ4RvhC5W69kyHmpGnk9tPIv2TdJhNZqj1alSZqyCpJdgqbCjJKYFPBx/ovHTyRssh/UOa6tYQkYtpUFCVnp4OoPpfhb/88gt0Op1cp9Pp0LdvXzzzzDOO7SERtVhXG4m6Y/4d2LNizzUnje9+dTeObT6G/e/uh91ml4+p0qoweM5gdB3XFd8++C2qLFVQGWoHVbZKGySthKPfHpXP41bqhuLTxRA2AUktQVRVPybUemrh5uOGmIUxnKRORFd1QxsqP/zww3j33Xe5KXADcUNlokuuNhJVcbECap0aNrMN7gHu0Bq0is8JIVCaV4qyvDIIu/Kvr86jO2PMqjEIiAyAsAt8NuYz5GfkKwKzmmOYzprgG+4L0zkTdF46+TwWkwWmXBNsZhtEVfXxQ28LxajXRjGdAlErVd/f7xta/bdu3ToGBUR0w65MX6A1aCGpJGgNWni184Kl1AJzsbnWvnvWcisu/nYRpedKFQGVT0cfTPp2Eu5PvB8BkQEAAEklIWZhDPReepjOmmAtt0LYBazlVpjOmqD31qP73d1ht9qh0V8atNd56eDfzR9+nf3gG+4LN183DFs8jAEVEV1XvR//3XPPPVi/fj28vb1xzz33XLPtN998c9MdI6KW63rpC9x83WA2mmEuMcPgVz03ynTOhPILypV3ar0aMYticMf8O2qNaAFAxMgIjP9wvPyIsbKoEiqdCoG9A9FzYk9U2aogIGCttELnrlN8tmZiu9ZDC49ADwffASJqieodVPn4+Mh/+Xl7e9e5+o+IqD7KC8phtyhHiC7n5usG01kTygvLYa+yw3TWJD+Kq+Ee6I5HUx9Fm05tFOVXTnwPHx6O8OHhcllRdhGO/r+j+GHZD6iyVKGyuBIVhRXw6egDN+9Lq5eFEJycTkQNUu+g6u6775bTJaxfv76x+kNErYB7gDtUOhVsZludI0y2Shs0HhpYS60wlhgVdZJagkeQB+75v3tqBVTXS8GQvSMb+97cp5jHpdFrYDxjRPHJYni184K7vztslTZUFFZA763n5HQiqrd6z6m6++67UVxcDABQq9U4f/58Y/WJiFq4kP4hCIgMQMXFilq7M9gsNhRnF8NcZIbdemlVHyRA76tH+PBw3PN/99Sa41Qz8T0/Ix86Tx08Qzyh89TJKRhObj9Z5zwu9wB3tOnUBiq1CmX5ZTCdM8FSZkFQnyCMX8u9/oio/uo9UtW2bVvs27cPd91111WTfxIR1UfNJPKEaQkwnTXB4GeAWq9GaW4pys6XAVesSe48pjP6PNAHAd0D6syoXp99+3a8sAPGc8Y653HpvfVo07kNKooqMOT5IQiLDrtm5nYiorrUO6iaPn06/vjHP0KSJEiShODg4Ku25TY1RHQ9l08iz/s5D5WFlYp8U0B1zqmgvkG449k7rjliVJ99+4pOFkHYBTwC6p50rnHTQIIE/67+CB0QevMXSEStTr2DqiVLluDee+/F8ePH8Yc//AHr1q2Dr69vI3aNiFo6/27+MLQxoPy8clWfpJLgGeoJnZcOxt+NigzqdbnexHeNmwbCLiCppGvO4+I2NER0MxqUUb179+7o3r07Fi9ejD//+c+KbWqIiOrLZrZh39v7sPuV3bCWWxV1Bn8DvNp5VW9sjOpHeFdufnyl+kx817pr4RXqheJTxdC009RKBsqVfkR0s24o+efixYvh7u6OCxcuICUlBSkpKbhw4YKj+0ZELdDxxOP4oPcHSF6UrAioVDoVfCJ84BvuKwdUQO3Nj+tyrYnvNQFTQPcAjHx15DWTgXKlHxHdjBsKqsrLy/HII48gNDQUQ4cOxdChQxEaGoqpU6eivLz8+gcgolan6GQRvpzwJT6P/xyFxwrlcvcAd0TNjoKbrxs0Og3MJWZYy6yK4EjjpoHdYkd5Qd1/v9Qne3rMwhh0iu2E8R+OR1CfIFjKLCjNLeVKPyJymAY9/qsxZ84cfP/99/jvf/+LO+64AwCQkpKCp556CvPmzcMHH3zg0E4SkeuylluR8noK9ry+B1XmS4tYJJWE2568DSNeHoHMrzJhLjGjsrDyf5XVgZRnsCf03vp6zXe6Wvb0oD5Bcp6qmnaXJwN1D3DnSj8icogbGqn6f//v/+HTTz9FfHw8vL294e3tjbFjx+Ljjz/Gv//973ofZ/fu3bjrrrsQGhoKSZKwceNGRb0QAi+99BJCQkJgMBgQGxuLY8eOKdoUFhbi/vvvh7e3N3x9fTF16lSUlpYq2mRkZGDIkCFwc3NDWFgYVqxYUasvGzZsQPfu3eHm5obevXtj8+bNDe4LEV0ihMCRb45gTY812P3ybkVA1WFoB0xLn4ax741FXnoe9r25DxDVn5G0ElRqFWwVNpTklKDSWJ3xPCAy4LrznSJGRuCBxAcw6ZtJ+OP6P2LSN5PwQOIDtUagJJWE0AGh6BLXBaEDQhlQEZFD3PDjv6CgoFrlgYGBDXr8V1ZWhr59+2LNmjV11q9YsQKrVq3C2rVrsX//fnh4eCAuLg6VlZVym/vvvx+//vorkpKSkJCQgN27d+Pxxx+X641GI0aPHo2OHTsiLS0NK1euxJIlS/DRRx/Jbfbu3YvJkydj6tSpSE9Px4QJEzBhwgRkZmY2qC9EVK3gaAE+i/sMX0/8GiU5JXK5pJJg8DdArVOjvKBckV/Kp6MPVBoVhLX6sZ+kkWC32VFyugQ6L1295zsxYCKi5iKJK2d11sOoUaPg7++Pf/7zn/LWNRUVFZgyZQoKCwuxffv2hndEkvDtt99iwoQJAKr/xRoaGop58+bhmWeeAQCUlJQgKCgI69evx7333osjR46gZ8+eOHDgAG677TYAQGJiIsaOHYszZ84gNDQUH3zwAZ5//nnk5eVBp6veMHXhwoXYuHEjjh49CgCYNGkSysrKkJCQIPdn8ODB6NevH9auXVuvvtSH0WiEj48PSkpK4O3t3eB7ROTszCYzvn/5e+x/Z3+tnFM1q/rsNjsqLlZA76XH4HmDkbIsBTpPHbQGLSwmC0y5JtjMNsAOCAioNCqMeXsMbpt+WzNdFRG1dvX9/b6hkap33nkHe/bsQfv27TFq1CiMGjUKYWFh2Lt3L959990b7vTlsrOzkZeXh9jYWLnMx8cHUVFRSE1NBQCkpqbC19dXDqgAIDY2FiqVCvv375fbDB06VA6oACAuLg5ZWVkoKiqS21x+npo2NeepT1/qYjabYTQaFS+ilkgIgYzPM7A6cjVS30hVBFRqnRoBPQPkVX1agxZe7bxgNpnx08c/ocpcJeeX0nnp4N/NH36d/eAb4Qu/zn4w+BrgG+HbTFdGRFR/NzRRvXfv3jh27Bg+//xzebRn8uTJuP/++2EwGBzSsby8PACo9ZgxKChIrsvLy0NgYKCiXqPRwM/PT9EmIiKi1jFq6tq0aYO8vLzrnud6fanLsmXLsHTp0utfLJELyzuUhy2ztiAnJUdR7hniCWuFFTovXfWo0/+2txJCwFZug1qnRvHpYqg0tfNLad2r/2wttzIhJxG5jAYHVVarFd27d0dCQgIee+yxxuhTi7Fo0SLMnTtXfm80GhEWFtaMPSJynIrCCux8aScOfnAQwq5Mf9DzLz2Rn5GP8xnnYS21okwqg8ZNA723HmajGbZKW/XEdLuA1kOLUkspfCN8mZCTiFxag4MqrVbbJJOza/YWzM/PR0jIpb9Q8/Pz0a9fP7nN+fPnFZ+z2WwoLCyUPx8cHIz8/HxFm5r312tzef31+lIXvV4PvV5fr+slaghhFziXdg5n9p8BALSPat8ok7KFXdRKPQAA6X9PR/Ki5Fp5oyL/GImef+6J75d8j4rCCkAFSOrq/UKtZVZYTBZIaglqrRpCCAhUj15ZSi0ozi6GZ7AnNG4a2CptqCisYEJOInIpN/T4b8aMGXj99dfxySefQKO5oUNcV0REBIKDg5GcnCwHLkajEfv378cTTzwBAIiOjkZxcTHS0tIwYMAAAMCOHTtgt9sRFRUlt3n++edhtVqh1VY/UkhKSkJkZCTatGkjt0lOTsbs2bPl8yclJSE6OrrefSFqKtk7spE0Pwnnfz0Pu6V67pJKq0Jgr0DcueJOhyWwzN6RLed8slvsUOlU8Az2hLnEjItZFxVt/br6IX5VPDqP7ozPxnxWvZov3AdVx6pgq7AB2upJ50B1oAYJEDYBrUGLNl3aoDi7GJIkwVxqvmp+KSIiZ3dDEdGBAweQnJyMbdu2oXfv3vDwUO76/s0339TrOKWlpTh+/Lj8Pjs7G4cOHYKfnx86dOiA2bNn49VXX0XXrl0RERGBF198EaGhofIKwR49emDMmDF47LHHsHbtWlitVsycORP33nsvQkOrd5m/7777sHTpUkydOhULFixAZmYm3n33Xbz99tvyeZ9++mkMGzYMb775JsaNG4cvv/wSBw8elNMuSJJ03b4QNYXsHdn49qFvUZpXnYtNpa1ea2K32ZF3KA/fPvgt7v6/u286EMnekY2EaQkwm8ww+BsgqSQYzxhhzFEuttB6aDH0xaEYPHswNHoNzqWdQ0FWQfVnJAleIV4oPl1cHfzVzF0XQJWlCiqNCl4hXpAkCZ7BnrCUWjDytZHwaOvBhJxE5JJuKKjy9fXFxIkTb/rkBw8exIgRI+T3NfOPpkyZgvXr12P+/PkoKyvD448/juLiYsTExCAxMVFO4wAAn3/+OWbOnIlRo0ZBpVJh4sSJWLVqlVzv4+ODbdu2YcaMGRgwYAACAgLw0ksvKXJZ3X777fjiiy/wwgsv4LnnnkPXrl2xceNG9OrVS25Tn74QNSZhF0hZloLyC9WP3NR6NSRUBx2SSoLdakdZQRl+WPbDVTcervd5/pc7yjPUExUFFTCdM0FUKbOv9Lq3F+584054t7u0vLi8oBx2i12xms+3oy9Kfi+pHrH6H7VODZ8wn+pJ7Kieh1VZVAmPth7oEtflhvpNRNTcGpSnym63Y+XKlfjvf/8Li8WCkSNHYsmSJQ5b8dfSMU8V3YxzaefwxfgvUF5QDrVGXStoEnYBu80OQ4AB9yXch9ABoTd8nq/u+QqSJKHsfJkiGAKqgzm9jx73b76/1jlqPluTd6qGtcyKwuOF1XOoIMGvix+0HpfVl1thKbNg0jeTbrjfRESNpVHyVP3tb3/Dc889B09PT7Rr1w6rVq3CjBkzbrqzRHR95QXl1Vu9CAB1DUL9r6zKXHXVjYfro+BIAcrPl6PktHJ0SVJL8A7zRkCPAKhUqjrPEdI/BAGRAai4WKHcENldA41BA1EloHZTKwKqmlV+9dmGhojImTUoqPrnP/+J999/H1u3bsXGjRvx3Xff4fPPP4fdbr/+h4noprgHuEOtV1cHT3WNL/+vTK1X31BeJ5vZhpTXU/Dd499Vpzy4jMHfgLa3tIVHoAeqzFVXzR0lqSTELIyB3ksP01kTrOVWCLuArcIGlUYFlUYFtVYtl1vLrTCdNXGVHxG1CA0KqnJycjB27Fj5fWxsLCRJwrlz5xzeMSJSCukfgqBeQVCpVKiyVsmr6YDq0R67zQ6ogMBegQ0e8TmeeBxr+6xF8sJkxeiUxl0D/+7+cjb0+owqRYyMwPgPxyOoTxAsZRaU5pbCUmZB6G2hGLVsFEJvC1WUB/UJwvi147nKj4hcXoMmqttstloTs7VaLaxWq0M7RUS1SSoJMYticOHIBZTmlVaPGGkurf4DAM+2nhiyaEi9R3yKsouwdc5WZP0nS1Gu99FDraseFZMkqXq0qQG5oyJGRiB8eHitHFeSSkL0nOg6y4mIXF2DJqqrVCrEx8crElp+9913GDlypCKtQn1TKrQ2nKhOjuCIPFXWCiv2vL4HKctTqudp/Y+kknDbk7dhxMsjkJeeVytPVUBkAHNHEVGrU9/f7wYFVQ8//HC92q1bt66+h2xVGFSRo9xoRnUhBI5uPIqtc7ai5HSJoq5DTAfEr45HcN9gxXk4qkRErV2jBFV0cxhUOZfWFjAUHC3Alqe24GTSSUW5Z4gnRr8xGr0m91LsvUdERNXq+/vdOHvMEDm5urZgaamPtswmM3a/shv73t4nz70CAJVGhajZURj20jDovbhHJRHRzWJQRa3OlVuwaPQa2Mw25GfkI2FaAsZ/6Fwr0W50RE0IgV+++AVJzyahNLdUUdfpzk6IXxWPgO4BjdVtIqJWh0EVtSqXb8Hi1c5LftylNWihaaeB6awJKctTbmqbF0e60RG1vJ/zsGXWFuT8kKMo9+nog7i349B9Qnc+6iMicjAGVdSq5KbnKjb8vZwkSTD4GVCQVYDc9Nxm3y7lRkbUKooqsPOlnTj4/kEI+6Xpkmq9GncsuAMxC2KgdddeeSoiInIABlXUqly54e+Vajb2vZltXhyhoSNqwi6Q/vd0JC9KrtX3yD9GIu6tOLTp1KY5LoWIqNVgUEWtinuAO1Q6FWxmm2LD3xq2SttVt2BpSg0ZURNVAptnbsa5A8qdDfy6+iF+VTy6jOnSlF0nImq1GFRRq1Kz4W9+Rj407TSKgKVmC5agPkHNvrFvfUbUygvKseO5HTix7YSiTuuhxdAXh2Lw7MFX/TwRETleg/b+I3J1V9vw19k29r18RO1KQgiYzplQXlBeK6DqdW8vzDw6EzELYhhQERE1MQZV1OpcbcNfZ9rYt2ZEreJiBS7Pz2s2mVFwuABl+WW4bD9ltL2lLabsnIKJ/5oI7/ZMLEtE1ByYUb0JMaO6c3H2jOqXr/7TeelQcbEClUWVijZ6bz1GvDICA58cKG+uTEREjsWM6kTXIamkZk+bcC0RIyMQvzoeW2ZtQeHxQsXIFAD0e7gfRi0bBc8gz+bpIBERKTCoInJSx7ceR+LTiSg8VqgoDxkQgrGrx6L94PbN1DMiIqoLgyoiJ1OUXYRtc7fh6MajinKDvwGjlo1C/0f6Q6Xmoz4iImfDoIrISVgrrNizYg/2LN8DW+WlVX+SSsJtT9yGES+PgMHP0Iw9JCKia2FQRdTMhBDI+k8Wts7ZiuJTxYq6sDvCMHb1WAT3C26ezhERUb0xqCJqRgVZBUh8OhEntirzTXkGe+LOlXei9/29ufExEZGLYFBF1AwspRZ8/8r32Pf2PtitdrlcpVEh6ukoDHtpGPTe+mbsIRERNRSDKnJqzp5Lqr5qrqPsQhly03Jx8P2DMJ0zKdp0iu2EMavGoG2Pts3USyIiuhkMqshpZe/IRsryFBRkFcBusUOlUyEgMgAxC2OcIut5fdVcR/4v+ai4WKEYmQIAnw4+iHs7Dt3v7s5HfURELoxBFTmly7OJG/wN0Og1sJltyM/IR8K0BIz/0Dm2k7me7B3Z+O+j/0VpXilsFVfs4ycBfe7vg/EfjofWXds8HSQiIodhshtyOsIukLI8BWaTGV7tvKA1aCGpJGgNWni184LZZEbK8hQIu3PvsGS32bF51mYUnyquFVDpffQw+BlQml8KjRv/bUNE1BIwqCKnk5uei4KsAhj8DbUeh0mSBIOfAQVZBchNz22mHl7f2QNnsbbfWhQcLlBsL6PWq9GmSxv4dfGDR6CH018HERHVH/+JTE6nvKAcdosdGn3dX0+NmwaVRZUoLyhv4p5dX9mFMiQ/l4z0T9MVwZSkkuAZ7AmPIA95or0zXwcRETUcgypyOu4B7lDpVLCZbdAaas81slXaoNKp4B7gfkPHl1finS9D+cVyuPu7wyPQ46ZWFtptdhz88CB2vrATlcWVijqdtw6+HX2h1qkdeh1ERORcGFSR0wnpH4KAyADkZ+RD006jeAQohEBFYQWC+gQhpH9Ig49dsxIv7+c8mIvNsFfZoVKroPfVI7hv8A2tLDz9w2lsmbkF+Rn5ivK2t7SFxk0D4xkjVFrlk/abvQ4iInI+nFNFTkdSSYhZGAO9lx6msyZYy60QdgFruRWmsybovfWIWRjT4FGlmhWF5w6eQ2VRpRxQCbtAZVElzh08h4RpCcjekV2v45nOmfDNA99g/dD1ioBK761H3DtxmJY+DXeuuNPh10FERM5JEkI49xKqFsRoNMLHxwclJSXw9vZu7u44PUfmqRJ2gc/GfIb8jHxYyiyoqqyCSqeCBAlCCNitdmgMGmgMGgT3DcYDiQ9cNdipslRh37v7sPvl3bCUWhR1/R7uh1HLRsEzyLNRroOIiJpefX+/+fiPnFbEyAiEDw93SEb1mhWFWnctKooqoNJUB1RA9YpClUYFW6UN7v7u8oq80AGhtY5zIukEtszagotZFxXlIQNCMHb1WLQf3L5Rr4OIiJwXgypyapJKqjO4aaiaFYVqDzVgB6C+ooEEQACSWoK90l5rRV7x6WJsm7sNR745oig3+Bsw6rVR6D+1P1Tqqz9Nd9R1EBGR82JQRa1CzYpCYRfVMwkFgMsHiv73XlQJxYo8a4UVe1fuRcqyFNgqLyXwlFQSBkwfgJGvjITBz9CUl0JERE7K6Seqh4eHQ5KkWq8ZM2YAAIYPH16rbvr06Ypj5OTkYNy4cXB3d0dgYCCeffZZ2GzKDNe7du3CrbfeCr1ejy5dumD9+vW1+rJmzRqEh4fDzc0NUVFR+PHHHxvtusmxalYUWsutUOvUsNvsEP9LJCWEgN1mh8ZNA0u5BQGRAQjuF4ys/2bh/Vvex67FuxQBVdjtYXjs4GMYt2YcAyoiIpI5/UjVgQMHUFVVJb/PzMzEnXfeiT//+c9y2WOPPYaXX35Zfu/ufinvT1VVFcaNG4fg4GDs3bsXubm5eOihh6DVavHaa68BALKzszFu3DhMnz4dn3/+OZKTk/Hoo48iJCQEcXFxAICvvvoKc+fOxdq1axEVFYV33nkHcXFxyMrKQmBgYGPfBrpJNSsKE6YloMpShSpz9atm9R8kQKVRwc3HDX3u74N/jf8XjiceVxzDM9gTsSti0eeBPtz4mIiIanG51X+zZ89GQkICjh07BkmSMHz4cPTr1w/vvPNOne23bNmC8ePH49y5cwgKCgIArF27FgsWLMCFCxeg0+mwYMECbNq0CZmZmfLn7r33XhQXFyMxMREAEBUVhYEDB2L16tUAALvdjrCwMMyaNQsLFy6sV9+5+q/5XStPVeAtgfAM9sThfx+G3WqXP6PSqBD1dBSGvTQMem99M/aeiIiaQ4tc/WexWPDZZ59h7ty5ipGCzz//HJ999hmCg4Nx11134cUXX5RHq1JTU9G7d285oAKAuLg4PPHEE/j111/Rv39/pKamIjY2VnGuuLg4zJ49Wz5vWloaFi1aJNerVCrExsYiNTX1qv01m80wm83ye6PReFPXTzfv8pV4NRnVDX4G5B/Kx4H3D+DUzlPK9qMiEL8qHm17tm2eDhMRkctwqaBq48aNKC4uxl//+le57L777kPHjh0RGhqKjIwMLFiwAFlZWfjmm28AAHl5eYqACoD8Pi8v75ptjEYjKioqUFRUhKqqqjrbHD169Kr9XbZsGZYuXXrD10uN4/KVeOczz2PzzM04/f1pRRvvMG/EvR2HHvf04KM+IiKqF5cKqj799FPEx8cjNPTS0vTHH39c/nPv3r0REhKCUaNG4cSJE+jcuXNzdFO2aNEizJ07V35vNBoRFhbWjD1qmWr28mtIDqjK4krsXLwTB9YcgKi69ARcrVPj9mdvx5DnhkDrXnvfQSIioqtxmaDq9OnT2L59uzwCdTVRUVEAgOPHj6Nz584IDg6utUovP796S5Hg4GD5vzVll7fx9vaGwWCAWq2GWq2us03NMeqi1+uh13MOTmNqaLZyYRc49I9D2L5gO8ovKHNRdburG+LejoNfZ7+m6j4REbUgTp9Soca6desQGBiIcePGXbPdoUOHAAAhIdWb1EZHR+OXX37B+fPn5TZJSUnw9vZGz5495TbJycmK4yQlJSE6OhoAoNPpMGDAAEUbu92O5ORkuQ01vZq9/PIz8qHz1MEzxBM6Tx3yM/Lr3MPv3MFz+PT2T/HfR/6rCKj8uvjhvk33YfJ/JzOgIiKiG+YSI1V2ux3r1q3DlClToNFc6vKJEyfwxRdfYOzYsfD390dGRgbmzJmDoUOHok+fPgCA0aNHo2fPnnjwwQexYsUK5OXl4YUXXsCMGTPkUaTp06dj9erVmD9/Ph555BHs2LEDX3/9NTZt2iSfa+7cuZgyZQpuu+02DBo0CO+88w7Kysrw8MMPN+3NIADVI04py1NgNpnh1c5LnvekNWihaaeB6awJKctTED48HBWFFUh+Lhk/ffITcNlaV627FkOeH4LoedHQ6F3ifwUiInJiLvFLsn37duTk5OCRRx5RlOt0Omzfvl0OcMLCwjBx4kS88MILchu1Wo2EhAQ88cQTiI6OhoeHB6ZMmaLIaxUREYFNmzZhzpw5ePfdd9G+fXt88sknco4qAJg0aRIuXLiAl156CXl5eejXrx8SExNrTV6nq7uRuU9XU7OXn8HfUGsiuSRJMPgZcOHoBSQ/l4y0j9JQWVSpaHPLX27BnW/cCZ8wnxu+HiIiosu5XJ4qV9aa81Q1dO7T9Rzfehz/+et/4BniWWdgZjaaUZxdDLvNrihv27Mt4t+Lv6FzEhFR69Qi81SRa6qZ+2Q2mWHwN0Cj18Bmtslzn8Z/OL7BQU7NXn42sw1aw6VVelXWKpjOmFBRWKFor/fWY/jS4Rg4YyDU2it3UyYiIrp5DKqoUTVk7lNDHgXW7OWXn5EPTTsNIICy82UozS2t3nbmMn2n9EXs8lh4Bns69NqIiIgu5zKr/8g11WfuU0FWAXLTcxt03Jq9/PReehSfLMaFXy/AdNakCKj8uvjhkT2PYML6CQyoiIio0XGkihpVeUE57Bb7VVfXadw0qCyqRHlBeZ3119Kmcxt4hnii8HiholylVWHQjEG48407oVLz3w1ERNQ0GFRRo7ra3KcatkobVDoV3APc631MW6UNe1buQcqyFNgqbJcqJKDHPT0w7oNx8Gjr4YjuExER1RuDKmpUV859uvwRoBACFYUVCOoThJD+Idc9lhACv333G7bO2Yqik0WKuvbR7TF29ViE3Hr94xARETUGBlXUqGrmPiVMS4DprAkGPwM0bhrYKm2oKKyA3luPmIUx152kfvHYRSQ+nYjjW44ryg3+Box+YzT6PtT3hnNeEREROQKDqhbEkck1HSliZATGfzhezlNVWVQJlU6FoD5B181TZSm1YPffdmPfW/tQZalS1GndtdAYNPjli1/g08GHuaeIiKhZMflnE2rM5J+OTq7ZGBoS9Akh8OvXv2LbvG0wnTUp6lQ6Fbzbe8PNxw02sw0VFyug99LfUL4rIiKi66nv7zeDqibUWEHV1ZJrumqwcT7zPLbM2oJTu04pytVuamj0Gvh28oVKdWlVnxACprMmBPUJwgOJDzjF6BwREbUc9f395npzF3dlck2tQQtJJUFr0MKrnRfMJjNSlqfUSojpjCpLKpE4JxFr+61VBFRqnRr9Hu4H9wB3eLXzUgRUwM3luyIiInIUzqlycQ1Jrhk6ILSZenltwi7w8//9jO3zt6PsfJmiruvYrhjz7hgUnijE8S3HGyXfFRERkSMwqHJxjZlcsymcSzuHLTO34My+M4ryNp3aYMy7Y9BtfDcA1aNYjs53RURE5EgMqlxcYyTXbArlF8ux4/kdSPsoDbjsyaTGoMGQ54bg9mduh8bt0tfTkfmuiIiIGgODKhfnasGGvcqOtI/SsOP5HagsqlTU9fxTT4x+czR8OvjU+pyj8l0RERE1Fk5Ud3GXbyxsOmuCtdwKYRewllthOmtyqmAjZ08OPr7tY2x+crMioAroEYAHtz+IP2/4c50BVY2afFdBfYJgKbOgNLcUljILgvoEYfxa11rhSERELQ9TKjSh1pqnypRrwvYF25HxfxmKcp2XDsMWD0PUU1FQa9X1Pp6zJjklIqKWiXmqnFBjBlWA8wUbVdYq/Pjej9i1ZBcsJouiru9DfTFq+Sh4hXg1U++IiIjqp76/35xT1YJIKslp0iacTD6JLbO2oOBIgaI8uF8w4lfHo8MdHZqpZ0RERI2DQRU5VElOCbbN24bD/z6sKHdr44aRfxuJAY8PgErNqXxERNTyMKgih7BV2rD3jb344bUfYKuwXaqQgAGPD8DIV0c6XVoHIiIiR2JQRTftt4TfkPh0IopOFinK2w9uj/jV8U7zSJKIiKgxMaiiG3bx2EVsnb0VxzYfU5R7BHogdkUs+j7Yl6vyiIio1WBQRQ1mKbPgh9d+QOobqaiyVMnlklpC1FNRGLZ4GNx83Jqxh0RERE2PQRXVmxAChzccxrZ522A8Y1TUhY8IR/yqeAT2Cmym3hERETUvBlVUL+d/PY8ts7bg1M5TinLv9t4Y/eZo9PxzT8UWOURERK0Ngyq6psqSSuxasgs/vvcjRNWlPLFqnRrRz0RjyHNDoPPQNWMPiYiInAODKqqTsAv8/H8/Y/uC7SjLL1PUdR3bFXHvxMG/q38z9Y6IiMj5MKiiWs6lncOWmVtwZt8ZRXmbTm0w5t0x6Da+WzP1jIiIyHkxqCJZ+cVy7Hh+B9I+SgMu2xFSY9BgyHNDcPszt0Pjxq8MERFRXfgLSbBX2fHTxz9hx/M7UFFYoajr+aeeGP3maPh08Gmm3hEREbkGBlWt3O97f8fmmZuRl56nKA/oHoD49+LRKbZTM/WMiIjItTCoaqVK80qxfcF2/PzPnxXlOk8dhi0ehqinoqDWqZupd0RERK6HQVUrU2Wtwo/v/YhdS3bBYrIo6vo80AexK2LhFeLVTL0jIiJyXQyqWpGTySexZdYWFBwpUJQH9Q3C2NVj0SGmQzP1jIiIyPUxqGoFSnJKsO2ZbTi84bCi3K2NG0a+OhIDpg2ASq1qpt4RERG1DE79S7pkyRJIkqR4de/eXa6vrKzEjBkz4O/vD09PT0ycOBH5+fmKY+Tk5GDcuHFwd3dHYGAgnn32WdhsNkWbXbt24dZbb4Ver0eXLl2wfv36Wn1Zs2YNwsPD4ebmhqioKPz444+Ncs03S9gFzqWdw/Gtx5GzNwe7X92N1d1XKwMqCbj18Vsx67dZGPjkQAZUREREDuD0I1W33HILtm/fLr/XaC51ec6cOdi0aRM2bNgAHx8fzJw5E/fccw/27NkDAKiqqsK4ceMQHByMvXv3Ijc3Fw899BC0Wi1ee+01AEB2djbGjRuH6dOn4/PPP0dycjIeffRRhISEIC4uDgDw1VdfYe7cuVi7di2ioqLwzjvvIC4uDllZWQgMdJ4NhLN3ZCNleQoKsgpgMVpgLjVD2ISiTbuodhi7eixCbwttpl4SERG1TJIQQly/WfNYsmQJNm7ciEOHDtWqKykpQdu2bfHFF1/gT3/6EwDg6NGj6NGjB1JTUzF48GBs2bIF48ePx7lz5xAUFAQAWLt2LRYsWIALFy5Ap9NhwYIF2LRpEzIzM+Vj33vvvSguLkZiYiIAICoqCgMHDsTq1asBAHa7HWFhYZg1axYWLlxY7+sxGo3w8fFBSUkJvL29b/S21Cl7RzYSpiWgoqgCNrMN1lKrot7N1w1xb8eh70N9Iam48TEREVF91ff32+mf+xw7dgyhoaHo1KkT7r//fuTk5AAA0tLSYLVaERsbK7ft3r07OnTogNTUVABAamoqevfuLQdUABAXFwej0Yhff/1VbnP5MWra1BzDYrEgLS1N0UalUiE2NlZuczVmsxlGo1HxagzCLrD7b7thyjWhorCiVkClcdcgqF8QAyoiIqJG5NRBVVRUFNavX4/ExER88MEHyM7OxpAhQ2AymZCXlwedTgdfX1/FZ4KCgpCXV53IMi8vTxFQ1dTX1F2rjdFoREVFBQoKClBVVVVnm5pjXM2yZcvg4+Mjv8LCwhp8D65HCIE9K/fg9PenYS2zKraX0XnqENAzAL4dfVF0sgi56bkOPz8RERFVc+o5VfHx8fKf+/Tpg6ioKHTs2BFff/01DAZDM/asfhYtWoS5c+fK741Go8MDq71v7EXywmRFmUqrgnd7b7i1cYMkSRB2gcqiSpQXlDv03ERERHSJU49UXcnX1xfdunXD8ePHERwcDIvFguLiYkWb/Px8BAcHAwCCg4NrrQaseX+9Nt7e3jAYDAgICIBara6zTc0xrkav18Pb21vxcrS+D/WF1kMrv/cI9kDbW9rC4GeAJFU/6rNV2qDSqeAe4O7w8xMREVE1lwqqSktLceLECYSEhGDAgAHQarVITr40SpOVlYWcnBxER0cDAKKjo/HLL7/g/PnzcpukpCR4e3ujZ8+ecpvLj1HTpuYYOp0OAwYMULSx2+1ITk6W2zQnzyBPjHhlBAz+Bhj8DfAK9VKkSBBCoKKwAgGRAQjpH9KMPSUiImrZnDqoeuaZZ/D999/j1KlT2Lt3L+6++26o1WpMnjwZPj4+mDp1KubOnYudO3ciLS0NDz/8MKKjozF48GAAwOjRo9GzZ088+OCD+Pnnn7F161a88MILmDFjBvR6PQBg+vTpOHnyJObPn4+jR4/i/fffx9dff405c+bI/Zg7dy4+/vhj/OMf/8CRI0fwxBNPoKysDA8//HCz3JcrDX56MP701Z9gaGOA6awJ1nIrhF3AWm6F6awJem89YhbGcJI6ERFRI3LqOVVnzpzB5MmTcfHiRbRt2xYxMTHYt28f2rZtCwB4++23oVKpMHHiRJjNZsTFxeH999+XP69Wq5GQkIAnnngC0dHR8PDwwJQpU/Dyyy/LbSIiIrBp0ybMmTMH7777Ltq3b49PPvlEzlEFAJMmTcKFCxfw0ksvIS8vD/369UNiYmKtyevNRVJJ6DSqE8Z/OF7OU1VZVAmVToWgPkGIWRiDiJERzd1NIiKiFs2p81S1NI2Zp6qGsAvkpueivKAc7gHuCOkfwhEqIiKim1Df32+nHqmihpNUEkIHMFs6ERFRU3PqOVVEREREroIjVS6Oj/uIiIicA4MqF3b5Bsp2ix0qnQoBkQGcmE5ERNQM+PjPRdVsoJyfkQ+dpw6eIZ7QeeqQn5GPhGkJyN6R3dxdJCIialUYVLkgYRdIWZ4Cs8kMr3Ze0Bq0kFQStAYtvNp5wWwyI2V5CoSdCzuJiIiaCoMqF5SbnouCrAIY/C9tRVNDkiQY/AwoyCrgBspERERNiEGVCyovKIfdYodGX/eUOI2bBnaLnRsoExERNSEGVS7IPcAdKp0KNrOtznpuoExERNT0GFS5oJD+IQiIDEDFxQpcmRCfGygTERE1DwZVLkhSSYhZGAO9l54bKBMRETkJBlUuKmJkBMZ/OB5BfYJgKbOgNLcUljILgvoEYfza8cxTRURE1MSY/NOFRYyMQPjwcGZUJyIicgIMqlwcN1AmIiJyDnz8R0REROQAHKlqIbixMhERUfNiUNUCcGNlIiKi5sfHfy6OGysTERE5BwZVLowbKxMRETkPBlUujBsrExEROQ8GVS6MGysTERE5DwZVLowbKxMRETkPBlUujBsrExEROQ8GVS6MGysTERE5DwZVLo4bKxMRETkHJv9sAbixMhERUfNjUNVCcGNlIiKi5sXHf0REREQOwKCKiIiIyAEYVBERERE5AIMqIiIiIgdgUEVERETkAAyqiIiIiByAQRURERGRAzCoIiIiInIABlVEREREDuDUQdWyZcswcOBAeHl5ITAwEBMmTEBWVpaizfDhwyFJkuI1ffp0RZucnByMGzcO7u7uCAwMxLPPPgubzaZos2vXLtx6663Q6/Xo0qUL1q9fX6s/a9asQXh4ONzc3BAVFYUff/zR4ddMRERErsmpg6rvv/8eM2bMwL59+5CUlASr1YrRo0ejrKxM0e6xxx5Dbm6u/FqxYoVcV1VVhXHjxsFisWDv3r34xz/+gfXr1+Oll16S22RnZ2PcuHEYMWIEDh06hNmzZ+PRRx/F1q1b5TZfffUV5s6di8WLF+Onn35C3759ERcXh/Pnzzf+jSAiIiKnJwkhRHN3or4uXLiAwMBAfP/99xg6dCiA6pGqfv364Z133qnzM1u2bMH48eNx7tw5BAUFAQDWrl2LBQsW4MKFC9DpdFiwYAE2bdqEzMxM+XP33nsviouLkZiYCACIiorCwIEDsXr1agCA3W5HWFgYZs2ahYULF9ar/0ajET4+PigpKYG3t/eN3gYiIiJqQvX9/XbqkaorlZSUAAD8/PwU5Z9//jkCAgLQq1cvLFq0COXl5XJdamoqevfuLQdUABAXFwej0Yhff/1VbhMbG6s4ZlxcHFJTUwEAFosFaWlpijYqlQqxsbFym7qYzWYYjUbFi4iIiFomTXN3oL7sdjtmz56NO+64A7169ZLL77vvPnTs2BGhoaHIyMjAggULkJWVhW+++QYAkJeXpwioAMjv8/LyrtnGaDSioqICRUVFqKqqqrPN0aNHr9rnZcuWYenSpTd+0UREROQyXCaomjFjBjIzM5GSkqIof/zxx+U/9+7dGyEhIRg1ahROnDiBzp07N3U3FRYtWoS5c+fK741GI8LCwpqxR0RERNRYXCKomjlzJhISErB79260b9/+mm2joqIAAMePH0fnzp0RHBxca5Vefn4+ACA4OFj+b03Z5W28vb1hMBigVquhVqvrbFNzjLro9Xro9fr6XSQRERG5NKeeUyWEwMyZM/Htt99ix44diIiIuO5nDh06BAAICQkBAERHR+OXX35RrNJLSkqCt7c3evbsKbdJTk5WHCcpKQnR0dEAAJ1OhwEDBija2O12JCcny22IiIiodXPqkaoZM2bgiy++wH/+8x94eXnJc6B8fHxgMBhw4sQJfPHFFxg7diz8/f2RkZGBOXPmYOjQoejTpw8AYPTo0ejZsycefPBBrFixAnl5eXjhhRcwY8YMeRRp+vTpWL16NebPn49HHnkEO3bswNdff41NmzbJfZk7dy6mTJmC2267DYMGDcI777yDsrIyPPzww01/Y4iIiMj5CCcGoM7XunXrhBBC5OTkiKFDhwo/Pz+h1+tFly5dxLPPPitKSkoUxzl16pSIj48XBoNBBAQEiHnz5gmr1apos3PnTtGvXz+h0+lEp06d5HNc7r333hMdOnQQOp1ODBo0SOzbt69B11NSUiIA1OofEREROa/6/n67VJ4qV8c8VURERK6nReapIiIiInJWDKqIiIiIHIBBFREREZEDMKgiIiIicgAGVUREREQOwKCKiIiIyAEYVBERERE5AIMqIiIiIgdw6m1q6PqEXSA3PRflBeVwD3BHSP8QSCqpubtFRETU6jCocmHZO7KRsjwFBVkFsFvsUOlUCIgMQMzCGESMvP7m00REROQ4fPznorJ3ZCNhWgLyM/Kh89TBM8QTOk8d8jPykTAtAdk7spu7i0RERK0KgyoXJOwCKctTYDaZ4dXOC1qDFpJKgtaghVc7L5hNZqQsT4Gwc1tHIiKipsKgygXlpueiIKsABn8DJEk5f0qSJBj