Bio_projects.ipynb

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  "metadata": {
    "colab": {
      "name": "Bio_projects.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/vimalkumarmdb/Brest-Cancer-Classification/blob/master/Bio_projects.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RnHLVurSUWT6",
        "colab_type": "text"
      },
      "source": [
        "# Bioinformatics mini projects\n",
        "\n",
        "---\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VV5m9dkhUiuT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xSg0Pw9DVNhJ",
        "colab_type": "text"
      },
      "source": [
        "## Download the dataset from UCI reposetry"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h8cHLGyi6uE0",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.datasets import load_breast_cancer\n",
        "cancer = load_breast_cancer()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MbWGgz6Q8FLi",
        "colab_type": "code",
        "outputId": "0c7f68f7-37b6-4093-c01a-dabdecdbfff8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "cancer.keys()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dict_keys(['data', 'target', 'target_names', 'DESCR', 'feature_names', 'filename'])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mN3iKHmQ8a_p",
        "colab_type": "code",
        "outputId": "d8cbdbdf-fa10-4717-95e8-d8e2026b5a05",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "print(cancer['DESCR'])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            ".. _breast_cancer_dataset:\n",
            "\n",
            "Breast cancer wisconsin (diagnostic) dataset\n",
            "--------------------------------------------\n",
            "\n",
            "**Data Set Characteristics:**\n",
            "\n",
            "    :Number of Instances: 569\n",
            "\n",
            "    :Number of Attributes: 30 numeric, predictive attributes and the class\n",
            "\n",
            "    :Attribute Information:\n",
            "        - radius (mean of distances from center to points on the perimeter)\n",
            "        - texture (standard deviation of gray-scale values)\n",
            "        - perimeter\n",
            "        - area\n",
            "        - smoothness (local variation in radius lengths)\n",
            "        - compactness (perimeter^2 / area - 1.0)\n",
            "        - concavity (severity of concave portions of the contour)\n",
            "        - concave points (number of concave portions of the contour)\n",
            "        - symmetry \n",
            "        - fractal dimension (\"coastline approximation\" - 1)\n",
            "\n",
            "        The mean, standard error, and \"worst\" or largest (mean of the three\n",
            "        largest values) of these features were computed for each image,\n",
            "        resulting in 30 features.  For instance, field 3 is Mean Radius, field\n",
            "        13 is Radius SE, field 23 is Worst Radius.\n",
            "\n",
            "        - class:\n",
            "                - WDBC-Malignant\n",
            "                - WDBC-Benign\n",
            "\n",
            "    :Summary Statistics:\n",
            "\n",
            "    ===================================== ====== ======\n",
            "                                           Min    Max\n",
            "    ===================================== ====== ======\n",
            "    radius (mean):                        6.981  28.11\n",
            "    texture (mean):                       9.71   39.28\n",
            "    perimeter (mean):                     43.79  188.5\n",
            "    area (mean):                          143.5  2501.0\n",
            "    smoothness (mean):                    0.053  0.163\n",
            "    compactness (mean):                   0.019  0.345\n",
            "    concavity (mean):                     0.0    0.427\n",
            "    concave points (mean):                0.0    0.201\n",
            "    symmetry (mean):                      0.106  0.304\n",
            "    fractal dimension (mean):             0.05   0.097\n",
            "    radius (standard error):              0.112  2.873\n",
            "    texture (standard error):             0.36   4.885\n",
            "    perimeter (standard error):           0.757  21.98\n",
            "    area (standard error):                6.802  542.2\n",
            "    smoothness (standard error):          0.002  0.031\n",
            "    compactness (standard error):         0.002  0.135\n",
            "    concavity (standard error):           0.0    0.396\n",
            "    concave points (standard error):      0.0    0.053\n",
            "    symmetry (standard error):            0.008  0.079\n",
            "    fractal dimension (standard error):   0.001  0.03\n",
            "    radius (worst):                       7.93   36.04\n",
            "    texture (worst):                      12.02  49.54\n",
