FMML_CLF1_Lab4_2022.ipynb

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        "<a href=\"https://colab.research.google.com/github/harika136/IIIT_IHUB_HYD/blob/main/FMML_CLF1_Lab4_2022.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    {
      "cell_type": "markdown",
      "metadata": {
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      "source": [
        "# **FOUNDATIONS OF MODERN MACHINE LEARNING, IIIT Hyderabad**\n",
        "### MODULE: CLASSIFICATION-1\n",
        "### LAB-4 : Understanding Cross-Validation and Standardization\n",
        "#### Module Coordinator: Tanvi Kamble\n",
        "\n",
        "---"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Section 1: Tweaking the parameters**"
      ],
      "metadata": {
        "id": "qH_EEbUzvRBM"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's stat the lab by writing knn code on a cancer detection dataset\n"
      ],
      "metadata": {
        "id": "ZPj-s_67uscz"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ITvv8HAmjquR"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import sklearn\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn import datasets"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cancer = datasets.load_breast_cancer(as_frame=True)"
      ],
      "metadata": {
        "id": "167jISBahmSu"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cancer.target_names"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IoKfdTQrKGnM",
        "outputId": "97fb2582-ae27-45d1-8b35-7ca32329a3b2"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['malignant', 'benign'], dtype='<U9')"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cancer.frame"
      ],
      "metadata": {
        "colab": {
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          "height": 522
        },
        "id": "l1Fp6m9jhrpP",
        "outputId": "d7ab7581-de8b-4e70-ac4b-3c348a26f512"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     mean radius  mean texture  mean perimeter  mean area  mean smoothness  \\\n",
              "0          17.99         10.38          122.80     1001.0          0.11840   \n",
              "1          20.57         17.77          132.90     1326.0          0.08474   \n",
              "2          19.69         21.25          130.00     1203.0          0.10960   \n",
              "3          11.42         20.38           77.58      386.1          0.14250   \n",
              "4          20.29         14.34          135.10     1297.0          0.10030   \n",
              "..           ...           ...             ...        ...              ...   \n",
              "564        21.56         22.39          142.00     1479.0          0.11100   \n",
              "565        20.13         28.25          131.20     1261.0          0.09780   \n",
              "566        16.60         28.08          108.30      858.1          0.08455   \n",
              "567        20.60         29.33          140.10     1265.0          0.11780   \n",
              "568         7.76         24.54           47.92      181.0          0.05263   \n",
              "\n",
              "     mean compactness  mean concavity  mean concave points  mean symmetry  \\\n",
              "0             0.27760         0.30010              0.14710         0.2419   \n",
              "1             0.07864         0.08690              0.07017         0.1812   \n",
              "2             0.15990         0.19740              0.12790         0.2069   \n",
              "3             0.28390         0.24140              0.10520         0.2597   \n",
              "4             0.13280         0.19800              0.10430         0.1809   \n",
              "..                ...             ...                  ...            ...   \n",
              "564           0.11590         0.24390              0.13890         0.1726   \n",
              "565           0.10340         0.14400              0.09791         0.1752   \n",
              "566           0.10230         0.09251              0.05302         0.1590   \n",
              "567           0.27700         0.35140              0.15200         0.2397   \n",
              "568           0.04362         0.00000              0.00000         0.1587   \n",
              "\n",
              "     mean fractal dimension  ...  worst texture  worst perimeter  worst area  \\\n",
              "0                   0.07871  ...          17.33           184.60      2019.0   \n",
              "1                   0.05667  ...          23.41           158.80      1956.0   \n",
              "2                   0.05999  ...          25.53           152.50      1709.0   \n",
              "3                   0.09744  ...          26.50            98.87       567.7   \n",
              "4                   0.05883  ...          16.67           152.20      1575.0   \n",
              "..                      ...  ...            ...              ...         ...   \n",
              "564                 0.05623  ...          26.40           166.10      2027.0   \n",
              "565                 0.05533  ...          38.25           155.00      1731.0   \n",
              "566                 0.05648  ...          34.12           126.70      1124.0   \n",
              "567                 0.07016  ...          39.42           184.60      1821.0   \n",
              "568                 0.05884  ...          30.37            59.16       268.6   \n",
              "\n",
              "     worst smoothness  worst compactness  worst concavity  \\\n",
              "0             0.16220            0.66560           0.7119   \n",
              "1             0.12380            0.18660           0.2416   \n",
              "2             0.14440            0.42450           0.4504   \n",
              "3             0.20980            0.86630           0.6869   \n",
              "4             0.13740            0.20500           0.4000   \n",
              "..                ...                ...              ...   \n",
              "564           0.14100            0.21130           0.4107   \n",
              "565           0.11660            0.19220           0.3215   \n",
              "566           0.11390            0.30940           0.3403   \n",
              "567           0.16500            0.86810           0.9387   \n",
              "568           0.08996            0.06444           0.0000   \n",
              "\n",
              "     worst concave points  worst symmetry  worst fractal dimension  target  \n",
              "0                  0.2654          0.4601                  0.11890       0  \n",
              "1                  0.1860          0.2750                  0.08902       0  \n",
              "2                  0.2430          0.3613                  0.08758       0  \n",
              "3                  0.2575          0.6638                  0.17300       0  \n",
              "4                  0.1625          0.2364                  0.07678       0  \n",
              "..                    ...             ...                      ...     ...  \n",
              "564                0.2216          0.2060                  0.07115       0  \n",
              "565                0.1628          0.2572                  0.06637       0  \n",
              "566                0.1418          0.2218                  0.07820       0  \n",
              "567                0.2650          0.4087                  0.12400       0  \n",
              "568                0.0000          0.2871                  0.07039       1  \n",
              "\n",
              "[569 rows x 31 columns]"
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>mean radius</th>\n",
              "      <th>mean texture</th>\n",
              "      <th>mean perimeter</th>\n",
              "      <th>mean area</th>\n",
              "      <th>mean smoothness</th>\n",
              "      <th>mean compactness</th>\n",
              "      <th>mean concavity</th>\n",
              "      <th>mean concave points</th>\n",
              "      <th>mean symmetry</th>\n",
              "      <th>mean fractal dimension</th>\n",
