Assignment_2_Classification.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "Question#1 (80 Marks): Use the Iris dataset iris.data file available at https://archivebeta.ics.uci.edu/ml/datasets/iris"
      ],
      "metadata": {
        "id": "EBVCyuM0zijQ"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "hjb5GVfOeeH0"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "\n",
        "URL = 'https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data'\n",
        "\n",
        "df = pd.read_csv(URL, names=['sepal length','sepal width', 'petal length', 'petal width','species'])"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "TNfEDTXzjHlx",
        "outputId": "5feb63bc-fe84-443f-d6b8-510b885bf7bf"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     sepal length  sepal width  petal length  petal width         species\n",
              "0             5.1          3.5           1.4          0.2     Iris-setosa\n",
              "1             4.9          3.0           1.4          0.2     Iris-setosa\n",
              "2             4.7          3.2           1.3          0.2     Iris-setosa\n",
              "3             4.6          3.1           1.5          0.2     Iris-setosa\n",
              "4             5.0          3.6           1.4          0.2     Iris-setosa\n",
              "..            ...          ...           ...          ...             ...\n",
              "145           6.7          3.0           5.2          2.3  Iris-virginica\n",
              "146           6.3          2.5           5.0          1.9  Iris-virginica\n",
              "147           6.5          3.0           5.2          2.0  Iris-virginica\n",
              "148           6.2          3.4           5.4          2.3  Iris-virginica\n",
              "149           5.9          3.0           5.1          1.8  Iris-virginica\n",
              "\n",
              "[150 rows x 5 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-9eefd15f-0654-40e6-91b6-2227d0b421ae\">\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>sepal length</th>\n",
              "      <th>sepal width</th>\n",
              "      <th>petal length</th>\n",
              "      <th>petal width</th>\n",
              "      <th>species</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5.1</td>\n",
              "      <td>3.5</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4.9</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>4.7</td>\n",
              "      <td>3.2</td>\n",
              "      <td>1.3</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4.6</td>\n",
              "      <td>3.1</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5.0</td>\n",
              "      <td>3.6</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>145</th>\n",
              "      <td>6.7</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.2</td>\n",
              "      <td>2.3</td>\n",
              "      <td>Iris-virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>146</th>\n",
              "      <td>6.3</td>\n",
              "      <td>2.5</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1.9</td>\n",
              "      <td>Iris-virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>147</th>\n",
              "      <td>6.5</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.2</td>\n",
              "      <td>2.0</td>\n",
              "      <td>Iris-virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>148</th>\n",
              "      <td>6.2</td>\n",
              "      <td>3.4</td>\n",
              "      <td>5.4</td>\n",
              "      <td>2.3</td>\n",
              "      <td>Iris-virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>149</th>\n",
              "      <td>5.9</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.1</td>\n",
              "      <td>1.8</td>\n",
              "      <td>Iris-virginica</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>150 rows × 5 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9eefd15f-0654-40e6-91b6-2227d0b421ae')\"\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",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-9eefd15f-0654-40e6-91b6-2227d0b421ae 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-9eefd15f-0654-40e6-91b6-2227d0b421ae');\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": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "1- Take a quick look at the data structure:\n",
        "a. Use head(), info(), and describe() (2.5 Marks)\n",
        "b. Plot the relationship between the Sepal Length and Sepal Width (sample figure\n",
        "shown below): (5 Marks)"
      ],
      "metadata": {
        "id": "zWDJy6qpj_XJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#1a\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "BF7_xVUVkGov",
        "outputId": "cf72f54a-ea96-4307-da06-a53e6c1d1d50"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   sepal length  sepal width  petal length  petal width      species\n",
              "0           5.1          3.5           1.4          0.2  Iris-setosa\n",
              "1           4.9          3.0           1.4          0.2  Iris-setosa\n",
              "2           4.7          3.2           1.3          0.2  Iris-setosa\n",
              "3           4.6          3.1           1.5          0.2  Iris-setosa\n",
              "4           5.0          3.6           1.4          0.2  Iris-setosa"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-4dfcc78b-4ef8-41b0-b4a7-b4d91040a5c3\">\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>sepal length</th>\n",
