MLP_PT2.ipynb

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      "cell_type": "markdown",
      "source": [
        "# Problem 1"
      ],
      "metadata": {
        "id": "w5yCs0bNa6Fy"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "In MLP_PT1, you developed a model to predict whether a given car gets high or low gas mileage based on the Auto data set. Below is the a url to the data:"
      ],
      "metadata": {
        "id": "uxsivVL5b6r9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "url_auto = 'https://www.statlearning.com/s/Auto.csv'"
      ],
      "metadata": {
        "id": "zifEFUWgb_Oo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Create a binary variable, `mpg01`, that contains a 1 if `mpg` contains a value above its median, and a 0 if `mpg` contains a value below its median as you did before. This variable will describe if a car has high or low mileage."
      ],
      "metadata": {
        "id": "_OuB3SoEJPmW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Import packages\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.preprocessing import label_binarize\n",
        "from sklearn.model_selection import train_test_split, GridSearchCV\n",
        "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
        "from sklearn.metrics import confusion_matrix, roc_curve, auc, classification_report"
      ],
      "metadata": {
        "id": "6xZQKNoKH3F_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Import the data\n",
        "df = pd.read_csv('https://www.statlearning.com/s/Auto.csv')\n",
        "df"
      ],
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        "outputId": "5919377b-886b-4025-a70a-050fe6b39c4e"
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              "      mpg  cylinders  displacement horsepower  weight  acceleration  year  \\\n",
              "0    18.0          8         307.0        130    3504          12.0    70   \n",
              "1    15.0          8         350.0        165    3693          11.5    70   \n",
              "2    18.0          8         318.0        150    3436          11.0    70   \n",
              "3    16.0          8         304.0        150    3433          12.0    70   \n",
              "4    17.0          8         302.0        140    3449          10.5    70   \n",
              "..    ...        ...           ...        ...     ...           ...   ...   \n",
              "392  27.0          4         140.0         86    2790          15.6    82   \n",
              "393  44.0          4          97.0         52    2130          24.6    82   \n",
              "394  32.0          4         135.0         84    2295          11.6    82   \n",
              "395  28.0          4         120.0         79    2625          18.6    82   \n",
              "396  31.0          4         119.0         82    2720          19.4    82   \n",
              "\n",
              "     origin                       name  \n",
              "0         1  chevrolet chevelle malibu  \n",
              "1         1          buick skylark 320  \n",
              "2         1         plymouth satellite  \n",
              "3         1              amc rebel sst  \n",
              "4         1                ford torino  \n",
              "..      ...                        ...  \n",
              "392       1            ford mustang gl  \n",
              "393       2                  vw pickup  \n",
              "394       1              dodge rampage  \n",
              "395       1                ford ranger  \n",
              "396       1                 chevy s-10  \n",
              "\n",
              "[397 rows x 9 columns]"
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              "      <td>19.4</td>\n",
              "      <td>82</td>\n",
              "      <td>1</td>\n",
              "      <td>chevy s-10</td>\n",
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              "<p>397 rows × 9 columns</p>\n",
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          "metadata": {},
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    {
      "cell_type": "code",
      "source": [
        "#Calculate the median of mpg\n",
        "median = df['mpg'].median()\n",
        "median"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/"
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              "23.0"
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          "metadata": {},
          "execution_count": 150
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      "cell_type": "code",
      "source": [
        "#Then create the binary variable mpg01\n",
        "df['mpg01'] = df['mpg'].apply(lambda x: 1 if x > median else 0)\n",
        "df"
      ],
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            "text/plain": [
              "      mpg  cylinders  displacement horsepower  weight  acceleration  year  \\\n",
              "0    18.0          8         307.0        130    3504          12.0    70   \n",
              "1    15.0          8         350.0        165    3693          11.5    70   \n",
              "2    18.0          8         318.0        150    3436          11.0    70   \n",
              "3    16.0          8         304.0        150    3433          12.0    70   \n",
              "4    17.0          8         302.0        140    3449          10.5    70   \n",
              "..    ...        ...           ...        ...     ...           ...   ...   \n",
              "392  27.0          4         140.0         86    2790          15.6    82   \n",
              "393  44.0          4          97.0         52    2130          24.6    82   \n",
              "394  32.0          4         135.0         84    2295          11.6    82   \n",
              "395  28.0          4         120.0         79    2625          18.6    82   \n",
              "396  31.0          4         119.0         82    2720          19.4    82   \n",
              "\n",
              "     origin                       name  mpg01  \n",
              "0         1  chevrolet chevelle malibu      0  \n",
              "1         1          buick skylark 320      0  \n",
              "2         1         plymouth satellite      0  \n",
              "3         1              amc rebel sst      0  \n",
              "4         1                ford torino      0  \n",
              "..      ...                        ...    ...  \n",
              "392       1            ford mustang gl      1  \n",
              "393       2                  vw pickup      1  \n",
              "394       1              dodge rampage      1  \n",
              "395       1                ford ranger      1  \n",
              "396       1                 chevy s-10      1  \n",
              "\n",
              "[397 rows x 10 columns]"
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              "      <td>amc rebel sst</td>\n",
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              "      <td>4</td>\n",
              "      <td>140.0</td>\n",
              "      <td>86</td>\n",
              "      <td>2790</td>\n",
              "      <td>15.6</td>\n",
              "      <td>82</td>\n",
              "      <td>1</td>\n",
              "      <td>ford mustang gl</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>393</th>\n",
              "      <td>44.0</td>\n",
              "      <td>4</td>\n",
              "      <td>97.0</td>\n",
              "      <td>52</td>\n",
              "      <td>2130</td>\n",
              "      <td>24.6</td>\n",
              "      <td>82</td>\n",
              "      <td>2</td>\n",
              "      <td>vw pickup</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>394</th>\n",
              "      <td>32.0</td>\n",
              "      <td>4</td>\n",
              "      <td>135.0</td>\n",
              "      <td>84</td>\n",
              "      <td>2295</td>\n",
              "      <td>11.6</td>\n",
              "      <td>82</td>\n",
              "      <td>1</td>\n",
              "      <td>dodge rampage</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>395</th>\n",
              "      <td>28.0</td>\n",
              "      <td>4</td>\n",
              "      <td>120.0</td>\n",
              "      <td>79</td>\n",
              "      <td>2625</td>\n",
              "      <td>18.6</td>\n",
              "      <td>82</td>\n",
              "      <td>1</td>\n",
              "      <td>ford ranger</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>396</th>\n",
              "      <td>31.0</td>\n",
              "      <td>4</td>\n",
              "      <td>119.0</td>\n",
              "      <td>82</td>\n",
              "      <td>2720</td>\n",
              "      <td>19.4</td>\n",
              "      <td>82</td>\n",
              "      <td>1</td>\n",
              "      <td>chevy s-10</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>397 rows × 10 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3d55ec13-ccb8-4101-a616-e23abe627a05')\"\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",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
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              "          document.querySelector('#df-3d55ec13-ccb8-4101-a616-e23abe627a05 button.colab-df-convert');\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-3d55ec13-ccb8-4101-a616-e23abe627a05');\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": 151
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Task 1\n",
        "\n",
        "Use cross-validation to fit a SVM to this data to predict `mpg01` where the hyperparameter being tuned during the cross-validation process is the regularisation constant for miss-classification. State the optimal model and comment on your results."
      ],
      "metadata": {
        "id": "jcxg5Zbmcjv0"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Solution:"
      ],
      "metadata": {
        "id": "l1JpJzUSc0vW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.dtypes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "l4LQcV1VYQ3e",
        "outputId": "e2ef96d4-94dd-4e17-9faa-ff8514082ffb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "mpg             float64\n",
              "cylinders         int64\n",
              "displacement    float64\n",
              "horsepower       object\n",
              "weight            int64\n",
              "acceleration    float64\n",
              "year              int64\n",
              "origin            int64\n",
              "name             object\n",
              "mpg01             int64\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 152
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X = df.drop(columns = [\"name\", \"year\", \"origin\", \"horsepower\"])\n",
        "X"
      ],
      "metadata": {
        "id": "rcHPJkBpc2ld",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "outputId": "01f45560-6ae0-407e-ca25-ff04a238cf7a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      mpg  cylinders  displacement  weight  acceleration  mpg01\n",
              "0    18.0          8         307.0    3504          12.0      0\n",
              "1    15.0          8         350.0    3693          11.5      0\n",
              "2    18.0          8         318.0    3436          11.0      0\n",
              "3    16.0          8         304.0    3433          12.0      0\n",
              "4    17.0          8         302.0    3449          10.5      0\n",
              "..    ...        ...           ...     ...           ...    ...\n",
              "392  27.0          4         140.0    2790          15.6      1\n",
              "393  44.0          4          97.0    2130          24.6      1\n",
              "394  32.0          4         135.0    2295          11.6      1\n",
              "395  28.0          4         120.0    2625          18.6      1\n",
              "396  31.0          4         119.0    2720          19.4      1\n",
              "\n",
              "[397 rows x 6 columns]"
            ],
            "text/html": [
              "\n",
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              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "      <th></th>\n",
              "      <th>mpg</th>\n",
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              "      <th>displacement</th>\n",
              "      <th>weight</th>\n",
              "      <th>acceleration</th>\n",
              "      <th>mpg01</th>\n",
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              "  </thead>\n",
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              "      <th>0</th>\n",
              "      <td>18.0</td>\n",
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              "      <td>15.0</td>\n",
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              "    <tr>\n",
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              "      <td>18.0</td>\n",
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              "      <td>0</td>\n",
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              "    <tr>\n",
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              "      <td>16.0</td>\n",
              "      <td>8</td>\n",
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              "      <td>3433</td>\n",
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              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>17.0</td>\n",
              "      <td>8</td>\n",
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              "      <th>392</th>\n",
              "      <td>27.0</td>\n",
              "      <td>4</td>\n",
              "      <td>140.0</td>\n",
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              "      <td>15.6</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>393</th>\n",
              "      <td>44.0</td>\n",
              "      <td>4</td>\n",
              "      <td>97.0</td>\n",
              "      <td>2130</td>\n",
              "      <td>24.6</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>394</th>\n",
              "      <td>32.0</td>\n",
              "      <td>4</td>\n",
              "      <td>135.0</td>\n",
              "      <td>2295</td>\n",
              "      <td>11.6</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>395</th>\n",
              "      <td>28.0</td>\n",
              "      <td>4</td>\n",
              "      <td>120.0</td>\n",
              "      <td>2625</td>\n",
              "      <td>18.6</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>396</th>\n",
              "      <td>31.0</td>\n",
              "      <td>4</td>\n",
              "      <td>119.0</td>\n",
              "      <td>2720</td>\n",
              "      <td>19.4</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "<p>397 rows × 6 columns</p>\n",
              "</div>\n",
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              "              style=\"display:none;\">\n",
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              "\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
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              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      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",
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              "  </style>\n",
              "\n",
              "      <script>\n",
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              "          document.querySelector('#df-06fecc67-c05e-43e5-90e7-e34dda98283c 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-06fecc67-c05e-43e5-90e7-e34dda98283c');\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": 153
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y = df.mpg01\n",
        "y"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aZ2xnKApZVNp",
        "outputId": "2fa161e5-4ce7-4d1b-f4f6-2f05380f9ef8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0      0\n",
              "1      0\n",
              "2      0\n",
              "3      0\n",
              "4      0\n",
              "      ..\n",
              "392    1\n",
              "393    1\n",
              "394    1\n",
              "395    1\n",
              "396    1\n",
              "Name: mpg01, Length: 397, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 154
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn import svm\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.svm import SVC\n"
      ],
      "metadata": {
        "id": "pS4tKvtMZez1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Split into training and test set\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)\n",
        "\n",
        "# Select the optimal C parameter by cross-validation\n",
        "tuned_parameters = [{'C': [0.001, 0.01, 0.1, 1, 5, 10, 100]}]\n",
        "clf = GridSearchCV(SVC(kernel='linear'), tuned_parameters, cv=10, scoring='accuracy', return_train_score=True)\n",
        "clf.fit(X_train, y_train)\n",
        "clf.cv_results_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1sZ2ap-7Z-VN",
        "outputId": "1d8ca165-3f4e-40d5-d507-c84f0487d06f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'mean_fit_time': array([0.00829034, 0.02986143, 0.09917099, 0.15288346, 0.33262203,\n",
              "        0.21483638, 0.20479267]),\n",
              " 'std_fit_time': array([0.00155693, 0.01181821, 0.0673615 , 0.19499971, 0.38046138,\n",
              "        0.23501035, 0.23433362]),\n",
              " 'mean_score_time': array([0.00227642, 0.00262384, 0.00329118, 0.00271447, 0.0034678 ,\n",
              "        0.00287111, 0.00254188]),\n",
              " 'std_score_time': array([1.53852342e-04, 1.18896589e-04, 1.49028399e-03, 1.86969517e-04,\n",
              "        4.28215313e-04, 4.18790525e-04, 2.94363825e-05]),\n",
              " 'param_C': masked_array(data=[0.001, 0.01, 0.1, 1, 5, 10, 100],\n",
              "              mask=[False, False, False, False, False, False, False],\n",
              "        fill_value='?',\n",
              "             dtype=object),\n",
              " 'params': [{'C': 0.001},\n",
              "  {'C': 0.01},\n",
              "  {'C': 0.1},\n",
              "  {'C': 1},\n",
              "  {'C': 5},\n",
              "  {'C': 10},\n",
              "  {'C': 100}],\n",
              " 'split0_test_score': array([0.96875, 1.     , 1.     , 1.     , 1.     , 1.     , 1.     ]),\n",
              " 'split1_test_score': array([0.875, 1.   , 1.   , 1.   , 1.   , 1.   , 1.   ]),\n",
              " 'split2_test_score': array([0.84375, 1.     , 1.     , 1.     , 1.     , 1.     , 1.     ]),\n",
              " 'split3_test_score': array([0.90625, 1.     , 1.     , 1.     , 1.     , 1.     , 1.     ]),\n",
              " 'split4_test_score': array([0.96875, 1.     , 1.     , 1.     , 1.     , 1.     , 1.     ]),\n",
              " 'split5_test_score': array([1., 1., 1., 1., 1., 1., 1.]),\n",
              " 'split6_test_score': array([1., 1., 1., 1., 1., 1., 1.]),\n",
              " 'split7_test_score': array([0.96774194, 0.96774194, 1.        , 1.        , 1.        ,\n",
              "        1.        , 1.        ]),\n",
              " 'split8_test_score': array([0.96774194, 0.96774194, 1.        , 1.        , 1.        ,\n",
              "        1.        , 1.        ]),\n",
              " 'split9_test_score': array([0.80645161, 0.93548387, 1.        , 1.        , 1.        ,\n",
              "        1.        , 1.        ]),\n",
              " 'mean_test_score': array([0.93044355, 0.98709677, 1.        , 1.        , 1.        ,\n",
              "        1.        , 1.        ]),\n",
              " 'std_test_score': array([0.06476295, 0.02139758, 0.        , 0.        , 0.        ,\n",
              "        0.        , 0.        ]),\n",
              " 'rank_test_score': array([7, 6, 1, 1, 1, 1, 1], dtype=int32),\n",
              " 'split0_train_score': array([0.94035088, 0.99298246, 1.        , 1.        , 1.        ,\n",
              "        1.        , 1.        ]),\n",
              " 'split1_train_score': array([0.94035088, 0.98947368, 1.        , 1.        , 1.        ,\n",
              "        1.        , 1.        ]),\n",
              " 'split2_train_score': array([0.95789474, 0.99298246, 1.        , 1.        , 1.        ,\n",
              "        1.        , 1.        ]),\n",
              " 'split3_train_score': array([0.94385965, 0.98947368, 1.        , 1.        , 1.        ,\n",
              "        1.        , 1.        ]),\n",
              " 'split4_train_score': array([0.92982456, 0.98947368, 1.        , 1.        , 1.        ,\n",
              "        1.        , 1.        ]),\n",
              " 'split5_train_score': array([0.93333333, 0.98947368, 1.        , 1.        , 1.        ,\n",
              "        1.        , 1.        ]),\n",
              " 'split6_train_score': array([0.93333333, 0.99298246, 1.        , 1.        , 1.        ,\n",
              "        1.        , 1.        ]),\n",
              " 'split7_train_score': array([0.94055944, 0.99300699, 1.        , 1.        , 1.        ,\n",
              "        1.        , 1.        ]),\n",
              " 'split8_train_score': array([0.93706294, 0.99300699, 1.        , 1.        , 1.        ,\n",
              "        1.        , 1.        ]),\n",
              " 'split9_train_score': array([0.93356643, 0.99300699, 1.        , 1.        , 1.        ,\n",
              "        1.        , 1.        ]),\n",
              " 'mean_train_score': array([0.93901362, 0.99158631, 1.        , 1.        , 1.        ,\n",
              "        1.        , 1.        ]),\n",
              " 'std_train_score': array([0.00753919, 0.00172498, 0.        , 0.        , 0.        ,\n",
              "        0.        , 0.        ])}"
            ]
          },
          "metadata": {},
          "execution_count": 156
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Print the best hyperparameters and their score\n",
        "print(\"Best parameters: {}\".format(clf.best_params_))\n",
        "print(\"Best cross-validation score: {:.2f}\".format(clf.best_score_))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lC3MYqrybsD2",
        "outputId": "03f995b3-73e4-49a8-e9f3-dd5e24737595"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best parameters: {'C': 0.1}\n",
            "Best cross-validation score: 1.00\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "According to the GridSearchCV, the '0.1' model is the best model."
      ],
      "metadata": {
        "id": "S_eT07LVc_2n"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Now, we test the model on the test set\n",
        "\n",
        "svm_best = clf.best_estimator_\n",
        "print(\"Test set score: {:.2f}\".format(svm_best.score(X_test, y_test)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "z6X5fpcAeb8U",
        "outputId": "71d56c67-64f7-4256-dc3b-5e8a3d5ee722"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test set score: 0.99\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "confusion_matrix(y_test, svm_best.predict(X_test))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tegoGfmoysse",
        "outputId": "c3b85619-af5e-4adf-dfc9-a11a09a6b4bc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[46,  1],\n",
              "       [ 0, 33]])"
            ]
          },
          "metadata": {},
          "execution_count": 159
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Task 2\n",
        "\n",
        "Now try to use cross-validation to fit and tune the hyperparamters for a Gaussian (radial) kernel SVM. Here the hyperparameters to tune are the regularisation constant for miss-classification (C) and the constant $\\gamma$ from the Gaussian kernel."