              "      <th>...</th>\n",
              "      <th>worst texture</th>\n",
              "      <th>worst perimeter</th>\n",
              "      <th>worst area</th>\n",
              "      <th>worst smoothness</th>\n",
              "      <th>worst compactness</th>\n",
              "      <th>worst concavity</th>\n",
              "      <th>worst concave points</th>\n",
              "      <th>worst symmetry</th>\n",
              "      <th>worst fractal dimension</th>\n",
              "      <th>target</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>17.99</td>\n",
              "      <td>10.38</td>\n",
              "      <td>122.80</td>\n",
              "      <td>1001.0</td>\n",
              "      <td>0.11840</td>\n",
              "      <td>0.27760</td>\n",
              "      <td>0.30010</td>\n",
              "      <td>0.14710</td>\n",
              "      <td>0.2419</td>\n",
              "      <td>0.07871</td>\n",
              "      <td>...</td>\n",
              "      <td>17.33</td>\n",
              "      <td>184.60</td>\n",
              "      <td>2019.0</td>\n",
              "      <td>0.16220</td>\n",
              "      <td>0.66560</td>\n",
              "      <td>0.7119</td>\n",
              "      <td>0.2654</td>\n",
              "      <td>0.4601</td>\n",
              "      <td>0.11890</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>20.57</td>\n",
              "      <td>17.77</td>\n",
              "      <td>132.90</td>\n",
              "      <td>1326.0</td>\n",
              "      <td>0.08474</td>\n",
              "      <td>0.07864</td>\n",
              "      <td>0.08690</td>\n",
              "      <td>0.07017</td>\n",
              "      <td>0.1812</td>\n",
              "      <td>0.05667</td>\n",
              "      <td>...</td>\n",
              "      <td>23.41</td>\n",
              "      <td>158.80</td>\n",
              "      <td>1956.0</td>\n",
              "      <td>0.12380</td>\n",
              "      <td>0.18660</td>\n",
              "      <td>0.2416</td>\n",
              "      <td>0.1860</td>\n",
              "      <td>0.2750</td>\n",
              "      <td>0.08902</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>19.69</td>\n",
              "      <td>21.25</td>\n",
              "      <td>130.00</td>\n",
              "      <td>1203.0</td>\n",
              "      <td>0.10960</td>\n",
              "      <td>0.15990</td>\n",
              "      <td>0.19740</td>\n",
              "      <td>0.12790</td>\n",
              "      <td>0.2069</td>\n",
              "      <td>0.05999</td>\n",
              "      <td>...</td>\n",
              "      <td>25.53</td>\n",
              "      <td>152.50</td>\n",
              "      <td>1709.0</td>\n",
              "      <td>0.14440</td>\n",
              "      <td>0.42450</td>\n",
              "      <td>0.4504</td>\n",
              "      <td>0.2430</td>\n",
              "      <td>0.3613</td>\n",
              "      <td>0.08758</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>11.42</td>\n",
              "      <td>20.38</td>\n",
              "      <td>77.58</td>\n",
              "      <td>386.1</td>\n",
              "      <td>0.14250</td>\n",
              "      <td>0.28390</td>\n",
              "      <td>0.24140</td>\n",
              "      <td>0.10520</td>\n",
              "      <td>0.2597</td>\n",
              "      <td>0.09744</td>\n",
              "      <td>...</td>\n",
              "      <td>26.50</td>\n",
              "      <td>98.87</td>\n",
              "      <td>567.7</td>\n",
              "      <td>0.20980</td>\n",
              "      <td>0.86630</td>\n",
              "      <td>0.6869</td>\n",
              "      <td>0.2575</td>\n",
              "      <td>0.6638</td>\n",
              "      <td>0.17300</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>20.29</td>\n",
              "      <td>14.34</td>\n",
              "      <td>135.10</td>\n",
              "      <td>1297.0</td>\n",
              "      <td>0.10030</td>\n",
              "      <td>0.13280</td>\n",
              "      <td>0.19800</td>\n",
              "      <td>0.10430</td>\n",
              "      <td>0.1809</td>\n",
              "      <td>0.05883</td>\n",
              "      <td>...</td>\n",
              "      <td>16.67</td>\n",
              "      <td>152.20</td>\n",
              "      <td>1575.0</td>\n",
              "      <td>0.13740</td>\n",
              "      <td>0.20500</td>\n",
              "      <td>0.4000</td>\n",
              "      <td>0.1625</td>\n",
              "      <td>0.2364</td>\n",
              "      <td>0.07678</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
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              "      <td>...</td>\n",
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              "      <th>564</th>\n",
              "      <td>21.56</td>\n",
              "      <td>22.39</td>\n",
              "      <td>142.00</td>\n",
              "      <td>1479.0</td>\n",
              "      <td>0.11100</td>\n",
              "      <td>0.11590</td>\n",
              "      <td>0.24390</td>\n",
              "      <td>0.13890</td>\n",
              "      <td>0.1726</td>\n",
              "      <td>0.05623</td>\n",
              "      <td>...</td>\n",
              "      <td>26.40</td>\n",
              "      <td>166.10</td>\n",
              "      <td>2027.0</td>\n",
              "      <td>0.14100</td>\n",
              "      <td>0.21130</td>\n",
              "      <td>0.4107</td>\n",
              "      <td>0.2216</td>\n",
              "      <td>0.2060</td>\n",
              "      <td>0.07115</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>565</th>\n",
              "      <td>20.13</td>\n",
              "      <td>28.25</td>\n",
              "      <td>131.20</td>\n",
              "      <td>1261.0</td>\n",
              "      <td>0.09780</td>\n",
              "      <td>0.10340</td>\n",
              "      <td>0.14400</td>\n",
              "      <td>0.09791</td>\n",
              "      <td>0.1752</td>\n",
              "      <td>0.05533</td>\n",
              "      <td>...</td>\n",
              "      <td>38.25</td>\n",
              "      <td>155.00</td>\n",
              "      <td>1731.0</td>\n",
              "      <td>0.11660</td>\n",
              "      <td>0.19220</td>\n",
              "      <td>0.3215</td>\n",
              "      <td>0.1628</td>\n",
              "      <td>0.2572</td>\n",
              "      <td>0.06637</td>\n",
              "      <td>0</td>\n",
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              "      <th>566</th>\n",
              "      <td>16.60</td>\n",
              "      <td>28.08</td>\n",
              "      <td>108.30</td>\n",
              "      <td>858.1</td>\n",
              "      <td>0.08455</td>\n",
              "      <td>0.10230</td>\n",
              "      <td>0.09251</td>\n",
              "      <td>0.05302</td>\n",
              "      <td>0.1590</td>\n",
              "      <td>0.05648</td>\n",
              "      <td>...</td>\n",
              "      <td>34.12</td>\n",
              "      <td>126.70</td>\n",
              "      <td>1124.0</td>\n",
              "      <td>0.11390</td>\n",
              "      <td>0.30940</td>\n",
              "      <td>0.3403</td>\n",
              "      <td>0.1418</td>\n",
              "      <td>0.2218</td>\n",
              "      <td>0.07820</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>567</th>\n",
              "      <td>20.60</td>\n",
              "      <td>29.33</td>\n",
              "      <td>140.10</td>\n",
              "      <td>1265.0</td>\n",
              "      <td>0.11780</td>\n",
              "      <td>0.27700</td>\n",
              "      <td>0.35140</td>\n",
              "      <td>0.15200</td>\n",
              "      <td>0.2397</td>\n",
              "      <td>0.07016</td>\n",
              "      <td>...</td>\n",
              "      <td>39.42</td>\n",
              "      <td>184.60</td>\n",
