TASK_3_Sales_Prediction_using_python.ipynb

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      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df=pd.read_csv(\"/content/drive/MyDrive/Datasets/Advertising.csv\")\n",
        "df.head()\n"
      ],
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              "   Unnamed: 0     TV  Radio  Newspaper  Sales\n",
              "0           1  230.1   37.8       69.2   22.1\n",
              "1           2   44.5   39.3       45.1   10.4\n",
              "2           3   17.2   45.9       69.3    9.3\n",
              "3           4  151.5   41.3       58.5   18.5\n",
              "4           5  180.8   10.8       58.4   12.9"
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            "text/plain": [
              "(200, 5)"
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          "execution_count": 8
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          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 200 entries, 0 to 199\n",
            "Data columns (total 5 columns):\n",
            " #   Column      Non-Null Count  Dtype  \n",
            "---  ------      --------------  -----  \n",
            " 0   Unnamed: 0  200 non-null    int64  \n",
            " 1   TV          200 non-null    float64\n",
            " 2   Radio       200 non-null    float64\n",
            " 3   Newspaper   200 non-null    float64\n",
            " 4   Sales       200 non-null    float64\n",
            "dtypes: float64(4), int64(1)\n",
            "memory usage: 7.9 KB\n"
          ]
        }
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              "       Unnamed: 0          TV       Radio   Newspaper       Sales\n",
              "count  200.000000  200.000000  200.000000  200.000000  200.000000\n",
              "mean   100.500000  147.042500   23.264000   30.554000   14.022500\n",
              "std     57.879185   85.854236   14.846809   21.778621    5.217457\n",
              "min      1.000000    0.700000    0.000000    0.300000    1.600000\n",
              "25%     50.750000   74.375000    9.975000   12.750000   10.375000\n",
              "50%    100.500000  149.750000   22.900000   25.750000   12.900000\n",
              "75%    150.250000  218.825000   36.525000   45.100000   17.400000\n",
              "max    200.000000  296.400000   49.600000  114.000000   27.000000"
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              "      <th>mean</th>\n",
              "      <td>100.500000</td>\n",
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              "      <th>std</th>\n",
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              "      <td>1.000000</td>\n",
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              "      <td>1.600000</td>\n",
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              "      <th>25%</th>\n",
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              "      <td>10.375000</td>\n",
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              "      <th>50%</th>\n",
              "      <td>100.500000</td>\n",
              "      <td>149.750000</td>\n",
              "      <td>22.900000</td>\n",
              "      <td>25.750000</td>\n",
              "      <td>12.900000</td>\n",
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              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>150.250000</td>\n",
              "      <td>218.825000</td>\n",
              "      <td>36.525000</td>\n",
              "      <td>45.100000</td>\n",
              "      <td>17.400000</td>\n",
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              "      <td>296.400000</td>\n",
              "      <td>49.600000</td>\n",
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              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-8882ee67-0656-46f7-a937-e1dd31565855 button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-dfa383ae-541f-4dfc-a2a1-9eeddfb39cc0 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-dfa383ae-541f-4dfc-a2a1-9eeddfb39cc0');\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",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df=df.drop(columns=[\"Unnamed: 0\"])"
      ],
      "metadata": {
        "id": "S2C9ZkC0gnCd"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "IEUxWYzWi0v_",
        "outputId": "69afb5d3-a24f-40a3-ecec-ab98d7e4ccb8"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        TV  Radio  Newspaper  Sales\n",
              "0    230.1   37.8       69.2   22.1\n",
              "1     44.5   39.3       45.1   10.4\n",
              "2     17.2   45.9       69.3    9.3\n",
              "3    151.5   41.3       58.5   18.5\n",
              "4    180.8   10.8       58.4   12.9\n",
              "..     ...    ...        ...    ...\n",
              "195   38.2    3.7       13.8    7.6\n",
              "196   94.2    4.9        8.1    9.7\n",