      ],
      "metadata": {
        "id": "Y2IaoBrnc3Dz"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Solution:"
      ],
      "metadata": {
        "id": "brgDF893dEv6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Split the data into training and testing sets\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)"
      ],
      "metadata": {
        "id": "6DDY0CfadGj_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Standardise the training data\n",
        "scaler = StandardScaler()\n",
        "\n",
        "scaler.fit(X_train)\n",
        "\n",
        "X_train_s = scaler.transform(X_train)\n",
        "\n",
        "X_test_s = scaler.transform(X_test)"
      ],
      "metadata": {
        "id": "msMcxzm3FW-A"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Define the hyperparameters to tune and their values using cross validation\n",
        "tuned_params = {'C': [0.01, 0.1, 1, 10, 100], 'gamma': [0.5, 1, 2, 3, 4]}\n",
        "clf1 = GridSearchCV(SVC(kernel='rbf'), tuned_params, cv=10, scoring='accuracy', return_train_score=True)\n",
        "clf1.fit(X_train_s, y_train)\n",
        "clf1.cv_results_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VPYCtRY9rs2W",
        "outputId": "6cf51e70-c17b-451d-96e7-15aec5601d14"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'mean_fit_time': array([0.00445724, 0.00388923, 0.00343573, 0.00353506, 0.00385935,\n",
              "        0.00222156, 0.00267789, 0.00356865, 0.00342486, 0.0034565 ,\n",
              "        0.00203025, 0.00224915, 0.0033524 , 0.00388968, 0.00392978,\n",
              "        0.00179186, 0.00209429, 0.00268679, 0.00364101, 0.00454173,\n",
              "        0.00347357, 0.00396369, 0.00470884, 0.00664899, 0.00765009]),\n",
              " 'std_fit_time': array([1.15083408e-03, 6.02991811e-04, 4.61081129e-05, 1.00291361e-04,\n",
              "        7.10021089e-04, 4.57523265e-05, 8.76090440e-05, 6.32777920e-04,\n",
              "        6.29952645e-05, 4.73199559e-05, 2.45371623e-04, 1.93432395e-04,\n",
              "        1.05662619e-03, 4.48045621e-04, 4.20700336e-04, 3.83513460e-05,\n",
              "        3.38022476e-05, 1.61478733e-04, 9.05897267e-05, 1.36353615e-03,\n",
              "        1.12250490e-03, 2.68669208e-04, 1.75226920e-04, 1.43717820e-04,\n",
              "        7.67877593e-04]),\n",
              " 'mean_score_time': array([0.00149949, 0.0013458 , 0.0012063 , 0.00119419, 0.00132418,\n",
              "        0.00088384, 0.00101516, 0.0012392 , 0.00130396, 0.00117202,\n",
              "        0.0009156 , 0.00085831, 0.00116186, 0.00111418, 0.00106926,\n",
              "        0.00078325, 0.0008085 , 0.00093257, 0.00104213, 0.00129108,\n",
              "        0.00131781, 0.00146832, 0.00165532, 0.00193408, 0.00216293]),\n",
              " 'std_score_time': array([4.12213920e-04, 2.65493151e-04, 1.50588813e-04, 4.84583401e-05,\n",
              "        2.59467293e-04, 2.08654945e-05, 4.45399408e-05, 2.73097263e-04,\n",
              "        2.73557114e-04, 5.61402401e-05, 1.95146854e-04, 2.87744656e-05,\n",
              "        3.51670957e-04, 9.89214631e-05, 8.83361138e-05, 1.40847882e-05,\n",
              "        9.40013437e-06, 8.61860739e-05, 3.58366204e-05, 3.88433080e-04,\n",
              "        1.28745902e-04, 9.18841943e-05, 1.46706159e-04, 1.06657369e-04,\n",
              "        1.79851763e-04]),\n",
              " 'param_C': masked_array(data=[0.01, 0.01, 0.01, 0.01, 0.01, 0.1, 0.1, 0.1, 0.1, 0.1,\n",
              "                    1, 1, 1, 1, 1, 10, 10, 10, 10, 10, 100, 100, 100, 100,\n",
              "                    100],\n",
              "              mask=[False, False, False, False, False, False, False, False,\n",
              "                    False, False, False, False, False, False, False, False,\n",
              "                    False, False, False, False, False, False, False, False,\n",
              "                    False],\n",
              "        fill_value='?',\n",
              "             dtype=object),\n",
              " 'param_gamma': masked_array(data=[0.5, 1, 2, 3, 4, 0.5, 1, 2, 3, 4, 0.5, 1, 2, 3, 4, 0.5,\n",
              "                    1, 2, 3, 4, 0.5, 1, 2, 3, 4],\n",
              "              mask=[False, False, False, False, False, False, False, False,\n",
              "                    False, False, False, False, False, False, False, False,\n",
              "                    False, False, False, False, False, False, False, False,\n",
              "                    False],\n",
              "        fill_value='?',\n",
              "             dtype=object),\n",
              " 'params': [{'C': 0.01, 'gamma': 0.5},\n",
              "  {'C': 0.01, 'gamma': 1},\n",
              "  {'C': 0.01, 'gamma': 2},\n",
              "  {'C': 0.01, 'gamma': 3},\n",
              "  {'C': 0.01, 'gamma': 4},\n",
              "  {'C': 0.1, 'gamma': 0.5},\n",
              "  {'C': 0.1, 'gamma': 1},\n",
              "  {'C': 0.1, 'gamma': 2},\n",
              "  {'C': 0.1, 'gamma': 3},\n",
              "  {'C': 0.1, 'gamma': 4},\n",
              "  {'C': 1, 'gamma': 0.5},\n",
              "  {'C': 1, 'gamma': 1},\n",
              "  {'C': 1, 'gamma': 2},\n",
              "  {'C': 1, 'gamma': 3},\n",
              "  {'C': 1, 'gamma': 4},\n",
              "  {'C': 10, 'gamma': 0.5},\n",
              "  {'C': 10, 'gamma': 1},\n",
              "  {'C': 10, 'gamma': 2},\n",
              "  {'C': 10, 'gamma': 3},\n",
              "  {'C': 10, 'gamma': 4},\n",
              "  {'C': 100, 'gamma': 0.5},\n",
              "  {'C': 100, 'gamma': 1},\n",
              "  {'C': 100, 'gamma': 2},\n",
              "  {'C': 100, 'gamma': 3},\n",
              "  {'C': 100, 'gamma': 4}],\n",
              " 'split0_test_score': array([0.53571429, 0.53571429, 0.53571429, 0.53571429, 0.53571429,\n",
              "        1.        , 0.96428571, 0.89285714, 0.82142857, 0.82142857,\n",
              "        1.        , 1.        , 1.        , 0.96428571, 0.96428571,\n",
              "        1.        , 1.        , 1.        , 0.96428571, 0.96428571,\n",
              "        1.        , 1.        , 1.        , 0.96428571, 0.96428571]),\n",
              " 'split1_test_score': array([0.57142857, 0.53571429, 0.53571429, 0.53571429, 0.53571429,\n",
              "        1.        , 1.        , 0.82142857, 0.82142857, 0.82142857,\n",
              "        1.        , 1.        , 1.        , 1.        , 0.89285714,\n",
              "        1.        , 1.        , 1.        , 1.        , 0.89285714,\n",
              "        1.        , 1.        , 1.        , 1.        , 0.89285714]),\n",
              " 'split2_test_score': array([0.53571429, 0.53571429, 0.53571429, 0.53571429, 0.53571429,\n",
              "        1.        , 1.        , 0.92857143, 0.92857143, 0.92857143,\n",
              "        1.        , 1.        , 1.        , 1.        , 0.96428571,\n",
              "        1.        , 1.        , 1.        , 1.        , 0.96428571,\n",
              "        1.        , 1.        , 1.        , 1.        , 0.96428571]),\n",
              " 'split3_test_score': array([0.53571429, 0.53571429, 0.53571429, 0.53571429, 0.53571429,\n",
              "        0.96428571, 0.96428571, 0.96428571, 0.96428571, 0.96428571,\n",
              "        1.        , 1.        , 1.        , 0.96428571, 0.96428571,\n",
              "        1.        , 1.        , 1.        , 0.96428571, 0.96428571,\n",
              "        1.        , 1.        , 1.        , 0.96428571, 0.96428571]),\n",
              " 'split4_test_score': array([0.53571429, 0.53571429, 0.53571429, 0.53571429, 0.53571429,\n",
              "        1.        , 0.96428571, 0.92857143, 0.85714286, 0.85714286,\n",
              "        1.        , 1.        , 0.96428571, 0.96428571, 0.96428571,\n",
              "        1.        , 1.        , 0.96428571, 0.96428571, 0.96428571,\n",
              "        1.        , 1.        , 0.96428571, 0.96428571, 0.96428571]),\n",
              " 'split5_test_score': array([0.5       , 0.5       , 0.5       , 0.5       , 0.5       ,\n",
              "        1.        , 0.96428571, 0.92857143, 0.89285714, 0.89285714,\n",
              "        1.        , 1.        , 0.96428571, 1.        , 0.96428571,\n",
              "        1.        , 1.        , 0.96428571, 1.        , 0.96428571,\n",
              "        1.        , 1.        , 0.96428571, 1.        , 0.96428571]),\n",
              " 'split6_test_score': array([0.5       , 0.5       , 0.5       , 0.5       , 0.5       ,\n",
              "        0.96428571, 0.96428571, 0.89285714, 0.85714286, 0.82142857,\n",
              "        1.        , 1.        , 0.96428571, 0.96428571, 0.96428571,\n",
              "        1.        , 1.        , 0.96428571, 0.96428571, 0.96428571,\n",
              "        1.        , 1.        , 0.96428571, 0.96428571, 0.96428571]),\n",
              " 'split7_test_score': array([0.51851852, 0.51851852, 0.51851852, 0.51851852, 0.51851852,\n",
              "        1.        , 1.        , 1.        , 0.88888889, 0.85185185,\n",
              "        1.        , 1.        , 1.        , 1.        , 1.        ,\n",
              "        1.        , 1.        , 1.        , 1.        , 1.        ,\n",
              "        1.        , 1.        , 1.        , 1.        , 1.        ]),\n",
              " 'split8_test_score': array([0.51851852, 0.51851852, 0.51851852, 0.51851852, 0.51851852,\n",
              "        1.        , 1.        , 0.96296296, 0.88888889, 0.85185185,\n",
              "        1.        , 1.        , 1.        , 1.        , 1.        ,\n",
              "        1.        , 1.        , 1.        , 1.        , 1.        ,\n",
              "        1.        , 1.        , 1.        , 1.        , 1.        ]),\n",
              " 'split9_test_score': array([0.51851852, 0.51851852, 0.51851852, 0.51851852, 0.51851852,\n",
              "        1.        , 0.96296296, 0.85185185, 0.85185185, 0.85185185,\n",
              "        1.        , 1.        , 1.        , 1.        , 1.        ,\n",
              "        1.        , 1.        , 1.        , 1.        , 1.        ,\n",
              "        1.        , 1.        , 1.        , 1.        , 1.        ]),\n",
              " 'mean_test_score': array([0.52698413, 0.5234127 , 0.5234127 , 0.5234127 , 0.5234127 ,\n",
              "        0.99285714, 0.97843915, 0.91719577, 0.87724868, 0.86626984,\n",
              "        1.        , 1.        , 0.98928571, 0.98571429, 0.96785714,\n",
              "        1.        , 1.        , 0.98928571, 0.98571429, 0.96785714,\n",
              "        1.        , 1.        , 0.98928571, 0.98571429, 0.96785714]),\n",
              " 'std_test_score': array([0.01987827, 0.01387376, 0.01387376, 0.01387376, 0.01387376,\n",
              "        0.01428571, 0.0176085 , 0.05116911, 0.04290611, 0.04575439,\n",
              "        0.        , 0.        , 0.01636634, 0.01749636, 0.02966651,\n",
              "        0.        , 0.        , 0.01636634, 0.01749636, 0.02966651,\n",
              "        0.        , 0.        , 0.01636634, 0.01749636, 0.02966651]),\n",
              " 'rank_test_score': array([21, 22, 22, 22, 22,  7, 14, 18, 19, 20,  1,  1,  8, 11, 15,  1,  1,\n",
              "         8, 11, 15,  1,  1,  8, 11, 15], dtype=int32),\n",
              " 'split0_train_score': array([0.53413655, 0.52208835, 0.52208835, 0.52208835, 0.52208835,\n",
              "        0.99196787, 0.98393574, 0.93172691, 0.91164659, 0.89156627,\n",
              "        1.        , 1.        , 1.        , 1.        , 1.        ,\n",
              "        1.        , 1.        , 1.        , 1.        , 1.        ,\n",
              "        1.        , 1.        , 1.        , 1.        , 1.        ]),\n",
              " 'split1_train_score': array([0.52610442, 0.52208835, 0.52208835, 0.52208835, 0.52208835,\n",
              "        0.99196787, 0.97991968, 0.93975904, 0.91164659, 0.87951807,\n",
              "        1.        , 1.        , 1.        , 1.        , 1.        ,\n",
              "        1.        , 1.        , 1.        , 1.        , 1.        ,\n",
              "        1.        , 1.        , 1.        , 1.        , 1.        ]),\n",
              " 'split2_train_score': array([0.52208835, 0.52208835, 0.52208835, 0.52208835, 0.52208835,\n",
              "        0.99196787, 0.97991968, 0.92771084, 0.90361446, 0.87550201,\n",
              "        1.        , 1.        , 1.        , 1.        , 1.        ,\n",
              "        1.        , 1.        , 1.        , 1.        , 1.        ,\n",
              "        1.        , 1.        , 1.        , 1.        , 1.        ]),\n",
              " 'split3_train_score': array([0.53413655, 0.52208835, 0.52208835, 0.52208835, 0.52208835,\n",
              "        1.        , 0.98393574, 0.93975904, 0.90361446, 0.87148594,\n",
              "        1.        , 1.        , 1.        , 1.        , 1.        ,\n",
              "        1.        , 1.        , 1.        , 1.        , 1.        ,\n",
              "        1.        , 1.        , 1.        , 1.        , 1.        ]),\n",
              " 'split4_train_score': array([0.52208835, 0.52208835, 0.52208835, 0.52208835, 0.52208835,\n",
              "        0.99196787, 0.98393574, 0.92771084, 0.91164659, 0.87951807,\n",
              "        1.        , 1.        , 1.        , 1.        , 1.        ,\n",
              "        1.        , 1.        , 1.        , 1.        , 1.        ,\n",
              "        1.        , 1.        , 1.        , 1.        , 1.        ]),\n",
              " 'split5_train_score': array([0.52610442, 0.52610442, 0.52610442, 0.52610442, 0.52610442,\n",
              "        0.99196787, 0.97991968, 0.92771084, 0.90763052, 0.87550201,\n",
              "        1.        , 1.        , 1.        , 1.        , 1.        ,\n",
              "        1.        , 1.        , 1.        , 1.        , 1.        ,\n",
              "        1.        , 1.        , 1.        , 1.        , 1.        ]),\n",
              " 'split6_train_score': array([0.52610442, 0.52610442, 0.52610442, 0.52610442, 0.52610442,\n",
              "        0.99598394, 0.98393574, 0.92369478, 0.90361446, 0.87550201,\n",
              "        1.        , 1.        , 1.        , 1.        , 1.        ,\n",
              "        1.        , 1.        , 1.        , 1.        , 1.        ,\n",
              "        1.        , 1.        , 1.        , 1.        , 1.        ]),\n",
              " 'split7_train_score': array([0.524, 0.524, 0.524, 0.524, 0.524, 0.992, 0.98 , 0.916, 0.9  ,\n",
              "        0.88 , 1.   , 1.   , 1.   , 1.   , 1.   , 1.   , 1.   , 1.   ,\n",
              "        1.   , 1.   , 1.   , 1.   , 1.   , 1.   , 1.   ]),\n",
              " 'split8_train_score': array([0.524, 0.524, 0.524, 0.524, 0.524, 0.992, 0.98 , 0.924, 0.896,\n",
              "        0.872, 1.   , 1.   , 1.   , 1.   , 1.   , 1.   , 1.   , 1.   ,\n",
              "        1.   , 1.   , 1.   , 1.   , 1.   , 1.   , 1.   ]),\n",
              " 'split9_train_score': array([0.524, 0.524, 0.524, 0.524, 0.524, 0.992, 0.98 , 0.924, 0.908,\n",
              "        0.884, 1.   , 1.   , 1.   , 1.   , 1.   , 1.   , 1.   , 1.   ,\n",
              "        1.   , 1.   , 1.   , 1.   , 1.   , 1.   , 1.   ]),\n",
              " 'mean_train_score': array([0.52627631, 0.52346506, 0.52346506, 0.52346506, 0.52346506,\n",
              "        0.99318233, 0.9815502 , 0.92820723, 0.90574137, 0.87845944,\n",
              "        1.        , 1.        , 1.        , 1.        , 1.        ,\n",
              "        1.        , 1.        , 1.        , 1.        , 1.        ,\n",
              "        1.        , 1.        , 1.        , 1.        , 1.        ]),\n",
              " 'std_train_score': array([0.00417466, 0.0015578 , 0.0015578 , 0.0015578 , 0.0015578 ,\n",
              "        0.00256706, 0.00194804, 0.00696273, 0.00504692, 0.00568942,\n",
              "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "        0.        , 0.        , 0.        , 0.        , 0.        ])}"
            ]
          },
          "metadata": {},
          "execution_count": 162
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Print the best hyperparameters and their score\n",
        "print(\"Best parameters: {}\".format(clf1.best_params_))\n",
        "print(\"Best cross-validation score: {:.2f}\".format(clf1.best_score_))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "i_b81-dZwfSu",
        "outputId": "9dd92020-e2b1-4bf2-8dd2-f8d97674b7e9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best parameters: {'C': 1, 'gamma': 0.5}\n",
            "Best cross-validation score: 1.00\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Test the model on the testing set\n",
        "svm_best1 = clf1.best_estimator_\n",
        "print(\"Test set score: {:.2f}\".format(svm_best1.score(X_test_s, y_test)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WjF8Mg2XxYsV",
        "outputId": "6ed0bffc-c30d-4259-c5b1-393631051c4b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test set score: 1.00\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "confusion_matrix(y_test, clf1.best_estimator_.predict(X_test_s))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NzEu4yzgxo2W",
        "outputId": "2971a5ed-f4c0-4cea-8709-be1a11d08324"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[61,  0],\n",
              "       [ 0, 59]])"
            ]
          },
          "metadata": {},
          "execution_count": 166
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Task 3\n",
        "\n",
        "Compare and discuss the models you obtain from the previous two tasks."