            "    perimeter (worst):                    50.41  251.2\n",
            "    area (worst):                         185.2  4254.0\n",
            "    smoothness (worst):                   0.071  0.223\n",
            "    compactness (worst):                  0.027  1.058\n",
            "    concavity (worst):                    0.0    1.252\n",
            "    concave points (worst):               0.0    0.291\n",
            "    symmetry (worst):                     0.156  0.664\n",
            "    fractal dimension (worst):            0.055  0.208\n",
            "    ===================================== ====== ======\n",
            "\n",
            "    :Missing Attribute Values: None\n",
            "\n",
            "    :Class Distribution: 212 - Malignant, 357 - Benign\n",
            "\n",
            "    :Creator:  Dr. William H. Wolberg, W. Nick Street, Olvi L. Mangasarian\n",
            "\n",
            "    :Donor: Nick Street\n",
            "\n",
            "    :Date: November, 1995\n",
            "\n",
            "This is a copy of UCI ML Breast Cancer Wisconsin (Diagnostic) datasets.\n",
            "https://goo.gl/U2Uwz2\n",
            "\n",
            "Features are computed from a digitized image of a fine needle\n",
            "aspirate (FNA) of a breast mass.  They describe\n",
            "characteristics of the cell nuclei present in the image.\n",
            "\n",
            "Separating plane described above was obtained using\n",
            "Multisurface Method-Tree (MSM-T) [K. P. Bennett, \"Decision Tree\n",
            "Construction Via Linear Programming.\" Proceedings of the 4th\n",
            "Midwest Artificial Intelligence and Cognitive Science Society,\n",
            "pp. 97-101, 1992], a classification method which uses linear\n",
            "programming to construct a decision tree.  Relevant features\n",
            "were selected using an exhaustive search in the space of 1-4\n",
            "features and 1-3 separating planes.\n",
            "\n",
            "The actual linear program used to obtain the separating plane\n",
            "in the 3-dimensional space is that described in:\n",
            "[K. P. Bennett and O. L. Mangasarian: \"Robust Linear\n",
            "Programming Discrimination of Two Linearly Inseparable Sets\",\n",
            "Optimization Methods and Software 1, 1992, 23-34].\n",
            "\n",
            "This database is also available through the UW CS ftp server:\n",
            "\n",
            "ftp ftp.cs.wisc.edu\n",
            "cd math-prog/cpo-dataset/machine-learn/WDBC/\n",
            "\n",
            ".. topic:: References\n",
            "\n",
            "   - W.N. Street, W.H. Wolberg and O.L. Mangasarian. Nuclear feature extraction \n",
            "     for breast tumor diagnosis. IS&T/SPIE 1993 International Symposium on \n",
            "     Electronic Imaging: Science and Technology, volume 1905, pages 861-870,\n",
            "     San Jose, CA, 1993.\n",
            "   - O.L. Mangasarian, W.N. Street and W.H. Wolberg. Breast cancer diagnosis and \n",
            "     prognosis via linear programming. Operations Research, 43(4), pages 570-577, \n",
            "     July-August 1995.\n",
            "   - W.H. Wolberg, W.N. Street, and O.L. Mangasarian. Machine learning techniques\n",
            "     to diagnose breast cancer from fine-needle aspirates. Cancer Letters 77 (1994) \n",
            "     163-171.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9fZpWX8W-O2w",
        "colab_type": "code",
        "outputId": "4be0fb89-b9b0-47dc-b50b-926b469cae9e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "print(cancer['target_names'])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['malignant' 'benign']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W-t-GlPkAp4D",
        "colab_type": "code",
        "outputId": "1a9def22-53f4-43d0-86a8-c87779473a92",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        }
      },
      "source": [
        "print(cancer['target'])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
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            "[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
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            " 1 1 0 1 0 1 0 1 1 1 0 1 1 1 1 1 1 1 0 0 0 1 1 1 1 1 1 1 1 1 1 1 0 0 1 0 0\n",
            " 0 1 0 0 1 1 1 1 1 0 1 1 1 1 1 0 1 1 1 0 1 1 0 0 1 1 1 1 1 1 0 1 1 1 1 1 1\n",
            " 1 0 1 1 1 1 1 0 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 0 1 0 0 1 0 1 1 1 1 1 0 1 1\n",
            " 0 1 0 1 1 0 1 0 1 1 1 1 1 1 1 1 0 0 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 0 1\n",
            " 1 1 1 1 1 1 0 1 0 1 1 0 1 1 1 1 1 0 0 1 0 1 0 1 1 1 1 1 0 1 1 0 1 0 1 0 0\n",
            " 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 0 1 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
            " 1 1 1 1 1 1 1 0 0 0 0 0 0 1]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bS7oPfdQAs26",