              "197  177.0    9.3        6.4   12.8\n",
              "198  283.6   42.0       66.2   25.5\n",
              "199  232.1    8.6        8.7   13.4\n",
              "\n",
              "[200 rows x 4 columns]"
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>TV</th>\n",
              "      <th>Radio</th>\n",
              "      <th>Newspaper</th>\n",
              "      <th>Sales</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>230.1</td>\n",
              "      <td>37.8</td>\n",
              "      <td>69.2</td>\n",
              "      <td>22.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>44.5</td>\n",
              "      <td>39.3</td>\n",
              "      <td>45.1</td>\n",
              "      <td>10.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>17.2</td>\n",
              "      <td>45.9</td>\n",
              "      <td>69.3</td>\n",
              "      <td>9.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>151.5</td>\n",
              "      <td>41.3</td>\n",
              "      <td>58.5</td>\n",
              "      <td>18.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>180.8</td>\n",
              "      <td>10.8</td>\n",
              "      <td>58.4</td>\n",
              "      <td>12.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
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              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>195</th>\n",
              "      <td>38.2</td>\n",
              "      <td>3.7</td>\n",
              "      <td>13.8</td>\n",
              "      <td>7.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>196</th>\n",
              "      <td>94.2</td>\n",
              "      <td>4.9</td>\n",
              "      <td>8.1</td>\n",
              "      <td>9.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>197</th>\n",
              "      <td>177.0</td>\n",
              "      <td>9.3</td>\n",
              "      <td>6.4</td>\n",
              "      <td>12.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>198</th>\n",
              "      <td>283.6</td>\n",
              "      <td>42.0</td>\n",
              "      <td>66.2</td>\n",
              "      <td>25.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>199</th>\n",
              "      <td>232.1</td>\n",
              "      <td>8.6</td>\n",
              "      <td>8.7</td>\n",
              "      <td>13.4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>200 rows × 4 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5ecb589c-e9f4-42a5-8cd3-f71621d0ea70')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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              "\n",
              "    <div id=\"df-51e6fa91-180b-4d92-a9ec-11d178a52062\">\n",
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              "      </button>\n",
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              "<style>\n",
              "  .colab-df-quickchart {\n",
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              "    width: 32px;\n",
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              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: #E2EBFA;\n",
              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: #174EA6;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart: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",
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              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-51e6fa91-180b-4d92-a9ec-11d178a52062 button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-5ecb589c-e9f4-42a5-8cd3-f71621d0ea70 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-5ecb589c-e9f4-42a5-8cd3-f71621d0ea70');\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",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x=df.iloc[:,0:-1]\n",
        "x"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "29GBM97gi5Di",
        "outputId": "057ec440-a795-47b8-f7d0-83ff4b219f31"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        TV  Radio  Newspaper\n",
              "0    230.1   37.8       69.2\n",
              "1     44.5   39.3       45.1\n",
              "2     17.2   45.9       69.3\n",
              "3    151.5   41.3       58.5\n",
              "4    180.8   10.8       58.4\n",
              "..     ...    ...        ...\n",
              "195   38.2    3.7       13.8\n",
              "196   94.2    4.9        8.1\n",
              "197  177.0    9.3        6.4\n",
              "198  283.6   42.0       66.2\n",
              "199  232.1    8.6        8.7\n",
              "\n",
              "[200 rows x 3 columns]"
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              "\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>TV</th>\n",
              "      <th>Radio</th>\n",
              "      <th>Newspaper</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>230.1</td>\n",
              "      <td>37.8</td>\n",
