Module3_Lab4_FMML20210660.ipynb

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        "<a href=\"https://colab.research.google.com/github/SreenadhSingamaneni/IIITH-FMML20210660/blob/main/Module3_Lab4_FMML20210660.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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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: Sahil Manoj Bhatt\n",
        "\n",
        "NOTE: YOU ONLY NEED TO MAKE CHANGES/WRITE CODE IN CELLS THAT SPECIFICALLY MENTION TASK-1, TASK-2, etc.\n",
        "\n",
        "WRITE ANY OBSERVATION(S), IF REQUIRED BY THE TASK, IN A SEPARATE CELL AT THE BOTTOM OF THE NOTEBOOK.  \n",
        "\n",
        "---"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ITvv8HAmjquR"
      },
      "outputs": [],
      "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"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "167jISBahmSu"
      },
      "outputs": [],
      "source": [
        "cancer = datasets.load_breast_cancer(as_frame=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IoKfdTQrKGnM",
        "outputId": "9162dfac-7572-4ece-c180-5c65dc1019d6"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['malignant', 'benign'], dtype='<U9')"
            ]
          },
          "metadata": {},
          "execution_count": 129
        }
      ],
      "source": [
        "cancer.target_names"
      ]
    },
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        "id": "l1Fp6m9jhrpP",
        "outputId": "fa7b8a71-cedc-4154-e2f0-96881d834af1"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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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>radius error</th>\n",
              "      <th>texture error</th>\n",
              "      <th>perimeter error</th>\n",
              "      <th>area error</th>\n",
              "      <th>smoothness error</th>\n",
              "      <th>compactness error</th>\n",
              "      <th>concavity error</th>\n",
              "      <th>concave points error</th>\n",
              "      <th>symmetry error</th>\n",
              "      <th>fractal dimension error</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",
              "      <th>target</th>\n",
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              "  <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>1.0950</td>\n",
              "      <td>0.9053</td>\n",
              "      <td>8.589</td>\n",
              "      <td>153.40</td>\n",
              "      <td>0.006399</td>\n",
              "      <td>0.04904</td>\n",
              "      <td>0.05373</td>\n",
              "      <td>0.01587</td>\n",
              "      <td>0.03003</td>\n",
              "      <td>0.006193</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",
              "      <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>0.5435</td>\n",
              "      <td>0.7339</td>\n",
              "      <td>3.398</td>\n",
              "      <td>74.08</td>\n",
              "      <td>0.005225</td>\n",
              "      <td>0.01308</td>\n",
              "      <td>0.01860</td>\n",
              "      <td>0.01340</td>\n",
              "      <td>0.01389</td>\n",
              "      <td>0.003532</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",
              "      <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>0.7456</td>\n",
              "      <td>0.7869</td>\n",
              "      <td>4.585</td>\n",
              "      <td>94.03</td>\n",
              "      <td>0.006150</td>\n",
              "      <td>0.04006</td>\n",
              "      <td>0.03832</td>\n",
              "      <td>0.02058</td>\n",
              "      <td>0.02250</td>\n",
              "      <td>0.004571</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",
              "      <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>0.4956</td>\n",
              "      <td>1.1560</td>\n",
              "      <td>3.445</td>\n",
              "      <td>27.23</td>\n",
              "      <td>0.009110</td>\n",
              "      <td>0.07458</td>\n",
              "      <td>0.05661</td>\n",
              "      <td>0.01867</td>\n",
              "      <td>0.05963</td>\n",
              "      <td>0.009208</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",
              "      <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>0.7572</td>\n",
              "      <td>0.7813</td>\n",
              "      <td>5.438</td>\n",
              "      <td>94.44</td>\n",
              "      <td>0.011490</td>\n",
              "      <td>0.02461</td>\n",
              "      <td>0.05688</td>\n",
              "      <td>0.01885</td>\n",
              "      <td>0.01756</td>\n",
              "      <td>0.005115</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",
              "      <td>0</td>\n",
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              "    <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>1.1760</td>\n",
              "      <td>1.2560</td>\n",
              "      <td>7.673</td>\n",
              "      <td>158.70</td>\n",
              "      <td>0.010300</td>\n",
              "      <td>0.02891</td>\n",
              "      <td>0.05198</td>\n",
              "      <td>0.02454</td>\n",
              "      <td>0.01114</td>\n",
              "      <td>0.004239</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",