        "colab_type": "code",
        "outputId": "88d1cf1b-6121-4fbb-a50d-15e56bd89b34",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        }
      },
      "source": [
        "print(cancer['feature_names'])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['mean radius' 'mean texture' 'mean perimeter' 'mean area'\n",
            " 'mean smoothness' 'mean compactness' 'mean concavity'\n",
            " 'mean concave points' 'mean symmetry' 'mean fractal dimension'\n",
            " 'radius error' 'texture error' 'perimeter error' 'area error'\n",
            " 'smoothness error' 'compactness error' 'concavity error'\n",
            " 'concave points error' 'symmetry error' 'fractal dimension error'\n",
            " 'worst radius' 'worst texture' 'worst perimeter' 'worst area'\n",
            " 'worst smoothness' 'worst compactness' 'worst concavity'\n",
            " 'worst concave points' 'worst symmetry' 'worst fractal dimension']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9VPEyFbkAv3p",
        "colab_type": "code",
        "outputId": "5f43d8fa-22ef-4ef1-f7ab-51eab492521d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "cancer['data'].shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(569, 30)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eTIBPrM1A00q",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df = pd.DataFrame(np.c_[cancer['data'], cancer['target']], columns = np.append(cancer['feature_names'], ['target']))\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3SO4OkPWA4ii",
        "colab_type": "code",
        "outputId": "ab5b4e94-9f1e-49a4-d96e-b69cf4b212de",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 261
        }
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "    .dataframe tbody tr th {\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>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",
              "    </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>0.3001</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>25.38</td>\n",
              "      <td>17.33</td>\n",
              "      <td>184.60</td>\n",
              "      <td>2019.0</td>\n",
              "      <td>0.1622</td>\n",
              "      <td>0.6656</td>\n",
              "      <td>0.7119</td>\n",
              "      <td>0.2654</td>\n",
              "      <td>0.4601</td>\n",
              "      <td>0.11890</td>\n",
              "      <td>0.0</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>0.0869</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>24.99</td>\n",
              "      <td>23.41</td>\n",
              "      <td>158.80</td>\n",
              "      <td>1956.0</td>\n",
              "      <td>0.1238</td>\n",
              "      <td>0.1866</td>\n",
              "      <td>0.2416</td>\n",
              "      <td>0.1860</td>\n",
              "      <td>0.2750</td>\n",
              "      <td>0.08902</td>\n",
              "      <td>0.0</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>0.1974</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>23.57</td>\n",
              "      <td>25.53</td>\n",
              "      <td>152.50</td>\n",
              "      <td>1709.0</td>\n",
              "      <td>0.1444</td>\n",
              "      <td>0.4245</td>\n",
              "      <td>0.4504</td>\n",
              "      <td>0.2430</td>\n",
              "      <td>0.3613</td>\n",
              "      <td>0.08758</td>\n",
              "      <td>0.0</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>0.2414</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>14.91</td>\n",
              "      <td>26.50</td>\n",
              "      <td>98.87</td>\n",
              "      <td>567.7</td>\n",
              "      <td>0.2098</td>\n",
              "      <td>0.8663</td>\n",
              "      <td>0.6869</td>\n",
              "      <td>0.2575</td>\n",
              "      <td>0.6638</td>\n",
              "      <td>0.17300</td>\n",
              "      <td>0.0</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>0.1980</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>22.54</td>\n",
              "      <td>16.67</td>\n",
              "      <td>152.20</td>\n",
              "      <td>1575.0</td>\n",
              "      <td>0.1374</td>\n",
              "      <td>0.2050</td>\n",
              "      <td>0.4000</td>\n",
              "      <td>0.1625</td>\n",
              "      <td>0.2364</td>\n",
              "      <td>0.07678</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   mean radius  mean texture  ...  worst fractal dimension  target\n",
              "0        17.99         10.38  ...                  0.11890     0.0\n",
              "1        20.57         17.77  ...                  0.08902     0.0\n",
              "2        19.69         21.25  ...                  0.08758     0.0\n",
              "3        11.42         20.38  ...                  0.17300     0.0\n",