              "      <td>69.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>44.5</td>\n",
              "      <td>39.3</td>\n",
              "      <td>45.1</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>17.2</td>\n",
              "      <td>45.9</td>\n",
              "      <td>69.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>151.5</td>\n",
              "      <td>41.3</td>\n",
              "      <td>58.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>180.8</td>\n",
              "      <td>10.8</td>\n",
              "      <td>58.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
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              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>195</th>\n",
              "      <td>38.2</td>\n",
              "      <td>3.7</td>\n",
              "      <td>13.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>196</th>\n",
              "      <td>94.2</td>\n",
              "      <td>4.9</td>\n",
              "      <td>8.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>197</th>\n",
              "      <td>177.0</td>\n",
              "      <td>9.3</td>\n",
              "      <td>6.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>198</th>\n",
              "      <td>283.6</td>\n",
              "      <td>42.0</td>\n",
              "      <td>66.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>199</th>\n",
              "      <td>232.1</td>\n",
              "      <td>8.6</td>\n",
              "      <td>8.7</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>200 rows × 3 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b5dc912a-47db-4128-b524-5f17ef39c9b3')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "\n",
              "\n",
              "\n",
              "    <div id=\"df-473a6b24-5194-4c1d-af9d-dc4e6efacc12\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-473a6b24-5194-4c1d-af9d-dc4e6efacc12')\"\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",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\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-quickchart:hover {\n",
              "    background-color: #E2EBFA;\n",
              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: #174EA6;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart: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",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-473a6b24-5194-4c1d-af9d-dc4e6efacc12 button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-b5dc912a-47db-4128-b524-5f17ef39c9b3 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-b5dc912a-47db-4128-b524-5f17ef39c9b3');\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",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y=df.iloc[:,-1]\n",
        "y"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uRRsfMt2kqqx",
        "outputId": "86c97304-ef97-4805-b38a-d89d1b554a19"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0      22.1\n",
              "1      10.4\n",
              "2       9.3\n",
              "3      18.5\n",
              "4      12.9\n",
              "       ... \n",
              "195     7.6\n",
              "196     9.7\n",
              "197    12.8\n",
              "198    25.5\n",
              "199    13.4\n",
              "Name: Sales, Length: 200, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.scatter(x.TV,y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 448
        },
        "id": "fW6AkViet8iT",
        "outputId": "fed56cf4-0ed8-4b55-abf4-9ddd55006db6"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7e49134e1510>"
            ]
          },
          "metadata": {},
          "execution_count": 30
        },
        {
          "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": [
        "plt.scatter(x.Radio,y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 448
        },
        "id": "bT6FtxzOuWNk",
        "outputId": "da7f0dfc-7ec6-42a6-f8d5-fb9bb9b3743f"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7e4910a97730>"
            ]
          },
          "metadata": {},
          "execution_count": 31
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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fJxKJfA6JiDSX7zy805UMXq58yBZEsccEhYHr4COZTGL58uW46qqrUFNTAwDo6upCUVERysrK0h47duxYdHV1Zf09DQ0NWLlypdvDIKIQMauFSK06sbM/htMNvrzaEMwsiLpt5gRb/z97TFhjvYa8XC+1ra+vR1tbGzZu3JjXATz00EOIx+P9/9577728fh8R6cmqFgI4Xwthp/GX0w2+RG8Ilmvp7hMvHUTZ8ELT7qsAUBABPuruzfEIamzrxNzVTVjydAvu2diKJU+3YO7qJm62JwlXmY9ly5bhxRdfxM6dOzF+/Pj+78diMZw9exYnTpxIy34cOXIEsVgs6+8qLi5GcXGxm8MgohARXQvhdIMvURuC2SkotZI0gPr1e7G2IMIln1mIyJCRtxxlPgzDwLJly7Bp0yY0NTWhqqoq7edXXnklCgsL8fLLL/d/78CBAzh8+DBqa2vFHDERhZIXtRCplQyLpo9D7eTRloGE08dnYyeIOnH6HO657lJY/Xq7mZ4wEZkhI+84ynzU19dj/fr12Lx5M0aOHNlfxxGNRjFs2DBEo1F87Wtfw3333Yfy8nKUlpbib/7mb1BbW8uVLkSUF13227AbHH2cTCLX/ZGrXrLjaiE1OAo+1q5dCwC45ppr0r7/zDPP4K677gIAPPHEEygoKMCtt96a1mSMiCgfuuy3YT84spdV4aqXdFwtpAZHwYedZqglJSVYs2YN1qxZ4/qgiIgyebXqxG92g6jZVeV4arv17xtzgf2auTCs/lApQxaGv4cZ7mpLRMpIrTrJXKIaU2i/DbtBVEHE5k3IZulCWPYpUSVDFpa/hxluLEdEytFhxGh283n4pmqMGlGE37R14p+a37X8PT++bToWTR9n+VxO9ooJgsi/aer1AtmDu6Bfrwp/Dzc821jODww+iCgsMm+4H3X3YtXWt3MWTGay2tAuqF15nfAiCyBrZkGFv4dbnm0sR0Qkk8yb95WTRmH3ux8pkxEZuGlZY1sn6tfvtTuLYnv6QPbVH1715BDVl0U02f8efmHwQURKyjayLYggbXmqDCNdO3L1psjGSYGtzKs/3Ozg64SMO9LK/Pfwk+v26kREQTFrT57ZFyM1epa9pbbVaDiTk7buMq/+cJIF0IXMfw8/MfNBREpxkiUQMXr2g91R7v+unYSFNRWOpg9kXv0RxiyAzH8PPzHzQURKcZolUGH0bHeUu7CmwnFb99TSXmBw27Kg+6OEMQsg89/DTww+iEgpbkfBMo+eU6Nhs9tNBOfrV9yOhkXvyiuKiNfdlzTQ3H4Mm1vfR3P7MSX2bJH17+EnTrsQkVLcjoKd/H9+9xHxo3urjKs/8n3dsi6ntUPGv4ef2OeDiJSS6pNgNmeeyWnfhCBvaCrfTPPh5nXr2qhLZWwyRkRaM+tgmcnpjcjpDc2LDIkO3VvdcPK6dW7UpTI2GSMirZnt8ZLZ58PJni9Oe054laWQsTeFH5y8bjbqUh+DDyJSUrY583w6nDq5ocXPnPWkKyfZE8Ylurph8EFEyso2WnY70rV7o+qKn8Hjvz3gWVdOshbGJbq6YfBBRNpxUzdh90Z1vPtsaFL+stafiGjUJetrCwsGH0SkFbe1GHZvaOUXFNs6DtVT/jKvvAnzEl1dsMkYEWnDbM8XO3u82O08GSvVP+Wfz3n0i9tGXSq8tjBg5oOItCBih1SzVTQDV830JQ2t9+bweqdZkZw26lLptemOwQcRaUHU8kurG5of3UiDpNoyVi7RVRODD6IQ0bnITuTyS6sbmp0Miap0Xsaq82tTDYMPopDQvcjO7+WXuu7NofMyVp1fm2oYfBApzG4mw6xtuOimWEFmVkQsv3RKx26kQZxHv+j82lTD4INIUXYzGX4V2QWdWdG9FsMvOp9HnV+barjUlkhBTpYLOimy8+N4vORk+WVf0kBz+zFsbn0fze3H0JfMFp7pyeq1u13GqgKdX5tKmPkgUozTTIbXRXayLV+0U4sRdJYmSHZfu6o1LXam/lR9bTph8EGkGKfLBb0ssutLGlj3aod0yxdz1WL4Vf8ikqhaGqevXbWaFidBpWqvTTcMPogU4zST4VWRXbYLvZ3jCZJsWRo7RGVpVHztTqgYVIYZaz6IFOM0k2G3bbiTG45ZjUcuH57sDby+wo/6F5FE1tKo9tqdsAqsgPOBVZjqemTH4INIMalMhlmoEMH5kfHATIbIIrtcF3ozBRFg1da3cc/GVix5ugVzVzcFsodGEE2m3Ba2ir6h6txgS+fASlecdiFSjNvlgqKK7Kwu9Nlk3h+DSoX73WQqnykT0a3AdW6wpXNgpStmPogU5DaTkSqyWzR9HGonj3Y1t+/kAm7264NKhbvJGrmV75SJ6Buqn6/dbzoHVrpi5oNIUUEtF7R7Af/KnIn4fy2HTX8e1CoYP5pMiSjuFH1D1bnBVlg6l+q0NxMzH0QKE5HJyGRVozCrqhyxUvMbXmoEPWPiKFvP53cq3I8mUyJqELzIVOjaYMuLomrZNLZ1Yu7qJix5uiXw2ikRmPkgon52ahS27e9Cz8d9Wf//gRf66LAiW88ZRCrc66yRiCkTrzIVujbY0nmnYR2XETP4ICIA9i5wALI+JqVseCEaFl+BBTUV6EsaUqfCvWwyJWrKxKsbqq4NtnQMrHTtz8Lgg4hsXeAe+fffA4jkXGJbPLQA11fHAOhdY2BFZA2CjjdUL+kWWIle9SQL1nwQka0LXFeiF12J3NMJXYnetDoGXWsMrIiuQfCitofUoOsyYmY+iEjohSvzd4V15K5zDQL5R9dlxAw+iEjohSvb79ItFW5XWAMvEkfXZcQMPkhrOq2L95KdC9zY0mIAERxJ6HUR9FpYAy8SQ9faKQYfpC1Ru4GGgZ0L3CNfuBwAtLsIkhrCPJDQcQovYhiGVNv8JRIJRKNRxONxlJaWBn04pCizZaPA+Rul6GJHmS6M+RyLnYCNQV24yPDe5nvuPBn+Frk4uX8z+CDt9CUNzF3dlHP1RkW0BLsemCfkgyvThVHEsdi5wMl+ESQxZHhvmw0kUu82nVdNqYbBB4Vac/sxLHm6xfJxG+6ek/dcvEwXRpmOhdQnw/vJaiCRqjMSNZCg/Di5f7PPB2mnK35GyOOs9jixaswF+Ldrq0zHQuqT5f0kYo8ckhMLTkk7x7vP5v04O+lmmToP2j2WJ7b9EVddMobTJJSTLO9tXRtsETMfpKHyC4rzelwq3Zx58U3tcZLaRVKmC6Pd53hq+yHld8Mk78ny3ta1wRYx+CAN5dru3epxTtLNMl0YnT5HZiClGqspMcqPLO/tVP8ZsxxdBOczkuwtox5Ou5B2Uhcsq9Uu2S5YTtLNVo25AKAgAnzU3evwFThn51gGUnk3TBlWYOhOlq6aujbYImY+SEOpC1YE2Tf1isD8guUk3Txw8zAzSQOoX7/X8wxDro3MzKhYrGd3SozyI3pjvHz4vTkhs2r+4FJb0pabEbKbZbq/3teJZRv2wOwa5edywGyv2cqPb5uORdPHeXhUYnDZpf9kyjL50VtGpterIif3b067kLbcbOrlJt08akSRaeABWK8MEHlRTb3mda92YNXWt239P6oU68myAkNmom/QMm2M5/UeOWZ9TVJZNfbJEYvBB2nN6QXLzRxzPisDvBhpDSmIYMxIeyt+yoYXKlOsJ8sKDFl5NWoPw8Z4VoXmqtZHyYw1H+QLleZRnc4xu10Z4GX9gt1j+qvPVilzMfVjBYZK79OBWAuTHzYz85/jzMfOnTvxgx/8ALt370ZnZyc2bdqEW265pf/nd911F5599tm0/2f+/PlobGzM+2BJTSrOozpJN7uZqvF6pGVn9UvZ8EIsm3eJ498dFK9XYKj4PgU4aheBWTX/Oc58dHd3Y9q0aVizZo3pYxYsWIDOzs7+fxs2bMjrIEldKo/IUunmRdPHoXbyaNMLt5uVAS3txzwdadlZ/fLY4itMX5OMGQAvV2Co/D7lqD1/svQ1CRPHmY+FCxdi4cKFOR9TXFyMWCzm+qBIfX1JAy3tx/Dgv72lxYjMqpAvNVWTOXKOZRk5N7Z14sF/e8vW8+Yz0jI7JqvRvMwZACfn2S7VMwcctedPlr4mYeJJwekrr7yCiy66CKNGjcK8efPw6KOPYvTo7AVLvb296O39SxOmRCLhxSGRj+wu91RldYLdm/HAqZqu+Bkc7z6L8guKER1WhL6kgSEFEdOKejP5jrScrlZQoeJf9AoM1VfRcNSevzA0M/NjqbITwoOPBQsWYPHixaiqqkJ7ezu+853vYOHChWhubsaQIUMGPb6hoQErV64UfRgUEKc3V0DuEZnTm/GQggjiZ87i8d8eGBSsPHzTp7Bq69u2zo3IkZbd1QoqZQBErsBQPXPAUbsYXmTVZCFjNlN48HHbbbf1//cVV1yBqVOnYvLkyXjllVdw3XXXDXr8Qw89hPvuu6//60QigQkTJog+LPJBrptXLrKOyNzcjHMFK99cv9fR8/s90so3AyDbyMou1TMHYRi1+yXIviZefX5kzWZ63ufj4osvxpgxY3Do0KGswUdxcTGKi+31JCC5Wd28Msk+InN6M7azKZ0dZcMK8ditVwi5IDi5oMnWr8QvOmQOdB61+y2IviZefX5kzmZ6Hnz86U9/wrFjx1BRwTe/7pykpVUYkTm9GTsNvsysuX0GrrpkTN6/x+kFLd9+JbKNrOzSJXMgUzdSss/Lz4/M9UyOl9qeOnUKra2taG1tBQB0dHSgtbUVhw8fxqlTp/Ctb30LLS0teOedd/Dyyy9j0aJFuOSSSzB//nzRx06ScZKW9mpTKJGc3oxF1ARUREsw5+L8LwJulo662b7cTrZn5Zb9UizVzcXvzcu8Ynd5OMnB68+PzPVMjjMfb775Jq699tr+r1P1GnfeeSfWrl2Lffv24dlnn8WJEydQWVmJG264AatWreLUSgjYamw1rBBrbp+BORfLf2F0mo4XURPwhWkVeZ8Xt6lWNxkAmUdWTjFzQH7z+vMjcz2T4+DjmmuuQa6NcH/729/mdUCkLjs3r8duvULIlIIfnN6M7QRfBRHk3ITu3/+rE99e8Km8bnj5XNCc1g7IPLIyk6sOJgz7mJA8vP78yFzPxI3lSChdCt9SN6jej5NYXncpNrx+GF2Jv/SjyfZ6rIIVA7kDD0BMliDfC5qTDIDMI6tsVC6MJf14/fmRuZ6JwQcJp3r6OtsNKlZagnvrPolPjBme8/XkCr5urInhF6++Y/n8+WYJRFzQ7GYAZB5ZZXJa2Kfq0mFShx+fH1kHhAw+yBOqpq/NblBHEj340Ut/xNo7Zli+LrPg6/WO47aCj3yzBH4GBDKPrAZyWgfDDAn5wa/Pj4wDQserXYh0JbLyPNuqAzerSdzwcgO2bFRYKeKkDkblTeZIPX59fmRbCcXMB9GfeV157meWwO9Uq4wjq4HsTmV1JXrweOMfpGzKRPqS/fPjBQYfRH/mx8oNP4MCvy9oMk+12Z3KOn6qV5ulw6QWmT8/XmDwQfRnfq3c8DMoCNsFzYzdOpjyEUW2fp9MS4eJVMTgg+jP/C7UZFDgH7tTXtFh9oIPWZYOUzC4Eip/DD6I/kyVlRvkjp0pr76koczSYQoGV0KJETFytSsNQCKRQDQaRTweR2lpadCHEzqZEf2Vk0Zh97sfaRfh5xq58OKiN6tRa2q1C5A9AJVlBQ/5z2wpPt8b5zm5fzP4oH7ZbrqZ7cB1uAnbCS6YVg03BqCUqS9pYO7qJtOC5FRWbNcD80J7rWDwQY6ZRfSZVI/wOXIhu3QPQHV/faI1tx/DkqdbLB+34e45oa3ncnL/Zs1HyGS74AAwba6VSeVeB253ew1SWKbBZKRzUTAzO86puImizBh8hIjZBee2mRNz9jbIpGqvA9W2fw/LNBj5y+keN3Seapsoyo7t1UMiV8voJ176o6vfqVqEr9LIxezvldnZnS2/yQmRWwiEjV/bI4QFg48QsHPBcUO1CF+VkUuuv1cm3jDICSfZP0rn955JuuO0SwhYXXCcyqfXQZBFbrJu/555TpJJIxTTYAOx+NEfKmX/ZCTr9vQqYvARIL8uuE4uJJnNtbL9HAAevqna8bEHXeQmYxOxbOekbFihq9+l6g0j6PdFmKiS/ZNZGDeB8wKDj4D4ecG1eyG5t+5SPPOf7+DE6XOmjykbXoj/9ZnxWLXV2bHLUuQm08jF7JycOGN+/nNR8YYhy/siLGTN/qlG55VQfmGfD8HsZDP87jWRao5jdcHZ8a1rcfXjTehK9Jr+ruiwoUic+djRscvYnCfoNL/VOXFC1eZGMr4vwoAdXMkr7PMRELudM/3uNWF3umH3ux/lDDwAIH7m46zfH3js86aMTetFYVXDEETNQtAjF1F1OCoXuqm29FlF2YJsmbJ/FF4MPgSxmz52esEVNUK3c8HZ3Pq+49+b7djnNLyE491/mTqwW8Ogas2CG3Zfa9mwwrRpmMw+HyrfMFj86C2rwZBV3ULQ2UHSG4MPAZxkM5xccEXXhVhdcETVDAwMPAD7NQwq1iy4Zfe1rrl9BgoiES07nLL40Tt2B0NmGSUWAZPXGHwI4CSbYfdC+s6H3fjRSweFF+Llmm6wKkbzip9FbrKM5uwW/s25ePSg49NlCoLFj97Id2qXRcDkBzYZE8BJNsNul7wNrx/2vQuhnSY6ZcMLTY/dDT9rFhrbOjF3dROWPN2Ceza2YsnTLZi7uimQ7qBsWMRz4JV8GomxAyr5hcGHAE7Sx6kLrtlH1wBw28yJOQs/UxePlvZjjo/VSqo2JBZNf02xaAl+escMPLb4CgCDbxZ2ZdZ/xKIlvoykzNqVdwbYnjzXuQ7L6JLnQLx8amnYAZX8wmkXAUSnj+Nnztp6XP36PXjs1iuEX6CtakOyFa6OHlGEY93Wx51Zw+DHtIdVu3IDwe1my4ZFPAei5VNLwyJg8guDDwGcdM5M3QjNRABsbv3A1vOeOHPOsznYXLUh2W4WV04ahc//YLurGgav2VnWGuSSzqCX/cqA50CcfAZDLAImv3DaRRCr9PH11TE0tx/DE9sOWKY1j3Wfxajh9uPCIOZgUzeLRdPHoXbyaBQNLZB2/r4rfkbo40hefUkDze3HsLn1fTS3HwtlbUI+tTTcuZX8wsyHQGbp49+2dWLm320btAQ1l9qLR+PXbUcsHydTIyZZmxcdtzEdBAB7Dn+EL0wfJzRAkmV1TRhweehfuP0syrj/EemJ7dU91vDr/fjZzg7H/9+yay/BU9sP2X78j2+bjkXTxzl+Hi/IdsPdtPd93PvLVluPFXmz4s3QP35vWaAKt59FvnfJDbZX94nVB/vX+zodBx6p+djayaMdBR8yzcHKNn8fK7V/bkT1MmCvBO9kfu6unDTK9y0LvCI6cHf7WWQRMHmNwYdLViODvqSB725uc/x7DQAP3/QpzLl4NCqiJZaFknZX0uS6qMmWqRAtNY9tZy8VETerIPbvCYtsn7vyEYU5pzRlmprMRbZsg2yDCNILgw8XzEa1nQNGtdFhRbZrDTKt2vo2CgbMvVrNi1nNwea6qAHI+rOHb6rGqBFFWgQkA+exAViez3xvVtwwzRtmnzu7tVQyLw9lpoxSdB8MpjD4cMhOz4iHfvUWvvc/ql0/x8ALTraisRQ7o6JcF7Vv/PlmnKkz3oNvrk//merzvWYFeLm4vVmxV4J4Vp87O2SamhyImTJKkS375SUGHw7Z6Rnx0elzePXQh66fY+AFZ9cD8/rnXrviZ3C8+yzKLyhGrNQ6IrbTKtkulUdgqZFE78dJ/N//OQ37OxP4u1+/bfn/ub1ZsVeCeHY+d2Zk3yOGmTICwpf9YvDhkN3R6ra3j2BkyVCc7PnY1fNkXnC8SP87PR4VR2DZRhKx0mKUDS9E/PQ5TzY044Zp4rnNEqmwPJSZMgpj9otNxhyyO1qNn/nYdeAxUD4XHNEXK9X2dTDbz+VIohcnTAIP4PzrzOdmFcSGaanmWpv2vo9f/Md/Y9OeP2nVZMvu5658RFHa1yrsEcNMGYVxTx1mPhyaVVWOsmGFOHHGfsOwfBw8chLN7cdcFR15dbFSYQQmcsrJDT8brmXL7qToMl9sN5u041vXYve7HylVrMdMGYUx+8Xgw6EhBRH81VVVeOKlP/ryfE9tb8dT29td3USsLmpuqTACy7dGQESK049eCWbzxCmdmswX2+28WTS0QLm6CHYVpTBmvzjt4sKyeZegbHih9QNtsnNJSRUdOdn63U763+7zpx6nyr4O+YwQRKY4M/fAET3VYmcFSGrXXtWnYKz2T1I5uNL5tZG1MO6pw8yHC0MKInhs8RWmS1WduLfuUmx84z3LUXpm0REAWyNqq/R/Mgl8d3ObZU8S1UZgIkYIsqc4nWR3dFktoXPnTZ1fG+UWxuwXgw+XFtRU4KcO+0YMlJrHXTbvUiybdyle7ziOVw99mLOlempE/lTTwUEBS65pGbOL2rb9XVi1dX9a4FE+ohC3zhiHF/d1SbU5nFMippzcBDB+NghyGhzJHkzZpXPnTZ1fmxthabgFyLsxp1cYfOQh86Y+5oJi3P8vrTiS6M15w8sWydZOHm375vDESwcHfc9qLXjmRc2sVuCj7nP4+X+8gzVf/jRGjShW9kOfayRhxW2Bn98NgpwGRzrNF5P+wtRwKyVM2S/WfORp4Jz+VZeMwSNfuBxA7joKs3ncfG4OqZurnbl9OytBVm19G7Oqyj2pVfCL2Tx6RbQEf311FSIQtxTWbFmvm1odu6zmiQfSbb6Y1JFaBr659X3by7/tfp7c/G7ZeVknJhNmPgQzS52VjyjEF6ePQ111zDSSzXeqwG4nRNEdFWVOjeYaSXx64ighKc6gGgRl7lljJgL95otJDW6yF3Y/T8kksGpruDIjOmHwIUjmDdhNvwGroiO7AYnV9I3INeXZdxktwi3TK3F9jkArF7+2FReV4gyyPbbVnjW8GFNQ3LYLt/t5ytx/ys7vJnkw+BAgV3S/aPo4R78rV9HRbTMn2uovYjV9I2pNufkuo2fxj6++g3989R3HNz+/53lFFPgF3SBoYBDVlejB8VO9KB9RhFh0mFRZKAqPfLKB+S6T17EVuY4YfOTJ7AacT3MnsxE5AGx843DenRBFdFS022PCyXmQfWMls4yMDA2CuEqCZJJPNjDfzwk34lMDg488WN2AU82d3ETgZjcTqzl+O3P7ItaUO+0ganUeZN9YKVdG5vrqGNtjEw1gN3vx6qEPBwXzojoz67K0XFdc7eJA/+Zde/6EX/zHf+OHvztgeQMWvRnQgpoKfP3qKmTefwsiwNevrrKdGci3o6KTD7adjqEyb6xkVXm/bX+X7xvJEcnMbvbiqe2HcM/GVix5ugVzVzehsa3TdmdmUcdAwWDmw6Zcm3dZ6YqfEXoc/7CzY9CIwDCAf9jZgU9PHOUoAHFbcOnmg50rYAm6bsKM3YzMrgfmhapBEFEubrIXmdOrZp+nh2/6FFZtfZuZRsUx+LDBavMuK1aty+3yYmrCba2Am4tLroBFhrqJbJxkZMLUIIgoFzdN/jKvYbk+TwUFkVC1ItcRp11M9E+x7H0f39n0Vl5zj+UXFAs5JpmmJnKlRjPZ2RRJ1o2VnGZkwtIgyCt2mkbp2FhKR2ZTu7lkXsPMPk/ciE99zHxkkc8USzaxUjGjddmmJqx6TAD2RyKybqwka0ZGR3aWWYex5bbKMrMXB4+cxFPb2y3/PzvXMGYa1eY487Fz507cfPPNqKysRCQSwQsvvJD2c8Mw8L3vfQ8VFRUYNmwY6urqcPDg4L1IZGVWXOiWyNG6jDfCBTUV2PXAPGy4ew6+dtUnUD6iMO3nTkYiMo5mZM3I6MZOO+0gWthT/tK3oLjQ1v9j9xrGTKO6HGc+uru7MW3aNHz1q1/F4sWLB/388ccfx5NPPolnn30WVVVVePjhhzF//nzs378fJSVyjw7t9q6ww4vRuoj+HF5IXQBqJ4/Gd26qzjkSsepeKttoRtaMjE7s1jIZhiHtUmyyR9ZrGPnPcfCxcOFCLFy4MOvPDMPAj370I3z3u9/FokWLAAD/9E//hLFjx+KFF17Abbfdlt/Resxp74pcvFjloMKNMFcBq92UuWwNs8K21bXf7NYy5cLGUmpQ4RpG/hBa89HR0YGuri7U1dX1fy8ajWL27Nlobm7OGnz09vait7e3/+tEIiHykBzJp1YiAmB53SfxiTHDhYzWzTIEIm+Efm4IZ7d76cBjGjOiGIgAH57qTevyGkRWRLaMjE5E1iixsZT8GMwTIDj46OrqAgCMHTs27ftjx47t/1mmhoYGrFy5UuRhuOa2VkJ0wZtVhkDEjdDPwj37u1QaWLX1bdNRbtnw8/UkJ06f8/yYs5EtI6MLkTVKLPxVA4N5Cny1y0MPPYT77ruv/+tEIoEJEyYEcix2eleUjyjC/1k4BSfOnEP5BcWIlYr90NjNEORzI/R7DxX7u1Tuzfl7BgYdKbLs+0Lu2a0DMAwDRxK9rBXQBIP5cBPa5yMWiwEAjhw5kvb9I0eO9P8sU3FxMUpLS9P+BcVO74qkYWBEyVB87XMX44ufFlthbZUhAM5nCPLpa+DHc2TyMhXu1TGTWLl6cwz83GVjAPjCtAo88oXLAbCFvcrYo4VShAYfVVVViMViePnll/u/l0gk8Nprr6G2tlbkUwk18AMRHVaENV+egeFFQ7I+9sTpc54t6/OjiVgQjcq8ToUHue8LWWts68Tc1U1Y8nTLoH08UlJ7Fpn5h50dACDdUmyyz877gMLD8bTLqVOncOjQof6vOzo60NraivLyckycOBHLly/Ho48+iksvvbR/qW1lZSVuueUWkcctTLbah7Lhheg+22f6/+SzW20ufjQRC6JRmahdKq2w2FA+TgqN//2/ct+EUnvosFZAPX5P9ZL8HAcfb775Jq699tr+r1P1GnfeeSfWrVuHb3/72+ju7sbXv/51nDhxAnPnzkVjY6OUPT7MPhDZagsyebGsz48mYqKew8lKGavldaICEhYbysXJXkROMnKpnjKkBi/2pFKRn6sLVeA4+LjmmmtgGOa3i0gkgu9///v4/ve/n9eBeU1EQzHRI20/GvCIeA43K2Vy71JZjVVb97vOjLDYUE5OAgrZtg7QSdA3PaeBpY64LcBgga92CYqIhmKiR9p+NODJ9znySZ/m3qUSjnbAdHLMFAwnAYWMWwfoQIabXtgDS045ZRfaXW3zfaOXjyj0ZKTtx/4mbp9DxEoZp7tUDjRqeGF/rw+7x0zBcRJQcA8d8WTZCyfMgWUQqwtVEdrMR75v9EcX1Xg20vajAY+b5/A6fZp5TDJ1OCXnnEzxse22WDLVWYR5PxdOOZkLbfCRzwqMv766CjdOrfTkuFL8aMDj9Dn8SJ/aOaawfUhV5TSgYNttcWS66YU5sAz7lFMuoQk+shVdWa3AKBtemLbyZfSIIqxaVIMbp4bzIhjm9Cm54zSgYNvt8/ItEpXtphfWwJLXTHOhCD5yFV3l+kDwIpguzOlTcs9pQBH2ttsiikRlvOmFMbDkNdNcxMi1bjYAiUQC0WgU8XhcSKt1s0rjlHvrPoml10zG7nc/UuoDEdTyudT5BLKnT1n8SeSe2fXK6eerL2lg7uomy5vergfmSX+tC4qoa2yYrplO7t9aBx+pD6DVktpYaTEe+cLlyrwBso2MykcU4VGfpoRkWL5HpBur65XTgCFMNz3RRF/jwnLNZPDxZ83tx7Dk6RZbj41AjQ+jVSbnr6+uwkM3mm/SJUrQjYtIT2F+X9m9Xm24e47taamw3PREEpV9yhSG97aT+7fWNR9Oi6lkb/Frpyvrz3Z2YNr4Mi1W41C4hP1G6UWRaBjrLPLh5RJlXjPTad1kzEkxlQo7o9rtyvrdzW1KNq3hdtvhJUtDrCB5VSRq1tiPBgti1++w0jrz4aaXh8zrre0e2/Huc8o1rQn7qDfMZGqIFSSujAiebEuUdaZ15iPV3MYJmddbOzk2lT4cHPWGG0eb5w28XmWGWLo345KFjEuUdaV18AEM2DOkNPebxe7eEUFODcyqKkf5iCJbj1Xlw8G9D4ijzb/wY28nMsc9hvyj9bRLSqro6qmmg3jipYODfm53VBH01MCQgggeXVSDb67fk/NxKn04ZGoDTcHgaDMdi0SDE+ZW8H7TPvORMqQggnvqPomf3jEDFS5GFbJMDdw4tQJ/fXWV6c8jUOvDwVEvcbQ5mCpFojoWiTP75I9QZD4GcjOqkK0g7qEbqzFtfBm+u7kNx7v/sveMigWaHPUSR5tqCjoT7CVmn7yndZMxUbxo/iOCDk1r2AaaUnS+menGq0ZcpDY2GRNM1qkBHZrWcNRLKRxtqkG2TDCpKTQ1H/ng1IC3OMdKKarUOoQZl0aTCMx82CCy+Y8OUyVe4KjXPb6nyE+yZoJJLQw+bBA1NcA57dx0mEbyG99T5DdmgkkETrvYlO/UgCxLdUkffE9RELg0mkRg5sMBt1MDLNAi0fieoqCwSJxEYObDQmYTHQCOC+JYoEWi8T1FfsnWSIxF4pQvZj5yEDWfzgKtwVgkmR++p8gPVtdAFonLSYXrK4MPE2ZNdFLz6U6iexZopWORZP74niKv2b0GskhcLqpcXzntkoXonVZZoPUXLJIUg+8p8hJ3m1aTStdXBh9ZiJ5PTxVoARh0swhTgRYvaOKIeE/puCkYicGaIvWodn1l8JGFF/PpLNDiBU20fN5TjW2dmLu6CUuebsE9G1ux5OkWzF3dJNXIiILDmiL1qHZ9Zc1HFl7Np4e9QIsXNPHcvKdE1jORnlhTpB7Vrq8MPrKYVVWOsuGFOHH6nOljRg0vdDWfHuYunrygecPJe4r9QcgOkVtKkD9Uu75y2sUlOWbN1OKmSJJ1CWKplpqlYLBOTT2qFaEz+Mji9Y7jObMeAHDi9DleoB1yekGTrS5Bh0BItdQsBceLOjUdPkOyUi1g5LRLFrxAeyd1Qctchx7LWIcuW12CKmvnraiWmqVgiaxT0+UzJDO711cZRAzDkCr0TCQSiEajiMfjKC0tDeQYmtuPYcnTLZaP23D3nNDWb+QrVwe+vqSBuaubTKcHUvPNux6Y50sUbxYIpZ5ZpQLN1Lm1msv369xSOOj0GVJBUB1Ondy/Oe2ShWpzZypKFUlm2yNHproE1dbOW1EtNUvq0+0zpIJc11dZMPjIghfoYMk07SVTICQKe86Qn3T8DFH+WPNhQqW5M93IVJcgUyAkUth7zpB/dP0MUX4YfOTAC3QwZOoxIFMgJFqYe86Qf3T+DJF7nHaxoMLcmW5kmvZi/Q9RfvgZomwYfJCUZKlLkCkQIlIRP0OUDZfaktSCWjKWiT0KiPLDz5D+nNy/GXwESJYbK9nDvxdRfvgZ0puT+zcLTgPCUYB6WKBJlB9+hiiFNR8BSHX7y1z7nmodHtTeJUQq4n4hROph5sNn3NKcSBxmEInUxMyHz9jtT00cXcuHGUQidTHz4TN2+1MPR9fycZJBBMAiRyLJMPjwGbv9qcVsN87U6Jp7oQTDbgbxqaaD2PjGewwciSTDaRefsdufOrgbp7zsZgafeOkgp2WIJBTa4COoOXx2+1MH63PklU9m0E3gyJofIrFCOe0S9Bw+d8xVA+tz5GW1+aCVgYGjVd+JoK8XRDoKXfAhyxw+d8yVH+tz5JXKIC59bg8iQNrnOfPrXKwCR1muF0S6CdW0i2xz+NwxV26sz5Fbrs0H7637pK3fkStwlO16QaQT4cHHI488gkgkkvZvypQpop/GFc7hkxOsz5HfgpoK7HpgHjbcPQc/vm06Ntw9B7semIdl8y7JO3Dk9YLIO55kPi6//HJ0dnb2/9u1a5cXT+MY5/DJqVyja6bc5ZAtgygicOT1gsg7ntR8DB06FLFYzItfnRdRc/jcmTFcWJ+jpnwLu1nzQ+QdT4KPgwcPorKyEiUlJaitrUVDQwMmTpzoxVM5YlUhH8H5C1OuVCwr38OJu3GqKZ/AUcT1goiyEz7tMnv2bKxbtw6NjY1Yu3YtOjo68LnPfQ4nT57M+vje3l4kEom0f17JNxXLvSSI1OO2sJs1P0TeiRiG4Wmp9okTJzBp0iT88Ic/xNe+9rVBP3/kkUewcuXKQd+Px+MoLS315JjcZC/6kgbmrm4yLUBLjYJ2PTCPFyMijTDbSWRPIpFANBq1df/2PPgAgJkzZ6Kurg4NDQ2Dftbb24ve3t7+rxOJBCZMmOBp8AE4r9tobj+GJU+3WP7eDXfPYXqeSDOs8yKy5iT48LzJ2KlTp9De3o6vfOUrWX9eXFyM4uJirw9jEKdz+Kx8Jwov1vwQiSW85uNv//ZvsWPHDrzzzjv4z//8T3zxi1/EkCFDsGTJEtFP5StWvhMREYkhPPPxpz/9CUuWLMGxY8dw4YUXYu7cuWhpacGFF14o+ql8xcp3IiIiMYQHHxs3bhT9K6VgtZcEwMp3IiIiO0K1t0u+2O2SiIgof6Hb1TZf7HZJRESUHwYfLrDynYiIyD1OuxAREZGvGHwQERGRrxh8EBERka8YfBAREZGvGHwQERGRrxh8EBERka8YfBAREZGvGHwQERGRrxh8EBERka/Y4ZSk0Zc02LaeiCgEGHyQFBrbOrFyy350xnv6v1cRLcGKm6u5YR8RkWY47UKBa2zrxNLn9qQFHgDQFe/B0uf2oLGtM6AjIyIiLzD4oED1JQ2s3LIfRpafpb63cst+9CWzPYKIiFTE4IMC9XrH8UEZj4EMAJ3xHrzecdy/gyIiIk8x+KBAHT1pHni4eRwREcmPwQcF6qKRJUIfR0RE8mPwQYGaVVWOimgJzBbURnB+1cusqnI/D4uIiDzE4IMCNaQgghU3VwPAoAAk9fWKm6vZ74OISCMMPihwC2oqsPaOGYhF06dWYtESrL1jBvt8EBFphk3GSAoLaipwfXWMHU6JiEKAwQdJY0hBBLWTRwd9GERE5DFOuxAREZGvGHwQERGRrxh8EBERka8YfBAREZGvGHwQERGRrxh8EBERka8YfBAREZGvGHwQERGRrxh8EBERka/Y4ZSIiALVlzS4tULIMPggIqLANLZ1YuWW/eiM9/R/ryJaghU3V3NTSY1x2oWIiALR2NaJpc/tSQs8AKAr3oOlz+1BY1tnQEdGXmPwQUREvutLGli5ZT+MLD9LfW/llv3oS2Z7BKmOwQcREfnu9Y7jgzIeAxkAOuM9eL3juH8HRb5h8EFERL47etI88HDzOFILgw8iIvLdRSNLhD6O1MLgg4iIfDerqhwV0RKYLaiN4Pyql1lV5X4eFvmEwQcREfluSEEEK26uBoBBAUjq6xU3V7Pfh6YYfBARUSAW1FRg7R0zEIumT63EoiVYe8cM9vnQGJuMERFRYBbUVOD66hg7nIYMgw8iIgrUkIIIaiePDvowyEecdiEiIiJfMfggIiIiXzH4ICIiIl8x+CAiIiJfMfggIiIiXzH4ICIiIl8x+CAiIiJfMfggIiIiXzH4ICIiIl9J1+HUMAwAQCKRCPhIiIiIyK7UfTt1H89FuuDj5MmTAIAJEyYEfCRERETk1MmTJxGNRnM+JmLYCVF8lEwm8cEHH2DkyJGIRMRuLJRIJDBhwgS89957KC0tFfq7KR3PtX94rv3Dc+0fnmt/iDzPhmHg5MmTqKysREFB7qoO6TIfBQUFGD9+vKfPUVpayjezT3iu/cNz7R+ea//wXPtD1Hm2yniksOCUiIiIfMXgg4iIiHwVquCjuLgYK1asQHFxcdCHoj2ea//wXPuH59o/PNf+COo8S1dwSkRERHoLVeaDiIiIgsfgg4iIiHzF4IOIiIh8xeCDiIiIfBWa4GPNmjX4xCc+gZKSEsyePRuvv/560IekhZ07d+Lmm29GZWUlIpEIXnjhhbSfG4aB733ve6ioqMCwYcNQV1eHgwcPBnOwCmtoaMDMmTMxcuRIXHTRRbjllltw4MCBtMf09PSgvr4eo0ePxgUXXIBbb70VR44cCeiI1bV27VpMnTq1v+lSbW0tfvOb3/T/nOfZG4899hgikQiWL1/e/z2ea3EeeeQRRCKRtH9Tpkzp/7nf5zoUwccvf/lL3HfffVixYgX27NmDadOmYf78+Th69GjQh6a87u5uTJs2DWvWrMn688cffxxPPvkkfvrTn+K1117DiBEjMH/+fPT09Ph8pGrbsWMH6uvr0dLSgm3btuHcuXO44YYb0N3d3f+Ye++9F1u2bMHzzz+PHTt24IMPPsDixYsDPGo1jR8/Ho899hh2796NN998E/PmzcOiRYvw+9//HgDPsxfeeOMN/OxnP8PUqVPTvs9zLdbll1+Ozs7O/n+7du3q/5nv59oIgVmzZhn19fX9X/f19RmVlZVGQ0NDgEelHwDGpk2b+r9OJpNGLBYzfvCDH/R/78SJE0ZxcbGxYcOGAI5QH0ePHjUAGDt27DAM4/x5LSwsNJ5//vn+x7z99tsGAKO5uTmow9TGqFGjjJ///Oc8zx44efKkcemllxrbtm0zPv/5zxv33HOPYRh8T4u2YsUKY9q0aVl/FsS51j7zcfbsWezevRt1dXX93ysoKEBdXR2am5sDPDL9dXR0oKurK+3cR6NRzJ49m+c+T/F4HABQXl4OANi9ezfOnTuXdq6nTJmCiRMn8lznoa+vDxs3bkR3dzdqa2t5nj1QX1+Pm266Ke2cAnxPe+HgwYOorKzExRdfjNtvvx2HDx8GEMy5lm5jOdE+/PBD9PX1YezYsWnfHzt2LP7whz8EdFTh0NXVBQBZz33qZ+RcMpnE8uXLcdVVV6GmpgbA+XNdVFSEsrKytMfyXLvz1ltvoba2Fj09PbjggguwadMmVFdXo7W1ledZoI0bN2LPnj144403Bv2M72mxZs+ejXXr1uGyyy5DZ2cnVq5cic997nNoa2sL5FxrH3wQ6aa+vh5tbW1p87Uk1mWXXYbW1lbE43H867/+K+68807s2LEj6MPSynvvvYd77rkH27ZtQ0lJSdCHo72FCxf2//fUqVMxe/ZsTJo0Cf/yL/+CYcOG+X482k+7jBkzBkOGDBlUtXvkyBHEYrGAjiocUueX516cZcuW4cUXX8T27dsxfvz4/u/HYjGcPXsWJ06cSHs8z7U7RUVFuOSSS3DllVeioaEB06ZNw49//GOeZ4F2796No0ePYsaMGRg6dCiGDh2KHTt24Mknn8TQoUMxduxYnmsPlZWV4ZOf/CQOHToUyPta++CjqKgIV155JV5++eX+7yWTSbz88suora0N8Mj0V1VVhVgslnbuE4kEXnvtNZ57hwzDwLJly7Bp0yY0NTWhqqoq7edXXnklCgsL0871gQMHcPjwYZ5rAZLJJHp7e3meBbruuuvw1ltvobW1tf/fZz7zGdx+++39/81z7Z1Tp06hvb0dFRUVwbyvPSljlczGjRuN4uJiY926dcb+/fuNr3/960ZZWZnR1dUV9KEp7+TJk8bevXuNvXv3GgCMH/7wh8bevXuNd9991zAMw3jssceMsrIyY/Pmzca+ffuMRYsWGVVVVcaZM2cCPnK1LF261IhGo8Yrr7xidHZ29v87ffp0/2O+8Y1vGBMnTjSampqMN99806itrTVqa2sDPGo1Pfjgg8aOHTuMjo4OY9++fcaDDz5oRCIR43e/+51hGDzPXhq42sUweK5Fuv/++41XXnnF6OjoMF599VWjrq7OGDNmjHH06FHDMPw/16EIPgzDMP7+7//emDhxolFUVGTMmjXLaGlpCfqQtLB9+3YDwKB/d955p2EY55fbPvzww8bYsWON4uJi47rrrjMOHDgQ7EErKNs5BmA888wz/Y85c+aM8c1vftMYNWqUMXz4cOOLX/yi0dnZGdxBK+qrX/2qMWnSJKOoqMi48MILjeuuu64/8DAMnmcvZQYfPNfifOlLXzIqKiqMoqIiY9y4ccaXvvQl49ChQ/0/9/tcRwzDMLzJqRARERENpn3NBxEREcmFwQcRERH5isEHERER+YrBBxEREfmKwQcRERH5isEHERER+YrBBxEREfmKwQcRERH5isEHERER+YrBBxEREfmKwQcRERH5isEHERER+er/A+fww0tGA1aYAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.scatter(x.Newspaper,y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 448
        },
        "id": "1vChwPS9ueiM",
        "outputId": "978322a6-c898-4f37-bea3-6ec6bdfba978"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7e4910b91240>"
            ]
          },
          "metadata": {},
          "execution_count": 32
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Train Test Split"
      ],
      "metadata": {
        "id": "x0FXnideo2IR"
      }
    },
    {
      "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=0.2,random_state=43)"
      ],
      "metadata": {
        "id": "XoN0nO3hlOzj"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_train"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "Bmf05OQdpBW3",
        "outputId": "24bcda9c-186d-4f3a-dab8-6c0f569e6d48"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        TV  Radio  Newspaper\n",
              "116  139.2   14.3       25.6\n",
              "138   43.0   25.9       20.5\n",
              "155    4.1   11.6        5.7\n",
              "82    75.3   20.3       32.5\n",
              "160  172.5   18.1       30.7\n",
              "..     ...    ...        ...\n",
              "58   210.8   49.6       37.7\n",
              "21   237.4    5.1       23.5\n",
              "49    66.9   11.7       36.8\n",
              "64   131.1   42.8       28.9\n",
              "68   237.4   27.5       11.0\n",
              "\n",
              "[160 rows x 3 columns]"
            ],
            "text/html": [
              "\n",
              "\n",
              "  <div id=\"df-b5068d47-edee-493a-bcbe-8c859a111f65\">\n",
              "    <div 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>TV</th>\n",
              "      <th>Radio</th>\n",
              "      <th>Newspaper</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>116</th>\n",
              "      <td>139.2</td>\n",
              "      <td>14.3</td>\n",
              "      <td>25.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>138</th>\n",
              "      <td>43.0</td>\n",
              "      <td>25.9</td>\n",
              "      <td>20.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>155</th>\n",
              "      <td>4.1</td>\n",
              "      <td>11.6</td>\n",
              "      <td>5.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>82</th>\n",
              "      <td>75.3</td>\n",
              "      <td>20.3</td>\n",
              "      <td>32.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>160</th>\n",
              "      <td>172.5</td>\n",
              "      <td>18.1</td>\n",
              "      <td>30.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>58</th>\n",