              "      <td>1821.0</td>\n",
              "      <td>0.16500</td>\n",
              "      <td>0.86810</td>\n",
              "      <td>0.9387</td>\n",
              "      <td>0.2650</td>\n",
              "      <td>0.4087</td>\n",
              "      <td>0.12400</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>568</th>\n",
              "      <td>7.76</td>\n",
              "      <td>24.54</td>\n",
              "      <td>47.92</td>\n",
              "      <td>181.0</td>\n",
              "      <td>0.05263</td>\n",
              "      <td>0.04362</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.1587</td>\n",
              "      <td>0.05884</td>\n",
              "      <td>...</td>\n",
              "      <td>30.37</td>\n",
              "      <td>59.16</td>\n",
              "      <td>268.6</td>\n",
              "      <td>0.08996</td>\n",
              "      <td>0.06444</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.2871</td>\n",
              "      <td>0.07039</td>\n",
              "      <td>1</td>\n",
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              "    <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-b8cea390-fae9-49e6-8498-29adffeb4b7d 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-b8cea390-fae9-49e6-8498-29adffeb4b7d');\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": "code",
      "metadata": {
        "id": "6P69_W-cnTF4"
      },
      "source": [
        "X = cancer.data\n",
        "y = cancer.target"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "v4SlDqmync4p"
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.33, random_state = 17)"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Section 1.1: Changing Value of K\n",
        "Let's try to run the knn with different values of knn and compare the results"
      ],
      "metadata": {
        "id": "A987m6FMvaLm"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "owajk896p9yc"
      },
      "source": [
        "neighbors = []\n",
        "scores_list = []\n",
        "  \n",
        "for k in range(1, 25):\n",
        "    neighbors.append(k)\n",
        "    knn = KNeighborsClassifier(n_neighbors = k)\n",
        "    knn.fit(X_train,y_train)\n",
        "    scores = knn.score(X_test,y_test)\n",
        "    scores_list.append(scores)"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 388
        },
        "id": "UvD57XSyqSVT",
        "outputId": "27a470e9-a855-4b4b-bdf4-11ccc9390d0e"
      },
      "source": [
        "Accuracy = [x*100 for x in scores_list]\n",
        "# plot Accuracy versus k\n",
        "plt.figure(figsize = (10, 6))\n",
        "plt.plot(neighbors, Accuracy)\n",
        "plt.xlim([0, 25])\n",
        "plt.xlabel('Number of neighbors')\n",
        "plt.ylabel('Accuracy in %')\n",
        "plt.show()"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# determining the best k\n",
        "max_acc = max(Accuracy)\n",
        "optimal_ks = [neighbors[i] for i, j in enumerate(Accuracy) if j == max_acc]\n",
        "print(max_acc)\n",
        "print(optimal_ks)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "L_PArXY3phVM",
        "outputId": "5c2e8948-4d0e-4a02-b067-ef201459549c"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "93.61702127659575\n",
            "[10, 12, 13, 14, 22, 24]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "In this case, we see that the ideal number of neighbours can take any one of the values in the list [10,12,13,14,22,24].  \n",
        "However, this only tells us the effectiveness of the KNN classifier only if we have the same train-test split considered above. It still does not tell us how well the algorithm would perform if the test set were different. The results would have been different if we had split the data differently."
      ],
      "metadata": {
        "id": "AYObfxqdsGqF"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### AUC-ROC Curve"
      ],
      "metadata": {
        "id": "uD1tc30Bvnec"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "The AUC - ROC curve is a performance measurement for classification problems at various threshold settings. ROC is a probability curve and AUC represents the degree or measure of separability.\n",
        "It tells how much the model is capable of distinguishing between classes.  \n",
        "The diagonal in the curve divides the ROC space. Points above the diagonal represent good classification results (better than random); points below the line represent bad results (worse than random).    \n",
        "Higher the AUC, the better the model is at predicting 0 classes as 0 and 1 classes as 1.\n",
        "\n",
        "The ROC curve is plotted with the TPR (True Positive Rate) on the y-axis, and the FPR (False Positive Rate) on the x-axis."
      ],
      "metadata": {
        "id": "3US9wcfViqNk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "## AUC-ROC\n",
        "from sklearn.metrics import roc_curve\n",
        "from sklearn.metrics import auc\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "def plot_roc(y_test,predicted):\n",
        "  y_scores = predicted\n",
        "  fpr, tpr, threshold = roc_curve(y_test, y_scores)\n",
        "  roc_auc = auc(fpr, tpr)\n",
        "  plt.plot(fpr, tpr, 'b', label = 'AUC = %0.2f' % roc_auc)\n",
        "  plt.legend(loc = 'lower right')\n",
        "  plt.plot([0, 1], [0, 1],'r--')\n",
        "  plt.xlim([0, 1])\n",
        "  plt.ylim([0, 1])\n",
        "  plt.ylabel('True Positive Rate')\n",
        "  plt.xlabel('False Positive Rate')\n",
        "  plt.title('Receiver Operating Characteristic')\n",
        "  plt.show()"
      ],
      "metadata": {
        "id": "SAp-lfCCiced"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "knn = KNeighborsClassifier(n_neighbors = 10)\n",
        "knn.fit(X_train,y_train)\n",
        "predictions = knn.predict(X_test)\n",
        "plot_roc(y_test,predictions)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "jcNAfOc6ihrv",
        "outputId": "a027259e-71b3-4cc9-ea58-f49b29ef9e31"
      },
      "execution_count": 11,
      "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"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Section 1.2: KNN using a different train-test split"
      ],
      "metadata": {
        "id": "CO7rXVXkiwsL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "## Changing the random_state value here so that data is split differently\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.33, random_state = 1089)"
      ],
      "metadata": {
        "id": "v-i6n6y3swm4"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "neighbors = []\n",
        "scores_list = []\n",
        "  \n",
        "for k in range(1, 25):\n",
        "    neighbors.append(k)\n",
        "    knn = KNeighborsClassifier(n_neighbors = k)\n",
        "    knn.fit(X_train,y_train)\n",
        "    scores = knn.score(X_test,y_test)\n",
        "    scores_list.append(scores)"
      ],
      "metadata": {
        "id": "yUQsEfAjsFaS"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Accuracy = [x*100 for x in scores_list]\n",
        "# plot Accuracy versus k\n",
        "plt.figure(figsize = (10, 6))\n",
        "plt.plot(neighbors, Accuracy)\n",
        "plt.xlim([0, 25])\n",
        "plt.xlabel('Number of neighbors')\n",
        "plt.ylabel('Accuracy in %')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 388
        },
        "id": "wy9EH2Yms5e_",
        "outputId": "fb17fc96-5e7b-4e49-b43c-0e627ea6c9b7"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# determining the best k\n",
        "max_acc = max(Accuracy)\n",
        "optimal_ks = [neighbors[i] for i, j in enumerate(Accuracy) if j == max_acc]\n",
        "print(max_acc)\n",
        "print(optimal_ks)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sEEUOUOfs7_E",
        "outputId": "c1293771-41a9-4995-94e7-74d459ed24f1"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "94.14893617021278\n",
            "[5, 7, 8]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "As you can see, the model gives different results when we split the data differently. Here, it suggests that the ideal k value is 5, 7 or 8."