8DCjIKkBuem4z9ZCIiKj1YVDlgsoLymG32KHR1z0lTuOmgd1iR3lBeRP3jIiIqPViUOWC3APcodKpYDPb6qy3Vdqg0qngHuDexD0jIiJqvRhUuaCQ/iEIiAxAxcUKCKGcNyWEQEVhBQIiAxDSP6SZekhERNT6MKhyQZJKQszCGOi99DCdNcFaboWwC1jLrTCdNUHvrUfMwhjmqyIiImpCDKpcVMTICIz/cDyC+gTBUmZBaW4pLGUWBPUJwvi145mnioiIqIkx+acLixgZgfDh4cyoTkRE5AQYVLk4SSUhdEBoc3eDiIio1ePjPyIiIiIHYFBFRERE5AAMqoiIiIgcgEEVERERkQMwqCIiIiJyAAZVRERERA7AoIqIiIjIARhUERERETkAgyoiIiIiB2BG9SYkhAAAGI3GZu4JERER1VfN73bN7/jVMKhqQiaTCQAQFhbWzD0hIiKihjKZTPDx8blqvSSuF3aRw9jtdpw7dw5eXl6QJMdtemw0GhEWFobff/8d3t7eDjuuK+M9UeL9UOL9UOL9UOL9UOL9qB6hMplMCA0NhUp19ZlTHKlqQiqVCu3bt2+043t7e7faL/zV8J4o8X4o8X4o8X4o8X4otfb7ca0RqhqcqE5ERETkAAyqiIiIiByAQVULoNfrsXjxYuj1+ubuitPgPVHi/VDi/VDi/VDi/VDi/ag/TlQnIiIicgCOVBERERE5AIMqIiIiIgdgUEVERETkAAyqiIiIiByAQVULsGbNGoSHh8PNzQ1RUVH48ccfm7tLDbZs2TIMHDgQXl5eCAwMxIQJE5CVlaVoM3z4cEiSpHhNnz5d0SYnJwfjxo2Du7s7AgMD8eyzz8Jmsyna7Nq1C7feeiv0ej26dOmC9evX1+pPc9/TJUuW1LrW7t27y/WVlZWYMWMG/P394enpiYkTJyI/P19xjJZyLwAgPDy81v2QJAkzZswA0PK/G7t378Zdd92F0NBQSJKEjRs3KuqFEHjppZcQEhICg8GA2NhYHDt2TNGmsLAQ999/P7y9veHr64upU6eitLRU0SYjIwNDhgyBm5sbwsLCsGLFilp92bBhA7p37w43Nzf07t0bmzdvbnBfHOFa98RqtWLBggXo3bs3PDw8EBoaioceegjnzp1THKOu79Xy5csVbVzlnlzvO/LXv/611rWOGTNG0aalfUeahSCX9uWXXwqdTif+/ve/i19//VU89thjwtfXV+Tn5zd31xokLi5OrFu3TmRmZopDhw6JsWPHig4dOojS0lK5zbBhw8Rjjz0mcnNz5VdJSYlcb7PZRK9evURsbKxIT08XmzdvFgEBAWLRokVym5MnTwp3d3cxd+5ccfjwYfHee+8JtVotEhMT5TbOcE8XL14sbrnlFsW1XrhwQa6fPn26CAsLE8nJyeLgwYNi8ODB4vbbb5frW9K9EEKI8+fPK+5FUlKSACB27twphGj5343NmzeL559/XnzzzTcCgPj2228V9cuXLxc+Pj5i48aN4ueffxZ/+MMfREREhKioqJDbjBkzRvTt21fs27dP/PDDD6JLly5i8uTJcn1JSYkICgoS999/v8jMzBT/+te/hMFgEB9++KHcZs+ePUKtVosVK1aIw4cPixdeeEFotVrxyy+/NKgvjX1PiouLRWxsrPjqq6/E0aNHRWpqqhg0aJAYMGCA4hgdO3YUL7/8suJ7c/nfOa50T673HZkyZYoYM2aM4loLCwsVbVrad6Q5MKhycYMGDRIzZsyQ31dVVYnQ0FCxbNmyZuzVzTt//rwAIL7//nu5bNiwYeLpp5++6mc2b94sVCqVyMvLk8s++OAD4e3tLcxmsxBCiPnz54tbbrlF8blJkyaJuLg4+b0z3NPFixeLvn371llXXFwstFqt2LBhg1x25MgRAUCkpqYKIVrWvajL008/LTp37izsdrsQonV9N678wbTb7SI4OFisXLlSLisuLhZ6vV7861//EkIIcfjwYQFAHDhwQG6zZcsWIUmSOHv2rBBCiPfff1+0adNGvh9CCLFgwQIRGRkpv//LX/4ixo0bp+hPVFSUmDZtWr370hjqCiKu9OOPPwoA4vTp03JZx44dxdtvv33Vz7jqPblaUPXHP/7xqp9p6d+RpsLHfy7MYrEgLS0NsbGxcplKpUJsbCxSU1ObsWc3r6SkBADg5+enKP/8888REBCAXr16YdGiRSgvL5frUlNT0bt3bwQFBcllcXFxMBqN+PXXX+U2l9+vmjY198uZ7umxY8cQGhqKTp064f7770dOTg4AIC0tDVarVdHH7t27o0OHDnIfW9q9uJzFYsFnn32GRx55RLExeWv6blwuOzsbeXl5in75+PggKipK8X3w9fXFbbfdJreJjY2FSqXC/v375TZDhw6FTqeT28TFxSErKwtFRUVym2vdo/r0pbmUlJRAkiT4+voqypcvXw5/f3/0798fK1euVDwSbmn3ZNeuXQgMDERkZCSeeOIJXLx4Ua7jd8QxuKGyCysoKEBVVZXihwIAgoKCcPTo0Wbq1c2z2+2YPXs27rjjDvTq1Usuv++++9CxY0eEhoYiIyMDCxYsQFZWFr755hsAQF5eXp33oqbuWm2MRiMqKipQVFTkFPc0KioK69evR2RkJHJzc7F06VIMGTIEmZmZyMvLg06nq/XjEBQUdN3rrKm7VhtnuxdX2rhxI4qLi/HXv/5VLmtN340r1fS/rn5dfm2BgYGKeo1GAz8/P0WbiIiIWseoqWvTps1V79Hlx7heX5pDZWUlFixYgMmTJys2BH7qqadw6623ws/PD3v37sWiRYuQm5uLt956C0DLuidjxozBPffcg4iICJw4cQLPPfcc4uPjkZqaCrVa3eq/I47CoIqczowZM5CZmYmUlBRF+eOPPy7/uXfv3ggJCcGoUaNw4sQJdO7cuam72aji4+PlP/fp0wdRUVHo2LEjvv76axgMhmbsWfP79NNPER8fj9DQULmsNX03qGGsViv+8pe/QAiBDz74QFE3d+5c+c99+vSBTqfDtGnTsGzZsha3Jcu9994r/7l3797o06cPOnfujF27dmHUqFHN2LOWhY//XFhAQADUanWtVV/5+fkIDg5upl7dnJkzZyIhIQE7d+5E+/btr9k2KioKAHD8+HEAQHBwcJ33oqbuWm28vb1hMBic9p76+vqiW7duOH78OIKDg2GxWFBcXKxoc3kfW+q9OH36NLZv345HH330mu1a03ej5tzX6ldwcDDOnz+vqLfZbCgsLHTId+by+uv1pSnVBFSnT59GUlKSYpSqLlFRUbDZbDh16hSAlnlPanTq1AkBAQGK/0da43fE0RhUuTCdTocBAwYgOTlZLrPb7UhOTkZ0dHQz9qzhhBCYOXMmvv32W+zYsaPWEHNdDh06BAAICQkBAERHR+OXX35R/MVQ8xdpz5495TaX36+aNjX3y1nvaWlpKU6cOIGQkBAMGDAAWq1W0cesrCzk5OTIfWyp92LdunUIDAzEuHHjrtmuNX03IiIiEBwcrOiX0WjE/v37Fd+H4uJipKWlyW127NgBu90uB6DR0dHYvXs3rFar3CYpKQmRkZFo06aN3OZa96g+fWkqNQHVsWPHsH37dvj7+1/3M4cOHYJKpZIfg7W0e3K5M2fO4OLFi4r/R1rbd6RRNPdMebo5X375pdDr9WL9+vXi8OHD4vHHHxe+vr6KVU6u4IknnhA+Pj5i165diiW/5eXlQgghjh8/Ll5++WVx8OBBkZ2dLf7zn/+ITp06iaFDh8rHqFk2P3r0aHHo0CGRmJgo2rZtW+ey+WeffVYcOXJErFmzps5l8819T+fNmyd27dolsrOzxZ49e0RsbKwICAgQ58+fF0JUp1To0KGD2LFjhzh48KCIjo4W0dHRLfJe1KiqqhIdOnQQCxYsUJS3hu+GyWQS6enpIj09XQAQb731lkhPT5dXsi1fvlz4+vqK//znPyIjI0P88Y9/rDOlQv/+/cX+/ftFSkqK6Nq1q2K5fHFxsQgKChIPPvigyMzMFF9++aVwd3evtVxeo9GIN954Qxw5ckQsXry4zuXy1+tLY98Ti8Ui/vCHP4j27duLQ4cOKf5OqVm5tnfvXvH222+LQ4cOiRMnTojPPvtMtG3bVjz00EMueU+udT9MJpN45plnRGpqqsjOzhbbt28Xt956q+jatauorKyUj9HSviPNgUFVC/Dee++JDh06CJ1OJwYNGiT27dvX3F1qMAB1vtatWyeEECInJ0cMHTpU+Pn5Cb1eL7p06SKeffZZRS4iIYQ4deqUiI+PFwaDQQQEBIh58+YJq9WqaLNz507Rr18/odPpRKdOneRzXK657+mkSZNESEiI0Ol0ol27dmLSpEni+PHjcn1FRYV48sknRZs2bYS7u7u4++67RW5uruIYLeVe1Ni6dasAILKyshTlreG7sXPnzjr//5gyZYoQonqZ+osvviiCgoKEXq8Xo0aNqnWfLl68KCZPniw8PT2Ft7e3ePjhh4XJZFK0+fnnn0VMTIzQ6/WiXbt2Yvny5bX68vXXX4tu3boJnU4nbrnlFrFp0yZFfX364gjXuifZ2dlX/TulJrdZWlqaiIqKEj4+PsLNzU306NFDvPbaa4ogw5XuybXuR3l5uRg9erRo27at0Gq1omPHjuKxxx6r9Y+BlvYdaQ6SEEI0wYAYERERUYvGOVVEREREDsCgioiIiMgBGFQREREROQCDKiIiIiIHYFBFRERE5AAMqoiIiIgcgEEVERERkQMwqCIiaqHWr18PX1/f5u4GUavBoIqIXMpf//pXSJIESZKg1WoRERGB+fPno7KyslbbN998E+Hh4TAYDIiMjMRHH31Uq82uXbvk46lUKvj4+KB///6YP38+cnNzr9ufb7/9FoMHD4aPjw+8vLxwyy23YPbs2Y64VCJyMZrm7gARUUONGTMG69atg9VqRVpaGqZMmQJJkvD666/LbXbv3o1nnnkGq1atwl133YUzZ87gwoULVz1mVlYWvL29YTQa8dNPP2HFihX49NNPsWvXLvTu3bvOzyQnJ2PSpEn429/+hj/84Q+QJAmHDx9GUlKSw6+ZiJwfR6qIyOXo9XoEBwcjLCwMEyZMQGxsbK1ARqVSQa1WY+rUqQgPD0dMTAzuvvvuqx4zMDAQwcHB6NatG+69917s2bMHbdu2xRNPPHHVz3z33Xe444478OyzzyIyMhLdunXDhAkTsGbNGrnNkiVL0K9fP3z44YcICwuDu7s7/vKXv6CkpERxrE8++QQ9evSAm5sbunfvjvfff1+uO3XqFCRJwjfffIMRI0bA3d0dffv2RWpqquIY69evR4cOHeDu7o67774bFy9erNf9JCLHYFBFRC4tMzMTe/fuhU6nU5T369cP7dq1w5NPPgm73d7g4xoMBkyfPh179uzB+fPn62wTHByMX3/9FZmZmdc81vHjx/H111/ju+++Q2JiItLT0/Hkk0/K9Z9//jleeukl/O1vf8ORI0fw2muv4cUXX8Q//vEPxXGef/55PPPMMzh06BC6deuGyZMnw2azAQD279+PqVOnYubMmTh06BBGjBiBV199tcHXTUQ3obl3dCYiaogpU6YItVotPDw8hF6vFwCESqUS//73v+U2VVVVYtSoUeKuu+4Sf/zjH8WkSZOE2WyW63v16iVWrlwphBBi586dAoAoKiqqda4tW7YIAGL//v119qW0tFSMHTtWABAdO3YUkyZNEp9++qmorKyU2yxevFio1Wpx5swZxXFVKpXIzc0VQgjRuXNn8cUXXyiO/corr4