              "      <td>0</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>0.7655</td>\n",
              "      <td>2.4630</td>\n",
              "      <td>5.203</td>\n",
              "      <td>99.04</td>\n",
              "      <td>0.005769</td>\n",
              "      <td>0.02423</td>\n",
              "      <td>0.03950</td>\n",
              "      <td>0.01678</td>\n",
              "      <td>0.01898</td>\n",
              "      <td>0.002498</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",
              "      <td>0</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>0.4564</td>\n",
              "      <td>1.0750</td>\n",
              "      <td>3.425</td>\n",
              "      <td>48.55</td>\n",
              "      <td>0.005903</td>\n",
              "      <td>0.03731</td>\n",
              "      <td>0.04730</td>\n",
              "      <td>0.01557</td>\n",
              "      <td>0.01318</td>\n",
              "      <td>0.003892</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",
              "      <td>0</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>0.7260</td>\n",
              "      <td>1.5950</td>\n",
              "      <td>5.772</td>\n",
              "      <td>86.22</td>\n",
              "      <td>0.006522</td>\n",
              "      <td>0.06158</td>\n",
              "      <td>0.07117</td>\n",
              "      <td>0.01664</td>\n",
              "      <td>0.02324</td>\n",
              "      <td>0.006185</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",
              "      <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>0.3857</td>\n",
              "      <td>1.4280</td>\n",
              "      <td>2.548</td>\n",
              "      <td>19.15</td>\n",
              "      <td>0.007189</td>\n",
              "      <td>0.00466</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.02676</td>\n",
              "      <td>0.002783</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",
              "      <td>1</td>\n",
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              "<p>569 rows × 31 columns</p>\n",
              "</div>\n",
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              "        \n",
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              "       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-c6d4be6d-0767-4a42-a2e4-acdb0e1c15ac 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-c6d4be6d-0767-4a42-a2e4-acdb0e1c15ac');\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",
              "  "
            ],
            "text/plain": [
              "     mean radius  mean texture  ...  worst fractal dimension  target\n",
              "0          17.99         10.38  ...                  0.11890       0\n",
              "1          20.57         17.77  ...                  0.08902       0\n",
              "2          19.69         21.25  ...                  0.08758       0\n",
              "3          11.42         20.38  ...                  0.17300       0\n",
              "4          20.29         14.34  ...                  0.07678       0\n",
              "..           ...           ...  ...                      ...     ...\n",
              "564        21.56         22.39  ...                  0.07115       0\n",
              "565        20.13         28.25  ...                  0.06637       0\n",
              "566        16.60         28.08  ...                  0.07820       0\n",
              "567        20.60         29.33  ...                  0.12400       0\n",
              "568         7.76         24.54  ...                  0.07039       1\n",
              "\n",
              "[569 rows x 31 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 130
        }
      ],
      "source": [
        "cancer.frame"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6P69_W-cnTF4"
      },
      "outputs": [],
      "source": [
        "X = cancer.data\n",
        "y = cancer.target"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "v4SlDqmync4p"
      },
      "outputs": [],
      "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)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "owajk896p9yc"
      },
      "outputs": [],
      "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)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 388
        },
        "id": "UvD57XSyqSVT",
        "outputId": "3619b4a7-c3fc-4cc9-9bfb-94041ad82580"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "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()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "L_PArXY3phVM",
        "outputId": "55dbdf4e-89f6-4ca3-e419-b5b5bcd0d6ed"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "93.61702127659575\n",
            "[10, 12, 13, 14, 22, 24]\n"
          ]
        }
      ],
      "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)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AYObfxqdsGqF"
      },
      "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."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        },
        "id": "KCmXsiag6CXJ",
        "outputId": "7f4825d8-aa54-45f0-f2cf-0c35c26c3016"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 15.0, 'Predicted label')"
            ]
          },
          "metadata": {},
          "execution_count": 136
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "from sklearn.metrics import confusion_matrix, classification_report\n",
        "\n",
        "knn = KNeighborsClassifier(n_neighbors = 10)\n",