      ],
      "metadata": {
        "id": "l-yZCIU8tdo2"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Section 2: Cross Validation**"
      ],
      "metadata": {
        "id": "0GOXxe-Wv_0w"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Pfnhgihivrn3"
      },
      "source": [
        "As you saw in the above two cases with different train-test splits, there is a risk of overfitting on the test set because the parameters can be tweaked until the estimator performs optimally. This way, knowledge about the test set can “leak” into the model and evaluation metrics no longer report on generalization performance.  \n",
        "\n",
        "Cross Validation is an important technique in Machine Learning to analyze the performance of a model on **unseen** data.  \n",
        "\n",
        "There are different kinds of cross-validation methods: exhaustive and non-exhaustive.   \n",
        "Exhaustive cross-validation methods are cross-validation methods which learn and test on all possible ways to divide the original sample into a training and a validation set.\n",
        "Non-exhaustive cross validation methods do not compute all ways of splitting the original sample.\n",
        "\n",
        "One of the most commonly cross-validation methods is **K-Fold Cross validation**. The procedure for K-Fold Cross Validation is as follows:  \n",
        "1. Split the dataset into k groups (typically without shuffling)\n",
        "2. For each unique group:  \n",
        "  a. Take the group as a hold out or test data set  \n",
        "  b. Take the remaining groups as a training data set  \n",
        "  c. Fit a model on the training set and evaluate it on the test set  \n",
        "  d. Retain the evaluation score and discard the model  \n",
        "3. Summarize the performance of the model using the mean of model evaluation scores over all the folds."
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Image source: https://scikit-learn.org/stable/modules/cross_validation.html  \n",
        "![Screenshot from 2021-12-13 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)"
      ],
      "metadata": {
        "id": "aKUDUmiS-g06"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.33, random_state = 17)"
      ],
      "metadata": {
        "id": "9xjLYb1ZvaqM"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import KFold\n",
        "from statistics import mean\n",
        "kf = KFold(n_splits=10)\n",
        "cv_scores = []\n",
        "neighbors = []\n",
        "for k in range(1,25):\n",
        "  knn = KNeighborsClassifier(n_neighbors = k)\n",
        "  neighbors.append(k)\n",
        "  acclist = []\n",
        "  for train_index, test_index in kf.split(X_train, y_train):\n",
        "    x_train_fold, x_test_fold = X_train.iloc[train_index], X_train.iloc[test_index]\n",
        "    y_train_fold, y_test_fold = y_train.iloc[train_index], y_train.iloc[test_index]\n",
        "    knn.fit(x_train_fold, y_train_fold)\n",
        "    acclist.append(knn.score(x_test_fold, y_test_fold))\n",
        "  cv_scores.append(mean(acclist))"
      ],
      "metadata": {
        "id": "RB0Te1S8uiT6"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 388
        },
        "id": "UjpQTP7QpokH",
        "outputId": "042cb9c3-68b9-41a2-c208-239f439a482d"
      },
      "source": [
        "Accuracy = [x*100 for x in cv_scores]\n",
        "  \n",
        "# plot misclassification error versus k\n",
        "plt.figure(figsize = (10, 6))\n",
        "plt.plot(neighbors, Accuracy)\n",
        "plt.xlabel('Number of neighbors')\n",
        "plt.ylabel('Mean Cross-validation accuracy in %')\n",
        "plt.show()"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# determining the best k\n",
        "max_acc = max(Accuracy)\n",
        "optimal_ks = [neighbors[i] for i, j in enumerate(Accuracy) if j == max_acc]\n",
        "print(optimal_ks)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "U-XBDufnxbuT",
        "outputId": "1779bc79-dbb2-44ec-d777-57e7bb947c72"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[10, 12]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Note that the accuracy in the above case is not the accuracy on the test set. We plot the mean of the accuracies obtained during cross-validation, when we considered different splits of the training set.  \n",
        "\n",
        "From the plot above, we can see that when the value of k is 10 or 12, our KNN model generalizes well to unseen data. Performing such cross validation gives us an idea about the parameters to choose when using a machine-learning algorithm."