jo6GghhBDZ2dkCgPjkk0/k+l9//VUAEEeOHBFCCDF58mQxduxYxTEmTZokfHx8rnovicixOFJFRC5nxIgROHToEPbv348pU6bg4YcfxsSJE+X6xMRE7NmzB+vXr8dXX32Fixcv4q677kJZWRkqKytx/PhxDBky5LrnEUIAACRJqrPew8MDmzZtwvHjx/HCCy/A09MT8+bNw6BBg1BeXi6369ChA9q1aye/j46Oht1uR1ZWFsrKynDixAlMnToVnp6e8uvVV1/FiRMnFOfr06eP/OeQkBAAkEfRjhw5gqioKEX76Ojo614jETkOJ6oTkcvx8PBAly5dAAB///vf0bdvX3z66aeYOnUqACAjIwMdOnSAn58fAGDjxo0YPXo0Ro0ahQkTJqBTp061ApC6HDlyBAAQHh5+zXadO3dG586d8eijj+L5559Ht27d8NVXX+Hhhx++7jlKS0sBAB9//HGtPqnVasV7rVYr/7km0LuRR5tE1Dg4UkVELk2lUuG5557DCy+8gIqKCgBAu3btkJ2djTNnzgCoDsI2b94Mi8WCRYsW1WuuUUVFBT766CMMHToUbdu2rXd/wsPD4e7ujrKyMrksJycH586dk9/v27cPKpUKkZGRCAoKQmhoKE6ePIkuXbooXhEREfU+b48ePbB//35F2b59++r9eSK6eQyqiMjl/fnPf4ZarZZX3U2cOBEdOnTAuHHjsH37dhw/fhxbtmxBYWEhPDw8sG7dulojPOfPn0deXh6OHTuGL7/8EnfccQcKCgrwwQcfXPW8S5Yswfz587Fr1y5kZ2cjPT0djzzyCKxWK+688065nZubG6ZMmYKff/4ZP/zwA5566in85S9/QXBwMABg6dKlWLZsGVatWoXffvsNv/zyC9atW4e33nqr3vfgqaeeQmJiIt544w0cO3YMq1evRmJiYkNuIxHdJAZVROTyNBoNZs6ciRUrVqCsrAzu7u7Yu3cvBg4ciIcffhi9evXCypUr8corr+DAgQPYtWtXrQSdkZGRCA0NxYABA7B8+XLExsYiMzMTPXv2vOp5hw0bhpMnT+Khhx5C9+7dER8fj7y8PGzbtg2RkZFyuy5duuCee+7B2LFjMXr0aPTp00eRMuHRRx/FJ598gnXr1qF3794YNmwY1q9f36CRqsGDB+Pjjz/Gu+++i759+2Lbtm144YUX6n8TieimSaJmJiYRETnckiVLsHHjRhw6dKi5u0JEjYwjVUREREQOwKCKiIiIyAH4+I+IiIjIAThSRUREROQADKqIiIiIHIBBFREREZEDMKgiIiIicgAGVUREREQOwKCKiIiIyAEYVBERERE5AIMqIiIiIgdgUEVERETkAP8fbJbCByx0pJYAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's draw a histogram over df to see the distribution of numerical variables."
      ],
      "metadata": {
        "id": "dsi0N2JbJOmO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.hist(color=\"black\",rwidth=0.7,alpha=0.8,figsize=(15,10))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 922
        },
        "id": "KtSWB2JoIUSX",
        "outputId": "02d722bd-7558-4eaa-9b17-f9fc55e812e4"
      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[<Axes: title={'center': 'R&D Spend'}>,\n",
              "        <Axes: title={'center': 'Administration'}>],\n",
              "       [<Axes: title={'center': 'Marketing Spend'}>,\n",
              "        <Axes: title={'center': 'Profit'}>]], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 55
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x1000 with 4 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "Let's view the basic statistics values ​​of the data frame."
      ],
      "metadata": {
        "id": "CL8EoKBaJrgY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.describe().T"
      ],
      "metadata": {
        "colab": {
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      "execution_count": 56,
      "outputs": [
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              "                 count         mean            std       min          25%  \\\n",
              "R&D Spend         50.0   73721.6156   45902.256482      0.00   39936.3700   \n",
              "Administration    50.0  121344.6396   28017.802755  51283.14  103730.8750   \n",
              "Marketing Spend   50.0  211025.0978  122290.310726      0.00  129300.1325   \n",
              "Profit            50.0  112012.6392   40306.180338  14681.40   90138.9025   \n",
              "\n",
              "                        50%          75%        max  \n",
              "R&D Spend         73051.080  101602.8000  165349.20  \n",
              "Administration   122699.795  144842.1800  182645.56  \n",
              "Marketing Spend  212716.240  299469.0850  471784.10  \n",
              "Profit           107978.190  139765.9775  192261.83  "
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              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 4,\n  \"fields\": [\n    {\n      \"column\": \"count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 50.0,\n        \"max\": 50.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          50.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mean\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 58108.762176291966,\n        \"min\": 73721.6156,\n        \"max\": 211025.09780000002,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          121344.63960000001\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"std\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 42764.872853944464,\n        \"min\": 28017.802755488683,\n        \"max\": 122290.31072584528,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          28017.802755488683\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"min\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 24205.191040124017,\n        \"min\": 0.0,\n        \"max\": 51283.14,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"25%\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 37581.07432207949,\n        \"min\": 39936.37,\n        \"max\": 129300.1325,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          103730.875\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"50%\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 59498.599405045854,\n        \"min\": 73051.08,\n        \"max\": 212716.24,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          122699.795\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"75%\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 87520.22098075329,\n        \"min\": 101602.8,\n        \"max\": 299469.08499999996,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          144842.18\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"max\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 146273.73569463688,\n        \"min\": 165349.2,\n        \"max\": 471784.1,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          182645.56\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's display the unique values ​​of State."