      ],
      "metadata": {
        "id": "iVOYoYbwdGur"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "From the first task when we used cross validation and tuning of just the missclasification constant, we got a cv score of 1 and the best model was the 0.1 model.The accuracy on the test score was 99% (.99). For the second model when we tuned two hyperparameters (missclasification and gamma constant), we got a cv score of 1 and the best model there was the '1' model. The gamma value was 0.5 and the accuracy on the test set was 100% (1.00).\n",
        "\n",
        "From the above obseravtions, there is not a lot of variance in terms of accuracy from the two models, with the gaussian kernel model showing a slightly better overall performance as it fit the test set better than the standard svm model. We can then say that in this case, the gaussian kernel modell was the best."
      ],
      "metadata": {
        "id": "ELXEpzAF0CVO"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Problem 2\n",
        "\n",
        "In this problem your task is to research how support vector machines can be applied to regression tasks.\n",
        "\n",
        "Please provide an overview of how this is done as well as an example below. (600 word equivalent)"
      ],
      "metadata": {
        "id": "YVTOU0scQHa0"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Solution:"
      ],
      "metadata": {
        "id": "4EIFr-60QeEc"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Support Vector Machines (SVMs) are widely used in machine learning for classification tasks. However, SVMs can also be applied to regression tasks, where the objective is to predict a continuous output variable given a set of input features. In this overview, we will discuss how SVMs can be used for regression tasks, including the basic principles behind SVM regression and an example of applying SVM regression to a real-world problem.\n",
        "\n",
        "SVM regression works by finding a hyperplane that best fits the data in the feature space. In a regression task, the hyperplane is a line (in 2D) or a plane (in 3D) that approximates the relationship between the input features and the output variable. The objective of SVM regression is to find the hyperplane that minimizes the prediction error, which is the difference between the predicted value and the true value of the output variable.\n",
        "\n",
        "The key idea behind SVM regression is to introduce a tolerance parameter, ε, which controls the amount of error that is allowed in the prediction.\n",
        "\n",
        "The optimization problem for SVM regression is formulated as follows:\n",
        "\n",
        "minimize: 1/2 * ||w||^2 + C * Σ (ε + ξi - yi)^2\n",
        "subject to: yi - wTxi - b ≤ ε + ξi\n",
        "wTxi + b - yi ≤ ε + ξi\n",
        "ξi ≥ 0 for i = 1, …, n\n",
        "\n",
        "where w is the weight vector, b is the bias term, C is a regularization parameter that controls the trade-off between the margin and the error, ξi are slack variables that allow for a soft margin, and ε is the tolerance parameter.\n",
        "\n",
        "The first term in the objective function represents the margin, which is the distance between the hyperplane and the closest data point. The second term penalizes the prediction error, where the sum of ξi represents the total amount of error that is allowed in the prediction. The constraints ensure that the prediction error is less than or equal to the tolerance parameter ε plus the slack variable ξi.\n",
        "\n",
        "SVM regression can be implemented using a kernel function, which maps the input features into a higher-dimensional space. The kernel function allows for nonlinear relationships between the input features and the output variable. The most commonly used kernel functions for SVM regression are the radial basis function (RBF) kernel and the polynomial kernel.\n",
        "\n",
        "Now, let's look at an example of applying SVM regression to a real-world problem. Suppose we want to predict the price of a house based on its size and number of bedrooms. We have a dataset of houses with their corresponding size, number of bedrooms, and price. We want to build a regression model that can predict the price of a new house given its size and number of bedrooms.\n",
        "\n",
        "We can use SVM regression with the RBF kernel to build the model. First, we split the dataset into training and test sets. We then standardize the input features to have zero mean and unit variance. Next, we use cross-validation to tune the hyperparameters, including the regularization parameter C, the tolerance parameter ε, and the kernel parameters for the RBF kernel.\n",
        "\n",
        "After tuning the hyperparameters, we train the SVM regression model on the training set. We can then use the trained model to predict the price of a new house given its size and number of bedrooms. We evaluate the performance of the model on the test set using metrics such as mean squared error (MSE) and R-squared (R2).\n",
        "\n",
        "The following plot is a visualization of a basic SVM interpretation.\n",
        "\n"
      ],
      "metadata": {
        "id": "GCP6UTzBRusp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Create the data\n",
        "from sklearn.datasets import make_classification, make_blobs\n",
        "\n",
        "\n",
        "X, y = make_blobs(n_samples=100, centers=2, random_state=6)\n",
        "\n",
        "plt.scatter(X[:,0], X[:,1], s=70, c=y, cmap=plt.cm.Paired)\n",
        "plt.xlabel('X1')\n",
        "plt.ylabel('X2');"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "f16xBrntTSo9",
        "outputId": "fe0b505c-12ac-4ae6-9887-e509d536062b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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u4SVGAoD9dW1INOn6TMQGEhfGVY5VNhcO1bf1KsbOTjBiYkZ8wLVfiqKg3uHGiRYXXF4JGlFAqkWP4mQL9GEqTB8NmPgQjTJGrQZaUVA96iMAqvZDaXV58elpSc/pFPgLSj8qbcTy8eksmIZ/Kqg0gDqf8WlxEAQBx5raVZ8jADja6MCcvCRVx5++vP703xSvrOCzihZIsqJ6o0NBEJBqMfRqRno6nUbEvPxkzMhORGWrs7NJqYBksw5ZCUZU2zvwUWmTqtfsjwL/yOSE9DgcrGtTfV6ySRdQ8hhMX9bYsL+2rc9WIjWdU3hzchNV/zxanB58Ut6MNrevR/JX2erCnmobJqYPb9+oaMaUkGiU0YgCilMsqnsxKQDGpg7+l/3henU3EAVAs8uLxvbBlxDHgmSzHrnWwVcKCQDiDVoUdjboLGtSP0qkAChTmVy5vBI+U7GnEAB8frI1ZNsdGLQixqTGYXq2FVOzEpBjNUEUBGQnGGHWaYLSS8wnKciKN6i+1oSM8NRKHW9sx/5a/39fff3Mux77/GQrqmyuQa/X7PRg/dEG/3RZH9eUFf+I2PbKFnYu6AMTH6JRSE0iA3x1s81MGPjG7JVklLeovxH7C6YdKo+OfgsKk5EZP/BIiEWvwZIxqdB2Tgm5AyyK9skKJBWjfMcDLnwf2Z+jKAg4qzgFojCU1qs9yYqC+QXJsOgHT6TGpVqQnzjyKxtlRcGXNepXxe0bpC2Joij4uKwJ8iA7eAP+rRZOqkikQk1RFFTbXPiwtBFvH6jF2wdq8VFpI2rsHWFJzJj4EI1CCUYdFhYmD/hmLwDQa0WcXZIKcZDhbqdXUr0XDdBVMM2NEbtoRf/3eX5+EpJPm0ox6zSYkW3FigkZPXbRHuxn0hc19cilAUyhKQEeHyzJZj2WjUtDvNH//RhqAmTWa2DUabB8fDryEk3d1xGFr66p14iYmWPFrNzEsEz71Ng70NFPZ/u+NLu8aHb2P5pa0+ZGu0dSldwKAA7Xh/cPlDa3D+8crMOW0iZU2zrQ5vahze1Dlc2/K/h7h+q6R65GCmt8iEap/CQzDFoRu6v8K7BOLSIdiZYVHEDvSRQEFKVYUJRigdPjg1uSoRVFxOk1fd5w0+IM/r94VV4/1aJXdeNW28G9i0dSICvKkBKx4Ug26/H1CRlobPfgRIsTbW4fatvU72odp9cgpbMBqUGrwaKiFDi9Ek40OzublHZNQ5qG3bNsOJraPQEXYDc5PUjup7lqWVO76uspABraPWj3+MLSuqbd48P6I/XwdCZ+p8bc9W97h/+YFRMyYB6h1aJMfIhGsYx4I1ZMMKLZ6UFdm3/PHYNWRE6iKaA3EbNOA1FAQDsQW418++iPWa+FeZBjxqXFodquvoWC2j5ToiBADnD6IFxpgSAISIszIK1zNdMHRxtQ73CruqmPT4/vlQiadRpMDFMdT38kRYEgIKCl9/IA/yE6vepGe07l8kphSXy+ONkKj08edId5t0/G7qpWLCxMGZG4ONVFFAWSzXpMzIjHlKwEjE2LC/gvJ51GREGSOaCC6TEpbPg4HJnxBv8oziDHdSWZuSo3oDQGuIw52ayLmJU/8wuSYNCKg35PchKMGKOyzi3czDpNQNPIAGAa4L9fzZCmSEf+5+v0+HDSpm5EUwFQ0eJCR4CjlUPFxIeIAPiXWautG0g06ZAW1/dQPKkjCAIWF6ciyTzw8up4gxZLxqSpmq5pdXnhCLCvViR1LDfrtVg+Ph2pnb9bp3/FguAvUj6zszhajaZ2Dz490Yx/76vBW3ur8d7BOhyqb4M7gLqb4cgP4A8KANCJwoC7rKtJlk+lEQUkhGGn6srWwIqqFWDECrE5Vk1EAIAksx7z8pPw2QCdxgUARp2IxcUpETNKMJoZtCLOHZuO0qZ2HG5og8P9VdJi1mkwLi0OY1It3ZsDKoqCujY3WlxeyIoCi16L3EQjtKL/+aONjoDqSfQaEfmJg03KjSyLXotlY9Nhc3lR2tzeuQ+QvzlrcYpZddsUryRja3kzqu0dPb4nHT4Zu6ps2FNtw/yCZBQkhfbrN+k0yEsyobLFNejPRQBQkmqBdoAktyTVgv0q9y4SAJSkWLpXEo4ktyQHNMUnACOWjDLxIaJuxSkWGLQi9lTbYOvw9VkwPSs3ccSKEGOBVhQwLi0OY1MtaHP74JUUaDX+v9JPTS6PN7Vjf60d7R6px89FWylgTKoFUzMTAtpBGgB0GiGshb8DsZp0mJmTOKRzJVnBh6WNaOjcIbmv74msAFvLmyHAPyoTSnNyE9HU7oFzgNVYAoAkkw5TswZuTWLRa1GUbFa1r5Om83crHLSCEFBFtwIMmPAFExMfIuohx2pCdoIRTU4P6tvckBQFBq0GeYmmAWsPaHgEQeh3h+091TYcOOWv/FPvJz5ZwaF6Bxo6G8oGYqT+wh5pZc3tvdpC9OezihZkW00hvekatBosH5eOreXNqHP07HfW9e9cqwnzCpK6R+8GckZeEjp8Mmr6KY4XAIiigLOLUxEfpoas6fEGKDWBnzMSmPgQUS9q2xNQ6FW0OHskPf1pcgbeqTwcRa+hpigKjjSo37vGJyuoaHGq7ls2VEadBkvHpsHm8uJ4Uzscbh8Ewb8nV0mKJaCO8RpRwOLiFBxvaseRegfsp+yDIwpAYbIZE9PjVbWqCZUUsx6JRi1aVe73lWLWI8k0MnWDTHyIiCKYmqRnKAQA6VFYoN7ukQLeXLOiNfSJTxerSYdZuYnDvo4oCBibGocxKRa0dnjR4ZWhEQQkmnQR0aBUEATMyEnE5uONgx8LYEaONfRBdQr/d4eIiHqRFQWH69vQ4gp8JEcNBVDdEHM0Gcr03Wie8hMEAUkmPbISjEiPN0RE0tMlK8GIBQX+Heb7GlsU4F+pt6goOeDO9MPBER8ioggiKwoO1LXhSL0DbinwG7JOFOAbpI+TACDFokfGCN5sRopWE/j0nU5FXQ0NTWGyGUlmHY42OFDa7OyuQ9N2Nlsem2oZ8Sk5Jj5E1IvbJ6G82d9GQBD8K4wKks3Qh2FZbCSQZAW1bR1w+2RoRAFpFj3MIdgJV1YUfFja1G/R6mAEAFkJBjQ5vWgfYD+fRJMOi4tTo3JLgniDFiatCFcAoziDNfGl4bEadZiTl4SZOYlweSUIAmDUasK2opCJDxF188kKdp1sRWlzO2TFPwwN+Pfi+KKqFWNT4zA92xqxS6CDzSvJ2F9rx7GmdnilnmMoOVYjpmYmIKmfnkpDsafaNuSkB/BPXyWZDTgjLxlHGx040uDo0SDTov9qbyA1q4cAfzLmcPvglRXoNf33HosUoiBgbFocvhyky3kXQQBKUiJrL6NopRGFgIq4QyX8ERBRRPDJCjYda0Bj+1fLgE/dfExWgMMNDrR2eHGOio7vo53bJ2Hj0QbYO3x9ThtV2zpQY+/A4uJUZAVhxMAryTgawGqkvggAipLN0GtFTM5MwMSMeNg6vPBJ/qQlwahVnbR4JRlHG9txtMEB5ymtBBIMWoxLj0NxsiViE+CxqXE43tQ+4L45XaZkJqjeFJGiQ2yOWxNRL19W23okPf2pa3NjX626v6ZHs4/LmvpNegD/6IqsAB+VNsHhDmwVUV/KW5yQAtuGp5fCZHOPvZbEzsLXtDgDrCb1PbmcHh/WHq7Hnmpbj6QHAOxuHz6vbMUHxxrgHUIN0kjQa0UsHZMGi77vhKbruzAhPQ6TI6ypKYUeEx8igleScaypXfXxRxocAW+WN5o0tXtQ7/Co2nhWVhQcbRzeSA0A2Ia5eivZrMOcICyT9o/8NQ6azDW1e/BRWROUADvBj5Q4gxZfm5CBOXmJPXpVCQDykkxYNjYNM3MSI3rajkKDU11EhMpWV0CJjFdSUGVzhXyr/3AJpOeVAuBYYzumZQ2v9ske4N4zXTSigJIUC6ZnJ6iu2xlIRYuzx4Z4/VHgH/1raPeM6FLkQOg0YvdeNz5Zga+zTilSp+hoZDDxISI4PL6AGwoOtGpotGtxeQPqeeWTFTi90pDbAyiKAltH4CM+E9PjMDkzobuJaTAEsuuxAOBogyNiE58ugiBApxHAjisEMPEhIgACAmsoCHy14isaDWX6ZjhTPrYOX4/VV2roRAHTs61BnaqRZCWgDRMVAPUOd9Ben2gkMPEhIiSadAHlPQqAxDD2AQq1OIN2wMLm0wnAsBq4nl5ArIYlCMvKZUVBvcONdo8EERjSUuNorvWi6MTEh4iQYzXCoBVVb91v0WuQMUKdlMOhJMWCKpu6/XQEAPlJpmFNNw2l5GQ4+6H422E4cLihDS7v8FZmxeqmljR68TeWiCAKAiYFsKx3ckZCVK+GyUowwqzX9Nlf6HQKgHHD7HmVZNKpeq1TpViGlnjKioKPSpuwu9o27KQHGN5IF1E4MPEhIgDA+LQ4VTvYTkiPQ3GU73QrCgLOLk6FRhQGTUhmZFuROsQkpItBq0F+kkl18jOc3YZ3V9lQPYzdoU/X0uGFHKFL2on6wsSHiAD4V76ckZeEM/IS+5xGSTBqMb8gKWb2Pkk06bB8fDpSLP6WFMIpHwBg1IqYl5+EiUHaAG9iRrzqgvFxqZYh7Tbs8clB2XPoVJKsBFyYTRROrPEhom6CIGBMahxKUixobPegrXM/lwSjFilmfUwkPKeyGnU4b1w6Wl1eVLQ40dHZpDQ9zoAcqzGobTuSTHqcWZSCj8uaoCj9L7LLSzRhRk7ikF6jvMWJUNQix9ZvBY12THyIqBdBEJAWZ0BahO/PMlISTTokmqwhf50cqwnLx6XjYH0bKlpcPZIfq1GL8WnxKE4xDzkBbXV5VW/MqJZOI8Cg5eQBjR5MfIiIIkiSWY+FhSmYlSOhtcMLWVZg1mthDaDBaH+CXYsjABiTYon6hrUUXZj4EBFFIKNOg8wgr5gazhL4vggCMCZ1eCvaiEYaxyeJiGJEUbI5qNNcZxalBD2ZIgo1Jj5ERDHCotci12oMSjFyolGLHKspCFciGllM1YmIYsjc/GSsP1IPh1t9S46+TAjSMv5Y1+T04GiDA/UONyRZgVGrQWGyGcUpFhaNhwgTHyIa9RRFQYPDg6ONju7O6vF6DUpS44K+7Hy0M2hFnDcuHZ9XtqCi1TXka+QnRvcmlqHmk2RsPdGMKltHj5V2HT4Zu6tt+LLGhnn5yShM5vc52Jj4ENGo5vJK+LC0Ec3Onku1290+1LS5YdZpsLgkBUkmfTjDjCgGrYhFRSloP1yHJqf6buyAv69Y167W1JNXkuGRZOg04oA9zGRFwYelTd2d7fsaeZMVYNuJZggCUJDE5CeYmPgQ0ajl9snYcLQe7W5/d/NTbyBd/3Z5JWw40oDl49NhjeKO8kNh1GkABJb4zM9P6t7NmvxJzMlWFw43ONDY7ul+PMWsx7i0OOQnmXqNOJY1O1HXmfQMZntFC3ISjNCyGWzQ8DtJRKPWvlo72t3SgLUqCvxtFXZUtIxUWKNGoMXJJp0GeRx96OaVZGw+1ohPypt7JD0A0Oz0YNuJZmw61gCv9FVLD0VRcKS+TfVr+GQFJ4Y4JUl944gPEY1KXknG8aZ2VQW6CoCGdg9sLi+sJo76dClIMuGLk63wqexjMS4tuJsV2ju8ONrYjmp7ByRJhkGrQUGSCcUpls7RqMilKAo+KftquqrX853/X+/w4P1DdbDotfDJCrQaAa0dvoBeq6LFiZIUyzAjpi5MfIhoVKpt86+CUUsAUNHqxNQRaD0xWmhFEbNzE/HZIKNhAoB4gxZjA9isUFYUNDs98EgKdKKAZLO+uy5IVhTsqGxBaZOzR12WyyejtcaLL2vsmJ2XGNDrjbQ6hxs1beqmqxweCQ6PNOTXcrMJbFAx8SGiUcntC/xGwhtIb8UpFkiygs9Ptvbq49X1udWowzljUqFTUWfikWQcrnfgaKOjx/dbr/E3wB2fZsEXVTacaPFP3/SVuioAPq9sBYCITX6ONjiC3vesPzoNC8mDiYkPEY1KQ1lVxJVIfRubFoesBCOONbWjrKkdHT4ZogCkWvQYlxaveksAl1fCxqMNfe4R5JEUHKxrw/GmdtUJ6BcnW5FnNUXktFdtm3tEkh4AyIw3jtArxYaoTHzcbjfmzZuHPXv2YNeuXZgxY0a4QyKiIEsPsHO8AiCD3eb7FWfQYka2FTOyrVAUJeCGqLKiYPOxvpOeLgoCG3WTFaC0uR2TMhJUn6MoClpdXji9EkRBQKJJB1MIEqdAplmHQwBY3xNkUZn43HnnncjOzsaePXvCHQoRhYhFr0V2ghE19g5Vf3mbdRpkJfAvZzWG0gW+2tYRcNGuGieaXaoSH0VRUNrkxKGGNthPiUMAkJtowqSMeCSbg7cMX68R4ZZCP3U6OTM+Ike8RrOoW87+3nvvYd26dXj00UfDHQoRhdjUrASovUfPzLEO6YZO6hzprHkJNrc0eC2XrCjYWt6M7ZUtPZIewD/KdLLVhXVH6lEZxGXh+UmmkHy9ALqvOyE9DlMy1Y92kTpRNeJTV1eHG264Af/6179gNqvba8LtdsPt/qoy3263hyo8olGl3eNDU7sHkqLApNUgPd4Qca0fks16nF2cig9LmyArSq+Rn67i0zl5icjn/jMh1eT0hKTmRSsO/vf5nirbgO03lM7/+aSsCcvHpwdl5GdsahyONrYP+zqAfzfsrpkzAUBeognj0uKQxqnZkIiaxEdRFFx77bX4wQ9+gDlz5qC8vFzVeWvWrMEvf/nL0AZHNIo0OT3YX2NHlb2jx+MGrYixqXGYmBEPbQQVCWcmGLFyUgaONrTjWJMDXsl/BxEFoDDJjHFpcUgK4hQH9U1Wgp/2CMCg05Nun4QjjQ7V1zxQ14Yzi1JUH2/r8KKs2QmnR4Io+JPtwmQzrCYdJmfGY3+t+s0I+yIAKEo2Y2ZOInyyAr1GZBF+iEV84rNq1So8/PDDAx5z8OBBrFu3Dm1tbVi9enVA11+9ejVuu+227s/tdjvy8vKGFCvRaFdlc+GjsqY+1+i6fTL21dpRY3dhyZg0VUubR4pFr8WMHCumZSegwytBVvztGCIpQYs2kqyg3uFGh0+CVhRh1IpweoNb86IAGJs6cGFvaZMTauuMu6a9XF5p0IJnp8eHbSeaUe/w9JjSKmt2YldVKyakx2NKZjxECNhbax/y0nYFwLi0eOg0IljKMzIERQlBmh5EDQ0NaGpqGvCY4uJiXHLJJfjvf//bYw5fkiRoNBpceeWVeP7551W9nt1uh9Vqhc1mQ0IC51Ypdtg6vHj/UN2gNxEBQI7ViLOKU0ckLoosHknGgdq2HqNroVKSYsbc/OQBj/mkrCngLvPnlKQOOJLk9Piw9nA93D55wGSmIMmEBQXJcHklHG9qR2WrCx5JhlYU4PLKkOTe06+ny0s0BTQCRf1Te/+O+BGftLQ0pKWlDXrcY489hl//+tfdn1dXV2PFihV47bXXMG/evFCGSBQVDtc7oObPIAXASVsH7B1eJLDpZ0zp6Nynp22AJevD1TVyUpBkwpy8pEGPH8qq8sGm5baeaB406QGAEy0uZMQ5UZJqwdQsK6ZmfbUruL3Di41HGwa8Tma8AfMLBk7sKPgiPvFRKz8/v8fncXH+3T5LSkqQm5sbjpCIRg2vJKOsWV3fK8B/czrW2I5ZuYkhjIoiiaIo+KisKaRJD+Cv2RqXakFWglHVKjyzXhPwNJN5gDklm8uLBoen3+dPd6ihDcUp5l6xJhh1OH9CBo42OnC0ob3H0nerUYvx6fEoSjZH3IKBWBA1iQ8RDV2b2xfQX84KgBaX+psDDUxWFLS5fZBkBUatCLM+8t6aG9s9vTqQh8LMbGtAjWSLks040qC+uNlq1CJxgOuXNrcHlEjZO3xocXn7XClm1GkwNcuKyZkJsHf44JP9jVjj9BpurRBGkfdfV5AUFhYiwsuXiCLGUP5TGaGNa6Nah1fyjwg09mzjkGLWY1xaHAqSTBFzgzxQF9hWHxoBGEoJkNMrBZT4JJv1SDHr0Oz0qkpWxqfFD/g9dXqkgEe0nB4JyQPsltC1gzRFhshZlkFEYWPWB7acRABgCfAc6snm8uK9Q3XYX9vWq41Ds9ODbSea8XFZ04i1RhhItc2Faru6TuRdJGXgKaX+DGUh3oLCFOg04qAbCuYlmlCcMvB+ToIgBLwxIRcPji5MfIgIJp0GWfEG1W/4Ctg/aDjcPgkfHOu/8LXrsZO2Dnx+smUkQ+ulqd3j3+JgCPKTTAGfYx1CwXy8QYvl49ORZPafe/rvsSgA49PisLAwedARtGSzLuARn0BGqCj8onaqi4gCMz49HjVtg/9VL8B/owm0SSh95WijvwO6GqVNTkzOSECcITxv11/W2IY0FSoK/hGWQ/Xq628ADHnzvniDFivGZ6DZ6em14WBxihkGrbrRp6JkM/ZU21RN5XZtsGiJwJos6h9/WkQEwP8GPikjHgfq+t+JVgCg1Qg4qzglYmpPRhtZUXA0gGJc/wo6B2bkJIYspv443D7UqkiGTycAKEgyD6kYusrWgcKBCmZOoygKGto9ON7YDofH36cryaTD1KyEIdXVGLQajE2Nw2GVP6PJmfEBvwaFFxMfIuo2LSsBJp0Ge2ts8EhKjykDBUCKRY/5BcmID9PoQzRweiTVoz2A//te5wg8+QiG+iG+rn834riANxYUALi8gzcl7eJw+/BRWRNaXd4eK7Ga2j042tiOzHgDFhWmQK8NrKpjRo4V7R4fTto6+ny+67XmFSQh1cKRz9GG715E1E0QBIxLi0NJigWVrS40trvhkxWYdBoUJJm5MiUIhlKsHK4CZ98QX3dmjhXJZj2qbB0BLQ1XANVtRto9Pqw7Ug9PZxJ56mt0/buuzY0NR+tx3rj0gFqsiIKARUUpON7YjkP1bXB4eiZjXaOjbCI6OjHxIaJeNKKAwmRzQFMOpI4hwNEHAIP2lQqVocSal2jEhHT/9E9anD7gQuHUOHUNZXdUtsIzyO7KCvz77OytsQe82aYoCBibFocxqRY0OT3+miFRQJJJx5qeUY6ruoiIRpBRp0F6nD6gJdOFSeFJQLMTjNAEWMo17ZS2DRlxBsQFsO1BilmPJNPgiY/D7UONvUNVUqUAON7UDp88tAaqgiAg1WJAfpIZuVYTk54owMSHiGiEjU+LVz0SotMIyA9T4qPTiChKsahK0gT4E51T+7cJgoDp2db+TzrNtGx1jaErWpwBJY4+WUFNgPsQUfRi6kpENMJyrEYUJplR3uIc9NiFBclDXuLdxSPJKG92oqLFCbdPhlYjIDvBiJIUy6DtMaZlJaDG3jHgjsZdq/3m5CX2ei4/yYwOn4ydJ1v7rPfp+srmFyQjM77/jumncvlkCEJgO453BFA0TdGNiQ8R0QgTBAHzCpKg1wo40tB3byi9RsSCwmRkJ6hLBvpT1tSOHZUtvdpHtDi92Ffbhonp8ZiendDv9gQGrQbLxqXjw+ONaDlt9VTXv016Dc4pTu0x2nOqcWlxSDbrcLjegcpWV/f5ouBf9j4+LQ5JffS66o9GEALeW2i4ySNFDyY+RERhIAoCZucmYWJ6Ao43OVDX1nMFXV6iadg369KmdnxW0ffOz115w8H6NrS4PJiSGY9Ui6HPBMis02DF+HTUO9w42tiOVpe/L1a8QYsxKRZkW42DdhlPtRiQWmSA2yfD2bnfjsWghT6A1VZd0uL0OFgf2DmpFvWJFUU3Jj5ERGGiKArcPglJZj1SzHokmnRB68zu9knYUamu3UVtmxu1bW5Y9BqMT4vD2LS4XomMIAjIiDciQ+V0VH8MWhEG7fCSkKwEI0w6ES7v4AXLAvyJUn+jURR7mPgQEY0wRVFQ1uzEofo22Dp8PZ7LDtIeMcebnKraLpyq3SPhiyobauwdOKs4NWKnh0RBwLQsa7+jWaebmqWuwFqSFTS2u+GVFOg0AlIsBtX7CtHowcSHiGgEKYqCTytaUN7cd2Fzjb0D1fYOzC9IQlHy0BvBlje3D/ncmjY3Pj/Zgnn5yUO+RqgVp1jQ4ZOwp9o+YNH0wsLkQfvKuX0SDta14VhTO7ynFEPpRAElqRZMTI+HMUx7KVHwMfEhIhpB+2rt/SY9wFc38E9PtMCiH3oz2EDaYvSlrMmJqVlWmCP4hj8pIwGpZgMON7T1aC/RXTSdHjfovkDtHh82HG2Aq49Va15ZweF6B060uLBsbFrYGsVScPGnSEQ0QrySjIMqu5ULAA7U2pE+Jm1Ir6UVBQx355rSxnZMyVK3t064pMcbkB5vQIdXgtMrQYD6omlJVrDpWGOfSU8XBf6l8JuONeDrEzMjdvqP1OMGhkREI+REi1N13y0F/iknh9s36LF9yYgzBLTJX1+v3+wMvLt6uBh1GiSb9Ugy61WvFDtpc6HN7Rt0M0kFgMMjoaJ18H2XKPIx8SEiGiHNTm/AyUiLyzuk1xqbFhdwn6zTDdwJa/Q70uAI6OdxpEHdaB1FNiY+REQjRA50170hngMAyWY98hJNQzoX8E+1WXTRXQ3R4vQElNq1OL1QhvjzoMjBxIeIaIQMpVB4OMXF8wuSkRXvL44OdKRJAVCUEp4eYSMl0OX+THmiAxMfIqIRUphsDujmadFrhrXjsFYUsLgkFQsKkpEcQEsIAUCSSYeUAM4ZjUwBJpVGrdhvaw8aPZj4EBGNkASjDpnx6ouOx6fFDftGKwoCCpPNWD4+HRdNzkSudeCdlwX4u7IvKkqJ+pt8Sar6fZKEAI+nyMXEh4hoBM3LT4ZRJw6a/GQnGDE2LS6or23Sa3FmUQqmZCZA05nUCKd8AP7aoOXj0xEfA3vWlKRYEMjq9JIUJj7RIPp/s4mIIohZr8HycenYdqIZ9Q5Pr12HBQEYk2LBrNzEQRt/DoUgCJialYAJ6XEob3aiyemBrPiboxYmmQPqkj7amXQazC9Ixtby5kGPnVeQBEuQ+qhRePGnSEQ0wsx6Lc4dmw6by4vS5nY4PRIEQUCSSYfiFDMM2tDvlqzTiBibFoexIX+lyFaQZIZGFLCjogUdPrk7Ee36f6NWxJy8pGGtkKPIwsSHiChMrCYdZuYkhjuMmJdrNSF7ihHV9g5U2Vz+JqWigGyrCTlWY0hG3ih8mPgQEVHMEwUBuVYTcq0c2Yl2LG4mIiKimMHEh4iIiGIGEx8iIiKKGUx8iIiIKGYw8SEiIqKYwcSHiIiIYgYTHyIiIooZTHyIiIgoZjDxISIiopjBxIeIiIhiBhMfIiIiihlMfIiIiChmMPEhIiKimMHEh4iIiGIGEx8iIiKKGUx8iIiIKGYw8SEiIqKYwcSHiIiIYgYTHyIiIooZTHyIiIgoZjDxISIiopjBxIeIiIhiBhMfIiIiihlRl/i88847mDdvHkwmE5KSknDRRReFOyQiIiKKENpwBxBM//znP3HDDTfgwQcfxNKlS+Hz+bBv375wh0VEREQRImoSH5/Ph1tuuQWPPPIIrr/++u7HJ02aFMaoiIiIKJJEzVTXF198gaqqKoiiiJkzZyIrKwvnn3/+oCM+brcbdru9xwcRERFFp6hJfEpLSwEA9913H+6++268/fbbSEpKwjnnnIPm5uZ+z1uzZg2sVmv3R15e3kiFTERERCMs4hOfVatWQRCEAT8OHToEWZYBAHfddRe+/e1vY/bs2Xj22WchCAJef/31fq+/evVq2Gy27o/KysqR+tKIiIhohEV8jc/tt9+Oa6+9dsBjiouLUVNTA6BnTY/BYEBxcTEqKir6PddgMMBgMAQlViIiIopsEZ/4pKWlIS0tbdDjZs+eDYPBgMOHD+PMM88EAHi9XpSXl6OgoCDUYRIREdEoEPGJj1oJCQn4wQ9+gHvvvRd5eXkoKCjAI488AgC4+OKLwxwdERERRYKoSXwA4JFHHoFWq8XVV18Nl8uFefPm4YMPPkBSUlK4QyMiIqIIICiKooQ7iEhit9thtVphs9mQkJAQ7nCIiIhIBbX374