      ],
      "metadata": {
        "id": "AHbBLzXWxzsh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "## TASK-1:\n",
        "## In the above case, we saw K-Fold cross validation, where we split the dataset into K consecutive folds without taking anything else into account.\n",
        "## Stratified KFold is another strategy that is commonly used, which preserves the percentage of samples for each class in the folds.\n",
        "## Implement Stratified KFold (refer Sklearn docs) and display the results you obtain\n",
        "from sklearn.model_selection import StratifiedKFold\n",
        "kf = StratifiedKFold(n_splits=10)\n",
        "cv_scores = []\n",
        "neighbors = []\n",
        "for k in range(1,25):\n",
        "  knn = KNeighborsClassifier(n_neighbors = k)\n",
        "  neighbors.append(k)\n",
        "  acclist = []\n",
        "  for train_index, test_index in kf.split(X_train, y_train):\n",
        "    x_train_fold, x_test_fold = X_train.iloc[train_index], X_train.iloc[test_index]\n",
        "    y_train_fold, y_test_fold = y_train.iloc[train_index], y_train.iloc[test_index]\n",
        "    knn.fit(x_train_fold, y_train_fold)\n",
        "    acclist.append(knn.score(x_test_fold, y_test_fold))\n",
        "  cv_scores.append(mean(acclist))\n",
        "print(neighbors)\n",
        "print(cv_scores)"
      ],
      "metadata": {
        "id": "ulg8ESJ77bIz",
        "outputId": "d5d9159a-07c4-4d2b-8db4-b47bf182e5cb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24]\n",
            "[0.9211201079622132, 0.9107962213225371, 0.9342780026990554, 0.926450742240216, 0.9368421052631579, 0.9368421052631579, 0.9342105263157895, 0.9342780026990554, 0.9289473684210526, 0.9316464237516869, 0.9290148448043185, 0.9290148448043185, 0.9263832658569501, 0.926450742240216, 0.9238191632928475, 0.9238191632928475, 0.9263832658569501, 0.9263832658569501, 0.9237516869095816, 0.9237516869095816, 0.9237516869095816, 0.9237516869095816, 0.9211875843454791, 0.9186234817813765]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# plotting misclassification error versus k for task-1\n",
        "Accuracy = [x*100 for x in cv_scores]\n",
        "plt.figure(figsize = (10, 6))\n",
        "plt.plot(neighbors, Accuracy)\n",
        "plt.xlabel('Number of neighbors')\n",
        "plt.ylabel('Mean Cross-validation accuracy in %')\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "OvYwhbwJel0b",
        "outputId": "aa213343-622e-401e-ebe8-981bc4ff272d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 388
        }
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# determining the best k for task-1\n",
        "max_acc = max(Accuracy)\n",
        "optimal_ks = [neighbors[i] for i, j in enumerate(Accuracy) if j == max_acc]\n",
        "print(optimal_ks)"
      ],
      "metadata": {
        "id": "i410hi56e6lq",
        "outputId": "42b611d8-4be5-4ed0-be4e-cca7d0856dde",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[5, 6]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Section 3: Standardization**\n"
      ],
      "metadata": {
        "id": "yyJCZFTxwIlp"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Scaling the data that we have available is a recommended pre-processing step when working with many machine learning algorithms. The scale and distribution of the data drawn from the domain may be different for each variable. Differences in the scales across input variables may increase the difficulty of the problem being modeled, and the model may suffer from poor performance during learning and sensitivity to input values resulting in higher generalization error.  \n",
        "\n",
        "Standardization (also called z-score normalization) is one such scaling technique that transforms your data such that the resulting distribution has a mean of 0 and a standard deviation of 1. \n",
        "\n",
        "$$ \\text{New (scaled) value} = {X - \\mu \\over \\sigma}$$ \n"
      ],
      "metadata": {
        "id": "LiST-3D1zVgA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X = cancer.data\n",
        "y = cancer.target"
      ],
      "metadata": {
        "id": "GNe4-JTI0tYh"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X"
      ],
      "metadata": {
        "id": "g7GxLTngfARX",
        "outputId": "e9f54381-5a9c-46a2-ac57-18de24b80aba",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 522
        }
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     mean radius  mean texture  mean perimeter  mean area  mean smoothness  \\\n",
              "0          17.99         10.38          122.80     1001.0          0.11840   \n",
              "1          20.57         17.77          132.90     1326.0          0.08474   \n",
              "2          19.69         21.25          130.00     1203.0          0.10960   \n",
              "3          11.42         20.38           77.58      386.1          0.14250   \n",
              "4          20.29         14.34          135.10     1297.0          0.10030   \n",
              "..           ...           ...             ...        ...              ...   \n",
              "564        21.56         22.39          142.00     1479.0          0.11100   \n",
              "565        20.13         28.25          131.20     1261.0          0.09780   \n",
              "566        16.60         28.08          108.30      858.1          0.08455   \n",
              "567        20.60         29.33          140.10     1265.0          0.11780   \n",
              "568         7.76         24.54           47.92      181.0          0.05263   \n",
              "\n",
              "     mean compactness  mean concavity  mean concave points  mean symmetry  \\\n",
              "0             0.27760         0.30010              0.14710         0.2419   \n",
              "1             0.07864         0.08690              0.07017         0.1812   \n",
              "2             0.15990         0.19740              0.12790         0.2069   \n",
              "3             0.28390         0.24140              0.10520         0.2597   \n",
              "4             0.13280         0.19800              0.10430         0.1809   \n",
              "..                ...             ...                  ...            ...   \n",
              "564           0.11590         0.24390              0.13890         0.1726   \n",
              "565           0.10340         0.14400              0.09791         0.1752   \n",
              "566           0.10230         0.09251              0.05302         0.1590   \n",
              "567           0.27700         0.35140              0.15200         0.2397   \n",
              "568           0.04362         0.00000              0.00000         0.1587   \n",
              "\n",
              "     mean fractal dimension  ...  worst radius  worst texture  \\\n",
              "0                   0.07871  ...        25.380          17.33   \n",
              "1                   0.05667  ...        24.990          23.41   \n",
              "2                   0.05999  ...        23.570          25.53   \n",
              "3                   0.09744  ...        14.910          26.50   \n",
              "4                   0.05883  ...        22.540          16.67   \n",
              "..                      ...  ...           ...            ...   \n",
              "564                 0.05623  ...        25.450          26.40   \n",
              "565                 0.05533  ...        23.690          38.25   \n",
              "566                 0.05648  ...        18.980          34.12   \n",
              "567                 0.07016  ...        25.740          39.42   \n",
              "568                 0.05884  ...         9.456          30.37   \n",
              "\n",