      ],
      "metadata": {
        "id": "K6d1wcJbJzLt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"State\"].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1RqSHDYiJgU1",
        "outputId": "6b4e8c3c-bcf3-4402-878e-3ec826478d84"
      },
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['New York', 'California', 'Florida'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's make categorical attribute inferences about State with the help of get_dummies. Because States have no superiority over each other, they are nominal. It's not ordinal"
      ],
      "metadata": {
        "id": "koHw9DZNJ8OF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"State\"] = pd.Categorical(df[\"State\"])\n",
        "df_dummy=pd.get_dummies(df[\"State\"], prefix=\"State\")"
      ],
      "metadata": {
        "id": "YXZCPrJjJgRt"
      },
      "execution_count": 58,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_dummy.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "JPjPfZYnKFpj",
        "outputId": "2fd3a34a-99b0-46a2-974b-2c20faaeefca"
      },
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   State_California  State_Florida  State_New York\n",
              "0                 0              0               1\n",
              "1                 1              0               0\n",
              "2                 0              1               0\n",
              "3                 0              0               1\n",
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              "      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-ec7ef3ef-c8cb-4920-b664-2b50ff4e93aa 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-ec7ef3ef-c8cb-4920-b664-2b50ff4e93aa');\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-18ca8d55-045e-4055-b842-568ddeb1ea3a\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-18ca8d55-045e-4055-b842-568ddeb1ea3a')\"\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-18ca8d55-045e-4055-b842-568ddeb1ea3a 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",
              "variable_name": "df_dummy",
              "summary": "{\n  \"name\": \"df_dummy\",\n  \"rows\": 50,\n  \"fields\": [\n    {\n      \"column\": \"State_California\",\n      \"properties\": {\n        \"dtype\": \"uint8\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"State_Florida\",\n      \"properties\": {\n        \"dtype\": \"uint8\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"State_New York\",\n      \"properties\": {\n        \"dtype\": \"uint8\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df =pd.concat([df,df_dummy], axis=1)\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "khp9BOMhKHI_",
        "outputId": "d73e9dad-f022-49cf-be29-8e96e40f74f1"
      },
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   R&D Spend  Administration  Marketing Spend       State     Profit  \\\n",
              "0  165349.20       136897.80        471784.10    New York  192261.83   \n",
              "1  162597.70       151377.59        443898.53  California  191792.06   \n",
              "2  153441.51       101145.55        407934.54     Florida  191050.39   \n",
              "3  144372.41       118671.85        383199.62    New York  182901.99   \n",
              "4  142107.34        91391.77        366168.42     Florida  166187.94   \n",
              "\n",
              "   State_California  State_Florida  State_New York  \n",
              "0                 0              0               1  \n",
              "1                 1              0               0  \n",
              "2                 0              1               0  \n",
              "3                 0              0               1  \n",
              "4                 0              1               0  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-d7d9da69-503e-46d0-a36b-c675a88b97ad\" 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",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>R&amp;D Spend</th>\n",
              "      <th>Administration</th>\n",
              "      <th>Marketing Spend</th>\n",
              "      <th>State</th>\n",
              "      <th>Profit</th>\n",
              "      <th>State_California</th>\n",
              "      <th>State_Florida</th>\n",
              "      <th>State_New York</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>165349.20</td>\n",
              "      <td>136897.80</td>\n",
              "      <td>471784.10</td>\n",
              "      <td>New York</td>\n",
              "      <td>192261.83</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>162597.70</td>\n",
              "      <td>151377.59</td>\n",
              "      <td>443898.53</td>\n",
              "      <td>California</td>\n",
              "      <td>191792.06</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>153441.51</td>\n",
              "      <td>101145.55</td>\n",
              "      <td>407934.54</td>\n",
              "      <td>Florida</td>\n",
              "      <td>191050.39</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>144372.41</td>\n",
              "      <td>118671.85</td>\n",
              "      <td>383199.62</td>\n",
              "      <td>New York</td>\n",
              "      <td>182901.99</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>142107.34</td>\n",
              "      <td>91391.77</td>\n",
              "      <td>366168.42</td>\n",
              "      <td>Florida</td>\n",
              "      <td>166187.94</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</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-d7d9da69-503e-46d0-a36b-c675a88b97ad')\"\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-d7d9da69-503e-46d0-a36b-c675a88b97ad 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-d7d9da69-503e-46d0-a36b-c675a88b97ad');\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-7efdc56c-20db-4ed3-bde3-324dd22ce580\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-7efdc56c-20db-4ed3-bde3-324dd22ce580')\"\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",
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              "            'suggestCharts', [key], {});\n",
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              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 50,\n  \"fields\": [\n    {\n      \"column\": \"R&D Spend\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 45902.25648230753,\n        \"min\": 0.0,\n        \"max\": 165349.2,\n        \"num_unique_values\": 49,\n        \"samples\": [\n          91992.39,\n          1000.23,\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Administration\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 28017.802755488683,\n        \"min\": 51283.14,\n        \"max\": 182645.56,\n        \"num_unique_values\": 50,\n        \"samples\": [\n          135495.07,\n          82982.09,\n          115641.28\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Marketing Spend\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 122290.31072584528,\n        \"min\": 0.0,\n        \"max\": 471784.1,\n        \"num_unique_values\": 48,\n        \"samples\": [\n          353183.81,\n          172795.67,\n          134050.07\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"State\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"New York\",\n          \"California\",\n          \"Florida\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Profit\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 40306.18033765055,\n        \"min\": 14681.4,\n        \"max\": 192261.83,\n        \"num_unique_values\": 50,\n        \"samples\": [\n          134307.35,\n          81005.76,\n          99937.59\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"State_California\",\n      \"properties\": {\n        \"dtype\": \"uint8\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"State_Florida\",\n      \"properties\": {\n        \"dtype\": \"uint8\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"State_New York\",\n      \"properties\": {\n        \"dtype\": \"uint8\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's update our data frame by deleting the State attribute and excluding one of the States created as dummy."
      ],
      "metadata": {
        "id": "yCUlCtEkKQin"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.drop([\"State\"], axis = 1, inplace= True)"
      ],
      "metadata": {
        "id": "wKewhVifKHFV"
      },
      "execution_count": 61,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "gDb2jKKUKHCV",
        "outputId": "25b3e118-373a-4731-f92a-1cf02a260e49"
      },
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "execute_result",
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              "   R&D Spend  Administration  Marketing Spend     Profit  State_California  \\\n",
              "0  165349.20       136897.80        471784.10  192261.83                 0   \n",
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              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 50,\n  \"fields\": [\n    {\n      \"column\": \"R&D Spend\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 45902.25648230753,\n        \"min\": 0.0,\n        \"max\": 165349.2,\n        \"num_unique_values\": 49,\n        \"samples\": [\n          91992.39,\n          1000.23,\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Administration\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 28017.802755488683,\n        \"min\": 51283.14,\n        \"max\": 182645.56,\n        \"num_unique_values\": 50,\n        \"samples\": [\n          135495.07,\n          82982.09,\n          115641.28\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Marketing Spend\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 122290.31072584528,\n        \"min\": 0.0,\n        \"max\": 471784.1,\n        \"num_unique_values\": 48,\n        \"samples\": [\n          353183.81,\n          172795.67,\n          134050.07\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Profit\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 40306.18033765055,\n        \"min\": 14681.4,\n        \"max\": 192261.83,\n        \"num_unique_values\": 50,\n        \"samples\": [\n          134307.35,\n          81005.76,\n          99937.59\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"State_California\",\n      \"properties\": {\n        \"dtype\": \"uint8\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"State_Florida\",\n      \"properties\": {\n        \"dtype\": \"uint8\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"State_New York\",\n      \"properties\": {\n        \"dtype\": \"uint8\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.drop([\"State_Florida\"], axis = 1, inplace = True)\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "JLQyP8mRKV6N",
        "outputId": "8926cc51-8c88-4b70-ef10-78b969075820"
      },
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   R&D Spend  Administration  Marketing Spend     Profit  State_California  \\\n",
              "0  165349.20       136897.80        471784.10  192261.83                 0   \n",
              "1  162597.70       151377.59        443898.53  191792.06                 1   \n",
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              "4  142107.34        91391.77        366168.42  166187.94                 0   \n",
              "\n",
              "   State_New York  \n",
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              "1               0  \n",
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              "      <th></th>\n",
              "      <th>R&amp;D Spend</th>\n",
              "      <th>Administration</th>\n",
              "      <th>Marketing Spend</th>\n",
              "      <th>Profit</th>\n",
              "      <th>State_California</th>\n",
              "      <th>State_New York</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>165349.20</td>\n",
              "      <td>136897.80</td>\n",
              "      <td>471784.10</td>\n",
              "      <td>192261.83</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>162597.70</td>\n",
              "      <td>151377.59</td>\n",
              "      <td>443898.53</td>\n",
              "      <td>191792.06</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>153441.51</td>\n",
              "      <td>101145.55</td>\n",
              "      <td>407934.54</td>\n",
              "      <td>191050.39</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>144372.41</td>\n",
              "      <td>118671.85</td>\n",
              "      <td>383199.62</td>\n",
              "      <td>182901.99</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>142107.34</td>\n",
              "      <td>91391.77</td>\n",
              "      <td>366168.42</td>\n",
              "      <td>166187.94</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "    [theme=dark] .colab-df-convert:hover {\n",
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              "\n",
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              "        document.querySelector('#df-0c9349f2-df27-4538-a864-0546cbc7a5b7 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
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              "                                                    [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",
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              "\n",
              "<style>\n",
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              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
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              "\n",
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              "  .colab-df-quickchart-complete:disabled,\n",
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              "\n",
              "  @keyframes spin {\n",
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              "      border-bottom-color: var(--fill-color);\n",
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              "      border-color: transparent;\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",
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              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-d47164f4-d005-47fe-85ca-3ff7c3f4d546 button');\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
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              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 50,\n  \"fields\": [\n    {\n      \"column\": \"R&D Spend\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 45902.25648230753,\n        \"min\": 0.0,\n        \"max\": 165349.2,\n        \"num_unique_values\": 49,\n        \"samples\": [\n          91992.39,\n          1000.23,\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Administration\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 28017.802755488683,\n        \"min\": 51283.14,\n        \"max\": 182645.56,\n        \"num_unique_values\": 50,\n        \"samples\": [\n          135495.07,\n          82982.09,\n          115641.28\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Marketing Spend\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 122290.31072584528,\n        \"min\": 0.0,\n        \"max\": 471784.1,\n        \"num_unique_values\": 48,\n        \"samples\": [\n          353183.81,\n          172795.67,\n          134050.07\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Profit\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 40306.18033765055,\n        \"min\": 14681.4,\n        \"max\": 192261.83,\n        \"num_unique_values\": 50,\n        \"samples\": [\n          134307.35,\n          81005.76,\n          99937.59\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"State_California\",\n      \"properties\": {\n        \"dtype\": \"uint8\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"State_New York\",\n      \"properties\": {\n        \"dtype\": \"uint8\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's segment our data frame into dependent and independent variables."
      ],
      "metadata": {
        "id": "wk_DP9ljKlfO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x = df.drop(\"Profit\", axis=1)\n",
        "y = df[\"Profit\"]"
      ],
      "metadata": {
        "id": "kTCm8KLVKV09"
      },
      "execution_count": 64,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's check the dependent and independent variables."
      ],
      "metadata": {
        "id": "QzxKengxKr82"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "T34RoLBiKVx_",
        "outputId": "9ad76700-a83a-4b68-87cd-556c29db4ac9"
      },
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   R&D Spend  Administration  Marketing Spend  State_California  \\\n",
              "0  165349.20       136897.80        471784.10                 0   \n",
              "1  162597.70       151377.59        443898.53                 1   \n",
              "2  153441.51       101145.55        407934.54                 0   \n",
              "3  144372.41       118671.85        383199.62                 0   \n",
              "4  142107.34        91391.77        366168.42                 0   \n",
              "\n",
              "   State_New York  \n",
              "0               1  \n",
              "1               0  \n",
              "2               0  \n",
              "3               1  \n",
              "4               0  "
            ],
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              "        const element = document.querySelector('#df-53edc672-190f-4cbc-b001-882dc22714b9');\n",
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              "          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",
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              "\n",
              "<div id=\"df-83efbf4a-fc2d-4820-afc2-62af87280b38\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-83efbf4a-fc2d-4820-afc2-62af87280b38')\"\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-83efbf4a-fc2d-4820-afc2-62af87280b38 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",
              "variable_name": "x",
              "summary": "{\n  \"name\": \"x\",\n  \"rows\": 50,\n  \"fields\": [\n    {\n      \"column\": \"R&D Spend\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 45902.25648230753,\n        \"min\": 0.0,\n        \"max\": 165349.2,\n        \"num_unique_values\": 49,\n        \"samples\": [\n          91992.39,\n          1000.23,\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Administration\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 28017.802755488683,\n        \"min\": 51283.14,\n        \"max\": 182645.56,\n        \"num_unique_values\": 50,\n        \"samples\": [\n          135495.07,\n          82982.09,\n          115641.28\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Marketing Spend\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 122290.31072584528,\n        \"min\": 0.0,\n        \"max\": 471784.1,\n        \"num_unique_values\": 48,\n        \"samples\": [\n          353183.81,\n          172795.67,\n          134050.07\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"State_California\",\n      \"properties\": {\n        \"dtype\": \"uint8\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"State_New York\",\n      \"properties\": {\n        \"dtype\": \"uint8\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wVMj3ZccKv-8",
        "outputId": "883910f2-72e9-4603-a587-33e3d0a517af"
      },
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    192261.83\n",
              "1    191792.06\n",
              "2    191050.39\n",
              "3    182901.99\n",
              "4    166187.94\n",
              "Name: Profit, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's create 4 parts, train and test, from these dependent and independent variables. Let's use train_test_split to do this."