              "      <th>sepal width</th>\n",
              "      <th>petal length</th>\n",
              "      <th>petal width</th>\n",
              "      <th>species</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5.1</td>\n",
              "      <td>3.5</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4.9</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>4.7</td>\n",
              "      <td>3.2</td>\n",
              "      <td>1.3</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4.6</td>\n",
              "      <td>3.1</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5.0</td>\n",
              "      <td>3.6</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4dfcc78b-4ef8-41b0-b4a7-b4d91040a5c3')\"\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",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-4dfcc78b-4ef8-41b0-b4a7-b4d91040a5c3 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-4dfcc78b-4ef8-41b0-b4a7-b4d91040a5c3');\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": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#1a\n",
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "u3ZL0v7ckNPp",
        "outputId": "d689b4a4-84de-41b9-bee1-fc8788f7575a"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 150 entries, 0 to 149\n",
            "Data columns (total 5 columns):\n",
            " #   Column        Non-Null Count  Dtype  \n",
            "---  ------        --------------  -----  \n",
            " 0   sepal length  150 non-null    float64\n",
            " 1   sepal width   150 non-null    float64\n",
            " 2   petal length  150 non-null    float64\n",
            " 3   petal width   150 non-null    float64\n",
            " 4   species       150 non-null    object \n",
            "dtypes: float64(4), object(1)\n",
            "memory usage: 6.0+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#1a\n",
        "df.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "pI-fsJ8VkRL-",
        "outputId": "1166258f-2e9b-4457-9797-d24394bb5290"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       sepal length  sepal width  petal length  petal width\n",
              "count    150.000000   150.000000    150.000000   150.000000\n",
              "mean       5.843333     3.054000      3.758667     1.198667\n",
              "std        0.828066     0.433594      1.764420     0.763161\n",
              "min        4.300000     2.000000      1.000000     0.100000\n",
              "25%        5.100000     2.800000      1.600000     0.300000\n",
              "50%        5.800000     3.000000      4.350000     1.300000\n",
              "75%        6.400000     3.300000      5.100000     1.800000\n",
              "max        7.900000     4.400000      6.900000     2.500000"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-e664f065-8068-4dce-93e6-e285c44556be\">\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>sepal length</th>\n",
              "      <th>sepal width</th>\n",
              "      <th>petal length</th>\n",
              "      <th>petal width</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>150.000000</td>\n",
              "      <td>150.000000</td>\n",
              "      <td>150.000000</td>\n",
              "      <td>150.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>5.843333</td>\n",
              "      <td>3.054000</td>\n",
              "      <td>3.758667</td>\n",
              "      <td>1.198667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.828066</td>\n",
              "      <td>0.433594</td>\n",
              "      <td>1.764420</td>\n",
              "      <td>0.763161</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>4.300000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.100000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>5.100000</td>\n",
              "      <td>2.800000</td>\n",
              "      <td>1.600000</td>\n",
              "      <td>0.300000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>5.800000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>4.350000</td>\n",
              "      <td>1.300000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>6.400000</td>\n",
              "      <td>3.300000</td>\n",
              "      <td>5.100000</td>\n",
              "      <td>1.800000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>7.900000</td>\n",
              "      <td>4.400000</td>\n",
              "      <td>6.900000</td>\n",
              "      <td>2.500000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e664f065-8068-4dce-93e6-e285c44556be')\"\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",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-e664f065-8068-4dce-93e6-e285c44556be 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-e664f065-8068-4dce-93e6-e285c44556be');\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": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#1b\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "print(df['species'].unique())\n",
        "\n",
        "setosa = df.loc[df['species'] == 'Iris-setosa']\n",
        "versicolor = df.loc[df['species'] == 'Iris-versicolor']\n",
        "virginica = df.loc[df['species'] == 'Iris-virginica']\n",
        "\n",
        "plt.scatter(setosa['sepal length'], setosa['sepal width'], label='setosa', marker='*', color='b')\n",
        "plt.scatter(versicolor['sepal length'], versicolor['sepal width'], label='versicolor', marker='o', color='r')\n",