              "     worst perimeter  worst area  worst smoothness  worst compactness  \\\n",
              "0             184.60      2019.0           0.16220            0.66560   \n",
              "1             158.80      1956.0           0.12380            0.18660   \n",
              "2             152.50      1709.0           0.14440            0.42450   \n",
              "3              98.87       567.7           0.20980            0.86630   \n",
              "4             152.20      1575.0           0.13740            0.20500   \n",
              "..               ...         ...               ...                ...   \n",
              "564           166.10      2027.0           0.14100            0.21130   \n",
              "565           155.00      1731.0           0.11660            0.19220   \n",
              "566           126.70      1124.0           0.11390            0.30940   \n",
              "567           184.60      1821.0           0.16500            0.86810   \n",
              "568            59.16       268.6           0.08996            0.06444   \n",
              "\n",
              "     worst concavity  worst concave points  worst symmetry  \\\n",
              "0             0.7119                0.2654          0.4601   \n",
              "1             0.2416                0.1860          0.2750   \n",
              "2             0.4504                0.2430          0.3613   \n",
              "3             0.6869                0.2575          0.6638   \n",
              "4             0.4000                0.1625          0.2364   \n",
              "..               ...                   ...             ...   \n",
              "564           0.4107                0.2216          0.2060   \n",
              "565           0.3215                0.1628          0.2572   \n",
              "566           0.3403                0.1418          0.2218   \n",
              "567           0.9387                0.2650          0.4087   \n",
              "568           0.0000                0.0000          0.2871   \n",
              "\n",
              "     worst fractal dimension  \n",
              "0                    0.11890  \n",
              "1                    0.08902  \n",
              "2                    0.08758  \n",
              "3                    0.17300  \n",
              "4                    0.07678  \n",
              "..                       ...  \n",
              "564                  0.07115  \n",
              "565                  0.06637  \n",
              "566                  0.07820  \n",
              "567                  0.12400  \n",
              "568                  0.07039  \n",
              "\n",
              "[569 rows x 30 columns]"
            ],
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>mean radius</th>\n",
              "      <th>mean texture</th>\n",
              "      <th>mean perimeter</th>\n",
              "      <th>mean area</th>\n",
              "      <th>mean smoothness</th>\n",
              "      <th>mean compactness</th>\n",
              "      <th>mean concavity</th>\n",
              "      <th>mean concave points</th>\n",
              "      <th>mean symmetry</th>\n",
              "      <th>mean fractal dimension</th>\n",
              "      <th>...</th>\n",
              "      <th>worst radius</th>\n",
              "      <th>worst texture</th>\n",
              "      <th>worst perimeter</th>\n",
              "      <th>worst area</th>\n",
              "      <th>worst smoothness</th>\n",
              "      <th>worst compactness</th>\n",
              "      <th>worst concavity</th>\n",
              "      <th>worst concave points</th>\n",
              "      <th>worst symmetry</th>\n",
              "      <th>worst fractal dimension</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>17.99</td>\n",
              "      <td>10.38</td>\n",
              "      <td>122.80</td>\n",
              "      <td>1001.0</td>\n",
              "      <td>0.11840</td>\n",
              "      <td>0.27760</td>\n",
              "      <td>0.30010</td>\n",
              "      <td>0.14710</td>\n",
              "      <td>0.2419</td>\n",
              "      <td>0.07871</td>\n",
              "      <td>...</td>\n",
              "      <td>25.380</td>\n",
              "      <td>17.33</td>\n",
              "      <td>184.60</td>\n",
              "      <td>2019.0</td>\n",
              "      <td>0.16220</td>\n",
              "      <td>0.66560</td>\n",
              "      <td>0.7119</td>\n",
              "      <td>0.2654</td>\n",
              "      <td>0.4601</td>\n",
              "      <td>0.11890</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>20.57</td>\n",
              "      <td>17.77</td>\n",
              "      <td>132.90</td>\n",
              "      <td>1326.0</td>\n",
              "      <td>0.08474</td>\n",
              "      <td>0.07864</td>\n",
              "      <td>0.08690</td>\n",
              "      <td>0.07017</td>\n",
              "      <td>0.1812</td>\n",
              "      <td>0.05667</td>\n",
              "      <td>...</td>\n",
              "      <td>24.990</td>\n",
              "      <td>23.41</td>\n",
              "      <td>158.80</td>\n",
              "      <td>1956.0</td>\n",
              "      <td>0.12380</td>\n",
              "      <td>0.18660</td>\n",
              "      <td>0.2416</td>\n",
              "      <td>0.1860</td>\n",
              "      <td>0.2750</td>\n",
              "      <td>0.08902</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>19.69</td>\n",
              "      <td>21.25</td>\n",
              "      <td>130.00</td>\n",
              "      <td>1203.0</td>\n",
              "      <td>0.10960</td>\n",
              "      <td>0.15990</td>\n",
              "      <td>0.19740</td>\n",
              "      <td>0.12790</td>\n",
              "      <td>0.2069</td>\n",
              "      <td>0.05999</td>\n",
              "      <td>...</td>\n",
              "      <td>23.570</td>\n",
              "      <td>25.53</td>\n",
              "      <td>152.50</td>\n",
              "      <td>1709.0</td>\n",
              "      <td>0.14440</td>\n",
              "      <td>0.42450</td>\n",
              "      <td>0.4504</td>\n",
              "      <td>0.2430</td>\n",
              "      <td>0.3613</td>\n",
              "      <td>0.08758</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>11.42</td>\n",
              "      <td>20.38</td>\n",
              "      <td>77.58</td>\n",
              "      <td>386.1</td>\n",
              "      <td>0.14250</td>\n",
              "      <td>0.28390</td>\n",
              "      <td>0.24140</td>\n",
              "      <td>0.10520</td>\n",
              "      <td>0.2597</td>\n",
              "      <td>0.09744</td>\n",
              "      <td>...</td>\n",
              "      <td>14.910</td>\n",
              "      <td>26.50</td>\n",
              "      <td>98.87</td>\n",
              "      <td>567.7</td>\n",
              "      <td>0.20980</td>\n",
              "      <td>0.86630</td>\n",
              "      <td>0.6869</td>\n",
              "      <td>0.2575</td>\n",
              "      <td>0.6638</td>\n",
              "      <td>0.17300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>20.29</td>\n",
              "      <td>14.34</td>\n",
              "      <td>135.10</td>\n",
              "      <td>1297.0</td>\n",
              "      <td>0.10030</td>\n",
              "      <td>0.13280</td>\n",
              "      <td>0.19800</td>\n",
              "      <td>0.10430</td>\n",
              "      <td>0.1809</td>\n",
              "      <td>0.05883</td>\n",