      ],
      "metadata": {
        "id": "ZdW7LPWTK0lV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size = 0.20, random_state = 42)\n",
        "x_test.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dyZPH5C3Kv78",
        "outputId": "84eb4345-ab0f-4009-f49c-26226f62ca0b"
      },
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 10 entries, 13 to 19\n",
            "Data columns (total 5 columns):\n",
            " #   Column            Non-Null Count  Dtype  \n",
            "---  ------            --------------  -----  \n",
            " 0   R&D Spend         10 non-null     float64\n",
            " 1   Administration    10 non-null     float64\n",
            " 2   Marketing Spend   10 non-null     float64\n",
            " 3   State_California  10 non-null     uint8  \n",
            " 4   State_New York    10 non-null     uint8  \n",
            "dtypes: float64(3), uint8(2)\n",
            "memory usage: 340.0 bytes\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's check 4 parts variables."
      ],
      "metadata": {
        "id": "9l73cuaFK-W9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x_train.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "wOrwIjzlK6fl",
        "outputId": "8ea1916f-1805-453a-cfaf-dd1d3b8aafe1"
      },
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    R&D Spend  Administration  Marketing Spend  State_California  \\\n",
              "12   93863.75       127320.38        249839.44                 0   \n",
              "4   142107.34        91391.77        366168.42                 0   \n",
              "37   44069.95        51283.14        197029.42                 1   \n",
              "8   120542.52       148718.95        311613.29                 0   \n",
              "3   144372.41       118671.85        383199.62                 0   \n",
              "\n",
              "    State_New York  \n",
              "12               0  \n",
              "4                0  \n",
              "37               0  \n",
              "8                1  \n",
              "3                1  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-e77ee01c-befa-41a6-a41e-9d9008ee7f1e\" 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",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>R&amp;D Spend</th>\n",
              "      <th>Administration</th>\n",
              "      <th>Marketing Spend</th>\n",
              "      <th>State_California</th>\n",
              "      <th>State_New York</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>93863.75</td>\n",
              "      <td>127320.38</td>\n",
              "      <td>249839.44</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>142107.34</td>\n",
              "      <td>91391.77</td>\n",
              "      <td>366168.42</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>44069.95</td>\n",
              "      <td>51283.14</td>\n",
              "      <td>197029.42</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>120542.52</td>\n",
              "      <td>148718.95</td>\n",
              "      <td>311613.29</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>144372.41</td>\n",
              "      <td>118671.85</td>\n",
              "      <td>383199.62</td>\n",
              "      <td>0</td>\n",
              "      <td>1</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-e77ee01c-befa-41a6-a41e-9d9008ee7f1e')\"\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-e77ee01c-befa-41a6-a41e-9d9008ee7f1e 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-e77ee01c-befa-41a6-a41e-9d9008ee7f1e');\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-676a55fe-4c0e-4677-bf47-77c03b1f1e41\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-676a55fe-4c0e-4677-bf47-77c03b1f1e41')\"\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-676a55fe-4c0e-4677-bf47-77c03b1f1e41 button');\n",
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              "  </script>\n",
              "</div>\n",
              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "x_train",
              "summary": "{\n  \"name\": \"x_train\",\n  \"rows\": 40,\n  \"fields\": [\n    {\n      \"column\": \"R&D Spend\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 47897.50259157002,\n        \"min\": 0.0,\n        \"max\": 165349.2,\n        \"num_unique_values\": 39,\n        \"samples\": [\n          76253.86,\n          119943.24,\n          144372.41\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Administration\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 27454.110044974677,\n        \"min\": 51283.14,\n        \"max\": 182645.56,\n        \"num_unique_values\": 40,\n        \"samples\": [\n          99814.71,\n          154806.14,\n          136897.8\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Marketing Spend\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 114863.93125361094,\n        \"min\": 0.0,\n        \"max\": 471784.1,\n        \"num_unique_values\": 40,\n        \"samples\": [\n          362861.36,\n          28334.72,\n          471784.1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"State_California\",\n      \"properties\": {\n        \"dtype\": \"uint8\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"State_New York\",\n      \"properties\": {\n        \"dtype\": \"uint8\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_test"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "vG0DZAnLK6ct",
        "outputId": "f476c48e-eab6-4e50-978d-468e987ca203"
      },
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    R&D Spend  Administration  Marketing Spend  State_California  \\\n",
              "13   91992.39       135495.07        252664.93                 1   \n",
              "39   38558.51        82982.09        174999.30                 1   \n",
              "30   61994.48       115641.28         91131.24                 0   \n",
              "45    1000.23       124153.04          1903.93                 0   \n",
              "17   94657.16       145077.58        282574.31                 0   \n",
              "48     542.05        51743.15             0.00                 0   \n",
              "26   75328.87       144135.98        134050.07                 0   \n",
              "25   64664.71       139553.16        137962.62                 1   \n",
              "32   63408.86       129219.61         46085.25                 1   \n",
              "19   86419.70       153514.11             0.00                 0   \n",
              "\n",
              "    State_New York  \n",
              "13               0  \n",
              "39               0  \n",
              "30               0  \n",
              "45               1  \n",
              "17               1  \n",
              "48               1  \n",
              "26               0  \n",
              "25               0  \n",
              "32               0  \n",
              "19               1  "
            ],
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              "type": "dataframe",
              "variable_name": "x_test",
              "summary": "{\n  \"name\": \"x_test\",\n  \"rows\": 10,\n  \"fields\": [\n    {\n      \"column\": \"R&D Spend\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 34359.18930684101,\n        \"min\": 542.05,\n        \"max\": 94657.16,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          63408.86,\n          38558.51,\n          542.05\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Administration\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 31730.339805253356,\n        \"min\": 51743.15,\n        \"max\": 153514.11,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          129219.61,\n          82982.09,\n          51743.15\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Marketing Spend\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 103279.88331993422,\n        \"min\": 0.0,\n        \"max\": 282574.31,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          137962.62,\n          174999.3,\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"State_California\",\n      \"properties\": {\n        \"dtype\": \"uint8\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"State_New York\",\n      \"properties\": {\n        \"dtype\": \"uint8\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_train.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5Xprfdq4LDcR",
        "outputId": "a51404b7-4a09-4f73-d5ed-3e3a5ec4fff9"
      },
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "12    141585.52\n",
              "4     166187.94\n",
              "37     89949.14\n",
              "8     152211.77\n",
              "3     182901.99\n",
              "Name: Profit, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_test"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eyvK-Dr5LDYO",
        "outputId": "fffe875f-d2f0-4af9-8fcc-564cf4ac91f7"
      },
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "13    134307.35\n",
              "39     81005.76\n",
              "30     99937.59\n",
              "45     64926.08\n",
              "17    125370.37\n",
              "48     35673.41\n",
              "26    105733.54\n",
              "25    107404.34\n",
              "32     97427.84\n",
              "19    122776.86\n",
              "Name: Profit, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 71
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's include LinearRegression into the core and build the model."
      ],
      "metadata": {
        "id": "tOL1mccfmuaJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "\n",
        "lm = LinearRegression()"
      ],
      "metadata": {
        "id": "sACDrNX3LDVN"
      },
      "execution_count": 72,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's give the training data created from dependent independent variables to train the model."
      ],
      "metadata": {
        "id": "Eq5HDWwum4zR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = lm.fit(x_train, y_train)\n",
        "model"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "xmmH63mpmvr0",
        "outputId": "07bf7694-f919-454d-d54e-b81951229aa9"
      },
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ],
            "text/html": [
              "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 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-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 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-2 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-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 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-2 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-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 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-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 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-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 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-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" 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-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" 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": 73
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's have the model predict the dependent test variable that it has not seen before. Let's assign these predicted values ​​to the y_pred variable."
      ],
      "metadata": {
        "id": "GK4PY4TdnIfx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred=lm.predict(x_test)\n",
        "y_pred"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fdl8eOj3mvoK",
        "outputId": "4fb79afa-e76a-44b8-d63e-fa50d096b343"
      },
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([126362.87908253,  84608.4538364 ,  99677.49425152,  46357.46068582,\n",
              "       128750.482885  ,  50912.41741896, 109741.35032701, 100643.24281645,\n",
              "        97599.27574598, 113097.42524435])"
            ]
          },
          "metadata": {},
          "execution_count": 74
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's gather the predictions and actual values ​​in a data frame and look at them."
      ],
      "metadata": {
        "id": "oLMmg1SZnEMI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df=pd.DataFrame({\"Actual Data\":y_test, \"Prediction Data\":y_pred,\"Margin of Error\":abs(y_test - y_pred), \"Percentage of Error\":(abs(y_test - y_pred)/y_test)*100})\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "0mEssxWXmvjM",
        "outputId": "3ec59720-0ab5-4ff1-8bdc-091754f92c41"
      },
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    Actual Data  Prediction Data  Margin of Error  Percentage of Error\n",
              "13    134307.35    126362.879083      7944.470917             5.915142\n",
              "39     81005.76     84608.453836      3602.693836             4.447454\n",
              "30     99937.59     99677.494252       260.095748             0.260258\n",
              "45     64926.08     46357.460686     18568.619314            28.599631\n",
              "17    125370.37    128750.482885      3380.112885             2.696102\n",
              "48     35673.41     50912.417419     15239.007419            42.718113\n",
              "26    105733.54    109741.350327      4007.810327             3.790482\n",
              "25    107404.34    100643.242816      6761.097184             6.294994\n",
              "32     97427.84     97599.275746       171.435746             0.175962\n",
              "19    122776.86    113097.425244      9679.434756             7.883761"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-2f16affc-f60a-4b8f-aa06-75a44785eff3\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
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              "    }\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Actual Data</th>\n",
              "      <th>Prediction Data</th>\n",
              "      <th>Margin of Error</th>\n",
              "      <th>Percentage of Error</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>134307.35</td>\n",
              "      <td>126362.879083</td>\n",
              "      <td>7944.470917</td>\n",
              "      <td>5.915142</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>81005.76</td>\n",
              "      <td>84608.453836</td>\n",
              "      <td>3602.693836</td>\n",
              "      <td>4.447454</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>99937.59</td>\n",
              "      <td>99677.494252</td>\n",
              "      <td>260.095748</td>\n",
              "      <td>0.260258</td>\n",
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              "    <tr>\n",
              "      <th>45</th>\n",
              "      <td>64926.08</td>\n",
              "      <td>46357.460686</td>\n",
              "      <td>18568.619314</td>\n",
              "      <td>28.599631</td>\n",
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              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>125370.37</td>\n",
              "      <td>128750.482885</td>\n",
              "      <td>3380.112885</td>\n",
              "      <td>2.696102</td>\n",
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              "    <tr>\n",
              "      <th>48</th>\n",
              "      <td>35673.41</td>\n",
              "      <td>50912.417419</td>\n",
              "      <td>15239.007419</td>\n",
              "      <td>42.718113</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>105733.54</td>\n",
              "      <td>109741.350327</td>\n",
              "      <td>4007.810327</td>\n",
              "      <td>3.790482</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>107404.34</td>\n",
              "      <td>100643.242816</td>\n",
              "      <td>6761.097184</td>\n",
              "      <td>6.294994</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>97427.84</td>\n",
              "      <td>97599.275746</td>\n",
              "      <td>171.435746</td>\n",
              "      <td>0.175962</td>\n",
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              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>122776.86</td>\n",
              "      <td>113097.425244</td>\n",
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              "      <td>7.883761</td>\n",
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              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
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              "\n",
              "  <div id=\"id_152c6f57-f195-4b33-ab21-3d205744d679\">\n",
              "    <style>\n",
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              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 10,\n  \"fields\": [\n    {\n      \"column\": \"Actual Data\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 29996.14926576417,\n        \"min\": 35673.41,\n        \"max\": 134307.35,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          97427.84,\n          81005.76,\n          35673.41\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Prediction Data\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 28165.81787375747,\n        \"min\": 46357.460685822385,\n        \"max\": 128750.48288499514,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          97599.27574597817,\n          84608.453836398,\n          50912.417418955556\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Margin of Error\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6105.388303252347,\n        \"min\": 171.43574597817496,\n        \"max\": 18568.619314177617,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          171.43574597817496,\n          3602.6938363980007,\n          15239.007418955553\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Percentage of Error\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14.002575047517574,\n        \"min\": 0.17596176408937628,\n        \"max\": 42.718112507202285,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          0.17596176408937628,\n          4.447453905003793,\n          42.718112507202285\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 75
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "Let's include the metrics hosted in sklearn into the core and display the MAE, MSE, RMSE values."