        "plt.scatter(virginica['sepal length'], virginica['sepal width'], label='virginica', marker='X', color='k')\n",
        "plt.legend(loc='best')\n",
        "plt.title('Iris Sepal Length vs Sepal Width')\n",
        "plt.xlabel('Sepal Length')\n",
        "plt.ylabel('Sepal Width')\n",
        "plt.figure(figsize=(10,4))\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 330
        },
        "id": "rcLuPXTqkZ0i",
        "outputId": "132777ed-e390-47e9-f869-1a76fa8efa93"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['Iris-setosa' 'Iris-versicolor' 'Iris-virginica']\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x288 with 0 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#1b\n",
        "plt.scatter(setosa['petal length'], setosa['petal width'], label='setosa', marker='*', color='b')\n",
        "plt.scatter(versicolor['petal length'], versicolor['petal width'], label='versicolor', marker='o', color='r')\n",
        "plt.scatter(virginica['petal length'], virginica['petal width'], label='virginica', marker='X', color='k')\n",
        "plt.legend(loc='best')\n",
        "plt.title('Iris Petal Length vs Petal Width')\n",
        "plt.xlabel('Petal Length')\n",
        "plt.ylabel('Petal Width')\n",
        "plt.figure(figsize=(10,4))\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        },
        "id": "7U4TjgZ2O2AG",
        "outputId": "f6f33774-5e09-4643-c568-39eaab861388"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x288 with 0 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "2- Check if there are null values and fill them with the average value of that attribute if it is\n",
        "numerical, and with the most common occurrence of that attribute if it is categorical\n",
        "(2.5 Marks)."
      ],
      "metadata": {
        "id": "feOv8nUXRB0B"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#2\n",
        "df.isnull().sum()\n",
        "# there are no null values"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vLWPZAK-Q6JY",
        "outputId": "5fa68297-cba8-491a-c04c-87e489726b3b"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "sepal length    0\n",
              "sepal width     0\n",
              "petal length    0\n",
              "petal width     0\n",
              "species         0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "3- Split the data into 80% training set and 20% testing set, use the stratify option to ensure\n",
        "you have all three classes represented in the training set (10 Marks)."
      ],
      "metadata": {
        "id": "oSx13ISNSd7M"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#3\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "key = {'Iris-setosa':0,'Iris-versicolor':1,'Iris-virginica':2}\n",
        "X = df.iloc[:,df.columns != 'species']\n",
        "y = df['species'].map(key)\n",
        "SEED = 4\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X,y, test_size=0.20,random_state=SEED, stratify=y)"
      ],
      "metadata": {
        "id": "O2RvHYK6SoEg"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "4- Train a Support Vector Machine Classifier (SVC) to predict the species of the iris, with a\n",
        "Polynomial Kernel. (10 Marks)"
      ],
      "metadata": {
        "id": "_7yB1F4hWEkp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#4\n",
        "from sklearn.pipeline import make_pipeline\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.svm import SVC\n",
        "\n",
        "poly_kernel_svm_clf = make_pipeline(StandardScaler(), SVC(kernel=\"poly\", degree=3, coef0=1, C=5))\n",
        "\n",
        "poly_kernel_svm_clf.fit(X_train, y_train)\n",
        "\n",
        "poly_kernel_svm_clf.score(X_test, y_test)\n",
        "\n",
        "\n",
        "y_pred = poly_kernel_svm_clf.predict(X_test)\n",
        "\n",
        "y_pred_vs_test = pd.DataFrame({'Prediction':y_pred,'Actual':y_test})\n",
        "print(y_pred_vs_test)\n",
        "\n",
        "print(f'Prediction vs Actual Score: {poly_kernel_svm_clf.score(X_test, y_test)}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MP-sOKhQWJaO",
        "outputId": "4b814be1-1c2a-4d77-bd48-c375f062a282"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "     Prediction  Actual\n",
            "68            1       1\n",
            "8             0       0\n",
            "53            1       1\n",
            "92            1       1\n",
            "147           2       2\n",
            "23            0       0\n",
            "72            2       1\n",
            "40            0       0\n",
            "49            0       0\n",
            "57            1       1\n",
            "115           2       2\n",
            "30            0       0\n",
            "113           2       2\n",
            "59            1       1\n",
            "5             0       0\n",
            "110           2       2\n",
            "39            0       0\n",
            "138           2       2\n",
            "9             0       0\n",
            "100           2       2\n",
            "143           2       2\n",
            "1             0       0\n",
            "61            1       1\n",
            "107           2       2\n",
            "140           2       2\n",
            "89            1       1\n",
            "46            0       0\n",
            "73            1       1\n",
            "122           2       2\n",
            "56            1       1\n",
            "Prediction vs Actual Score: 0.9666666666666667\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "5- Make predictions using cross-validation with cv=3."