              "      <td>...</td>\n",
              "      <td>22.540</td>\n",
              "      <td>16.67</td>\n",
              "      <td>152.20</td>\n",
              "      <td>1575.0</td>\n",
              "      <td>0.13740</td>\n",
              "      <td>0.20500</td>\n",
              "      <td>0.4000</td>\n",
              "      <td>0.1625</td>\n",
              "      <td>0.2364</td>\n",
              "      <td>0.07678</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>564</th>\n",
              "      <td>21.56</td>\n",
              "      <td>22.39</td>\n",
              "      <td>142.00</td>\n",
              "      <td>1479.0</td>\n",
              "      <td>0.11100</td>\n",
              "      <td>0.11590</td>\n",
              "      <td>0.24390</td>\n",
              "      <td>0.13890</td>\n",
              "      <td>0.1726</td>\n",
              "      <td>0.05623</td>\n",
              "      <td>...</td>\n",
              "      <td>25.450</td>\n",
              "      <td>26.40</td>\n",
              "      <td>166.10</td>\n",
              "      <td>2027.0</td>\n",
              "      <td>0.14100</td>\n",
              "      <td>0.21130</td>\n",
              "      <td>0.4107</td>\n",
              "      <td>0.2216</td>\n",
              "      <td>0.2060</td>\n",
              "      <td>0.07115</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>565</th>\n",
              "      <td>20.13</td>\n",
              "      <td>28.25</td>\n",
              "      <td>131.20</td>\n",
              "      <td>1261.0</td>\n",
              "      <td>0.09780</td>\n",
              "      <td>0.10340</td>\n",
              "      <td>0.14400</td>\n",
              "      <td>0.09791</td>\n",
              "      <td>0.1752</td>\n",
              "      <td>0.05533</td>\n",
              "      <td>...</td>\n",
              "      <td>23.690</td>\n",
              "      <td>38.25</td>\n",
              "      <td>155.00</td>\n",
              "      <td>1731.0</td>\n",
              "      <td>0.11660</td>\n",
              "      <td>0.19220</td>\n",
              "      <td>0.3215</td>\n",
              "      <td>0.1628</td>\n",
              "      <td>0.2572</td>\n",
              "      <td>0.06637</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>566</th>\n",
              "      <td>16.60</td>\n",
              "      <td>28.08</td>\n",
              "      <td>108.30</td>\n",
              "      <td>858.1</td>\n",
              "      <td>0.08455</td>\n",
              "      <td>0.10230</td>\n",
              "      <td>0.09251</td>\n",
              "      <td>0.05302</td>\n",
              "      <td>0.1590</td>\n",
              "      <td>0.05648</td>\n",
              "      <td>...</td>\n",
              "      <td>18.980</td>\n",
              "      <td>34.12</td>\n",
              "      <td>126.70</td>\n",
              "      <td>1124.0</td>\n",
              "      <td>0.11390</td>\n",
              "      <td>0.30940</td>\n",
              "      <td>0.3403</td>\n",
              "      <td>0.1418</td>\n",
              "      <td>0.2218</td>\n",
              "      <td>0.07820</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>567</th>\n",
              "      <td>20.60</td>\n",
              "      <td>29.33</td>\n",
              "      <td>140.10</td>\n",
              "      <td>1265.0</td>\n",
              "      <td>0.11780</td>\n",
              "      <td>0.27700</td>\n",
              "      <td>0.35140</td>\n",
              "      <td>0.15200</td>\n",
              "      <td>0.2397</td>\n",
              "      <td>0.07016</td>\n",
              "      <td>...</td>\n",
              "      <td>25.740</td>\n",
              "      <td>39.42</td>\n",
              "      <td>184.60</td>\n",
              "      <td>1821.0</td>\n",
              "      <td>0.16500</td>\n",
              "      <td>0.86810</td>\n",
              "      <td>0.9387</td>\n",
              "      <td>0.2650</td>\n",
              "      <td>0.4087</td>\n",
              "      <td>0.12400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>568</th>\n",
              "      <td>7.76</td>\n",
              "      <td>24.54</td>\n",
              "      <td>47.92</td>\n",
              "      <td>181.0</td>\n",
              "      <td>0.05263</td>\n",
              "      <td>0.04362</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.1587</td>\n",
              "      <td>0.05884</td>\n",
              "      <td>...</td>\n",
              "      <td>9.456</td>\n",
              "      <td>30.37</td>\n",
              "      <td>59.16</td>\n",
              "      <td>268.6</td>\n",
              "      <td>0.08996</td>\n",
              "      <td>0.06444</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.2871</td>\n",
              "      <td>0.07039</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>569 rows × 30 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e1b586ff-8cdb-49a7-a6ea-596dd5afc901')\"\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",
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              "  </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-e1b586ff-8cdb-49a7-a6ea-596dd5afc901 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-e1b586ff-8cdb-49a7-a6ea-596dd5afc901');\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",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "scaler = StandardScaler()\n",
        "X_scaled = scaler.fit_transform(X)"
      ],
      "metadata": {
        "id": "dwS4wRtFzUK_"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size = 0.33, random_state = 17)"
      ],
      "metadata": {
        "id": "rVg1mxfc0HgB"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "neighbors = []\n",
        "scores_list = []\n",
        "  \n",
        "for k in range(1, 25):\n",
        "    neighbors.append(k)\n",
        "    knn = KNeighborsClassifier(n_neighbors = k)\n",
        "    knn.fit(X_train,y_train)\n",
        "    scores = knn.score(X_test,y_test)\n",
        "    scores_list.append(scores)"
      ],
      "metadata": {
        "id": "yq6yq6FjzmcR"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Accuracy = [x*100 for x in scores_list]\n",
        "# plot Accuracy versus k\n",
        "plt.figure(figsize = (10, 6))\n",
        "plt.plot(neighbors, Accuracy)\n",
        "plt.xlim([0, 25])\n",
        "plt.xlabel('Number of neighbors')\n",
        "plt.ylabel('Accuracy in %')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 388
        },
        "id": "ID5KNvw5zyOb",
        "outputId": "96b99bd5-e662-4633-dbb8-75c008971fb9"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# determining the best k\n",
        "max_acc = max(Accuracy)\n",
        "optimal_ks = [neighbors[i] for i, j in enumerate(Accuracy) if j == max_acc]\n",
        "print(max_acc)\n",
        "print(optimal_ks)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8tm1WNW1z0I7",
        "outputId": "bcbeb4e2-6624-4895-b641-9c658f315989"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "96.80851063829788\n",
            "[2]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## TASK-2:\n",
        "## Plot a confusion matrix for the classification above on the scaled data (using the optimal value of k)\n",
        "## Also print out the Precision, Recall and F1-score values\n",
        "\"\"\"----------------------------------------------------\"\"\"\n",
        "#classifier for optimal value of k\n",
        "knn = KNeighborsClassifier(n_neighbors = int(optimal_ks[0]))\n",
        "knn.fit(X_train,y_train)\n",
        "predictions_k= knn.predict(X_test)\n",
        "\"\"\"----------------------------------------------------\"\"\"\n",
        "#confusion matrix for optimal value of k\n",