        "knn.fit(X_train,y_train)\n",
        "predictions = knn.predict(X_test)\n",
        "cnf_matrix = confusion_matrix(y_test, predictions)\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')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3US9wcfViqNk"
      },
      "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."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "SAp-lfCCiced"
      },
      "outputs": [],
      "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()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "jcNAfOc6ihrv",
        "outputId": "667ceeb9-83a2-4988-9477-4fb13986fe74"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plot_roc(y_test,predictions)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CO7rXVXkiwsL"
      },
      "source": [
        "#### KNN using a different train-test split"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "v-i6n6y3swm4"
      },
      "outputs": [],
      "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)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "yUQsEfAjsFaS"
      },
      "outputs": [],
      "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)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wy9EH2Yms5e_",
        "outputId": "749c24be-f0ef-491a-c3c8-8f90405cf13e"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "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()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sEEUOUOfs7_E",
        "outputId": "a25a8cfc-dbdb-4d6e-87ab-0100580b7a69"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "94.14893617021278\n",
            "[5, 7, 8]\n"
          ]
        }
      ],
      "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)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "l-yZCIU8tdo2"
      },
      "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."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Y51amBOH5AGI",
        "outputId": "273d8e3a-3a4a-4c8e-d9be-54f151c4de78"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 15.0, 'Predicted label')"
            ]
          },
          "metadata": {},
          "execution_count": 143
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "\n",
        "knn = KNeighborsClassifier(n_neighbors = 5)\n",
        "knn.fit(X_train,y_train)\n",
        "predictions = knn.predict(X_test)\n",
        "cnf_matrix = confusion_matrix(y_test, predictions)\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')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Pfnhgihivrn3"
      },
      "source": [
        "## Cross Validation\n",
        "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",
      "metadata": {
        "id": "aKUDUmiS-g06"
      },
      "source": [
        "Image source: https://scikit-learn.org/stable/modules/cross_validation.html  \n",
        "![Screenshot from 2021-12-13 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)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "9xjLYb1ZvaqM"
      },
      "outputs": [],
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.33, random_state = 17)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "RB0Te1S8uiT6"
      },
      "outputs": [],
      "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))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UjpQTP7QpokH",
        "outputId": "a703142b-404a-4033-f8c0-8f51de304e10"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "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()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "U-XBDufnxbuT",
        "outputId": "0193a23d-45e0-46e2-c135-259c29a62265"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[10, 12]\n"
          ]
        }
      ],
      "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)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AHbBLzXWxzsh"
      },
      "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."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ulg8ESJ77bIz"
      },
      "outputs": [],
      "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"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "RaXQP3xsC3r9"
      },
      "outputs": [],
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.33, random_state = 17)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7QdNB_djFEU-"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import StratifiedKFold\n",
        "skf = StratifiedKFold(n_splits=2)\n",
        "skf_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 skf.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",
        "  skf_scores.append(mean(acclist))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SV18cRu3HZ-R",
        "outputId": "7089c257-8270-4ab5-fe3d-a1fc102b68d1"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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6Eq3xKW3DN08aQoe0FG57YQWV1Yv57cVjyUgNBx1LRCQh6VfZJFGsxdilDbnq+EH87JyRvLZ6O1c9+C7lVTVBRxIRSUgq1JLAvspqSvYd0EACaVMuOq4/d04fzbx1O7hs9kL2VlYHHUlEJOGoUEsC60vLAQ0kkLbnnGP7cs9FY1mycTeXPLCA3eVVQUcSEUkoMRdqZnaWmb1uZvPN7Pp4hpJDUxQd8ZmvOdSkDTpjZG9mXTaOVdv2ccGs+ZTsOxB0JBGRhNFooWZmYz6z61LgRGAy8I14hpJDU1RShhn0z84MOorIYTnp6J784YrxbNhRzvmz5rF1T0XQkUREEkJTPWrfMLP7zaxX9PNG4N+AHwBb4p5MYlZcWkbfbh1IT9HIOWm7phTk8NBVE9i+9wDTZ85j487yoCOJiASu0ULN3a8F7gFmmtmtwK3APOADYFrrxJNYREZ8aiCBtH3jB3bnsRnHsa+yhq/dN491JfuDjiQiEqgm51Fz96XA2WZ2FvAs8JC7P9QqySQm7k5xaRnjBnQLOopIixjVtytPXDORSx5YwLm/fYfBuR0xIGSGGRgGBqHoe7N/fAeRiXUj3/3jPZ8eFzmnQ1qYH54xjNys9OB+UBGRGDRaqJnZdcBl0Y93A6cD15vZS8B/ufubrZBPmlGy/wD7D9QwWAMJJIkc3aszT147idvnrKasqgZ3qHPHHZzIa10dOHXRfQd/D7hTd9Cx9edHv2L1J/sYnNORG08eEuSPKSLSrKZ61K5391Fmlg684+5PAHeb2cPAjwEVagmgqKR+MXYVapJc8nM7cd+l4+LS9vT75vHc0i1886QCzLQ4vIgkrqYGE2w2sx8SKcpW1e90913u/u24J5OYFJeqUBM5VGeNyeOj7ftZtW1f0FFERJrUVKF2NpGBA2/xj1ugkmCKS8tISwmR16VD0FFE2owzRvQiHDKeW6oB7CKS2Joa9Vnl7s+7+xx3r23NUBK7opIyBmV3JBTS7RuRWGV3Suf4ghyeX7oFjz67JiKSiLSEVBtXXLpfAwlEDsO00Xls2lXBex/vDjqKiEijVKi1YTW1dXy8s1zPp4kchlOH9yQ9JcRzSzYHHUVEpFExFWpmFjazPDPrX7/FO5g0b9OuCqprXYWayGHIykjlpKN78JcPtlJTWxd0HBGRBjVbqJnZjcAnwMvAX6LbC7E0bmY3mdlyM/vQzG6O7rvdzFaZ2TIze8bMujZy7noz+8DMlpjZoph/onakfsSnbn2KHJ5po/Mo3V/FvKIdQUcREWlQLD1qNwFHuftwdx8Z3UY1d5KZjQBmABOA0cCZZlZApOAbEW1jDZG1QxtzoruPcffCGHK2O0WfTs2h5aNEDseJR/egU3oKzy3R6E8RSUyxFGobgT2H0fYwYIG7l7t7DfAGcK67z41+BpgP9D2MtoXIQIKumal075gWdBSRNikjNcxpw3sx58NtHKjR4HYRSTyxFGpFwOtm9gMz+3b9FsN5y4GpZpZtZpnAGUC/zxzzdeCvjZzvwFwzW2xm18RwvXanqKRMz6eJHKFpY/LYV1nD66tLgo4iIvI5sRRqHxO5XZkGZB20NcndVwK/AOYCc4AlwKe/sprZj4Aa4NFGmjje3ccCXwZuMLMTGjrIzK4xs0VmtqikpH39QVtcqkJN5EhNyc8mu2OaJr8VkYTU1FqfALj7Tw+3cXefDcwGMLOfAZui768AzgRO9kZmm3T3zdHX7Wb2DJFn3T63vqi7zwJmARQWFrabmSvLq2rYuqeSwSrURI5ISjjEGSN78/TijZQdqKFjerN/LIqItJpGe9TM7K7o6/Nm9txnt1gaN7Me0df+wLnAY2Z2OvCvwDR3L2/kvI5mllX/HjiVyK1UiVpfGvlHp4EEIkdu2pg8KqvreHnFJ0FHERH5J0396vhw9PWOI2j/z2aWDVQDN7j7bjO7B0gHXjYzgPnufp2Z5QEPuPsZQE/gmej3KcBj7j7nCHIkHU3NIdJyxvXvRl6XDJ5buoWvHtsn6DgiIp9qtFBz98XR1zcOt3F3n9rAvoJGjt1CZMAB7l5EZEoPaURRyX4ABmarUBM5UqGQcdboPGa/Vcyusiq6aSS1iCQILSHVRhWXlpHXJYMOaeGgo4gkhbNG51FT5/x1+bago4iIfEqFWhtVVFrGIN32FGkxw/M6Mzi3I88t1dqfIpI4YllCamRrBJHYuTtFJfs1NYdICzIzpo3OY0HxTrbtqQw6jogIEFuP2m/NbKGZXW9mXeKeSJq1q7yavZU1DNaIT5EWNW10Hu7wwjLNqSYiiaHZQi06IOBiIqsKLDazx8zslLgnk0bVDyTQrU+RljU4txMj+nTW5LcikjBiekbN3T8C/g34f8AXgLvNbJWZnRvPcNKw+sXYNdmtSMubNjqPZZv2fDoFjohIkGJ5Rm2Umf0aWAmcBJzl7sOi738d53zSgOLSMlLDRp+uHYKOIpJ0zhyVB8Dz6lUTkQQQS4/ab4D3gNHufoO7vwefznv2b/EMJw0rLimjf/dMUsIatCvS0vK6dmDCwO48t3QLjaxwJyLSamL5m/4rRFYGqAAws5CZZQK4+8NNnilxUVxaxuBcDSQQiZdpY/JYu30/K7fuCzqKiLRzsRRqrwAH32PLjO6TANTWOcU7yvR8mkgcnTGyNykh06ACEQlcLIVahrvvr/8QfZ8Zv0jSlC27K6iqqdMcaiJx1L1jGscPyeF53f4UkYDFUqiVmdnY+g9mNg6oiF8kaUr9SDQVaiLxNW10Hpt3V/Dex7uCjiIi7Viji7If5GbgaTPbAhjQCzg/rqmkUZ8WappDTSSuTh3ei/SUD3huyRbGDegedBwRaadimfD2XeBo4BvAdcAwd18c72DSsOLSMrLSU8jtlB50FJGk1ik9hZOH9eAvH2ylprYu6Dgi0k7FOr/DUcAxwFjgQjO7LH6RpCnrSvYzKLcjZhZ0FJGkN210HqX7q5hXtCPoKCLSTsUy4e1PiMyl9hvgROCXwLQ455JGFJeW6fk0kVbyxaN6kJWewnNLNPpTRIIRS4/avwAnA9vc/UpgNKDF2QNQWV3L5t0VKtREWklGaphTh/dizofbOFBTG3QcEWmHYinUKty9Dqgxs87AdiILtEsr+3hnOe4a8SnSmqaNyWNfZQ2vry4JOoqItEOxFGqLzKwrcD+wmMhyUvPimkoaVFQSGfGZr1UJRFrNlPxssjum6faniASiyek5LPLE+n+7+27gPjObA3R292Wtkk7+SVFpZN7hgepRE2k1KeEQZ4zszVOLNrL/QA2d0mOZ1UhEpGU02aPmkSm5Xzzo83oVacEpLimjR1a6/qIQaWXTxuRxoKaOl1dsCzqKiLQzsdz6fM/Mxsc9iTRLIz5FgjGufzfyumTo9qeItLpYCrXjgHlmts7MlpnZB2amXrUAFJeWMVgrEoi0ulDIOGtMHn//qJRdZVVBxxGRdiSWQu00IB84CTgLODP6Kq1oT3k1O8qqGJyjgQQiQZg2Oo+aOufF5VuDjiIi7UgshZo3skkrqh9IoFufIsE4pndn8nM76vaniLSqWJ5K/wuRwsyADGAQsBoYHsdc8hlajF0kWGbGtNF9uOvVNWzbU0mvLhlBRxKRdiCWRdlHuvuo6OsQYAKaR63VFZeWEQ4Z/bplBh1FpN2aNiYPd3hhmXrVRKR1xLoo+6fc/T0iAwykFRWVltGvWwfSUg75X5mItJBBOR0Z2acLzy1VoSYiraPZW59m9u2DPoaAsYD+lGplxSVlDNaKBCKBmzY6j/96caWmyxGRVhFL90zWQVs6kWfWzo5nKPlndXWuvxREEsSZo3tjBs+rV01EWkGzPWru/tPWCCKN+2RfJRXVtSrURBJA7y4dGD+wO88t3cKNJxUQWWlPRCQ+mu1RM7OXo4uy13/uZmYvxTeWHKw4uhj7YBVqIglh2ug81m7fz8qt+4KOIiJJLpZbn7nRRdkBcPddQI9YGjezm8xsuZl9aGY3R/fdbmaroqscPHNwEfiZc083s9VmttbMvh/L9ZJVkabmEEkoZ4zsTUrIeHbp5qCjiEiSi6VQqzWz/vUfzGwAMUx4a2YjgBlEpvMYDZxpZgXAy8AIdx8FrAF+0MC5YeBe4MvAMcCFZnZMDFmTUnFpGR1Sw/TqrHmbRBJB945pHD8khxeWbqWuTvN/i0j8xFKo/Qh4y8weNrNHgDdpoLhqwDBggbuXu3sN8AZwrrvPjX4GmA/0beDcCcBady9y9yrgCdrxAIaikv0MyumoZ2FEEsi00Xls3l3Bex/vCjqKiCSxWCa8nUNkSo4niRRM49w9lmfUlgNTzSzbzDKBM4B+nznm68BfGzi3D7DxoM+bovs+x8yuMbNFZraopKQkhlhtT3FpmW57iiSYU4f3Ij0lpDnVRCSuYhlMcA5Q7e4vuPsLQI2ZfbW589x9JfALYC4wB1gC1B7U7o+AGuDRw8xef51Z7l7o7oW5ublH0lRCqqqpY+OuCg0kEEkwndJT+NKwnrz4wVZqauuCjiMiSSqWW58/cfc99R+iAwt+Ekvj7j7b3ce5+wnALiLPpGFmVwBnAhe7e0MPeGzmn3vf+kb3tTsbd5VTW+eamkMkAZ01Oo/S/VW8s25H0FFEJEnFUqg1dEwsi7ljZj2ir/2Bc4HHzOx04F+Bae5e3sip7wJDzGyQmaUBFwDPxXLNZPPp1BxalUAk4XzxqFyy0lN0+1NE4iaWQm2Rmd1pZvnR7U5gcYzt/9nMVgDPAzdEe+PuIbLKwctmtsTM7gMwszwzexEgOtjgm8BLwErgKXf/8NB+tORQVLofgEHZ6lETSTQZqWFOG9GLl5Zvo7K6tvkTREQOUSw9YzcCPyYymAAi02vcEEvj7j61gX0FjRy7hciAg/rPLwIvxnKdZFZcWkZ2xzS6ZKYGHUVEGjBtdB5/WryJ11eXcPqIXkHHEZEkE8sSUmVAu55wNkhFJVrjUySRTc7PJqdTGs8v3aJCTURaXLOFmpnlEnmmbDjw6Yyr7n5SHHNJVHFpGV8YmnyjWUWSRUo4xBkje/PkuxvZf6CGTukxPcIrIhKTWJ5RexRYBQwCfgqsJ/Kwv8TZ/gM1bN93QAMJRBLctNF5HKip4+UV24KOIiJJJpZCLdvdZxOZS+0Nd/86oN60VlA/4lO3PkUS29j+3ejTtQPPLdHoTxFpWbH00VdHX7ea2VeALUD3+EWSevUjPgdrVQKRhBYKGWeO7s3svxfz6spPSE8JY0Zkw6KvkeOM6H6rf2+EDjoOIGT26fmh6HEOuEOdO+7gRF8Pel/n/ulxftD7xs4JmzF2QDcyUsMB/ZMTkebEUqj9p5l1Ab4D/AboDNwS11QCRJ5PM4P+3TODjiIizTjn2D7MfKOIqx5cFHSUQ1I4oBt/uHI8WRkaWS6SiGIZ9flC9O0e4MT4xpGDFZeW0adrB/22K9IGHN2rMy/fcgJ7KqpxoK6uid4tDt73j16upnrEDu5di/S8RXviovtCoUivHPW9d/W9ctHjOKjXrv67jz7Zz63PLueSBxbw4Ncn0DUzLZB/diLSOA1PSmDFpWUaSCDShgzpmRV0hEMyfmB3emSlc/2j73Hh/Qt45KoJZHdKDzqWiBwklsEEEgB3p6ikTIuxi0hcfemYnsy+opDi0v2cP2s+2/dWBh1JRA6iQi1Blew/wP4DNRrxKSJxN3VILn+8cgJbd1cwfeY8Nu+uCDqSiEQ1W6iZWbqZXWRmPzSzW+u31gjXnmlqDhFpTRMHZ/Pw1cexo6yK6ffNY8OOsqAjiQix9ag9C5wN1ABlB20SR8WlKtREpHWN7d+Nx2dMpLyqhukz57F2+/6gI4m0e7EMJujr7qfHPYn8k+LSMtJSQvTp2iHoKCLSjozo04UnrpnExQ8s4IJZ83jk6uM4ulfnoGOJtFux9Ki9Y2Yj455E/sm6kjIGZXckFLKgo4hIO3NUryyevHYiKaEQF8yazweb9gQdSaTdiqVQOx5YbGarzWyZmX1gZsviHay9Ky7dr9ueIhKY/NxOPHXtJDqlp3DR/fNZvGFn0JFE2qVYCrUvA0OAU4GzgDOjrxInNbV1fLyznEFaOkpEAtQ/O5Onrp1ETlY6l85eyLx1O4KOJNLuNFuoufsGoLua4/gAACAASURBVCuR4uwsoGt0n8TJ5t0VVNe6etREJHB5XTvw5DUT6dO1A1f8YSFvrCkJOpJIuxLL9Bw3AY8CPaLbI2Z2Y7yDtWdF0RGfmuxWRBJBj84ZPHHNRPJzOzHjwUXM/XBb0JFE2o1Ybn1eBRzn7re6+63ARGBGfGO1b0XROdS0fJSIJIrsTuk8PmMiw/I6c/2j7/HCsi1BRxJpF2Ip1AyoPehzbXSfxElx6X66dEilW2Zq0FFERD7VJTOVR66awNj+3fjW4+/z58Wbgo4kkvRimUftD8ACM3sm+vmrwOz4RZLi0jIG5XTETPWwiCSWrIxU/vj18Vzz0GK+8/RSKmtqufi4AUHHEklasQwmuBO4EtgZ3a5097viHaw9K9Zi7CKSwDLTUnjg8kJOOroHP3pmObPfKg46kkjSarRHzcw6u/teM+sOrI9u9d91d3dNqhMHFVW1bNlTqRGfIpLQMlLD3HfJOG564n1ue2EFldW13HBiQdCxRJJOU7c+HyMyZ9piwA/ab9HPg+OYq92qX+NTAwlEJNGlpYT4zYXH8p2nl3L7S6s5UF3LLacM1WMbIi2o0ULN3c+Mvg5qvTiixdhFpC1JCYe4c/oYMlLC3P23tVTW1PGDLx+tYk2khcQyj9qrsexrDzbsKGNXWVVcr1Fcuh+AgTmZcb2OiEhLCYeM/z53JJdPGsCsN4u49dkPqavz5k8UkWY19YxaBpAJ5JhZN/4xJUdnoE8rZEsoNbV1XPmHd0kNh3j46gn0yMqIy3WKSsvo3SWDzLRYBuSKiCSGUMj492nDyUgNM/PNIgbmdOSq43VDRuRINdWjdi2R59OOjr7Wb88C98Q/WmJJCYe47asj2LirnOn3zWPTrvK4XKd+ag4RkbbGzPj+l4/mmN6dtXqBSAtptFBz9/+JPp/2XXcf7O6Dottod293hRrAlIIcHr7qOHaUVTH9vnmfPk/WUtydopIyBmsxdhFpo8yM44fk8P7Hu6moqm3+BBFpUizzqP3GzEaY2XQzu6x+a41wiWjcgG48cc1EDtTU8bX75rFq294Wa3tXeTV7KqoZlKMRnyLSdk3Oz6aqto5FGzSLk8iRimUwwU+A30S3E4FfAtPinCuhDc/rwpPXTiIlZJw/cz5LNu5ukXbrBxJoslsRacsmDOpOSsh4e+2OoKOItHmxrPX5L8DJwDZ3vxIYDXSJpXEzu8nMlpvZh2Z2c3Tf16Kf68yssIlz15vZB2a2xMwWxXK91lTQoxNPXzeJzh1SuPj++cwvOvI/kOoXY9czaiLSlmWmpXBs/668s6406CgibV4shVqFu9cBNWbWGdgO9GvuJDMbAcwAJhAp7s40swJgOXAu8GYM1z7R3ce4e6MFXZD6dc/k6Wsn07trBy7//UJeX739iNorLi0jNWz07dahhRKKiARjcn4OH2zew57y6qCjiLRpsRRqi8ysK3A/kVGf7wHzYjhvGLDA3cvdvQZ4AzjX3Ve6++rDTpxgenXJ4MlrJlLQoxMzHlrEXz/YethtFZWU0b97JinhWP61iIgkrikFObjDvBa42yDSnsUymOB6d9/t7vcBpwCXR2+BNmc5MNXMss0sEziDGHriDr40MNfMFpvZNY0dZGbXmNkiM1tUUlJyCM23nOxO6Tw2YyKj+nblhsfe40+LNx1WO5GpOTSQQETavjH9utIhNazbnyJHqKkJb8c29Z27v9dUw+6+0sx+AcwFyoAlwKGM1T7e3TebWQ/gZTNb5e6fu13q7rOAWQCFhYWBTYXdpUMqD181gRkPLeK7Ty+loqqGSycNjPn8ujqneEcZXzgqN34hRURaSVpKiAmDuvP2WhVqIkeiqR61X0W3e4EFRIqh+6Pv742lcXef7e7j3P0EYBewJtZg7r45+rodeIbIs24JLTMthdmXj+dLw3ry42c/5Hevr4v53C17KqiqqdNAAhFJGlMKsllXUsa2PZVBRxFps5qa8PZEdz8R2AqMdfdCdx8HHAtsjqXxaG8YZtafyACCx2I8r6OZZdW/B04lcis14WWkhvndJWOZNjqPX8xZxe0vrcK9+Y4+LcYuIslmcn4OAPOK1KsmcrhieWr9KHf/oP6Duy8nMlAgFn82sxXA88AN7r7bzM4xs03AJOAvZvYSgJnlmdmL0fN6Am+Z2VJgIfAXd58T4zUDlxoO8evzx3DhhH7c+9o6fvr8imYXKK6fmkOrEohIsjimd2e6ZqZqPjWRIxDLyt/LzOwB4JHo54uBZbE07u5TG9j3DJFbmZ/dv4XIgAPcvYjIlB5tVjhk/OyckXRMS+GBt4opO1DDz88bRThkDR5fXFpGp/QUcjult3JSEZH4CIWMSYOzeWdtKe6OWcN//olI42LpUbsS+BC4KbqtiO6TZpgZP/rKMG46eQhPL97Etx5/n6qaugaPLYouxq4/yEQkmUwuyGHLnkrW7ygPOopIm9Rsj5q7VwK/jm5yiMyMW04ZSqf0FP7rxZVUVNfy24vHkpEa/qfjikv3c2y/bgGlFBGJjyn52QC8vbZUz+CKHIZGe9TM7Kno6wdmtuyzW+tFTA4zThjMz84ZyWurt3PlH95l/4GaT787UFPLpl0V+kNMRJLOoJyO9O6SofnURA5TUz1qN0Vfz2yNIO3BRcf1JzMtzHeeXsolDyzgwSsn0CUzlQ07ynHXQAIRST5mxuT8HP626hPq6pxQI8/pikjDmpqeY2v0dUNDW+tFTC5fPbYPv714LCu27OX8WfMo2XfgHyM+tSqBiCShKQXZ7CqvZsXWvUFHEWlzmrr1uc/M9jaw7TMz/d92BE4b3ovZVxSyYUc558+cx7zoLYGBOZkBJxMRaXlTCiLzqen2p8iha6pHLcvdOzewZbl759YMmYymDsnloasmULLvAA/O20BuVjpZGalBxxIRaXE9O2eQn9uRd9ZpPjWRQxXL9BxAZJUBM+tfv8UzVHsxfmB3HpsxkW6ZqRzdKyvoOCIicTOlIIeFxTsbnaJIRBrWbKFmZtPM7COgGHgDWA/8Nc652o2Rfbvw0i0n8OvzxwQdRUQkbibnZ1NeVcvSTbuDjiLSpsTSo3YbMBFY4+6DgJOB+XFN1c70yMogRysSiEgSmzg4G7PIfGoiErtYCrVqd98BhMws5O6vAYVxziUiIkmka2YaI/K68I7W/RQ5JLEUarvNrBPwJvComf0PUBbfWCIikmwmF2Tz/sZdlFfVNH+wiACxFWpnA+XALcAcYB1wVjxDiYhI8pmSn0N1rbOweGfQUUTajFgKtWuB3u5e4+4Puvvd0VuhIiIiMRs/sDtp4ZCm6RA5BLEUalnAXDP7u5l908x6xjuUiIgknw5pYY7t31UT34ocgmYLNXf/qbsPB24AegNvmNkrcU8mIiJJZ0pBDh9u2cvu8qqgo4i0CTFPeAtsB7YBO4Ae8YkjIiLJbHJ+Nu4wT7c/RWISy4S315vZ68CrQDYww91HxTuYiIgkn9H9utIxLczbuv0pEpOUGI7pB9zs7kviHUZERJJbajjEhEHdNZ+aSIxieUbtB/VFmpnNin8kERFJZlMKcigqLWPrnoqgo4gkvEN5Rg20IoGIiByhyfk5ALytXjWRZh1qobY9LilERKTdOLpXFt07pvGO1v0UadahFmpnmFnnuCQREZF2IRQyJuVn8866Hbh70HFEElosoz4fM7POZtYRWA6sMLPvxT+aiIgkqyn5OWzbW0lRqZaOFmlKLD1qx7j7XuCrwF+BQcClcU0lIiJJbXJ+NoBuf4o0I5ZCLdXMUokUas+5ezWgvmoRETlsA7Iz6dO1gwYUiDQjlkJtJrAe6Ai8aWYDgL3xDCUiIsnNzJicn828oh3U1ul3f5HGxDKP2t3u3sfdz/CIDcCJrZBNRESS2JSCHPZUVLNii373F2lMLIMJbooOJjAzm21m7wEntUI2ERFJYvXPqWk5KZHGxXLr8+vRwQSnAt2IDCT4eVxTiYhI0uvROYMhPTrxtgYUiDQqlkLNoq9nAA+7+4cH7RMRETlsUwpyeHf9Tqpq6oKOIpKQYinUFpvZXCKF2ktmlgXE9H9U9LbpcjP70Mxuju77WvRznZk1uiSVmZ1uZqvNbK2ZfT+W64mISNsyOT+byuo63v94V9BRRBJSLIXaVcD3gfHuXg6kAVc2d5KZjQBmABOA0cCZZlZAZNLcc4E3mzg3DNwLfBk4BrjQzI6JIauIiLQhxw3OJmTw9jpN0yHSkFhGfdYBfYF/M7M7gMnuviyGtocBC9y93N1rgDeAc919pbuvbubcCcBady9y9yrgCeDsGK4pIiJtSJcOqYzs00UT34o0IpZRnz8HbgJWRLdvmdnPYmh7OTDVzLLNLJPIrdN+MebqA2w86POm6D4REUkykwtyWLJxN2UHaoKOIpJwYrn1eQZwirv/3t1/D5wOnNncSe6+EvgFMBeYAywBao8ga4PM7BozW2Rmi0pKSlq6eRERibMp+TnU1DkLi3cGHUUk4cRSqAF0Peh9l1gbd/fZ7j7O3U8AdgFrYjx1M//c+9Y3uq+ha8xy90J3L8zNzY01moiIJIjCgd1ISwlpmg6RBqTEcMzPgPfN7DUi03KcQGRwQbPMrIe7bzez/kQGEEyMMde7wBAzG0SkQLsAuCjGc0VEpA3JSA0zrn83DSgQaUCTPWpmFiIyFcdE4H+BPwOT3P3JGNv/s5mtAJ4HbnD33WZ2jpltAiYBfzGzl6LXyjOzFwGigw++CbwErASeis7fJiIiSWhKQTYrt+5lZ1lV0FFEEkqTPWruXmdm/+ruTwHPHWrj7j61gX3PAM80sH8Lkefh6j+/CLx4qNcUEZG2Z3JBDsxdw7x1O/jKqN5BxxFJGLE8o/aKmX3XzPqZWff6Le7JRESk3RjVpwud0lO07qfIZ8TyjNr50dcbDtrnwOCWjyMiIu1RSjjEcYO6az41kc9otlBz90GtEURERNq3yQU5vLpqO5t3V9Cna4eg44gkhEZvfZrZJWZ2aQP7LzUzjcAUEZEWNaUgG0DTdIgcpKln1G6kgYf+iYz+/E584oiISHt1VM8scjql6fanyEGaKtRS3X3/Z3e6exmQGr9IIiLSHpkZk/JzeHvdDtw96DgiCaGpQq2DmXX87E4zywLS4hdJRETaqyn52ZTsO8C6ks/1E4i0S00VarOBP5nZgPodZjYQeCL6nYiISIuaUpADwNtrtUqBCDRRqLn7HcCzwJtmtsPMdgBvAC+4++2tFVBERNqPft0z6de9gwYUiEQ1tzLBfcB90duduPu+VkklIiLt1uTBOfx1+VZq65xwyIKOk3CeX7qFcMg4Y6RWcGgPYlmZAHffpyJNRERaw+SCbPZW1rB8856goyScT/ZW8r0/LeW7Ty+ldP+BoONIK4ipUBMREWktk/Ojz6lpOanPuedva6mpdSqra/nd6+uCjiOtQIWaiIgklNysdI7qmcU7GlDwTzbuLOfxhR9z/vh+nDe2Lw/P38DWPRVBx5I4i6lQM7PJZnaRmV1Wv8U7mIiItF+TC7J5d/1OKqtrg46SMO565SPCIePGk4bwrZOH4O7c/eraoGNJnDVbqJnZw8AdwPHA+OhWGOdcIiLSjk3Jz+FATR3vf7w76CgJYe32fTzz/iYumzSAXl0y6Nc9kwsn9OfpRRvZsKMs6HgSR7H0qBUCU9z9ene/Mbp9K97BRESk/TpucHfCIeMdPacGwJ0vr6FDaphvfLHg033fPLGAlLBx1ysfBZhM4i2WQm050CveQUREROplZaQyqm8XzacGfLBpDy9+sI2rpg6me8d/LAzUo3MGl08eyP8t2czqbZqYIVnFUqjlACvM7CUze65+i3cwERFp36bk57B00x72VVYHHSVQd8xdTdfMVK6eOuhz3113Qj6d0lK48+XVASST1tDkhLdR/x7vECIiIp81OT+be15by8LinZw8rGfQcQKxsHgnb6wp4QdfPprOGamf+75bxzSumjqIu175iGWbdjOqb9cAUko8Nduj5u5vNLS1RjgREWm/xg7oRnpKqN2u++nu3PHSanKz0rls0sBGj7vq+EF0y0zljrlrWi+ctJpYRn1ONLN3zWy/mVWZWa2Z7W2NcCIi0n5lpIYpHNit3Q4oePOjUhau38m3TiqgQ1q40eOyMlL5xhfzeXNNCQuK2mdRm8xieUbtHuBC4COgA3A1cG88Q4mIiEBklYJV2/a1u+WS3J3bX1pF324dOH98/2aPv2zSQHpkpXPH3NW4eysklNYS61qfa4Gwu9e6+x+A0+MbS0REBKYURJaTmreuffUUzVm+jeWb93Lzl4aSltL8X9UZqWFuPKmAd9fv4o01Ja2QUFpLLIVauZmlAUvM7JdmdkuM54mIiByRkX26kJWR0q5uf9bWOb96eQ0FPTpxzrF9Yj7v/PH96dutA7+au0a9akkkloLr0uhx3wTKgH7AefEMJSIiAhAOGRMHZ7erAQX/9/5m1m7fz7dPGUo4ZDGfl5YS4uYvDeWDzXt46cNtcUworSmWUZ8bAAN6u/tP3f3b0VuhIiIicTclP5uPd5azcWd50FHirqqmjrteXcOIPp05ffihzzV/zrF9yM/tyB1z11Bbp161ZBDLqM+zgCXAnOjnMZrwVkREWsvk6HNq7eH255OLNrJxZwXfOfUoQofQm1YvHDK+fcpRrN2+n2eXbI5DQmltsdz6/HdgArAbwN2XAJ+fHllERCQOhvToRG5WetLf/qyoquU3r37E+IHd+OLQ3MNu58sjenFM787c9cpHVNfWtWBCCUIshVq1u+/5zD71p4qISKswMybnZ/POuh1J/ZD8w/PXs33fAb532tGYHXpvWr1QyPjeaUfx8c5ynlq0sQUTShBiKdQ+NLOLgLCZDTGz3wDvxDmXiIjIp6bk51C6/wBrPtkfdJS42FdZzW9fX8cJQ3OZMKj7Ebf3xaNyGTegG3e/+hGV1bUtkFCCEstanzcCPwIOAI8DLwG3xTOUiIjIwSYXZANw67PLGZjdETOIdDoZoeh7+/R9pDcqZBbdH+llih5+0HH/eH/KMb0Y2bdLQD8dzH6rmN3l1Xzv1KNapD0z47unHsWF98/nkfkbuHrq4BZpV1pfs4Wau5cTKdR+FP84IiIin9e3WyanHtOTZZv2sH5HGe4QGdTouEeex6nz6Hv/xz53/+fv+Pz3dQ6z/l7ErEsLOeEIng07XDvLqnjg78WcPrxli8VJ+dkcX5DDb19fxwUT+tMpPZa+GUk0jf5ba25kp7tPa65xM7sJmEHkF5r73f0uM+sOPAkMBNYD0919VwPn1gIfRD9+HMv1REQkec26rDAu7ZbuP8Clsxdy9YOLuPfisZxyTM+4XKcx972xjrKqGr5z6tAWb/u7px3FV+99mz+8VcyNJw9p8fYl/pp6Rm0S0Bf4O3AH8KvPbE0ysxFEirQJwGjgTDMrAL4PvOruQ4BXo58bUuHuY6KbijQREYmLnE7pPD7jOIb1zuIbjyzm+aVbWu3an+yt5MF31nPOsX0Y0jOrxdsf068rpxzTk1l/L2J3eVWLty/x11Sh1gv4ITAC+B/gFKDU3d9w9zdiaHsYsMDdy929BngDOBc4G3gwesyDwFcPN7yIiEhL6JqZxiNXH8fY/t246Yn3+dPiTa1y3d/87SNq65ybT2753rR63zl1KPsP1DDzzaK4XUPip9FCLboA+xx3vxyYCKwFXjezb8bY9nJgqpllm1kmcAaR5ad6uvvW6DHbgMb6mDPMbJGZzTezRos5M7smetyikhItRCsiIocnKyOVP359PFMKcvju00t5eN76uF7v4x3lPLFwIxdM6Ef/7My4XefoXp05a1Qef3x7Pdv3VcbtOhIfTU7PYWbpZnYu8AhwA3A38EwsDbv7SuAXwFwiqxosAWo/c4zT+JxsA9y9ELgIuMvM8hu5zix3L3T3wtzc1n8IVEREkkdmWgr3X1bIl4b14MfPfsisN9fF7Vp3vbqGcMi48aT4Pzt2yylDqaqt47evxe/nkfhotFAzs4eAecBY4KfuPt7db3P3mNekcPfZ7j7O3U8AdgFrgE/MrHf0Gr2B7Y2cuzn6WgS8Dhwb63VFREQOV0ZqmN9dMo6vjOrNz15cxf+88lGLT7T70Sf7eOb9zVw+eSA9O2e0aNsNGZTTka+N68tjCz5m8+6KuF9PWk5TPWqXAEOAm4B3zGxvdNtnZntjadzMekRf+xN5Pu0x4Dng8ughlwPPNnBeNzNLj77PAaYAK2L7kURERI5MajjE3Rccy3lj+/LrV9bw8zmrWrRYu/PlNXRMS+G6LzR4sygu6kd93v3KR612TTlyjU7P4e6xrFrQnD+bWTZQDdzg7rvN7OfAU2Z2FbABmA5gZoXAde5+NZGBCDPNrI5IMflzd1ehJiIirSYcMm7/l1F0SAsx840iKqtq+clZww9rsfSDLdu0m78u38ZNJw+he8e0FkrbvD5dO3DRcf15eP4GrvtiPoNyOrbateXwxXX2O3ef2sC+HcDJDexfBFwdff8OMDKe2URERJoTChm3nT2CDqlh7v97MRXVtfz3uaMIH0GxdsfcNXTNTOXqqYNaMGlsbjixgCff3civX17D3RfqiaK2oCV6zURERJKWmfHDM4bxrZOH8NSiTdzy5BKqa+sOq60FRTt4c00J3/hCPlkZqS2ctHm5WelcOWUgzy/bwsqtMT3FJAFToSYiItIMM+Pbpwzl/51+NM8t3cL1j77HgZpDW+zc3blj7mp6ZKVz2aSB8Qkag2tPyKdTegq/mrsmsAwSOxVqIiIiMfrGF/P56bThvLziE2Y8tJiKqtiLtTfWlPDu+l3cePIQOqSF45iyaV0yU7lm6mBeWfkJ73/8uRUcJcGoUBMRETkEl08eyC/PG8XfPyrhij8sZP+BmmbPqatzbn9pNX27deD8wn6tkLJpVx4/iO4d09Sr1gaoUBMRETlE08f3467zx7Bowy4unb2APRXVTR4/58NtfLhlL7d8aShpKcH/1dspPYXrv5jPW2tLmbduR9BxpAnB/9ciIiLSBp09pg/3XjSW5Zv3cNH989lZ1vCi57V1zq/mrqagRye+emyfVk7ZuEsmDqBX5wzumLu6xSf0lZajQk1EROQwnT6iF/dfVsja7fs5f+Y8tu/9/Fqaz7y/mXUlZXznlKFHNK1HS8tIDXPjyQUs3rCL11Y3uEiQJAAVaiIiIkfgi0f14I9XTmDz7gqmz5z3T0s0VdXUcdcraxjZpwunj+gVYMqGTS/sR//umdzx0hrq6tSrlohUqImIiByhSfnZPHL1cewoq2L6ffNYX1oGwJPvfsymXRV859ShmCVOb1q91HCIW04Zwoqte/nr8m1Bx5EGqFATERFpAWP7d+PxGRMpr6ph+sx5fLBpD3f/bS0TBnbnC0Nzg47XqGmj+zCkRyfufHk1NYc5ka/EjyXTA4SFhYW+aNGioGOIiEg7tuaTfVz8wAJ2llVRW+c8de0kJgzqHnSsJs1ZvpXrHnmPb58ylMIB3TAzzMCILKNlEPls9e8jr6HocRz0PnKeEYq+B6Nn5/RAVmJIdGa22N0Lmzomrmt9ioiItDdDe2bx1LWTuOSBBQzP65zwRRrAacN7MbpvF+58OT7zqg3IzuTlW76QEFOTtDXqURMREYmD6to6DEgJt43iZHd5Fau37cMBd3A88hp9X+eRZbAcwKHOo9/zj/dEz6k76PyNu8r55ZzV3Hb2cC4NcOmsRKQeNRERkYCktpECrV7XzDSOG5zd4u26O6+vKuE3f1vLv4zrF+jyWW1R2/qvSERERNoUM+O7px3F9n0HeHj++qDjtDkq1ERERCSuJgzqzglDc/nt6+vYV9n0clvyz1SoiYiISNx999Sh7C6vZvZbxUFHaVNUqImIiEjcjerbldOH9+KBvxezq5F1UeXzVKiJiIhIq/j2qUMpq6rhvjfXBR2lzVChJiIiIq1iaM8svjqmDw++s77BBezl81SoiYiISKu5+UtDqKl17nltbdBR2gQVaiIiItJqBmR3ZPr4fjy+8GM27iwPOk7CU6EmIiIirerGkwowM+5+9aOgoyQ8FWoiIiLSqnp36cClEwfw5/c2sXb7/qDjJDQVaiIiItLqvvHFfDJSw/z6lfgsBJ8sVKiJiIhIq8vplM5Vxw/iL8u28uGWPUHHSVgq1ERERCQQV08dTOeMFO6cq161xqhQExERkUB06ZDKtV/I59VV21m8YVfQcRKSCjUR+f/t3XmQVeWZx/Hvj4aWsAQBARdAEBCMRiObEDdQIcaY0TFuGAc0RogxbokzWslUtEylyjWxTDIKCoqjQZlxHJ2kEiAOGmPYEQUhDQjIIpu0EBZlfeaPe0jdtA10N337nHvn96mi7r3vfZfn9ltv8/R7zrnHzCw115/ZhaNalPPwpIq0Q8kkJ2pmZmaWmmbljbl5cHemLdvEW0s/SjuczHGiZmZmZqm65ozOHNuqKQ9NqiAi0g4nUwqaqEm6TdICSe9Juj0payNpiqQlyWPrA7QdkdRZImlEIeM0MzOz9BzRuIxbz+/BvFWb+cOiDanGsnPPXirWbU01hnwFS9QknQLcCPQHTgMultQduBt4LSJ6AK8lr6u2bQPcA5yRtL/nQAmdmZmZFb9v9OlIl7bNeGRyBfv2pbOr9unuvYx8dg5XPPFnKrfvSiWGqgq5o3YSMCMidkTEHuAN4DLgEmB8Umc8cGk1bb8CTImIyoj4GJgCXFjAWM3MzCxFTcoacceQE/nLuq38Zv7aBh9/x649XP/0LP64ZCM/vOgk2jQvb/AYqlPIRG0BcLaktpKaARcBnYAOEbF/BtYBHappexywKu/16qTsMySNlDRb0uyNGzfWX/RmZmbWoL5+6rH0OrolP5+ymD179zXYuFs/3c2IcTOZsXwTj1xxGlf379xgYx9KwRK1iFgEPABMBn4PzAP2VqkTwGHtb0bEmIjoGxF927VrdzhdhCDfVAAAC8pJREFUmZmZWYoaNRLfH3Iiyz/azktzVzfImFt27ObasTN5e+VmHht2Opf17tgg49ZUQS8miIixEdEnIs4BPgYWA+slHQOQPFZ31uAacrtv+3VMyszMzKyEDflCB07rdCSPvbaUnXv2HrrBYajcvotrnprOwg+38G/f7M3Fpx5b0PHqotBXfbZPHjuTOz/t18CrwP6rOEcAr1TTdBIwVFLr5CKCoUmZmZmZlTBJ/PPQnqzZ/AkTZqws2Dgbt+5k2JjpLN2wjTHD+zL05KMLNtbhKPT3qL0kaSHwP8DNEbEZuB8YImkJcEHyGkl9JT0FEBGVwE+AWcm/+5IyMzMzK3Fndm/LgBPa8Mup77Nj155673/dlk+5asw0VlbuYNx1/Rjcs329j1FfVEpfLNe3b9+YPXt22mGYmZnZYZrzQSXfeHwad13Yi5sGdau3fld/vINrnpxB5fZdjLuuH/27tqm3vmtL0pyI6HuwOr4zgZmZmWVOn+PbcF6v9jzxxvts+WR3vfT5wabtXDV6Oh/v2MW/39A/1SStppyomZmZWSb9YOiJbPlkN2PfXHbYfb2/cRtXjp7Gjl17mHDjAE7vXBzfo+9EzczMzDLp5GNb8bUvHsPYPy1n07adde6nYt1Wrho9nb37ggkjB3DKca3qMcrCcqJmZmZmmXXHkBP5ZPdennjj/Tq1X7BmC1ePmUZZI3hh5EB6Hf35eo6wsJyomZmZWWZ1b9+Cy3p3ZPy0D1i35dNatZ23ajPXPDmdZuWNmThqIN3btyhQlIXjRM3MzMwy7bbzexAR/OJ/l9S4zawVlVz71AyObFbOi6MGcHzb5gWMsHCcqJmZmVmmdWrTjKv7debFWatYuWnHIev/eelHDB87k/Ytj2DiqIF0bN2sAaIsDCdqZmZmlnnfO687ZY3Eo68tPmi91ys2cP0zs+jU5nO8MGoAR7dq2kARFoYTNTMzM8u8Dp9vynVf7sLLb69hyfqt1daZsnA9I5+dQ7d2LXhh5EDatyzuJA2cqJmZmVmR+M653Whe3pifTfnsrtpv313LTc/N4aRjWjLhxgG0aV6eQoT1z4mamZmZFYXWzcu54ayu/G7BOhas2fK38pffXs0tE+bypU5H8ty3z6BVsyYpRlm/nKiZmZlZ0fj22V05slkTHp5cAcDEWav4/sR3OKNrW8Z/qz8tm5ZOkgZO1MzMzKyItGzahJvO7cbrFRv50cvz+ZeX3uXsHu14+vp+ND+icdrh1TsnamZmZlZUhg/sQruWR/D8jJVccFJ7nhzeh6ZNytIOqyBKL/U0MzOzkva58jIevPxUpi/bxA+G9KS8cenuOzlRMzMzs6IzuGd7Bvdsn3YYBVe6KaiZmZlZkXOiZmZmZpZRTtTMzMzMMsqJmpmZmVlGOVEzMzMzyygnamZmZmYZ5UTNzMzMLKOcqJmZmZlllBM1MzMzs4xyomZmZmaWUU7UzMzMzDLKiZqZmZlZRjlRMzMzM8soRUTaMdQbSRuBD5KXRwEfpRiO1Y3nrTh53oqX5644ed6KU9V5Oz4i2h2sQUklavkkzY6IvmnHYbXjeStOnrfi5bkrTp634lSXefOhTzMzM7OMcqJmZmZmllGlnKiNSTsAqxPPW3HyvBUvz11x8rwVp1rPW8meo2ZmZmZW7Ep5R83MzMysqJVcoibpQkkVkpZKujvteKzmJK2QNF/SPEmz047HqidpnKQNkhbklbWRNEXSkuSxdZox2mcdYN7ulbQmWXPzJF2UZoz2WZI6SZoqaaGk9yTdlpR7zWXcQeauVuuupA59SioDFgNDgNXALGBYRCxMNTCrEUkrgL4R4e8GyjBJ5wDbgGcj4pSk7EGgMiLuT/5Aah0Rd6UZp/29A8zbvcC2iHg4zdjswCQdAxwTEXMltQTmAJcC1+E1l2kHmbsrqcW6K7Udtf7A0ohYFhG7gBeAS1KOyaykRMQfgcoqxZcA45Pn48n9MrIMOcC8WcZFxNqImJs83wosAo7Day7zDjJ3tVJqidpxwKq816upww/FUhPAZElzJI1MOxirlQ4RsTZ5vg7okGYwVivfk/RucmjUh88yTFIX4HRgBl5zRaXK3EEt1l2pJWpW3M6KiN7AV4Gbk0M1VmQidz5F6ZxTUdoeB7oBXwLWAo+kG44diKQWwEvA7RHx1/z3vOayrZq5q9W6K7VEbQ3QKe91x6TMikBErEkeNwAvkzuUbcVhfXI+xv7zMjakHI/VQESsj4i9EbEPeBKvuUyS1ITcf/TPR8R/JcVec0Wgurmr7bortURtFtBDUldJ5cDVwKspx2Q1IKl5crIlkpoDQ4EFB29lGfIqMCJ5PgJ4JcVYrIb2/0ef+Ee85jJHkoCxwKKI+FneW15zGXeguavtuiupqz4BkstcHwXKgHER8dOUQ7IakHQCuV00gMbArz132SRpAjAIOApYD9wD/DcwEegMfABcGRE+cT1DDjBvg8gdfglgBTAq77wnywBJZwFvAvOBfUnxD8md6+Q1l2EHmbth1GLdlVyiZmZmZlYqSu3Qp5mZmVnJcKJmZmZmllFO1MzMzMwyyomamZmZWUY5UTMzMzPLKCdqZlbvJIWkR/Je35ncALw++n5G0uX10dchxrlC0iJJU+uhr/skXXCIOvdKurOa8i6S/P1mZv9POVEzs0LYCVwm6ai0A8knqXEtqt8A3BgRgw933Ij4cUT84XD7qQtJZWmMa2b1w4mamRXCHmAMcEfVN6ruiEnaljwOkvSGpFckLZN0v6RvSpopab6kbnndXCBptqTFki5O2pdJekjSrORmx6Py+n1T0qvAwmriGZb0v0DSA0nZj4GzgLGSHqpSf5Ck1yX9p6S/SHo++QZyJPVJPsMcSZPybvHzt88s6aKk3RxJj0n6TV73X0j6Xibp1rzyxsk4i5JxmyV9nS/p7ST+cZKOSMpXSHpA0lzgCkm3SlqY/FxeqMH8mVlG1OavSzOz2vgV8K6kB2vR5jTgJKASWAY8FRH9Jd0G3ALcntTrQu7+eN2AqZK6A8OBLRHRL0lY3pI0OanfGzglIpbnDybpWOABoA/wMTBZ0qURcZ+k84A7I2J2NXGeDpwMfAi8BZwpaQbwC+CSiNgo6Srgp8C38sZrCowGzomI5cndAvL1AgYDLYEKSY8n5T2BGyLiLUnjgO9K+iXwDHB+RCyW9CxwE7k7swBsiojeybgfAl0jYqekI6v/0ZtZFnlHzcwKIiL+CjwL3HqounlmRcTaiNgJvA/sT7Tmk0vO9psYEfsiYgm5hK4XufvDDpc0j9ztddoCPZL6M6smaYl+wOsRsTEi9gDPA+fUIM6ZEbE6uanyvCS2nsApwJQkhn8FOlZp1wtYlhdL1UTttxGxMyI+IneT7Q5J+aqIeCt5/hy53b6ewPKIWJyUj68S+4t5z98Fnpd0LbndTjMrEt5RM7NCehSYCzydV7aH5I9ESY2A8rz3duY935f3eh9///uq6r3vAhBwS0RMyn9D0iBge93CP6D8OPcmsQl4LyIG1nO/UP3nPZT8z/w1cknc14EfSfpikpiaWcZ5R83MCia5SfREcifm77eC3KFGgH8AmtSh6yskNUrOWzsBqAAmATdJagIg6URJzQ/Rz0zgXElHJSfdDwPeqEM8JDG0kzQwGb+JpJOrqXOCpC7J66tq2Hfn/f0C1wB/Svrqkhz2Bfin6mJPkuFOETEVuAtoBbSo4bhmljInamZWaI8A+Vd/PkkuOXoHGEjddrtWkkuyfgd8JyI+BZ4id7HA3OTrLEZziKMGEbEWuBuYCrwDzImIV+oQDxGxC7gceCD5bPOAL1ep8wnwXeD3kuYAW4EtNei+ArhZ0iKgNfB48pmvB/5D0nxyu45PVNO2DHguqfM28FhEbK7LZzSzhqeImuygm5lZfZDUIiK2JVeK/gpYEhE/TzsuM8sm76iZmTWsG5OLDd4jdxhydMrxmFmGeUfNzMzMLKO8o2ZmZmaWUU7UzMzMzDLKiZqZmZlZRjlRMzMzM8soJ2pmZmZmGeVEzczMzCyj/g8A+wO4gRJQYwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "Accuracy = [x*100 for x in skf_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()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FXuXM51GHjAx",
        "outputId": "2fdfeac7-428d-4982-9fbc-8bd2ea3663f9"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[5, 6]\n"
          ]
        }
      ],
      "source": [
        "max_acc = max(Accuracy)\n",
        "optimal_ks = [neighbors[i] for i, j in enumerate(Accuracy) if j == max_acc]\n",
        "print(optimal_ks)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LiST-3D1zVgA"
      },
      "source": [
        "## Standardization\n",
        "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"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "GNe4-JTI0tYh"
      },
      "outputs": [],
      "source": [
        "X = cancer.data\n",
        "y = cancer.target"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "dwS4wRtFzUK_"
      },
      "outputs": [],
      "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)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "rVg1mxfc0HgB"
      },
      "outputs": [],
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size = 0.33, random_state = 17)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "yq6yq6FjzmcR"
      },
      "outputs": [],
      "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)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 388
        },
        "id": "ID5KNvw5zyOb",
        "outputId": "5cd9f727-fa6c-40e4-a47c-bf156a81db34"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "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()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8tm1WNW1z0I7",