      ],
      "metadata": {
        "id": "KxEGwHtsX-lZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#5\n",
        "from sklearn.model_selection import cross_val_score\n",
        "from sklearn.model_selection import cross_val_predict\n",
        "\n",
        "cross_val_score(poly_kernel_svm_clf, X_train, y_train, cv=3)\n",
        "y_predcv = cross_val_predict(poly_kernel_svm_clf, X_train, y_train, cv=3)\n",
        "\n",
        "y_predcv_vs_train = pd.DataFrame({'Prediction':y_predcv,'Actual':y_train})\n",
        "y_predcv_vs_train\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "nSWI7RnWX93M",
        "outputId": "9b43b7e0-9a66-4062-f0db-18ec63a9a902"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Prediction  Actual\n",
              "137           2       2\n",
              "109           2       2\n",
              "64            1       1\n",
              "103           2       2\n",
              "101           2       2\n",
              "..          ...     ...\n",
              "11            0       0\n",
              "94            1       1\n",
              "0             0       0\n",
              "104           2       2\n",
              "44            0       0\n",
              "\n",
              "[120 rows x 2 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-10b0af25-50e4-4cad-a6e0-c5d05c81dfcc\">\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>Prediction</th>\n",
              "      <th>Actual</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>137</th>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>109</th>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>64</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>103</th>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>101</th>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>94</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>104</th>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>120 rows × 2 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-10b0af25-50e4-4cad-a6e0-c5d05c81dfcc')\"\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",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-10b0af25-50e4-4cad-a6e0-c5d05c81dfcc 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-10b0af25-50e4-4cad-a6e0-c5d05c81dfcc');\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": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "6- Report on the following performance metrics (10 Marks)"
      ],
      "metadata": {
        "id": "rTi8NBJrdIMF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#6\n",
        "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay\n",
        "from sklearn.metrics import accuracy_score\n",
        "\n",
        "\n",
        "ConfusionMatrixDisplay.from_predictions(y_train, y_predcv)\n",
        "\n",
        "print(f'accuracy score: {accuracy_score(y_train, y_predcv)}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "uZNrn-oedHBE",
        "outputId": "f1c79256-30bc-4877-dd06-a09dcd86dd1f"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "accuracy score: 0.95\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "7- Repeat steps 4 to 6 with a Gaussian RBF Kernel. (20 Marks)"
      ],
      "metadata": {
        "id": "ULKREN1-rejf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#7-4\n",
        "from sklearn.gaussian_process import GaussianProcessClassifier\n",
        "from sklearn.gaussian_process.kernels import RBF\n",
        "\n",
        "kernel = 1.0 * RBF(1.0)\n",
        "\n",
        "rbf_kernel_gaussian_process_clf = make_pipeline(StandardScaler(), GaussianProcessClassifier(kernel=kernel))\n",
        "\n",
        "rbf_kernel_gaussian_process_clf.fit(X_train, y_train)\n",
        "\n",
        "rbf_kernel_gaussian_process_clf.score(X_test, y_test)\n",
        "\n",
        "\n",
        "y_pred_gprbf = rbf_kernel_gaussian_process_clf.predict(X_test)\n",
        "\n",
        "y_pred_gprbf_vs_test = pd.DataFrame({'Prediction':y_pred_gprbf,'Actual':y_test})\n",
        "print(y_pred_gprbf_vs_test)\n",
        "\n",
        "print(f'Prediction vs Actual Score: {rbf_kernel_gaussian_process_clf.score(X_test, y_test)}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ro1iZ-39ri3T",
        "outputId": "b7cda824-31c1-4c59-a14a-ca138ac40a90"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "     Prediction  Actual\n",
            "68            2       1\n",
            "8             0       0\n",
            "53            1       1\n",
            "92            1       1\n",
            "147           2       2\n",