        "print(pd.crosstab(y_test, predictions_k, rownames=['True'], colnames=['Predicted'], margins=True))\n",
        "\n",
        "from sklearn import metrics\n",
        "cnf_matrix = metrics.confusion_matrix(y_test, predictions_k)\n",
        "p = sns.heatmap(pd.DataFrame(cnf_matrix), annot=True, cmap=\"viridis\" ,fmt='g')\n",
        "plt.title('Confusion matrix', y=1.1)\n",
        "plt.ylabel('Actual label')\n",
        "plt.xlabel('Predicted label')"
      ],
      "metadata": {
        "id": "8m5kl4pU6oV7",
        "outputId": "121d334a-53e2-40fa-cb15-357b040844c2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 399
        }
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Predicted   0    1  All\n",
            "True                   \n",
            "0          63    4   67\n",
            "1           2  119  121\n",
            "All        65  123  188\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 15.0, 'Predicted label')"
            ]
          },
          "metadata": {},
          "execution_count": 32
        },
        {
          "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": "code",
      "source": [
        "# print out the Precision, Recall and F1-score values for task-2\n",
        "from sklearn.metrics import classification_report\n",
        "print(classification_report(y_test,predictions_k))"
      ],
      "metadata": {
        "id": "atvhs-UYfjzx",
        "outputId": "5160701a-9089-4f42-84c7-75b0773aedcf",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.97      0.94      0.95        67\n",
            "           1       0.97      0.98      0.98       121\n",
            "\n",
            "    accuracy                           0.97       188\n",
            "   macro avg       0.97      0.96      0.96       188\n",
            "weighted avg       0.97      0.97      0.97       188\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## TASK-3:\n",
        "## Perform K-Fold Cross validation after standardizing the data. Display your results.\n",
        "from sklearn.model_selection import KFold\n",
        "from statistics import mean\n",
        "kf = KFold(n_splits=10)\n",
        "cv_scores = []\n",
        "neighbors = []\n",
        "#after standardizing data i.e, X_scaled\n",
        "X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size=0.33, random_state=17)\n",
        "y_train= y_train.reset_index(drop=True)\n",
        "for k in range(1,25):\n",
        "  knn = KNeighborsClassifier(n_neighbors = k)\n",
        "  neighbors.append(k)\n",
        "  acclist = []\n",
        "  for train_index, test_index in kf.split(X_train,y_train):\n",
        "    x_train_fold, x_test_fold = X_train[train_index], X_train[test_index]\n",
        "    y_train_fold, y_test_fold = y_train[train_index], y_train[test_index]\n",
        "    knn.fit(x_train_fold, y_train_fold)\n",
        "    acclist.append(knn.score(x_test_fold, y_test_fold))\n",
        "  cv_scores.append(mean(acclist))\n",
        "print(neighbors)\n",
        "print(cv_scores)"
      ],
      "metadata": {
        "id": "NIiLVOEPJFLN",
        "outputId": "811e8ec8-9547-4fc6-95cd-656355f0aaf9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24]\n",
            "[0.9421727395411605, 0.9553981106612686, 0.971187584345479, 0.9685560053981106, 0.9711875843454791, 0.9712550607287449, 0.9659244264507423, 0.9686234817813765, 0.9632928475033739, 0.9659244264507423, 0.9658569500674764, 0.9606612685560054, 0.9605937921727395, 0.9605937921727395, 0.9605937921727395, 0.9527665317139001, 0.9579622132253711, 0.9553981106612686, 0.9527665317139001, 0.9475033738191633, 0.9501349527665317, 0.9501349527665317, 0.9553306342780027, 0.9527665317139001]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Accuracy = [x*100 for x in cv_scores]\n",
        "  \n",
        "# plot misclassification error versus k for task-3\n",
        "plt.figure(figsize = (10, 6))\n",
        "plt.plot(neighbors, Accuracy)\n",
        "plt.xlabel('Number of neighbors')\n",
        "plt.ylabel('Mean Cross-validation accuracy in %')\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "k7v0hFs6f6eU",
        "outputId": "28a49a79-de2a-49e5-e3d5-0138a68d057d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 388
        }
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# determining the best k for task-3.\n",
        "max_acc = max(Accuracy)\n",
        "optimal_ks = [neighbors[i] for i, j in enumerate(Accuracy) if j == max_acc]\n",
        "print(f\"The maximum accuracy is {max_acc}\")\n",
        "print(f\"The optimal value of k is {optimal_ks[0]}\")"
      ],
      "metadata": {
        "id": "5vpf4uZpgE7l",
        "outputId": "f0fb05df-2790-4898-a114-0b6b836f4d69",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The maximum accuracy is 97.12550607287449\n",
            "The optimal value of k is 6\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Questions to Think About and Answer\n",
        "1. Instead of using cross-validation, we could also split our dataset into three sets- train, validation, and test set. The training proceeds on the training set, after which evaluation is done on the validation set, and when the experiment seems to be successful, final evaluation can be done on the test set.  \n",
        "What do you think are the pros/cons of this method ?  \n",
        "2. Is K-Fold cross validation an exhaustive or non-exhaustive cross validation method ? Justify your answer."
      ],
      "metadata": {
        "id": "W0HPRG2m_XAN"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Answer for Question 1**\n",
        "\n",
        "**Pros:**\n",
        "\n",
        "1. Easy to implement and interpret.\n",
        "2. Takes less time to train the model.\n",
        "3. It is computationally less expensive in terms of processing power required.\n",
        "4. We can get a single trained model.\n",
        "\n",
        "**Cons:**\n",
        "\n",
        "1. May increase the chance of overfitting.\n",
        "2. It may not perform well on a unknown data set.\n",
        "3. The splitting of data in some cases can be biased.\n",
        "4. Cannot get the better estimation of error rate.\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "w3NnVALMgLo0"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Answer for Question 2**\n",
        "\n",
        "K-Fold cross validation is an non-exhaustive cross validation method because the original data set is not separated into all the possible permutations and combinations.If we remove some part of the data, it poses a threat of overfitting the Machine Learning model. It is also possible that it may not recognize a dominant pattern if enough data is not provided for the training phase.\n",
        "\n",
        "By reducing the data, we also face the risk of reduced accuracy due to the error induced by bias. To overcome this problem, we need a method that would provide sample data for training and some data for testing for k iterations covering all the data. K-fold Cross-validation does exactly that. By this we can say that k-fold cross validation is an non-exhaustive method."
      ],
      "metadata": {
        "id": "elMpasdqhVvi"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Useful Resources for further reading\n",
        "1. Cross validation (scikit-learn): https://scikit-learn.org/stable/modules/cross_validation.html  \n",
        "2. Wikipedia: https://en.wikipedia.org/wiki/Cross-validation_(statistics)  \n",
        "3. Standardization: https://machinelearningmastery.com/standardscaler-and-minmaxscaler-transforms-in-python/  \n",
        "4. AUC-ROC : https://en.wikipedia.org/wiki/Receiver_operating_characteristic\n"
      ],
      "metadata": {
        "id": "p2VvIoDbB15O"
      }
    }
  ]
}