hf1UVEREQULEx8iIiIKGYElPj8+c9/xrJly3DJJZdg48aNPZ5rbGxEcXFxUIMjIiIiCibVic9jjz2GO+64AxMmTIDBYMDXv/51rFmzpvt5SZJw4sSJkARJREREFAyqV3U99dRT+Mtf/oIrrrgCAHDjjTfioosugsvlwq9+9auQBUhEREQULKoTn7KyMixcuLD784ULF+KDDz7AsmXL4PV6ceutt4YiPiIiIqKgUZ34pKamorKyEoWFhd2PTZkyBR988AGWLl2K6urqUMRHREREFDSqa3zOPPNMvPnmm70enzRpEjZu3Ij33nsvqIERERERBZvqEZ9Vq1Zh586dfT43efJkfPDBB3jjjTeCFhgRERFRsKke8Xn99ddx9dVX9/t8QkICPvnkk6AERURERBQKqhOf559/HnPnzsW+fft6PffUU09hypQp0GqjqvUXERERRRnVic++ffswZcoUzJkzB2vWrIEsy6ioqMCyZctw55134tFHH2WdDxEREUW0gJuU/vvf/8b3v/99ZGZmoqysDHPnzsVf//pXFBQUhCrGEcUmpURERKNPyJqUzp8/H1OnTsWXX34JWZZx9913R03SQ0RERNEtoMTnlVdewaRJkyDLMg4ePIgbb7wRy5cvx09+8hN0dHSEKkYiIiKioFCd+Hz729/GDTfcgPvuuw8bN27E+PHj8Zvf/AabNm3Cu+++i+nTp2Pbtm2hjJWIiIhoWFQvw6qtrcWuXbswduzYHo8vXLgQu3fvxqpVq3D22WfD4/EEPUgiIiKiYFBd3CzLMkRx4AGiDz/8EIsXLw5KYOHC4mYiIqLRJ+jFzYMlPQBGfdJDRERE0S3gVV1EREREoxUTHyIiIooZ7DFBRFHDUXsCzroKAALisotgTssJd0hEFGGY+BDRqFf7xSYce/sZNB/a2ePxtCkLUXLBdUifuihMkRFRpGHiQ0Sj2qE3HseRN/8M9LEAo/HAZ2jYtxWTrrwTY1b+bxiiI6JIwxofIhq1Kjb/05/0AIAs93pekSUAwIGXfoPqz9aOZGhEFKGY+BDRqKTIEg7984/qDhYEHHrjcQTYk5mIohATHyIaleq//AQdTbXqDlYUOKqOo+Xo7pDGRESRj4kPEY1KraX7IIga9ScIAlqP7w1dQEQ0KjDxIaJRSfF5AUFQfbwgiJB93hBGRESjARMfIhqVjCmZ3cXLaiiyBGNKZggjIqLRgIkPEY1K2XOXQ9Co35FDYzAjc9aSEEZERKMBEx8iGpX08UnIXXSBujofUUTB0ouhNZpDHxgRRTQmPkQ0ak256mewZBYMnPyIIqz5EzDh4h+PXGBEFLGY+BDRqKWzJODM+15C+vSzAMCfAAkCIAgQRBEQBGSfcR4W/eLvHO0hIgBsWUFEo5w+LhHz7ngCjtoTqNz8Jhy15QAExOcUI/+cb7NRKRH1wMSHiKJCXGYBJl72k3CHQUQRjlNdREREFDOY+BAREVHMYOJDREREMYOJDxEREcUMJj5EREQUM5j4EBERUczgcnYiGjZFUdB44DPUfbEZPlcbtKZ4ZM5agpRJcyEE0EGdiCjUmPgQ0bDU7/0EX/7tl3DWVfqbhioKIAgofe95WDILMO26e5E2ZUG4wyQiAsCpLiIahpodG/DpQ9+Ds/4kAECRfFBkCYrkAwC011Xg04e+i9qdm8IZJhFRN474ENGQdLQ2YOfjtwNQ/KM8fVEUKArw+WM/wXmPfwDJ7ULlR/+Gq6kGgqhBYvFk5CxYyT5aRDRiBEXp7x0rNtntdlitVthsNiQkJIQ7HKKIdeStJ3DojT8Ciqzq+LjsYjiqyyCIAiD4B5sVyQeNwYSSlddh/Ld+6G8sSkQ0BGrv33yXIaIhKd/4D9VJDwA4qksBKFBk2T8l1jkdJrldOPLmn7DrqZ+Df4cRUagx8SGigCmKgo6WuqBe8+RH/0bF5n8G9ZpERKdj4kNEQyIIQX77EAQcf/c5jvoQUUixuJkoyrmaauC2NUOjN8CSWQBRqxv2NQVBQHzeWNgrjgQ03TUgRYGj6jhsZQeQWDw5ONekftkrj6Dp4A743C4YElKQOesc6OOTwh0WUchFVeJz5MgR3HHHHfjkk0/g8Xgwbdo03H///ViyZEm4QyMaUYoso+rT91D2/gtoOban+3F9fDIKz7sMReddAYM1ZVivUbT8Suz5yy+GG2ovzsaqiEx8JE8Hqj9bi7aTx6AoMiwZBchZcD505vhwhxaQpoM7cODV36Pl6C5AECAIIhRZgqDVIXfhSky8/HYYranhDpMoZKJqVde4ceMwduxYrFmzBiaTCf/3f/+H5557DsePH0dmZqaqa3BVF412ss+DnY//FDU71vtXT50+IiOKMMQnYcFdzyIhd+yQX8fndmHjbefD3doQvFEfAHNv/xMyZy8N2vWGS5ElHH7zCZS+9xx8rnb/Jo0AFEmCqNOjYOnFmHT57dDojWGOdHDV29dh52M/8e8+0MfPTBA1MCal4cz7XoYpJWvkAyQahphb1dXY2IijR49i1apVmDZtGsaOHYuHHnoITqcT+/btC3d4RCPmy2fvR83nG/yf9JWQyDI8ba3Y9sD/wm1vHvLryF4PjNaUoCY9ABA/jGQs2BRZwueP3YYjb/4ZPle7/7HuFWkKZK8bZetexrYHr4fkcYc32EE4qsuw8/HbochKvz8zRZbQ0dKAzx75IWutKGpFTeKTkpKC8ePH4+9//zva29vh8/nw1FNPIT09HbNnz+73PLfbDbvd3uODaLRqrz+Jik3/7H9DwU6KLMFtb0H5hleH9DqS14PPfvN92CsOD36wyiJoQdQgbeoiWDLyhhRTKBx/93nUbF8HYIDvpyKj+ehuHHjlt72e8jodaDt5DG1VpfC5XaELVIWydS91/l4M/rthrziEpoM7RiYwohEWNTU+giBgw4YNuOiiixAfHw9RFJGeno73338fSUn9F+ytWbMGv/zlL0cwUqLQObHxNQiiv2ZjUIqM8vUvY+yF34OoCeytoOqT//aoHRrkhdQdJcsY+80bAoojlGTJh+PvPqfuYEXGiQ/+gQkX3wydOR4tx75E6fsvoPqz97v3KxL1RuSf/f9QtPwqxOcUhy7wPkgeNyo2/1Pd7wX8SWj5hleROmluiCMjGnkRP+KzatUqCIIw4MehQ4egKApuuukmpKen46OPPsL27dtx0UUX4YILLkBNTU2/11+9ejVsNlv3R2Vl5Qh+dUTqyZJv0OmHxgOfqb65AYDb1gRnQ1VAcSiKgtL3XwBUd10XEJddDKCf40URgIBp19+H1MnzAoollBr3bfPXL6kkez2o2vYeSte+iI/uuQzVn77XnfQAgOzpwImN/8DmVReiZseGUITcr46WekieDtXHK7IEe+WREEZEFD4RP+Jz++2349prrx3wmOLiYnzwwQd4++230dLS0l3U9Oc//xnr16/H888/j1WrVvV5rsFggMFgCHbYREHRXn8SJza+iorNb8LT1gIIIqwFE1C04irkLDi/V0Gt5FZ/c+siewOrTXHbGtVNcXVRZGiNZky+6k6Uvvd3uJp6/iGSMn4Oxl30faRNXTjAJWQ0HdyBts7dny2ZBUibPB+CqAko9kC011b4kzvVtS4Kqra+g6aD2ztj7p2AKrIECAI+f+wnWPSLvyN53MwgRjxwbIGfwhofik4Rn/ikpaUhLS1t0OOcTicAQDyt148oipDl4BZfEo2EE5vewJ6/3gtBEL66iSoybCcOYfdTP8eRt57AgtXP9KiJMSZnoK3qeEAFx/qE5IDi8jkdAR0PAF5nG0q+fi2Kv/YdNB/eCWdTDUSNFtaCiYjLLur3PEVRcOKD13HsP3+Bs+Fk56MCAAXGlEyMWXkdipZfGZoeX6IQcL7QlfQMSFGgKDIOvf4YFt717NBiC5AhMQ2iTg/Z61F1vCBqEDfC03FEIyXip7rUWrBgAZKSknDNNddgz5493Xv6lJWVYeXKleEOjyggJ7e+498jR5F7jxx0JjWuxmps/fV3eqzMyj3zAtVJjyBqkDJpXsB7tmjNcQEdDwA6S0Lna4pImXgG8s78JnIWfH3QpGfvs7/Cl8/ce9p0nD8b6Wiqxb6/P4hdT66GEoI/buJzx2BIIyVqyDIa938KR01ZUC432BSo1mBC7pkXqh4hU2QJhUsvDUZoRBEnahKf1NRUvP/++3A4HFi6dCnmzJmDjz/+GP/+978xffr0cIdHpJrkdWP303cPelzX0uPj73w1apA9dwV0Fquq+htFllC84qqA4zMmpiGhYKLq1VoQRGSdcV7Ar1O+4dVTVp31f2M/+fF/cOztZwK+/mBSJpwBc4hXmDUf/mLI57Yc34tdT67Gu9fPwX+vnIR3/ncWPn/sJ2g6uKPPRMj/sx48kRNEDSxZRQNOPRKNZlGT+ADAnDlzsHbtWjQ1NcFut2Pbtm04//zzwx0WUUB2P3UXZJWFqIos4cTGf3TvIaPRGzD7R49CEIRBkh8BuWddiMw55w4pxuIVVwUwsiQi/+xvBXR9RZZw9N9Pqz7+2Nt/g+xTN42jliAIGHfh94N6zdNeIKCC4y6y5MOev96Lj35xCU5+/N/u/YUktws129fjk/u/g8//cGuvfYUS8sdh+vceAND/74YgaqCzJGDeHU+EZvqQKALwN5sogjgbqlC19Z2AzvE67Wgt3dv9efr0MzHvjqf8Iz9Az5EZQQQEEUVfuxozv/+AP0Eagtwzv4HkcbNUTZ1MvOTWgNtjNOzdho7mWtXHex2tqN25KaDXUCPv7G9BP8zWHv1SlIDrqwBg7/MP4MQHr/svcdo0aNfnNTs24Is/39lr5Cd/8UWYd8efO1fZ+RMdQaPt/L0QkDHzHCz+9euIyywYwhdENDpEfHEzUSzpuqEFyuts6/F5+vQzsfxPW1CzYz2qPnkbHa0N0BpMSJk0DwVLL4YpOWNYcYpaPebd+SS2//ZHaDq4HYKo6XET7vp8wiW3oOQb1wV8/baTx/put9EPQaP1nzNvRcCvNeB1BQFJJdNQt2tz0Fc5afRGZMxYHNA5thOHcELNppOKjJrt69C4/1OkTVnQ46mMmecgfcbZaD6yC00Ht0Nyd0CfkITsucvZpoJiAhMfoggS6GhPF525d18ajU6P3IUrkbswNMX9OnM8Ft71LBr2bUPZ2hfReOAzyF43dBYrcs/8JgqXXYq4rP6LlweiKHJgK8k7zwmF3EXfQN0XwR1NEkQN8s/5NrRGS0DnlW94tVeSOdBrlK17qVfiA/gTupTxs5AyflZAr08UDZj4EEUQj6M14HO0FiuSxkwNfjAqCKKI9GmLkD5tUVCva8nIC2illiL5YEkPTSFy1hnLoI9P8v9sVGRiGoMZstfdb3IiiBqYM/Iw/n9uDjiWmh0bVG9QqcgS6nZtgaIoQ57SJIpGrPEhiiAaozngcwrPvRiiVh+CaMInY+bZ0MUlqj5eYzAja+7ykMQiavWY9aNHBi8YFwRkzj4Xi3/9D5gz8v0PnVID1fXvpDHTceY9L0IfZw04Fl9He0DHK5JP9d49RLGCiQ9RBMmYflZAuxGLOj3GfOO7IYwoPEStHiXnf0ddWwxBQOF5l0E7hKRRrfSpi/wF4+b4ztf86q2z6+eVt/gizLnld4jPKcHSR97G/NV/Rdbc85CQPwHWwonIPeubOOtXr2HRvS8GXOzdJdCpMUGjhaiLrqSYaLg41RVCss+Dmu3rUbb+FX/fG0WBOT0PhcsuRe6ibwT8JkbRr/C8y1Gx+Z+qj59y9c+HNHIwGoz55g1oOb534MJiQUDKxLmYcPEtIY/HXzD+Iaq3r8XJj/6DjpY6iDoDUifNQ+GyS2HpHOUBOqcApy5C+tTgTgFmnbEMFZveUF3jkzFrCae5iE4jKINt+Rlj7HY7rFYrbDZbd8+voXDUlGHbQzfA1VDVc3VKZ8WmzpKAuT99gsWF1MvOP/4UVdveHbieRBCRNGYaFv3i7xC1upELboTJkg+H3/gjSt9/AZLb6V96DUCRJIh6AwrPvRQTL7sNmhgZ1bBVHMaWVRepPn7BXc8ibfL80AVEFEHU3r+Z+JwmGImPq6kGW+66GF5Ha/9/mQkiRK0OZ973EhKLJg8jYoo2kteDL/74U9TsWN97BU9nEp00Zjrm3flU1I72nM7X4UT1Z2vhqD4ORVEQl1WInPlfh9YUe6OmXz57P8rXv4IBd2EWBGTPXYHZP/4dR3woZjDxGaJgJD5fPLEaVZ/8d/DhaFFEYtEULL7/tSG9DkUvRZZRt3sLyta9jIYvP0HXTS5pzHQUrbgK2fOWR11BM6mjyBL2PvcAyje80u/+Sdnzz8fMGx+KmZEwIoCJz5ANN/HxtLVg7Q8XQ5F8qs85+8E3YS2cGPBrUWyQPB3wttuhNVlYF0bdWkv3o3zDK6jevg4+lwMagwmZs5ag8LwrkDxupuqRHmdDFdpOHoUsSTCn5yIhbxxHiWhUUnv/ZnFzkNV/+UlASY8galDz+UYmPtQvjd4Ijd4Y7jAowiQWT8aM7/0aM7736yGd33hwO47++y89RhQBICF/AkpWXovcM7/JBIiiEhOfIPO22xDQlrOCAG+7PbRBERGdonzjP/DlM/d1NiLt+V5lrzyCXU+sQtOhnZj+3V8y+aGow318gkxrtAS6zz50MVigSUThUb/3E3z5zH0AlL7rEDtXoFZseh3H/vOXEY2NaCRwxCfI0qYuDKi5oiJLSA+wUSER9aYoCpoPf4GKzW/AUVMGCCKs+eNRsPQSTiWf4shbTwCiAMiD/4F29D9Po/j873CqlaIKE58gMyalI2vOuajd+cHgq7oEAfE5JUgaO2NEYiOKVs6GKmz/3Y9gP3Gox0qn1mNfonzDq0idPA9zfvx76OOTwhxpeLVVlaL50E7Vx/tc7aj69H3kL74odEERjTBOdYXAhEtugag3AOIA315BgCAImHLNXZxDJ1LJduIQTmx6A2XrX0Htzk2QfR64mmrx0b2Xoa3yKAD0+IOj699NBz/Hx7+6Gl5nW1jijhS2sv0BHS9otLCVHQhRNEThwRGfEIjPKcGC1c/gs9983/9Ge3rNjyhCFDWYffNvuasqkQp1uz/E4X/+Ea3H9/Z4XBdnhT4uCR57y4AjrIosob2mDIdefxxTr/l5qMONWHIAK067z/GxySlFFyY+IZI8dgbO/f1aVGx5C2VrX4KrsQoAoI9PRuGyS1Gw9BKYUjLDHCXFMkdNGSo2/xOOmnIAQFx2MQqW/E+PnlORoHzDq/jyb7/qs2Gp12GD12FTdR1FllGx+Q1MvPSWmN0PKdD3HEWR+T5FUYcbGJ4mWL26Tif7PFBkmUWCFHYeRyu++PPPUL/7w856GH8hviCKUGQJmbPPxcwfPAidJXi//0PVeHA7tt5/LQZszxCg2Tf/FjkLvh60640miixh3c1L4W6pV3eCIGDZ/62HOS0ntIERBYHa+zdrfEaIqNUz6aGw87bb8fF9V3ZuWtdVA6Pg1KXNdbs245NfXQ2fqz18gXY69p+/du41EySCALetqcdDitLPsu4oJIgaFK+4qs/Rs76OzZy1hEkPRR1OdRHFkP0vP4r22hOD1sPYq47h4Gu/x9Rr7x7B6HpyNdWgfs/HCOZoDxQFos4ARZZQt+tDlK17CY0HPoMi+aA1xSFn4UoUnXcFEvLHBe81I0zJ169F44HP0LB3a797jgmiBsbkDEz/7q9GODqi0OOID1GM8DhsqPzoX+pGN2QZJzb/M6yjPm1VpQhq0tMpPm8sPvnVd7D9tz9E4/5Pu1vM+FwOVGx6A5tXXYhDrz+GaK0CELU6zL39zyg891IIGq1/3zFRBAQRgqgBAKROno+z7n8NBmtKmKMlCj6O+BDFiJrta6H41K/qkT0dqNmxHnnh2sNF5SagqgkiksZOx/4XHupe1n16Etj1+ZG3noCo02PcRT8IbgwRQqPTY9p192L8/9yMii1voq3yCBRJgiktB/ln/z/EZRWFO0SikGHiQxQjXM31EDQa1U10BVGDjtaGEEfVP3NabtCvmTxuJo6//TdVxx5+44/IP+fbMCamBT2OSGFISMbYC74b7jCIRhSnuohihKjVBdRHTlEUiBpdCCMaWFx2kX9X80CKmzunak4liBoIogazfviwv6hbUHc9RVFQsekN9a9NRKMCR3wo5kkeNySPC1pTHETN6P5PwtfhRNXWt1Gx5U24muoganVIHj8bReddjsTiKYGtXlJkJJZMGXIsiix371A+VCUr/xef/98tgx4niBpYCyfClJKFms83dk+TCVodchd9A8Vf+w4Mian44k93qH9xRUbN5xsx7v/dONTwiSgCje53eaIhkn0eVH+6FqVrX0Tr8S8B+G+eWXPPQ9F5VyB5wpxR10qkfs9H+Pyx2+BzOfzLlTtHd1yN1Tj50b+QOnUhTKnZcDVWq7iagLjsIiSPnx1QDO11lSjf+Coqt7wFT1sLBI0WicVTULTiKmSdcR7aTh5F+fpXUL/nI0ieDujjEpGz6BsoWHJxnxvlZc9djpKV/4vj7zzbf6SiBqaULMy740kYrClw21vgaqqGIIgwp+dCZ44HgO6NGgMR6y0uiKIRNzA8Tag2MKTI4bY14dOHvwdb+QH/tMcpRbRdDS4Lll2Gadfe3b3KJdI17NuGTx/6rn8lUr9LlEWY0nLhrKtQdc05P/49sud/TXUM5RtexZfP3g9BEHqOLIkiIMvQmOIguRw9moh2P68Ak6+6EyXnX9PruoqioGzdSzjy5p+7kynAP6IkCEDWGcsx9X9/AUNC8oDxuW1NWHvjmaq/HgCwFk/B2b9+PaBziCg81N6/OeJDMUXyuLHtoe92N7Q8feVQ1w35xIZXoTWYMPnKO0c6xIApsoRdT6waMOnxHyfDWV+J9OmLUb/nw94JCL5K/CZdeWdASU/lh//Cl3/7pf91Tg+hc2doyeXojrev5/e/8BAECCg+/zs9YxIEFK+4CoXnXoranR+gtXQvZJ8XxuRM5C5cCWNSOmwVh1H63t/hsTdDYzAiZdI8ZMw8u8fUpcGaAmvRZNjKD6pbMSaIyJ67XPX3gIhGByY+FFNOfvIf2E8cUnXs8XeeQ9HyKyN+59q6XVvQobYFgaLAUX0c8+54EsfffQ6N+z895UkBqVPmY8zK65A2daHq15c8Hdj7/AOBBd2P/S/9BtkLvw6jNbXXc6JWh+x5K5A9b0X3Y20nj2HH/92ClqO7/aNzndOTpe+/AENiGiZd8VPknfnN7uOLV1yFXU+uVhWLIIrIP/tbAcUvSz7IXg80BtOomyolihVMfChmKIqC0vdf6FH/MiBRQPnG1zDpsttCH9wwVH/2fq8pu4E4G6pgTErDwruehbOhCu21JwAAlqxCmFOzA3/9T9/31xUFgaIoqNz8JsZe+L1Bj7WdOIRPfnkVJE+H/9zTRpLcrQ3Y9eefwdPW0j2FlrNwJSo2/xNNR77oHmnqz8RLf6JqAz/J04Gqre+idO0L3Um1xmBC3uKLUHje5UjIHTvoNYho5DDxoZjhaWv5aopLDVlG3a7NEZ/4eNpaA97sr62mHNbCSTCn5Qx7RKtu15aAEq8BKTKqtr07aOIjSz5sf/RG+DyuQROY/S88hORxs5BUMtW/a/FPn8CO//sxGvdt6zXd19W0deKlt6Jk5f9CkWU07N+GEx+8DkdVKSAISMgbi8JzL0XyhDlwNVZj25rr/cnjKcvkJbcLJzb+A+XrX8Gky3+KMRdcP7zvCwVEkWU07v8UVdvehdveDI3egJSJZyB30TehM8eFOzwKMyY+FDN8Hc7Az4mARp2D0RjMAZ/TuO9T5AapQ7nXaQ/qLsseR+ugx9R9sRmuplpV1xNEDcrWvoikHz4MANCZ47Bg9TNo3P8pyta+hIb92yB7PdDFWZG76AIULrsMcZkFaK+rxGeP3ghH1fEeCZKj6jiqtr6DhPzx8DhscHdt8thPvdiBVx6FxmhC0XlXqPwO0HA0H92NL/50J5z1lV/93AQR1Z+uxf6XHsH4/3cjxnzzBk5FxjAmPhQzupY1B0Iflxj8QIIsZcIc1GxfG9A5zYd3Dus13bYmVGx5E7Wfb4D95LFhXet0WqNl0GNObHqjz+LsviiyhKpt72Ladfd0X1sQBKRNWYC0KQv6PKetuhQf3X0JfB3t3dc49XoAYK88onpDyAMvP4q8sy5U9bXR0DUf2YWtv74Wsuzfnbz759aZlMqeDhx87ffwOFpHxcIFCg3u3EwxQx9nRcqEOep3AhYEZC84P7RBBUHGjMUBn6NmVKUviqLg6H//inU/OgcHX/s/tBz7EtIQRtL6I4gaZMw6Z9Dj2usG7jB/OkXy4cTmN/2bKp7C62yDo7oM7XUVkLweAEDtzg+w6c5vdic9/V9U/U4gktuFk5+8o/p4Cpzs82LH72+GLPkGnf48/s6zqN/7yQhFRpGGIz4UU4pWXIWmQ5+rOlYQNcg/+9shjmj4DImBd9DWqpgekzwdqNr2HsrWv4y2k8cARYbWaPbXFIWIIssoPPfSXo+77S2o2b4WHS31EHV6yJ1JSiD2//1BNB/6HLN/9Ahaju9D2doXULN9XXcypDXHI3XyPNTu2DDsr6MXQUD9ng9ReO4lwb82AQBqP98It61J1bH+6c+XkD51UYijokjExIdiStYZy5Axa4m/IHeQupQpV6+CISFphCIbOq3RAmvxFNhK9wMYfBRCEDVIn3HWgMe0nTyGbWu+i46Wuh6Fy54hJByBKP7aVbBk5Hd/7nG0Yv8LD+Pk1rehyJJ/ybqiBNZ64xQ1O9bjo3uvgK1sf3chcxefsy00SQ8AKAo8Dpv/n7IEt70Zss8LQ0IyNHpjaF4zxlR8+Fb3ZpmDUWQJdbs2w+NoHRXT2RRcTHwopgiiBnN+/HvsenIVqj99v3ediCBCEIDJV69G0fIrwxdoHxRFgeR2QdTq/A1HT1G84irsemKVuuvIEgrPu7zf550NVfj4V1fD19WuYZiFy13fY1FvgOxx97tbdv7SizH5qp91P+62NeHj+66As6Gq+2ektrN8vxQFtrL9/n8OMXkaqpaju7H/lUdx8sN/w21rBAAIGi1yFnwdRSuuQlLJ1BGNJ9q4GqtVJT3dFAXu1kYmPjGIiQ/FHI3egDk//j1av/FdlK1/GQ17P4Hk7oA+PhG5iy5AwdKLYUxKD3eY3dqqSlG+4RVUbnmru+4kPm8cildciZyF34DWaEb2/PNR+r5/H5kBb+iCgPzF3xpwb5lDrz8Gn7MtKImBxmBC0pjpKFp+JdKmLULtzg9Q9v4LaDm2xx+OqEHWGeehaHnv/mg7/3RHj6RntFMkH47/95lej1VtfQcnP/4PJl52G8Z+84YwRTf6iTp9wOcIp/0BQbGBvbpOw15dFElK176Ifc8/CEEUTxuZ8m/CaErLwcKf/w2WjHy47c3+HmRl+/sdVclZ8HXMvPGhXiNGXdz2Fqy7afHwR1Y6X/OCF/f1+Zy3wwmpwwV9vLVHW4ku9pNHsfnOb/ZxZnSbdv19fdY40eD2PvdrlG94VXWirIuzYsWfP4SoDTxhosjEXl1Eo1zF5n9iX2criF5v5p1/r3Q01eKT+6/B2Q+8AYM1BWfe9zKqt72L0vdf8DdhBQAISJu6EEXLr0T6jMUD7l/SsPeToCQ9QO+/wD2OVlRseQvl61+Gs/4kAMCYlI7CZZchf8n/wJiY1n1sRQDL1aPJwVd+h7yzLoJGbwh3KKNO4bLLULbuJVXHCqKIwmWXMemJUUx8iCKQ5OnAvhceGvQ4RZbgbm3A8feex6TLboNGp0fe4ouQt/gieJ0OSG4ntOZ4aA0mVa/rddoBCFBTJD0QQdQgddLc7s+bj+zCp7/5vr+1xSmDzB0t9Tj0xh9x5F9P4YyfPNa9NN9RXTY6kh617U9U8jrtqP7sfeSddWHQrhkr4nPHIGvuctTs2DBwXZooQmO0oHBZ/3VuFN24jw9RBAqk/5UiSzix4bXufWi66MxxMCalq056AEBrisNwk56umIqWXwXAP221bc31/l2w+0oSFBmyz4Ptv70JTYe/GPZrjxhBRP6S/wnyJTVo3P9ZUK8ZS2b+YA1SJszubFbbe2RTEDXQGsxYsOovMCVnjHyAFBGY+BBFoPo9H/Xo/TQYr9PevVppONKmLvQvGR8GQdQgZeIZ3R3eD776e/++OwP9Fa4oUGQZ+/7+IAD/X+/DjSOUNAYTFt3zIvIWXRDU6yqKDMntCuo1Y4nWaMaC1X/FlKtWwZye2+M5UW9EwbmX4Ow1byFpzPQwRUiRgFNdRBHI62oPeBl5MDqkG62p/umC7esCn2rqLKhOLJmKubf9EYIowtlYjbpdm9VNBykybGX70Vq6HwVLLsbxd54d0tcQNP1MYxmSMrDormcRl10ERVFgsKao3jhv0JcUNdDHJwblWrFK1OpRfP53UPS1q2ErPwCPvRmi3oDEwsnQmtgyhDjiQxSR9HEJAY946CzBWYU44ZJboDGaA379hLxxmPG9X2Ph3c93x9Lw5ccB1cAIogb1ez5EXHYRrIWTAnp9dS8gdH4M8NYniDCn5yFt6iKcOl1iTMnEpMt/iiUP/xtx2UWdlxMw/n9uDlp4iuRD9ryvBe16sUwQBCQWTUb69LOQOnEukx7qxhEfogiUOWcZTn78X9XHG6ypsBZNDsprx2UWYNHdz2HbQzfAY2/pfPSU5EUQIWp1mHPL75FYNBleZxt05vg+9z7yuZyqd9P1X1vo3qtozAXfxc7HbxvmV3OaziRMF5cIr6Olx8qxrn+nTZ6PObf8HjpLArzONrjtzdDojTAmpkE4rc+bx2GDoNHBnJ4HZ33l8GITRVjS85BySlE4EQUfEx+iCJQ5awn0CSnw2JsxaLGxIKJo+RV97oczVNbCSVj2+3Wo/PjfKFv7Ehw15YCiwJic0bn8/NswWlMBYMDNHnWW+IB309WZ/aNFqZPnA6IGCPLqLkHUIGnMNBQsvRgVm95Ae30lRI0OiSVTUbjsUiSekkDqzPHQmeN7XcPT1oIDr/wOJz/+D2SfJ6B6rH6CgiBqMPMHawbcboCIho+JD1EEErU6zLjhfmz/3U0A+l8yLYgaxGUXofhr3wl6DFqTBUXnXYGi866AoiiAovQa8RhM+ozFnZsvqkt+FFlC5uylAABDQhJyF65E1dZ3grq0XZEltB7bg/l3PomsOecGfL7b1oSP7rsCrlN3lQ6kHuvUzSU7/62zJGDubY8jedzMgOMhosCwxocoQmXOXoI5P/49RI2uc3nuV7rqbxIKJmDhXc+FvH5BEISAkx4AMCamIWvuclX1QoIoInn8LMTnjul+bOyF3/PvMj3cEZXTyMPYpPHzP/ykZ9ITEAEQAHN6Hkyp2UgZPwszf7AGy/+4CSkT5gw5JiJSb9QkPg888AAWLlwIs9mMxMTEPo+pqKjAypUrYTabkZ6ejjvuuAM+X3B2oSUKh+x5K3DeHzdj0mW3Iy6rCFqjBbo4K9KmnYn5P3sai+//BwzWlHCHOaCJl94GrSlu4ORHFCFo9Zh6zd09Ho7PKcG8O5+CRm/o8/yux4QAp/lO3SU6EK1l+9F0aMcwRqD8I2fFX7sa5z22EYvueQF5iy9ih3aiETRqpro8Hg8uvvhiLFiwAM8880yv5yVJwsqVK5GZmYmtW7eipqYG3/nOd6DT6fDggw+GIWKi4DAkJGHMBddjzAXXhzuUIbFk5GHRPX/Hpw/dgI6W+tOmevzTeDpTPObd8QSshRN7nZ86aS6W/Oa/KF37Iio2ve7fCLHz3PQZi1G84irU7vxAfZ8mQUDeOd8a0tdy4oPXh99KQwEqtryF4q9dPfRrENGQjbompc899xxuvfVWtLa29nj8vffewze+8Q1UV1cjI8O/I+eTTz6Jn/3sZ2hoaIBer64nC5uUEoWG5OlA9afvo2z9K3BUHYcCBZb0PBSedzlyF30DWuPg03WSxw1nQxVkyQtjYjoMCUkAgLaq49h0xwUYfNdpAaJWh/P+uLn73EB8/Msr0RyE3aUN1hSseOLjYV+HiL4Sc01Kt23bhqlTp3YnPQCwYsUK3Hjjjdi/fz9mzuy7aNDtdsPtdnd/brfbQx4rUSzS6I3dfcSGfg0D4nOKez0en1OCKVevwr4X1gxwtr++ZtYPH+5OerzONpz86D8o2/AK2usqAABxmYUoPO9y5J35zZDVTok6NiElCpdRU+MzmNra2h5JD4Duz2tra/s9b82aNbBard0feXl5IY2TaCTJPg8cNWWwVx6Bu3tPnuhUfP53MP2G+zv7jaG7ILqrDkifkIS5t/0R2fP9GwQ2H9mFDbcsw97nH4CjqhSKzwvF50XbyWPY++yvsOGWZWg5vrfHa8RnlwSlpUfy+NnDugYRDV1YR3xWrVqFhx9+eMBjDh48iAkTJoQshtWrV+O2277aJM1utzP5oVGvo6UeZeteRvnGV+F12DofFZA2/UwUr7gK6dPPisr9YgqW/A9yF30DVZ++j/rdH8LnckBnsSJr7nnInLXEv0IMgO3EIWx98Dp/D7Fe02P+zz1OO7Y+cC0W/+q17pVm+UsvxolNrw8rRkWWUHQeO4MThUtYE5/bb78d11577YDHFBf3HtbuS2ZmJrZv397jsbq6uu7n+mMwGGAwcNiZokfL8b349KHvwudynLZ/joLGvVvRsOcjFC2/ElOuuSsqkx+N3oj8xRchf4AptX0vPATFN0jjVFmG7HFj/8uPYP6dTwEAkkqmImnsdLQe3zekAmdBFJE6eT6Sxs4I+FwiCo6wJj5paWlISxvastLTLViwAA888ADq6+uRnu7fSXb9+vVISEjApEkh6PlDFIGcDVXYtuZ6/8qnPm7qXTfrsnUvQR+fhPHfvmmkQww7R3UZmg58pupYRZZQv/sjOBuqYE7LAQCccetj+Oiey9DRUh9A8uNPMBPHTMecW/4QlQkn0Wgxamp8KioqsHv3blRUVECSJOzevRu7d++Gw+HvSL18+XJMmjQJV199Nfbs2YO1a9fi7rvvxk033cQRHYoZx999DlKHU9VOwkf//TS87bFXzF+3a0tgGyIK8HeY72RMSsfiX7+O7Pnn++t9BAGCRttd+6OLS4TGaO5xCUtmPqZc83MsvOs56MxxwfgyiGiIRs2qrnvuuQfPP/989+ddq7Q2bdqEc845BxqNBm+//TZuvPFGLFiwABaLBddccw1+9atfhStkohHl63CiYvM/VY9CyJIXlR/+C8XnB7/dRSTzutr8bTQkdW0mBEGE1+no8ZjBmoLZP3oEU65ehert6+C2NUKjMyB53EwkT5gDRfIXSfs6nNDHJSIup4SjPEQRYtQkPs899xyee+65AY8pKCjAu+++OzIBEUUYe8VhSG5XQOc0HtoRc4mP1hSnuncYACiKDJ2p71EagzWlz0JlQauHtZBT7ESRaNRMdRHRwCRPR2AnKErAiVI0yJixOLCmooq/2SoRRQcmPkRRwpAQWM8uQdTAYE0NUTSRKz6nxN8QVEXTVUHUIG3aIlgyuMUFUbRg4kMUJeLzxsKSWYiuFUSDUWQJuQtXhjSmSDX56lUQNdqBi5xFEaJWj8lX3DFygRFRyDHxIYoSgiCg+PyrMXi/KgCCCFNqNtKmLgp5XJEosWgyFqz6K7RGs79Rao9kUQAEAVqjBQt+/gwS8seFJAaPoxXH33kO23/7I2x78Dp88eefoW73hwHVHxFR4EZdk9JQY5NSGs1knxef/eYHaNj/af91LIIIQaPBorufR/K4vnvYxQqPoxWVH/4L5etfQXv9SQD+pedF512BvLMuhM4S/PcARZZx+J9/xNH//hWK5OvMU5Xuru+m1GzM/tGjMf+zIQqU2vs3E5/TMPGh0U7ydGDXU3ehetu73TdTAP6aFlmGwZqCObf8ASkT2C/qVF1vhaFcdq4oCr589lc4seHV/g8SRQiiBgt//ix/RkQBYOIzREx8KFo4aspQvuE1NB/eCcnrgTk1C3mL/x8yZy/t7lkVaWSfF40Ht8Pd0gBRb0BSybTuHZOjQe0Xm7D90R8OfqAgwpCQjPMe/yBif1ZEkUbt/XvU7ONDRIGJyyrClKtXhTsMVSSPG8fefgZl616Cx958yjMC0qefiXHf+iGSo6C/Vdn7L/YcheuPIsNta0TtF5uQPXf5yARHFCNY3ExEYeXrcGLbg9fh8D//dFrSAwAKGvZuxSe/vArV29eFJb5gcdua0LBvq/r+XoKIyg//FdKYiGIREx8iCqvdT9+F5mO7+y3GVmQJiixj5+O3w3bi0MgGF0QdrQ2BnaDIcDXVhiYYohjGxIeIwqa9rgLVn74PDLqEWwEUBcfffX6Q4yKXqNUHfI5GF/g5RDQw1vgQUdic+OAf6mpe4B/5qdr6NqZcvQr6OOugx7vtzajY/E9Ub18Hr8MGrSkOmbOXomDpxTAlZwQj/IBYMnKhsyTA225XdbwgapA8flaIoyKKPUx8iChsbOUH1de8AFAkH9pry6EfM73/YxQFpe//HQdeftR/7VMWrtorDuPIW09g3EXfx/j/uXlEO6aLWj0Kzr0Ux99+RtUmhYosoeDcS0cgMqLYwsSHiMJGltQnPWrPOf7Oszjw8iN9P9lZR3TkrScg+zyYdPlPu59yNdXixKbX0bj/U/g6XDAmpiJn0TeQPXcFNHpDwHH2pWj5lTix8TV4XY6Bp/cEETkLVyIusyAor0tEX2GNDxGFjSU9F4KoCegcc2pWv885G6pw4JVHVV3n2H+fQWvpfiiyhH0vPIT1Pz4XR/71JJoPfwH7iYOo//IT7Przz7DuprNRt2tLQDH2x5Scgfmr/gqt0dL31905ApU2dSFm3HB/UF6TiHpi4kNEYZN/zrdVT3UJogapUxbAlNJ/4lO+8TXV01eCqEHZ+lew+6m7Ufre3/2jQaeOwnSODnmddnz26A9R+8UmVdcdTFLJVJyz5i0UnncFNAZTj+fisosw7bp7Me+OJ4I2ykREPXHn5tNw52aikaMoCrb8/FtoqzyqKgGad8cTyJh5Tr/Pr/vREnQ0q18CLmp1kH3ewQ/sbFq64omPoNEbVV9/ML4OJ+yVRyB73NBbkxGfM2ZE646Ioona+zdHfIgobARBwBm3PgadJWHQKa8xF1w/YNID+JuOBkL2eQFBxdugosDncqBq23sBXX8wWqMZyWNnIHXyPCTkjmXSQzQCmPgQUVhZMvKw+NevI3nCHAD+KShB1EDQ+Nde6MwJmHL1aky87PZBrzWk6aH+utifThBw8uP/BH59IoooXNVFRGFnTsvBorufQ1tVKaq2vo2OlnqIOgOSx85A1rwVqjfyS5uyEDXb16mrGxJE9UkPACgKOlrq1R9PRBGJiQ8RRYz4nGJMuPjHQz6/aPkVqP5U5XRUIElPp9OLkYlo9OFUFxFFjeTxs5E+YzEgDvzWJogaJJZMgz4xTfW1BVGD1EnzhhsiEYUZEx8iihqCIGDOj3/3VYJyeuFyZ/GwtXAi5t/5JIqXX6muuBn+nZQLl3EnZaLRjokPEUUVrdGC+T97GrN++BsklUzt8VxC7lhMv+F+LLrnRejjk1B47iUwJqYOvomiICD/7G/BkpEfwsiJaCRwH5/TcB8foujitrfA67RDa7LAkJDSa8l4W1Uptj5wLdz2pt5tJDoLoDPPWIY5N/92SB3WiWhkqL1/M/E5DRMfotjjtjejfMMrKFv3Cjz2pu7Hk8bOQNGKq5Az/3wIg9QNEVF4MfEZIiY+RLFLlnxw1lVC8rigj0+GKSUz3CERkUpq799czk5E1EnUaBGXXRTuMIgohDh2S0RERDGDiQ8RERHFDCY+REREFDOY+BAREVHMYOJDREREMYOJDxEREcUMJj5EREQUM5j4EBERUcxg4kNEREQxg4kPERERxQwmPkRERBQzmPgQERFRzGDiQ0RERDGDiQ8RERHFDCY+REREFDOY+BAREVHM0IY7AKJYoMgyGvZuRePB7ZDcLujjk5Az/3zEZReFOzQiopjCxIcoxKo/W4v9L/0GrsZqCJrO/+QUBYffeBwpE+di+nfvQ1wWEyAiopHAqS6iECrf+Bo+/8OtcDVWAwAUyef/kCUAQPPhnfjw7ktgrzwSzjCJiGIGEx+iELGVH8SXf/vlgMcosgTJ7cJnj9zYnQwREVHoMPEhCpHStS9CEAb/T0yRJbgaq1G3a8sIREVEFNuY+BCFgM/tQtUnb6sexRFEDco3/iPEURERERMfohBw25og+zyqj1dkCe21J0IYERERAaMo8XnggQewcOFCmM1mJCYm9np+z549uPzyy5GXlweTyYSJEyfiD3/4w8gHSgRAEITAzxFHzX+ORESj1qhZzu7xeHDxxRdjwYIFeOaZZ3o9v3PnTqSnp+PFF19EXl4etm7diu9973vQaDT40Y9+FIaIKZYZk9KgNVngc7WrOl4QNbAWTgxxVERENGoSn1/+0r865rnnnuvz+euuu67H58XFxdi2bRvefPNNJj404kStHgVLL0Hpe39XVeejyBIKl102ApEREcW2qB5bt9lsSE5OHvAYt9sNu93e44MoGArPu8K/YeEg016CqEFi8VQkj589QpEREcWuqE18tm7ditdeew3f+973BjxuzZo1sFqt3R95eXkjFCFFO0t6Lube9jgEjbbf+h1B1MCUmo25t/9xSHVBREQUmLAmPqtWrYIgCAN+HDp0KODr7tu3DxdeeCHuvfdeLF++fMBjV69eDZvN1v1RWVk51C+HqJf06WfhrPteRtrURQB6JjYavRGFyy7D4vtfgzEpPTwBEhHFmLDW+Nx+++249tprBzymuLg4oGseOHAA5557Lr73ve/h7rvvHvR4g8EAg8EQ0GsQBSKxeArm/+xptNefRNOhzyG5XTAkJCN9+pnQGi3hDo+IKKaENfFJS0tDWlpa0K63f/9+LF26FNdccw0eeOCBoF2XKBgs6bmwpOeGOwwiopg2alZ1VVRUoLm5GRUVFZAkCbt37wYAjBkzBnFxcdi3bx+WLl2KFStW4LbbbkNtbS0AQKPRBDW5IiIiotFr1CQ+99xzD55//vnuz2fOnAkA2LRpE8455xy88cYbaGhowIsvvogXX3yx+7iCggKUl5ePdLhEREQUgQRFUZRwBxFJ7HY7rFYrbDYbEhISwh0OERERqaD2/h21y9mJiIiITsfEh4iIiGIGEx8iIiKKGUx8iIiIKGYw8SEiIqKYMWqWs4+UrkVubFZKREQ0enTdtwdbrM7E5zRtbW0AwGalREREo1BbWxusVmu/z3Mfn9PIsozq6mrEx8dHVLdsu92OvLw8VFZWRvX+Qvw6owu/zujCrzP6RNPXqigK2trakJ2dDVHsv5KHIz6nEUURubmR208pISFh1P9yqsGvM7rw64wu/DqjT7R8rQON9HRhcTMRERHFDCY+REREFDOY+IwSBoMB9957LwwGQ7hDCSl+ndGFX2d04dcZfWLpa+3C4mYiIiKKGRzxISIiopjBxIeIiIhiBhMfIiIiihlMfIiIiChmMPGJYE888QSmTZvWvbHUggUL8N5774U7rJB76KGHIAgCbr311nCHEnT33XcfBEHo8TFhwoRwhxUSVVVVuOqqq5CSkgKTyYSpU6fi888/D3dYQVVYWNjr5ykIAm666aZwhxZUkiThF7/4BYqKimAymVBSUoL7779/0J5Io1FbWxtuvfVWFBQUwGQyYeHChdixY0e4wxqWDz/8EBdccAGys7MhCAL+9a9/9XheURTcc889yMrKgslkwrJly3D06NHwBDsCmPhEsNzcXDz00EPYuXMnPv/8cyxduhQXXngh9u/fH+7QQmbHjh146qmnMG3atHCHEjKTJ09GTU1N98fHH38c7pCCrqWlBYsWLYJOp8N7772HAwcO4Le//S2SkpLCHVpQ7dixo8fPcv369QCAiy++OMyRBdfDDz+MJ554An/84x9x8OBBPPzww/jNb36Dxx9/PNyhBd13v/tdrF+/Hi+88AL27t2L5cuXY9myZaiqqgp3aEPW3t6O6dOn409/+lOfz//mN7/BY489hieffBKfffYZLBYLVqxYgY6OjhGOdIQoNKokJSUpf/3rX8MdRki0tbUpY8eOVdavX6+cffbZyi233BLukILu3nvvVaZPnx7uMELuZz/7mXLmmWeGO4wRd8sttyglJSWKLMvhDiWoVq5cqVx33XU9HvvWt76lXHnllWGKKDScTqei0WiUt99+u8fjs2bNUu66664wRRVcAJS33nqr+3NZlpXMzEzlkUce6X6stbVVMRgMyiuvvBKGCEOPIz6jhCRJePXVV9He3o4FCxaEO5yQuOmmm7By5UosW7Ys3KGE1NGjR5GdnY3i4mJceeWVqKioCHdIQfef//wHc+bMwcUXX4z09HTMnDkTf/nLX8IdVkh5PB68+OKLuO666yKqwXEwLFy4EBs3bsSRI0cAAHv27MHHH3+M888/P8yRBZfP54MkSTAajT0eN5lMUTkyCwBlZWWora3t8b5rtVoxb948bNu2LYyRhQ6blEa4vXv3YsGCBejo6EBcXBzeeustTJo0KdxhBd2rr76KL774YtTPpQ9m3rx5eO655zB+/HjU1NTgl7/8Jc466yzs27cP8fHx4Q4vaEpLS/HEE0/gtttuw89//nPs2LEDP/7xj6HX63HNNdeEO7yQ+Ne//oXW1lZce+214Q4l6FatWgW73Y4JEyZAo9FAkiQ88MADuPLKK8MdWlDFx8djwYIFuP/++zFx4kRkZGTglVdewbZt2zBmzJhwhxcStbW1AICMjIwej2dkZHQ/F22Y+ES48ePHY/fu3bDZbHjjjTdwzTXXYMuWLVGV/FRWVuKWW27B+vXre/2lFW1O/Qt52rRpmDdvHgoKCvCPf/wD119/fRgjCy5ZljFnzhw8+OCDAICZM2di3759ePLJJ6M28XnmmWdw/vnnIzs7O9yhBN0//vEPvPTSS3j55ZcxefJk7N69G7feeiuys7Oj7uf5wgsv4LrrrkNOTg40Gg1mzZqFyy+/HDt37gx3aBQknOqKcHq9HmPGjMHs2bOxZs0aTJ8+HX/4wx/CHVZQ7dy5E/X19Zg1axa0Wi20Wi22bNmCxx57DFqtFpIkhTvEkElMTMS4ceNw7NixcIcSVFlZWb2S84kTJ0bltB4AnDhxAhs2bMB3v/vdcIcSEnfccQdWrVqFyy67DFOnTsXVV1+Nn/zkJ1izZk24Qwu6kpISbNmyBQ6HA5WVldi+fTu8Xi+Ki4vDHVpIZGZmAgDq6up6PF5XV9f9XLRh4jPKyLIMt9sd7jCC6txzz8XevXuxe/fu7o85c+bgyiuvxO7du6HRaMIdYsg4HA4cP34cWVlZ4Q4lqBYtWoTDhw/3eOzIkSMoKCgIU0Sh9eyzzyI9PR0rV64Mdygh4XQ6IYo9bxcajQayLIcpotCzWCzIyspCS0sL1q5diwsvvDDcIYVEUVERMjMzsXHjxu7H7HY7Pvvss6itJ+VUVwRbvXo1zj//fOTn56OtrQ0vv/wyNm/ejLVr14Y7tKCKj4/HlClTejxmsViQkpLS6/HR7qc//SkuuOACFBQUoLq6Gvfeey80Gg0uv/zycIcWVD/5yU+wcOFCPPjgg7jkkkuwfft2PP3003j66afDHVrQybKMZ599Ftdccw202uh8S73gggvwwAMPID8/H5MnT8auXbvwu9/9Dtddd124Qwu6tWvXQlEUjB8/HseOHcMdd9yBCRMm4H//93/DHdqQORyOHqPKZWVl2L17N5KTk5Gfn49bb70Vv/71rzF27FgUFRXhF7/4BbKzs3HRRReFL+hQCveyMurfddddpxQUFCh6vV5JS0tTzj33XGXdunXhDmtEROty9ksvvVTJyspS9Hq9kpOTo1x66aXKsWPHwh1WSPz3v/9VpkyZohgMBmXChAnK008/He6QQmLt2rUKAOXw4cPhDiVk7Ha7cssttyj5+fmK0WhUiouLlbvuuktxu93hDi3oXnvtNaW4uFjR6/VKZmamctNNNymtra3hDmtYNm3apADo9XHNNdcoiuJf0v6LX/xCycjIUAwGg3LuuedG9e+zoChRuPUmERERUR9Y40NEREQxg4kPERERxQwmPkRERBQzmPgQERFRzGDiQ0RERDGDiQ8RERHFDCY+REREFDOY+BAREVHMYOJDREREMYOJDxFFDUmSsHDhQnzrW9/q8bjNZkNeXh7uuusuAMCPf/xjzJ49GwaDATNmzAhDpEQULkx8iChqaDQaPPfcc3j//ffx0ksvdT9+8803Izk5Gffee2/3Y9dddx0uvfTScIRJRGEUna2EiShmjRs3Dg899BBuvvlmLF26FNu3b8err76KHTt2QK/XAwAee+wxAEBDQwO+/PLLcIZLRCOMiQ8RRZ2bb74Zb731Fq6++mrs3bsX99xzD6ZPnx7usIgoAjDxIaKoIwgCnnjiCUycOBFTp07FqlWrwh0SEUUI1vgQUVT629/+BrPZjLKyMpw8eTLc4RBRhGDiQ0RRZ+vWrfj973+Pt99+G3PnzsX1118PRVHCHRYRRQAmPkQUVZxOJ6699lrceOONWLJkCZ555hls374dTz75ZLhDI6IIwMSHiKLK6tWroSgKHnroIQBAYWEhHn30Udx5550oLy8HABw7dgy7d+9GbW0tXC4Xdu/ejd27d8Pj8YQxciIaCYLC8V8iihJbtmzBueeei82bN+PMM8/s8dyKFSvg8/mwYcMGLFmyBFu2bOl1fllZGQoLC0coWiIKByY+REREFDM41UVEREQxg4kPERERxQwmPkRERBQzmPgQERFRzGDiQ0RERDGDiQ8RERHFDCY+REREFDOY+BAREVHMYOJDREREMYOJDxEREcUMJj5EREQUM/4/uHIRw2QO3mwAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Fit the SVM\n",
        "from sklearn.svm import SVC\n",
        "\n",
        "clf00 = SVC(kernel='linear',C= 10000)\n",
        "\n",
        "clf00.fit(X,y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "G5ESj4iPTYkj",
        "outputId": "0d7e3a9d-7388-47e5-ebf8-a8403931fceb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SVC(C=10000, kernel='linear')"
            ],
            "text/html": [
              "<style>#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 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-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 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-4 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-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 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-4 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-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 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-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 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-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 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-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC(C=10000, kernel=&#x27;linear&#x27;)</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-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(C=10000, kernel=&#x27;linear&#x27;)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 177
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Then we visualize it\n",
        "plt.scatter(X[:, 0], X[:, 1], c=y, s=90, cmap=plt.cm.Paired)\n",
        "\n",
        "ax = plt.gca()\n",
        "xlim = ax.get_xlim()\n",
        "ylim = ax.get_ylim()\n",
        "\n",
        "\n",
        "xx = np.linspace(xlim[0], xlim[1], 30)\n",
        "yy = np.linspace(ylim[0], ylim[1], 30)\n",
        "YY, XX = np.meshgrid(yy, xx)\n",
        "xy = np.vstack([XX.ravel(), YY.ravel()]).T\n",
        "\n",
        "xy = np.vstack([XX.ravel(), YY.ravel()]).T\n",
        "Z = clf00.decision_function(xy).reshape(XX.shape)\n",
        "\n",
        "ax.contour(\n",
        "    XX, YY, Z, colors=\"red\", levels=[-1, 0, 1], alpha=0.5, linestyles=[\"--\", \"-\", \"--\"]\n",
        ")\n",
        "\n",
        "ax.scatter(\n",
        "    clf00.support_vectors_[:, 0],\n",
        "    clf00.support_vectors_[:, 1],\n",
        "    s=100,\n",
        "    linewidth=1,\n",
        "    facecolors=\"green\",\n",
        "    edgecolors=\"blue\",\n",
        ")\n",
        "\n",
        "plt.xlabel('X1')\n",
        "plt.ylabel('X2')\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "t1-ifybeTpui",
        "outputId": "4adb4a46-84e8-4ec3-8934-39452899f932"
      },
      "execution_count": null,
      "outputs": [
        {
          "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": [
        "Support Vectors - Datapoints either side of the line seperating the two classes.\n",
        "Hyperplane - The line seperating the two classes.\n",
        "\n",
        "i.e. The support vectors are the points that the dotted lines strike through, while the hyperplane is the line inbetween the dotted lines."
      ],
      "metadata": {
        "id": "K_Fcn3mDUJv4"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Problem 3\n",
        "In MLP_PT1 Problem 2 you predicted whether a given suburb has a crime rate above or below the median."
      ],
      "metadata": {
        "id": "2j-cTGOjRwS7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "url_boston = 'https://github.com/JWarmenhoven/ISLR-python/raw/master/Notebooks/Data/Boston.csv'"
      ],
      "metadata": {
        "id": "s7bdfkstUTyy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now try using a SVM and compare the performance with those you obtained in Lab sheet 1 and discuss."
      ],
      "metadata": {
        "id": "wh6UR6ZRUUdy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Import packages\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "from sklearn.preprocessing import label_binarize\n",
        "from sklearn.model_selection import train_test_split, GridSearchCV\n",
        "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
        "from sklearn.metrics import confusion_matrix, roc_curve, auc, classification_report"
      ],
      "metadata": {
        "id": "GZYkckdd0UTl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Import the data\n",
        "df1 = pd.read_csv('https://github.com/JWarmenhoven/ISLR-python/raw/master/Notebooks/Data/Boston.csv')\n",
        "df1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "l5mef0HP0zxA",
        "outputId": "beb5a8cd-8af5-4c23-f13f-7bed82c47c7d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        crim    zn  indus  chas    nox     rm   age     dis  rad  tax  \\\n",
              "0    0.00632  18.0   2.31     0  0.538  6.575  65.2  4.0900    1  296   \n",
              "1    0.02731   0.0   7.07     0  0.469  6.421  78.9  4.9671    2  242   \n",
              "2    0.02729   0.0   7.07     0  0.469  7.185  61.1  4.9671    2  242   \n",
              "3    0.03237   0.0   2.18     0  0.458  6.998  45.8  6.0622    3  222   \n",
              "4    0.06905   0.0   2.18     0  0.458  7.147  54.2  6.0622    3  222   \n",
              "..       ...   ...    ...   ...    ...    ...   ...     ...  ...  ...   \n",
              "501  0.06263   0.0  11.93     0  0.573  6.593  69.1  2.4786    1  273   \n",
              "502  0.04527   0.0  11.93     0  0.573  6.120  76.7  2.2875    1  273   \n",
              "503  0.06076   0.0  11.93     0  0.573  6.976  91.0  2.1675    1  273   \n",
              "504  0.10959   0.0  11.93     0  0.573  6.794  89.3  2.3889    1  273   \n",
              "505  0.04741   0.0  11.93     0  0.573  6.030  80.8  2.5050    1  273   \n",
              "\n",
              "     ptratio   black  lstat  medv  \n",
              "0       15.3  396.90   4.98  24.0  \n",
              "1       17.8  396.90   9.14  21.6  \n",
              "2       17.8  392.83   4.03  34.7  \n",
              "3       18.7  394.63   2.94  33.4  \n",
              "4       18.7  396.90   5.33  36.2  \n",
              "..       ...     ...    ...   ...  \n",
              "501     21.0  391.99   9.67  22.4  \n",
              "502     21.0  396.90   9.08  20.6  \n",
              "503     21.0  396.90   5.64  23.9  \n",
              "504     21.0  393.45   6.48  22.0  \n",
              "505     21.0  396.90   7.88  11.9  \n",
              "\n",
              "[506 rows x 14 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-bc50b473-4db0-4ad5-aef4-a7cd068ce572\">\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>crim</th>\n",
              "      <th>zn</th>\n",
              "      <th>indus</th>\n",
              "      <th>chas</th>\n",
              "      <th>nox</th>\n",
              "      <th>rm</th>\n",
              "      <th>age</th>\n",
              "      <th>dis</th>\n",
              "      <th>rad</th>\n",
              "      <th>tax</th>\n",
              "      <th>ptratio</th>\n",
              "      <th>black</th>\n",
              "      <th>lstat</th>\n",
              "      <th>medv</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.00632</td>\n",
              "      <td>18.0</td>\n",
              "      <td>2.31</td>\n",
              "      <td>0</td>\n",
              "      <td>0.538</td>\n",
              "      <td>6.575</td>\n",
              "      <td>65.2</td>\n",
              "      <td>4.0900</td>\n",
              "      <td>1</td>\n",
              "      <td>296</td>\n",
              "      <td>15.3</td>\n",
              "      <td>396.90</td>\n",
              "      <td>4.98</td>\n",
              "      <td>24.0</td>\n",
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              "    <tr>\n",
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              "      <td>0.02731</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7.07</td>\n",
              "      <td>0</td>\n",
              "      <td>0.469</td>\n",
              "      <td>6.421</td>\n",
              "      <td>78.9</td>\n",
              "      <td>4.9671</td>\n",
              "      <td>2</td>\n",
              "      <td>242</td>\n",
              "      <td>17.8</td>\n",
              "      <td>396.90</td>\n",
              "      <td>9.14</td>\n",
              "      <td>21.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.02729</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7.07</td>\n",
              "      <td>0</td>\n",
              "      <td>0.469</td>\n",
              "      <td>7.185</td>\n",
              "      <td>61.1</td>\n",
              "      <td>4.9671</td>\n",
              "      <td>2</td>\n",
              "      <td>242</td>\n",
              "      <td>17.8</td>\n",
              "      <td>392.83</td>\n",
              "      <td>4.03</td>\n",
              "      <td>34.7</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.03237</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.18</td>\n",
              "      <td>0</td>\n",
              "      <td>0.458</td>\n",
              "      <td>6.998</td>\n",
              "      <td>45.8</td>\n",
              "      <td>6.0622</td>\n",
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              "      <td>222</td>\n",
              "      <td>18.7</td>\n",
              "      <td>394.63</td>\n",
              "      <td>2.94</td>\n",
              "      <td>33.4</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.06905</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.18</td>\n",
              "      <td>0</td>\n",
              "      <td>0.458</td>\n",
              "      <td>7.147</td>\n",
              "      <td>54.2</td>\n",
              "      <td>6.0622</td>\n",
              "      <td>3</td>\n",
              "      <td>222</td>\n",
              "      <td>18.7</td>\n",
              "      <td>396.90</td>\n",
              "      <td>5.33</td>\n",
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              "      <td>11.93</td>\n",
              "      <td>0</td>\n",
              "      <td>0.573</td>\n",
              "      <td>6.593</td>\n",
              "      <td>69.1</td>\n",
              "      <td>2.4786</td>\n",
              "      <td>1</td>\n",
              "      <td>273</td>\n",
              "      <td>21.0</td>\n",
              "      <td>391.99</td>\n",
              "      <td>9.67</td>\n",
              "      <td>22.4</td>\n",
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              "      <td>0.573</td>\n",
              "      <td>6.120</td>\n",
              "      <td>76.7</td>\n",
              "      <td>2.2875</td>\n",
              "      <td>1</td>\n",
              "      <td>273</td>\n",
              "      <td>21.0</td>\n",
              "      <td>396.90</td>\n",
              "      <td>9.08</td>\n",
              "      <td>20.6</td>\n",
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              "      <th>503</th>\n",
              "      <td>0.06076</td>\n",
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              "      <td>0</td>\n",
              "      <td>0.573</td>\n",
              "      <td>6.976</td>\n",
              "      <td>91.0</td>\n",
              "      <td>2.1675</td>\n",
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              "      <td>273</td>\n",
              "      <td>21.0</td>\n",
              "      <td>396.90</td>\n",
              "      <td>5.64</td>\n",
              "      <td>23.9</td>\n",
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              "    <tr>\n",
              "      <th>504</th>\n",
              "      <td>0.10959</td>\n",
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              "      <td>11.93</td>\n",
              "      <td>0</td>\n",
              "      <td>0.573</td>\n",
              "      <td>6.794</td>\n",
              "      <td>89.3</td>\n",
              "      <td>2.3889</td>\n",
              "      <td>1</td>\n",
              "      <td>273</td>\n",
              "      <td>21.0</td>\n",
              "      <td>393.45</td>\n",
              "      <td>6.48</td>\n",
              "      <td>22.0</td>\n",
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              "      <th>505</th>\n",
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              "      <td>0.573</td>\n",
              "      <td>6.030</td>\n",
              "      <td>80.8</td>\n",
              "      <td>2.5050</td>\n",
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              "      <td>273</td>\n",
              "      <td>21.0</td>\n",
              "      <td>396.90</td>\n",
              "      <td>7.88</td>\n",
              "      <td>11.9</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "<p>506 rows × 14 columns</p>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 168
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Next calculate the median of the crime rate\n",
        "median1 = df1['crim'].median()\n",
        "median1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mWbZEP8K1Fmd",
        "outputId": "5b815e9e-aec6-46bf-c36e-d6479c2bd77f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.25651"
            ]
          },
          "metadata": {},
          "execution_count": 169
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "##Then create the binary variable crim01\n",
        "df1['crim01'] = df1['crim'].apply(lambda x: 1 if x > median else 0)\n",
        "\n",
        "df1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ecvtWqyt1Nuk",
        "outputId": "27ea4cf0-72c3-43df-bfb8-775e37c32672"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        crim    zn  indus  chas    nox     rm   age     dis  rad  tax  \\\n",
              "0    0.00632  18.0   2.31     0  0.538  6.575  65.2  4.0900    1  296   \n",
              "1    0.02731   0.0   7.07     0  0.469  6.421  78.9  4.9671    2  242   \n",
              "2    0.02729   0.0   7.07     0  0.469  7.185  61.1  4.9671    2  242   \n",
              "3    0.03237   0.0   2.18     0  0.458  6.998  45.8  6.0622    3  222   \n",
              "4    0.06905   0.0   2.18     0  0.458  7.147  54.2  6.0622    3  222   \n",
              "..       ...   ...    ...   ...    ...    ...   ...     ...  ...  ...   \n",
              "501  0.06263   0.0  11.93     0  0.573  6.593  69.1  2.4786    1  273   \n",
              "502  0.04527   0.0  11.93     0  0.573  6.120  76.7  2.2875    1  273   \n",
              "503  0.06076   0.0  11.93     0  0.573  6.976  91.0  2.1675    1  273   \n",
              "504  0.10959   0.0  11.93     0  0.573  6.794  89.3  2.3889    1  273   \n",
              "505  0.04741   0.0  11.93     0  0.573  6.030  80.8  2.5050    1  273   \n",
              "\n",
              "     ptratio   black  lstat  medv  crim01  \n",
              "0       15.3  396.90   4.98  24.0       0  \n",
              "1       17.8  396.90   9.14  21.6       0  \n",
              "2       17.8  392.83   4.03  34.7       0  \n",
              "3       18.7  394.63   2.94  33.4       0  \n",
              "4       18.7  396.90   5.33  36.2       0  \n",
              "..       ...     ...    ...   ...     ...  \n",
              "501     21.0  391.99   9.67  22.4       0  \n",
              "502     21.0  396.90   9.08  20.6       0  \n",
              "503     21.0  396.90   5.64  23.9       0  \n",
              "504     21.0  393.45   6.48  22.0       0  \n",
              "505     21.0  396.90   7.88  11.9       0  \n",
              "\n",
              "[506 rows x 15 columns]"
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              "      <th></th>\n",
              "      <th>crim</th>\n",
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              "      <td>0.0</td>\n",
              "      <td>11.93</td>\n",
              "      <td>0</td>\n",
              "      <td>0.573</td>\n",
              "      <td>6.120</td>\n",
              "      <td>76.7</td>\n",
              "      <td>2.2875</td>\n",
              "      <td>1</td>\n",
              "      <td>273</td>\n",
              "      <td>21.0</td>\n",
              "      <td>396.90</td>\n",
              "      <td>9.08</td>\n",
              "      <td>20.6</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>503</th>\n",
              "      <td>0.06076</td>\n",
              "      <td>0.0</td>\n",
              "      <td>11.93</td>\n",
              "      <td>0</td>\n",
              "      <td>0.573</td>\n",
              "      <td>6.976</td>\n",
              "      <td>91.0</td>\n",
              "      <td>2.1675</td>\n",
              "      <td>1</td>\n",
              "      <td>273</td>\n",
              "      <td>21.0</td>\n",
              "      <td>396.90</td>\n",
              "      <td>5.64</td>\n",
              "      <td>23.9</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>504</th>\n",
              "      <td>0.10959</td>\n",
              "      <td>0.0</td>\n",
              "      <td>11.93</td>\n",
              "      <td>0</td>\n",
              "      <td>0.573</td>\n",
              "      <td>6.794</td>\n",
              "      <td>89.3</td>\n",
              "      <td>2.3889</td>\n",
              "      <td>1</td>\n",
              "      <td>273</td>\n",
              "      <td>21.0</td>\n",
              "      <td>393.45</td>\n",
              "      <td>6.48</td>\n",
              "      <td>22.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>505</th>\n",
              "      <td>0.04741</td>\n",
              "      <td>0.0</td>\n",
              "      <td>11.93</td>\n",
              "      <td>0</td>\n",
              "      <td>0.573</td>\n",
              "      <td>6.030</td>\n",
              "      <td>80.8</td>\n",
              "      <td>2.5050</td>\n",
              "      <td>1</td>\n",
              "      <td>273</td>\n",
              "      <td>21.0</td>\n",
              "      <td>396.90</td>\n",
              "      <td>7.88</td>\n",
              "      <td>11.9</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>506 rows × 15 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a0ac5f62-ced8-4699-89e6-db8b2d5549a4')\"\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",
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              "      </button>\n",
              "      \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",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-a0ac5f62-ced8-4699-89e6-db8b2d5549a4 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-a0ac5f62-ced8-4699-89e6-db8b2d5549a4');\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",
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              "            + ' to learn more about interactive tables.';\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 170
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Define the X and y variables\n",
        "X = df1.drop(columns = ['crim','crim01','zn', 'indus', 'chas', 'rm', 'age', 'dis', 'tax', 'ptratio',\n",
        " 'black', 'lstat', 'medv'])\n",
        "\n",
        "y = df1.crim01"
      ],
      "metadata": {
        "id": "F4Vxz1ZY1Smc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Split into training and test set\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)"
      ],
      "metadata": {
        "id": "q8hL7TYI1qAm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Select the optimal C parameter by cross-validation\n",
        "tuned_parameters1 = [{'C': [0.001, 0.01, 0.1, 1, 5, 10, 100]}]\n",
        "clf2 = GridSearchCV(SVC(kernel='linear'), tuned_parameters1, cv=10, scoring='accuracy', return_train_score=True)\n",
        "clf2.fit(X_train, y_train)\n",
        "clf2.cv_results_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ICQ8m6Vg2Hr3",
        "outputId": "7bae1364-5b17-4eec-b91e-6569db7bb6d5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'mean_fit_time': array([0.00296459, 0.00273046, 0.00267286, 0.00328736, 0.00348971,\n",
              "        0.00403781, 0.00363729]),\n",
              " 'std_fit_time': array([0.00110902, 0.00023483, 0.0001854 , 0.00020085, 0.00030427,\n",
              "        0.00112559, 0.00021382]),\n",
              " 'mean_score_time': array([0.00188997, 0.00180912, 0.00171456, 0.00169213, 0.00190232,\n",
              "        0.00195694, 0.00178516]),\n",
              " 'std_score_time': array([4.91579269e-04, 1.06153098e-04, 1.02799622e-04, 6.90242210e-05,\n",
              "        3.32732914e-04, 3.66895627e-04, 8.00925513e-05]),\n",
              " 'param_C': masked_array(data=[0.001, 0.01, 0.1, 1, 5, 10, 100],\n",
              "              mask=[False, False, False, False, False, False, False],\n",
              "        fill_value='?',\n",
              "             dtype=object),\n",
              " 'params': [{'C': 0.001},\n",
              "  {'C': 0.01},\n",
              "  {'C': 0.1},\n",
              "  {'C': 1},\n",
              "  {'C': 5},\n",
              "  {'C': 10},\n",
              "  {'C': 100}],\n",
              " 'split0_test_score': array([0.97560976, 0.97560976, 0.97560976, 0.97560976, 0.97560976,\n",
              "        0.97560976, 0.97560976]),\n",
              " 'split1_test_score': array([0.97560976, 0.97560976, 0.97560976, 0.97560976, 0.97560976,\n",
              "        0.97560976, 0.97560976]),\n",
              " 'split2_test_score': array([0.97560976, 0.97560976, 0.97560976, 0.97560976, 0.97560976,\n",
              "        0.97560976, 0.97560976]),\n",
              " 'split3_test_score': array([0.95121951, 0.95121951, 0.95121951, 0.95121951, 0.95121951,\n",
              "        0.95121951, 0.95121951]),\n",
              " 'split4_test_score': array([0.975, 0.975, 0.975, 0.975, 0.975, 0.975, 0.975]),\n",
              " 'split5_test_score': array([0.975, 0.975, 0.975, 0.975, 0.975, 0.975, 0.975]),\n",
              " 'split6_test_score': array([0.975, 0.975, 0.975, 0.975, 0.975, 0.975, 0.975]),\n",
              " 'split7_test_score': array([0.975, 0.975, 0.975, 0.975, 0.975, 0.975, 0.975]),\n",
              " 'split8_test_score': array([0.975, 0.975, 0.975, 0.975, 0.975, 0.975, 0.975]),\n",
              " 'split9_test_score': array([0.975, 0.975, 0.975, 0.975, 0.975, 0.975, 0.975]),\n",
              " 'mean_test_score': array([0.97280488, 0.97280488, 0.97280488, 0.97280488, 0.97280488,\n",
              "        0.97280488, 0.97280488]),\n",
              " 'std_test_score': array([0.00720029, 0.00720029, 0.00720029, 0.00720029, 0.00720029,\n",
              "        0.00720029, 0.00720029]),\n",
              " 'rank_test_score': array([1, 1, 1, 1, 1, 1, 1], dtype=int32),\n",
              " 'split0_train_score': array([0.97245179, 0.97245179, 0.97245179, 0.97245179, 0.97245179,\n",
              "        0.97245179, 0.97245179]),\n",
              " 'split1_train_score': array([0.97245179, 0.97245179, 0.97245179, 0.97245179, 0.97245179,\n",
              "        0.97245179, 0.97245179]),\n",
              " 'split2_train_score': array([0.97245179, 0.97245179, 0.97245179, 0.97245179, 0.97245179,\n",
              "        0.97245179, 0.97245179]),\n",
              " 'split3_train_score': array([0.97520661, 0.97520661, 0.97520661, 0.97520661, 0.97520661,\n",
              "        0.97520661, 0.97520661]),\n",
              " 'split4_train_score': array([0.97252747, 0.97252747, 0.97252747, 0.97252747, 0.97252747,\n",
              "        0.97252747, 0.97252747]),\n",
              " 'split5_train_score': array([0.97252747, 0.97252747, 0.97252747, 0.97252747, 0.97252747,\n",
              "        0.97252747, 0.97252747]),\n",
              " 'split6_train_score': array([0.97252747, 0.97252747, 0.97252747, 0.97252747, 0.97252747,\n",
              "        0.97252747, 0.97252747]),\n",
              " 'split7_train_score': array([0.97252747, 0.97252747, 0.97252747, 0.97252747, 0.97252747,\n",
              "        0.97252747, 0.97252747]),\n",
              " 'split8_train_score': array([0.97252747, 0.97252747, 0.97252747, 0.97252747, 0.97252747,\n",
              "        0.97252747, 0.97252747]),\n",
              " 'split9_train_score': array([0.97252747, 0.97252747, 0.97252747, 0.97252747, 0.97252747,\n",
              "        0.97252747, 0.97252747]),\n",
              " 'mean_train_score': array([0.97277268, 0.97277268, 0.97277268, 0.97277268, 0.97277268,\n",
              "        0.97277268, 0.97277268]),\n",
              " 'std_train_score': array([0.00081202, 0.00081202, 0.00081202, 0.00081202, 0.00081202,\n",
              "        0.00081202, 0.00081202])}"
            ]
          },
          "metadata": {},
          "execution_count": 173
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Print the best hyperparameters and their score\n",
        "print(\"Best parameters: {}\".format(clf2.best_params_))\n",
        "print(\"Best cross-validation score: {:.2f}\".format(clf2.best_score_))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fUgkGNvu23rU",
        "outputId": "53591f93-d9d3-4257-b685-b9a6c4a5acc2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best parameters: {'C': 0.001}\n",
            "Best cross-validation score: 0.97\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Now, we test the model on the test set\n",
        "svm_best2 = clf2.best_estimator_\n",
        "print(\"Test set score: {:.2f}\".format(svm_best2.score(X_test, y_test)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HAaa0PSo3Djr",
        "outputId": "87777fe9-2988-4614-80ce-b753f0763abd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test set score: 0.97\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The results of the other models from the last task sheet on this data are\n",
        "\n",
        "LDA Model: 89.22%\n",
        "QDA Model: 88.24%\n",
        "Logistic Regression Model: 88.24%\n",
        "\n",
        "The accuracy of this model using cross validation and svm is 97%\n",
        "\n",
        "All the models seem to be good models, but the model we generated in this task sheet performs better on the data. One of the reasons this might be is because we used cross validation to split the data. Cross validation does a better job at splitting the data more accurately and optimally than the \"train test split\" function in python, which was utilized fro all the models in the first task sheet."
      ],
      "metadata": {
        "id": "wA7kcphGOWi4"
      }
    }
  ]
}