            "23            0       0\n",
            "72            2       1\n",
            "40            0       0\n",
            "49            0       0\n",
            "57            1       1\n",
            "115           2       2\n",
            "30            0       0\n",
            "113           2       2\n",
            "59            1       1\n",
            "5             0       0\n",
            "110           2       2\n",
            "39            0       0\n",
            "138           2       2\n",
            "9             0       0\n",
            "100           2       2\n",
            "143           2       2\n",
            "1             0       0\n",
            "61            1       1\n",
            "107           2       2\n",
            "140           2       2\n",
            "89            1       1\n",
            "46            0       0\n",
            "73            1       1\n",
            "122           2       2\n",
            "56            1       1\n",
            "Prediction vs Actual Score: 0.9333333333333333\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#7-5\n",
        "cross_val_score(rbf_kernel_gaussian_process_clf, X_train, y_train, cv=3)\n",
        "y_predcv_gprbf = cross_val_predict(rbf_kernel_gaussian_process_clf, X_train, y_train, cv=3)\n",
        "\n",
        "y_predcv_gprbf_vs_train = pd.DataFrame({'Prediction':y_predcv_gprbf,'Actual':y_train})\n",
        "y_predcv_gprbf_vs_train"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "lxR3O3VMu832",
        "outputId": "e211368d-2c59-4efd-d96f-539b9a0f5a0a"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Prediction  Actual\n",
              "137           2       2\n",
              "109           2       2\n",
              "64            1       1\n",
              "103           2       2\n",
              "101           2       2\n",
              "..          ...     ...\n",
              "11            0       0\n",
              "94            1       1\n",
              "0             0       0\n",
              "104           2       2\n",
              "44            0       0\n",
              "\n",
              "[120 rows x 2 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-a998d1d2-f9fe-4a49-85bc-812e2bca63d3\">\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>Prediction</th>\n",
              "      <th>Actual</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>137</th>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>109</th>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>64</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>103</th>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>101</th>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>94</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>104</th>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>120 rows × 2 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a998d1d2-f9fe-4a49-85bc-812e2bca63d3')\"\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",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-a998d1d2-f9fe-4a49-85bc-812e2bca63d3 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-a998d1d2-f9fe-4a49-85bc-812e2bca63d3');\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": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#7-6\n",
        "ConfusionMatrixDisplay.from_predictions(y_train, y_predcv_gprbf)\n",
        "\n",
        "print(f'accuracy score: {accuracy_score(y_train, y_predcv_gprbf)}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "yug-i5Hav5YB",
        "outputId": "bbad5fc6-b6ef-4c48-a891-574e5ea66dc0"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "accuracy score: 0.9583333333333334\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "8- Repeat steps 4 to 6 with a k-NN classifier and k=10. (20 Marks)\n"
      ],
      "metadata": {
        "id": "NHZxlQAMuxIL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#8-4\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "\n",
        "knn_clf = make_pipeline(StandardScaler(), KNeighborsClassifier(n_neighbors=10))\n",
        "\n",
        "knn_clf.fit(X_train, y_train)\n",
        "\n",
        "knn_clf.score(X_test, y_test)\n",
        "\n",
        "\n",
        "y_pred_knn = knn_clf.predict(X_test)\n",
        "\n",
        "y_pred_knn_vs_test = pd.DataFrame({'Prediction':y_pred_knn,'Actual':y_test})\n",
        "print(y_pred_knn_vs_test)\n",
        "\n",
        "print(f'Prediction vs Actual Score: {knn_clf.score(X_test, y_test)}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CLkDR8VUuzR0",