              "      <td>210.8</td>\n",
              "      <td>49.6</td>\n",
              "      <td>37.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>237.4</td>\n",
              "      <td>5.1</td>\n",
              "      <td>23.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49</th>\n",
              "      <td>66.9</td>\n",
              "      <td>11.7</td>\n",
              "      <td>36.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>64</th>\n",
              "      <td>131.1</td>\n",
              "      <td>42.8</td>\n",
              "      <td>28.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>68</th>\n",
              "      <td>237.4</td>\n",
              "      <td>27.5</td>\n",
              "      <td>11.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>160 rows × 3 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b5068d47-edee-493a-bcbe-8c859a111f65')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "\n",
              "\n",
              "\n",
              "    <div id=\"df-66c44d4b-4b2a-49be-a019-5673371482cf\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-66c44d4b-4b2a-49be-a019-5673371482cf')\"\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",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\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-quickchart:hover {\n",
              "    background-color: #E2EBFA;\n",
              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: #174EA6;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart: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",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-66c44d4b-4b2a-49be-a019-5673371482cf button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-b5068d47-edee-493a-bcbe-8c859a111f65 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-b5068d47-edee-493a-bcbe-8c859a111f65');\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",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "Sc=StandardScaler()\n",
        "x_train_scaled=Sc.fit_transform(x_train)\n",
        "x_test_scaled=Sc.fit_transform(x_test)"
      ],
      "metadata": {
        "id": "CpbhGL7ZqW_o"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**APPLYING LINEAR REGRESSION**"
      ],
      "metadata": {
        "id": "wzHBJhP1sB2P"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "lr=LinearRegression()\n",
        "lr.fit(x_train_scaled,y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "4uKfK8Pzq2QL",
        "outputId": "529bdff8-7ea3-4cde-b73c-98681505172c"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 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-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 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-1 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-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 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-1 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-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 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-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 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-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 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-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" 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-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" 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": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred=lr.predict(x_test_scaled)"
      ],
      "metadata": {
        "id": "eWkw9pSPsoBS"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Evaluate the performance of a Linear Regerssion Model"
      ],
      "metadata": {
        "id": "whaJKhposv_V"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import r2_score\n",
        "r2_score(y_test,y_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sHsLF5L9stag",
        "outputId": "5edb1281-5b4b-4c97-a7f6-ab2e671ce475"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9260553930062493"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Analyzing Data By Scatter Plot"
      ],
      "metadata": {
        "id": "8JOF-sNas-0y"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt"
      ],
      "metadata": {
        "id": "V5unnQUys0wx"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.scatter(y_test,y_pred,c='g')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 452
        },
        "id": "fkxBaLA4tD_P",
        "outputId": "141fd3e8-19a3-4d9e-df47-34f953a34844"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7e4913320ac0>"
            ]
          },
          "metadata": {},
          "execution_count": 28
        },
        {
          "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": [],
      "metadata": {
        "id": "JfIzVmmYtGdr"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}