        "outputId": "9f84b28b-e904-47f2-ec42-4d1feb3ba276"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "96.80851063829788\n",
            "[2]\n"
          ]
        }
      ],
      "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)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8m5kl4pU6oV7"
      },
      "outputs": [],
      "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"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        },
        "id": "YVXOvKYHJU6M",
        "outputId": "31f64a8c-c2bb-49ca-c6e2-733c71656469"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 15.0, 'Predicted label')"
            ]
          },
          "metadata": {},
          "execution_count": 160
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "from sklearn.metrics import confusion_matrix, classification_report\n",
        "\n",
        "knn = KNeighborsClassifier(n_neighbors = 2)\n",
        "knn.fit(X_train,y_train)\n",
        "predictions = knn.predict(X_test)\n",
        "cnf_matrix = confusion_matrix(y_test, predictions)\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')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7r9-BmgkQY6K",
        "outputId": "81e2324f-10e1-4f5e-aaff-da656a11f8fe"
      },
      "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"
          ]
        }
      ],
      "source": [
        "from sklearn.metrics import classification_report\n",
        "print(classification_report(y_test,predictions))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "NIiLVOEPJFLN"
      },
      "outputs": [],
      "source": [
        "## TASK-3:\n",
        "## Perform K-Fold Cross validation after standardizing the data. Display your results."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "K2Yahm_pObJi"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import StratifiedKFold, KFold\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "scaler = StandardScaler()\n",
        "X_scaled=scaler.fit_transform(X)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "vRu82KebSzh-"
      },
      "outputs": [],
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size = 0.33, random_state = 17)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "TJRHFjFHTCvn"
      },
      "outputs": [],
      "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)"
      ]
    },
    {
      "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",
        "  \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": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 236
        },
        "id": "GbGsj3PPvbHE",
        "outputId": "ce9126c0-c76e-446c-c282-d4603ddb0ad7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "AttributeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-169-e97e6b16aa9f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m   \u001b[0;32mfor\u001b[0m \u001b[0mtrain_index\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest_index\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mkf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 12\u001b[0;31m     \u001b[0mX_train_fold\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX_test_fold\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mX_train\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtrain_index\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX_train\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtest_index\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     13\u001b[0m     \u001b[0my_train_fold\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_test_fold\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtrain_index\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtest_index\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     14\u001b[0m     \u001b[0mknn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_train_fold\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train_fold\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mAttributeError\u001b[0m: 'numpy.ndarray' object has no attribute 'iloc'"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "W0HPRG2m_XAN"
      },
      "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."
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**ANSWERS:**\n",
        "1. splitting of data set into train, validation and test set.\n",
        "pros- For larger data set this splitting gives a better result. cons- For the same model using small data sets could result in les.s accuracy\n",
        "2. K-Fold cross-validation is an non-exhaustive cross validation method, the original sample is randomly partitioned into k equal sized subsamples. Of the k subsamples, a single subsample is retained as the validation data for testing the model, and the remaining k minus one subsamples are used as training data. The cross-validation process is then repeated k times with each of the k subsamples used exactly once as the validation data. The k results from the folds can then be averaged to produce a single estimation. When k is equal to n, the k-fold cross-validation is exactly the leave-one-out cross-validation. "
      ],
      "metadata": {
        "id": "fhf60TkLptTY"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "p2VvIoDbB15O"
      },
      "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"
      ]
    }
  ],
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      "name": "Module3_Lab4_FMML20210660.ipynb",
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    "kernelspec": {
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