        "outputId": "1767ba67-b875-40ba-cea3-692d276dbdd3"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "     Prediction  Actual\n",
            "68            1       1\n",
            "8             0       0\n",
            "53            1       1\n",
            "92            1       1\n",
            "147           2       2\n",
            "23            0       0\n",
            "72            2       1\n",
            "40            0       0\n",
            "49            0       0\n",
            "57            1       1\n",
            "115           2       2\n",
            "30            0       0\n",
            "113           2       2\n",
            "59            1       1\n",
            "5             0       0\n",
            "110           2       2\n",
            "39            0       0\n",
            "138           1       2\n",
            "9             0       0\n",
            "100           2       2\n",
            "143           2       2\n",
            "1             0       0\n",
            "61            1       1\n",
            "107           2       2\n",
            "140           2       2\n",
            "89            1       1\n",
            "46            0       0\n",
            "73            1       1\n",
            "122           2       2\n",
            "56            1       1\n",
            "Prediction vs Actual Score: 0.9333333333333333\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#8-5\n",
        "cross_val_score(knn_clf, X_train, y_train, cv=3)\n",
        "y_predcv_knn = cross_val_predict(knn_clf, X_train, y_train, cv=3)\n",
        "\n",
        "y_predcv_knn_vs_train = pd.DataFrame({'Prediction':y_predcv_knn,'Actual':y_train})\n",
        "y_predcv_knn_vs_train"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "G1yRtlTYyVoK",
        "outputId": "7f415908-3405-49ac-d6bd-494f8581ec93"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Prediction  Actual\n",
              "137           2       2\n",
              "109           2       2\n",
              "64            1       1\n",
              "103           2       2\n",
              "101           2       2\n",
              "..          ...     ...\n",
              "11            0       0\n",
              "94            1       1\n",
              "0             0       0\n",
              "104           2       2\n",
              "44            0       0\n",
              "\n",
              "[120 rows x 2 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-d74dd057-6116-4dd9-944f-ca72f16446d8\">\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>Prediction</th>\n",
              "      <th>Actual</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>137</th>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>109</th>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>64</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>103</th>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>101</th>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>94</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>104</th>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>120 rows × 2 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d74dd057-6116-4dd9-944f-ca72f16446d8')\"\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",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-d74dd057-6116-4dd9-944f-ca72f16446d8 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-d74dd057-6116-4dd9-944f-ca72f16446d8');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#8-6\n",
        "ConfusionMatrixDisplay.from_predictions(y_train, y_predcv_knn)\n",
        "\n",
        "print(f'accuracy score: {accuracy_score(y_train, y_predcv_knn)}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "KBTSugt3y6Sz",
        "outputId": "464e594d-bce2-4dc5-dc61-b4980f842094"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "accuracy score: 0.9583333333333334\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Question#2 (20 Marks): Create a moons dataset with make_moons command, and choose\n",
        "the number of samples and noise level."
      ],
      "metadata": {
        "id": "8BzP_RyizQRO"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "1- Train a SVM classifier on the moons dataset you created. (10 Marks)"
      ],
      "metadata": {
        "id": "zd14MB1KzTLd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#1\n",
        "from sklearn.datasets import make_moons\n",
        "import numpy as np\n",