      ],
      "metadata": {
        "id": "sTCC6gxBnxZR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import sklearn.metrics as metrics\n",
        "import math\n",
        "\n",
        "MAE = metrics.mean_absolute_error(y_test, y_pred)\n",
        "MSE = metrics.mean_squared_error(y_test,y_pred)\n",
        "RMSE = math.sqrt(MSE)"
      ],
      "metadata": {
        "id": "bVsbYjYemvfx"
      },
      "execution_count": 76,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Mean absolute error value (MAE):\", MAE)\n",
        "print(\"Mean square error value (MSE):\", MSE)\n",
        "print(\"Square root mean square error value (RMSE):\", RMSE) #To find the distance between the estimated and real value"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Y1TvK79rmvcg",
        "outputId": "2761840b-e217-4d47-9d3d-452f0de1ff11"
      },
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean absolute error value (MAE): 6961.477813266986\n",
            "Mean square error value (MSE): 82010363.04475\n",
            "Square root mean square error value (RMSE): 9055.957323483255\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's print the R Squared value of the model on the training data."
      ],
      "metadata": {
        "id": "Gbw8Sjj4oA4h"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"R Squared: \", lm.score(x_train, y_train))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d8Zv-L0On8hw",
        "outputId": "44e519c9-0962-4cde-8332-a561ca3bdcbe"
      },
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "R Squared:  0.9537019995248526\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import statsmodels.api as sm"
      ],
      "metadata": {
        "id": "BFuA5MAGn8dL"
      },
      "execution_count": 79,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model2 = sm.OLS(y,x).fit()"
      ],
      "metadata": {
        "id": "6LgxdKMNn8Zo"
      },
      "execution_count": 80,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model2.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 559
        },
        "id": "O0ZOmP1DoMw3",
        "outputId": "0ca9031e-7b8a-4ee7-d2bb-3e115f59f774"
      },
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<class 'statsmodels.iolib.summary.Summary'>\n",
              "\"\"\"\n",
              "                                 OLS Regression Results                                \n",
              "=======================================================================================\n",
              "Dep. Variable:                 Profit   R-squared (uncentered):                   0.988\n",
              "Model:                            OLS   Adj. R-squared (uncentered):              0.987\n",
              "Method:                 Least Squares   F-statistic:                              765.9\n",
              "Date:                Thu, 21 Mar 2024   Prob (F-statistic):                    2.49e-42\n",
              "Time:                        20:12:53   Log-Likelihood:                         -543.87\n",
              "No. Observations:                  50   AIC:                                      1098.\n",
              "Df Residuals:                      45   BIC:                                      1107.\n",
              "Df Model:                           5                                                  \n",
              "Covariance Type:            nonrobust                                                  \n",
              "====================================================================================\n",
              "                       coef    std err          t      P>|t|      [0.025      0.975]\n",
              "------------------------------------------------------------------------------------\n",
              "R&D Spend            0.7217      0.064     11.260      0.000       0.593       0.851\n",
              "Administration       0.2847      0.038      7.465      0.000       0.208       0.362\n",
              "Marketing Spend      0.0830      0.022      3.833      0.000       0.039       0.127\n",
              "State_California  7720.4710   4539.299      1.701      0.096   -1422.146    1.69e+04\n",
              "State_New York    7126.6916   4530.146      1.573      0.123   -1997.491    1.63e+04\n",
              "==============================================================================\n",
              "Omnibus:                        0.862   Durbin-Watson:                   1.480\n",
              "Prob(Omnibus):                  0.650   Jarque-Bera (JB):                0.929\n",
              "Skew:                          -0.212   Prob(JB):                        0.628\n",
              "Kurtosis:                       2.484   Cond. No.                     8.02e+05\n",
              "==============================================================================\n",
              "\n",
              "Notes:\n",
              "[1] R² is computed without centering (uncentered) since the model does not contain a constant.\n",
              "[2] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
              "[3] The condition number is large, 8.02e+05. This might indicate that there are\n",
              "strong multicollinearity or other numerical problems.\n",
              "\"\"\""
            ],
            "text/html": [
              "<table class=\"simpletable\">\n",
              "<caption>OLS Regression Results</caption>\n",
              "<tr>\n",
              "  <th>Dep. Variable:</th>         <td>Profit</td>      <th>  R-squared (uncentered):</th>      <td>   0.988</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared (uncentered):</th> <td>   0.987</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th>          <td>   765.9</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Date:</th>             <td>Thu, 21 Mar 2024</td> <th>  Prob (F-statistic):</th>          <td>2.49e-42</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Time:</th>                 <td>20:12:53</td>     <th>  Log-Likelihood:    </th>          <td> -543.87</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>No. Observations:</th>      <td>    50</td>      <th>  AIC:               </th>          <td>   1098.</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Df Residuals:</th>          <td>    45</td>      <th>  BIC:               </th>          <td>   1107.</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Df Model:</th>              <td>     5</td>      <th>                     </th>              <td> </td>   \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>              <td> </td>   \n",
              "</tr>\n",
              "</table>\n",
              "<table class=\"simpletable\">\n",
              "<tr>\n",
              "          <td></td>            <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>R&D Spend</th>        <td>    0.7217</td> <td>    0.064</td> <td>   11.260</td> <td> 0.000</td> <td>    0.593</td> <td>    0.851</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Administration</th>   <td>    0.2847</td> <td>    0.038</td> <td>    7.465</td> <td> 0.000</td> <td>    0.208</td> <td>    0.362</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Marketing Spend</th>  <td>    0.0830</td> <td>    0.022</td> <td>    3.833</td> <td> 0.000</td> <td>    0.039</td> <td>    0.127</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>State_California</th> <td> 7720.4710</td> <td> 4539.299</td> <td>    1.701</td> <td> 0.096</td> <td>-1422.146</td> <td> 1.69e+04</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>State_New York</th>   <td> 7126.6916</td> <td> 4530.146</td> <td>    1.573</td> <td> 0.123</td> <td>-1997.491</td> <td> 1.63e+04</td>\n",
              "</tr>\n",
              "</table>\n",
              "<table class=\"simpletable\">\n",
              "<tr>\n",
              "  <th>Omnibus:</th>       <td> 0.862</td> <th>  Durbin-Watson:     </th> <td>   1.480</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Prob(Omnibus):</th> <td> 0.650</td> <th>  Jarque-Bera (JB):  </th> <td>   0.929</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Skew:</th>          <td>-0.212</td> <th>  Prob(JB):          </th> <td>   0.628</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Kurtosis:</th>      <td> 2.484</td> <th>  Cond. No.          </th> <td>8.02e+05</td>\n",
              "</tr>\n",
              "</table><br/><br/>Notes:<br/>[1] R² is computed without centering (uncentered) since the model does not contain a constant.<br/>[2] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[3] The condition number is large, 8.02e+05. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
            ],
            "text/latex": "\\begin{center}\n\\begin{tabular}{lclc}\n\\toprule\n\\textbf{Dep. Variable:}    &      Profit      & \\textbf{  R-squared (uncentered):}      &     0.988   \\\\\n\\textbf{Model:}            &       OLS        & \\textbf{  Adj. R-squared (uncentered):} &     0.987   \\\\\n\\textbf{Method:}           &  Least Squares   & \\textbf{  F-statistic:       }          &     765.9   \\\\\n\\textbf{Date:}             & Thu, 21 Mar 2024 & \\textbf{  Prob (F-statistic):}          &  2.49e-42   \\\\\n\\textbf{Time:}             &     20:12:53     & \\textbf{  Log-Likelihood:    }          &   -543.87   \\\\\n\\textbf{No. Observations:} &          50      & \\textbf{  AIC:               }          &     1098.   \\\\\n\\textbf{Df Residuals:}     &          45      & \\textbf{  BIC:               }          &     1107.   \\\\\n\\textbf{Df Model:}         &           5      & \\textbf{                     }          &             \\\\\n\\textbf{Covariance Type:}  &    nonrobust     & \\textbf{                     }          &             \\\\\n\\bottomrule\n\\end{tabular}\n\\begin{tabular}{lcccccc}\n                           & \\textbf{coef} & \\textbf{std err} & \\textbf{t} & \\textbf{P$> |$t$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n\\midrule\n\\textbf{R\\&D Spend}        &       0.7217  &        0.064     &    11.260  &         0.000        &        0.593    &        0.851     \\\\\n\\textbf{Administration}    &       0.2847  &        0.038     &     7.465  &         0.000        &        0.208    &        0.362     \\\\\n\\textbf{Marketing Spend}   &       0.0830  &        0.022     &     3.833  &         0.000        &        0.039    &        0.127     \\\\\n\\textbf{State\\_California} &    7720.4710  &     4539.299     &     1.701  &         0.096        &    -1422.146    &     1.69e+04     \\\\\n\\textbf{State\\_New York}   &    7126.6916  &     4530.146     &     1.573  &         0.123        &    -1997.491    &     1.63e+04     \\\\\n\\bottomrule\n\\end{tabular}\n\\begin{tabular}{lclc}\n\\textbf{Omnibus:}       &  0.862 & \\textbf{  Durbin-Watson:     } &    1.480  \\\\\n\\textbf{Prob(Omnibus):} &  0.650 & \\textbf{  Jarque-Bera (JB):  } &    0.929  \\\\\n\\textbf{Skew:}          & -0.212 & \\textbf{  Prob(JB):          } &    0.628  \\\\\n\\textbf{Kurtosis:}      &  2.484 & \\textbf{  Cond. No.          } & 8.02e+05  \\\\\n\\bottomrule\n\\end{tabular}\n%\\caption{OLS Regression Results}\n\\end{center}\n\nNotes: \\newline\n [1] R² is computed without centering (uncentered) since the model does not contain a constant. \\newline\n [2] Standard Errors assume that the covariance matrix of the errors is correctly specified. \\newline\n [3] The condition number is large, 8.02e+05. This might indicate that there are \\newline\n strong multicollinearity or other numerical problems."
          },
          "metadata": {},
          "execution_count": 81
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's obtain the success score by folding the data through the cross_val_score and cross_val_predict methods."
      ],
      "metadata": {
        "id": "2zzX2bwtoY9n"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn import model_selection\n",
        "from sklearn.model_selection import cross_val_score, cross_val_predict"
      ],
      "metadata": {
        "id": "YEsXiPYwoMuA"
      },
      "execution_count": 82,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "repeated_values = cross_val_score(model, x_train, y_train, cv=10, scoring=\"r2\")\n",
        "repeated_values"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "h8qwhJeBoMqz",
        "outputId": "656e09b5-b04e-4550-8c58-7426e1368063"
      },
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.9226481 , 0.97501191, 0.93143071, 0.96694209, 0.9408353 ,\n",
              "       0.93863795, 0.9631974 , 0.93125019, 0.87182566, 0.70645281])"
            ]
          },
          "metadata": {},
          "execution_count": 83
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "repeated_values.mean()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-5RYHBK0oMnt",
        "outputId": "775aaecc-77d7-416f-997e-1adc0d40765a"
      },
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9148232109573252"
            ]
          },
          "metadata": {},
          "execution_count": 84
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "As a result, we achieved a success rate of 95 in the training data for lm.score() above, but when we performed the same operation on 10 different parts, the average value was 91, which allowed us to obtain a healthier result."
      ],
      "metadata": {
        "id": "8V3UMuJAo3MH"
      }
    }
  ]
}