        "\n",
        "X_moon, y_moon = make_moons(n_samples=1000, noise=0.1, random_state=4)\n",
        "\n",
        "poly_kernel_svm_clf_moon = make_pipeline(StandardScaler(), SVC(kernel=\"poly\", degree=3, coef0=1, C=5))\n",
        "poly_kernel_svm_clf_moon.fit(X_moon, y_moon)\n",
        "poly_kernel_svm_clf_moon.score"
      ],
      "metadata": {
        "id": "KzcqKi7JzYM1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2a356077-22a9-453f-82c3-bac0dffefcbb"
      },
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<function sklearn.pipeline.Pipeline.score(self, X, y=None, sample_weight=None)>"
            ]
          },
          "metadata": {},
          "execution_count": 78
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "2- Plot the dataset along the decision boundary. (10 Marks)"
      ],
      "metadata": {
        "id": "zoCFbNNOzXZ4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#2\n",
        "def plot_dataset(X, y, axes):\n",
        "    plt.plot(X[:, 0][y==0], X[:, 1][y==0], \"bs\")\n",
        "    plt.plot(X[:, 0][y==1], X[:, 1][y==1], \"g^\")\n",
        "    plt.axis(axes)\n",
        "    plt.grid(True)\n",
        "    plt.xlabel(\"$x_1$\")\n",
        "    plt.ylabel(\"$x_2$\", rotation=0)\n",
        "  \n",
        "def plot_predictions(clf, axes):\n",
        "    x0s = np.linspace(axes[0], axes[1], 100)\n",
        "    x1s = np.linspace(axes[2], axes[3], 100)\n",
        "    x0, x1 = np.meshgrid(x0s, x1s)\n",
        "    X = np.c_[x0.ravel(), x1.ravel()]\n",
        "    y_pred = clf.predict(X).reshape(x0.shape)\n",
        "    y_decision = clf.decision_function(X).reshape(x0.shape)\n",
        "    plt.contourf(x0, x1, y_pred, cmap=plt.cm.brg, alpha=0.2)\n",
        "    plt.contourf(x0, x1, y_decision, cmap=plt.cm.brg, alpha=0.1)\n",
        "\n",
        "poly100_kernel_svm_clf_moon = make_pipeline(StandardScaler(), SVC(kernel=\"poly\", degree=10, coef0=100, C=5))\n",
        "poly100_kernel_svm_clf_moon.fit(X_moon, y_moon)\n",
        "\n",
        "fig, axes = plt.subplots(ncols=2, figsize=(20, 8), sharex=True, sharey=True)\n",
        "\n",
        "plt.sca(axes[0])\n",
        "plot_predictions(poly_kernel_svm_clf_moon, [-1.5, 2.45, -1, 1.5])\n",
        "plot_dataset(X_moon, y_moon, [-1.5, 2.4, -1, 1.5])\n",
        "plt.title(\"degree=3, coef0=1, C=5\")\n",
        "\n",
        "plt.sca(axes[1])\n",
        "plot_predictions(poly100_kernel_svm_clf_moon, [-1.5, 2.45, -1, 1.5])\n",
        "plot_dataset(X_moon, y_moon, [-1.5, 2.4, -1, 1.5])\n",
        "plt.title(\"degree=10, coef0=100, C=5\")\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "5YfSiOluzYsM",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 515
        },
        "outputId": "b0d83a12-b37b-4c0c-f7ef-9cc7a87d6559"
      },
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x576 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#2\n",
        "G1, G2 = 0.1, 5\n",
        "C1, C2 = 0.001, 1000\n",
        "hyperparams = (G1, C1), (G1, C2), (G2, C1), (G2, C2)\n",
        "\n",
        "models = []\n",
        "\n",
        "for g, c in hyperparams:\n",
        "  rbf_kernel_svm_clf_moon = make_pipeline(StandardScaler(), SVC(kernel=\"rbf\", gamma=g, C=c))\n",
        "  rbf_kernel_svm_clf_moon.fit(X_moon, y_moon)\n",
        "  models.append(rbf_kernel_svm_clf_moon)\n",
        "\n",
        "fig, axes = plt.subplots(nrows=2, ncols=2, figsize=(20, 8), sharex=True, sharey=True)\n",
        "\n",
        "for i, model in enumerate(models):\n",
        "  plt.sca(axes[i // 2, i % 2])\n",
        "  plot_predictions(model, [-1.5, 2.45, -1, 1.5])\n",
        "  plot_dataset(X_moon, y_moon, [-1.5, 2.45, -1, 1.5])\n",
        "  G, C = hyperparams[i]\n",
        "  plt.title(f\"gamma={G}, C={C}\")\n",
        "  if i in (0, 1):\n",
        "        plt.xlabel(\"\")\n",
        "  if i in (1, 3):\n",
        "        plt.ylabel(\"\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 515
        },
        "id": "gCEImHfyP5lw",
        "outputId": "51ad9e84-42a4-4dc1-86cf-ff4f6d827d62"
      },
      "execution_count": 87,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x576 with 4 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**References**\n",
        "\n",
        "handson-ml3/05_support_vector_machines.ipynb at main · ageron/handson-ml3. (n.d.). GitHub. Retrieved October 23, 2022, from https://github.com/ageron/handson-ml3/blob/main/05_support_vector_machines.ipynb"
      ],
      "metadata": {
        "id": "115eVP4cPXjM"
      }
    }
  ]
}