              "4        20.29         14.34  ...                  0.07678     0.0\n",
              "\n",
              "[5 rows x 31 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Fmb7tMIxA6Hq",
        "colab_type": "code",
        "outputId": "44eeea6b-9080-4f21-ef63-d468ba321e2a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 261
        }
      },
      "source": [
        "df.tail()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>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",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\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>1.1760</td>\n",
              "      <td>1.256</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>0.0</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>2.463</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>0.0</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>1.075</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>0.0</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>1.595</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>0.0</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>0.3857</td>\n",
              "      <td>1.428</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>1.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     mean radius  mean texture  ...  worst fractal dimension  target\n",
              "564        21.56         22.39  ...                  0.07115     0.0\n",
              "565        20.13         28.25  ...                  0.06637     0.0\n",
              "566        16.60         28.08  ...                  0.07820     0.0\n",
              "567        20.60         29.33  ...                  0.12400     0.0\n",
              "568         7.76         24.54  ...                  0.07039     1.0\n",
              "\n",
              "[5 rows x 31 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Zq7Y8mbTBeKA",
        "colab_type": "text"
      },
      "source": [
        "## Visulization of the some feature V/S target"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-cj5wG9xBD2R",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import seaborn as sn"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MwsPronTB25a",
        "colab_type": "code",
        "outputId": "788d6aeb-0ac7-4cb4-a38b-11c3f4ff891f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 905
        }
      },
      "source": [
        "sn.pairplot (df, hue = 'target', vars = ['mean radius', 'mean texture', 'mean area', 'mean perimeter', 'mean smoothness'] )\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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5ory9vI5lr5WxeFoRxw3JxNFcQzUafERr+wnREYah2V3npd4XoikY5p5Xy2LZ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\nxb1gHDn+jeR6VfDdbmF47cj+jWVNpb5As3aG9jE2xZX1GKYlQei2YAROoWr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BKUTR1TBN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dKvt+sjO71tJVgPobqooutHj8Qb3EsvVWJB0/8Dmo7lcBFKysHpFvEJoYOwHbSmoktpvdUC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\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XPgBOGdL5c319WLYIIDnBzVHDM1n12W7sPgixVnqB/RlljlHocslzMOnmUDki57jwsGPH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\nTkSuQmCC\n",
            "text/plain": [
              "<Figure size 951.875x900 with 30 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hBdpOCkaCS66",
        "colab_type": "code",
        "outputId": "094b978a-cfc1-4c92-bbe6-b5441f673f19",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        }
      },
      "source": [
        "sn.countplot(df['target'], label = \"Count\") \n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nHoM2SkgDfK5",
        "colab_type": "code",
        "outputId": "cc745088-f868-44e5-fb83-a140f8567bc4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        }
      },
      "source": [
        "sn.scatterplot(data = df,x = 'mean area', y = 'mean smoothness', hue = 'target')\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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+y41ZpTwwewNvT+wb9ApACEHbKBtPj+mByxcg3GpE38gYisfnZ1l6AXd9thaX\nN0Co2cBHkwfQp23kccdjXF4/Bp3A0EhDpVA0B0c0EFLKE9WKyAZq/3cnVo0FhRAiDPge+LuUcsUJ\nzqVhSvbDzAlaKqk1Esa8A+2Gg+nEnoqPiNEK7UZomUuH6HYluMvh8/E1Y5m/w9Q0iGoHaJk7eeUu\nLEYd7WPsHCxzsbewErevfivRQxh0glCLkRnL9/L29f1IyyxicIcY4kLNtAqz1LuBOVw+Plmxj7hQ\nMwad4IMlGXSKD2XioGTiwy24vIFq43CIjIIKXLWe/ivcXhZuy2ft3iL6pUTxw6Ycbj2nAzklRcSF\nmWkfa6+uAD9EtN3EhIFt+XyVttiMtBm589wO3D9rPSN7tG7wvTncvmrjcIjVGUV4/Ef+PI6EzWzA\ndpxhlKIKD/d+sR6XV7tuudvHnz9bxzdThzVadqSs0svWA2VMX5ZJ2ygbNw9NoVWYpW5VvEJxkgkm\ni2mhlPKCY401wGqgoxCiHZphGA9cF8ykhBAmYA4w41BmU5NTWQLf3asZB9AqpWfdAPdsbD4DYYuG\nce/DmumaEUgeCp1HwtJX6u4X8Gm1Dn1uAHMoFpOeF0anMrQNGHLW4o/uyA5nKLZaOfuFDje78x0U\nVXjonRRJTIiJf4zsyri3lzGoXRSJkTbu/2I9Lq+fycPaMXlouzo+fofby44DZeh1YTz7ww4AftyS\ny/ebDvLprQPR6wStwy0cqJIcB00w0OsPUOL0EGo2UOL0ERtmYvygJMa/u5yPbh7IzR+tptipFQF2\naxPG9MkD6xiJCJuJv17amclD25FZ6CTMauCFn3aQVVzJ2L4JDX6MIWYDVqO+jmuqV9sIjM1cwX04\nLm+AssOC3TmlruMyVCszipgyo6Yj4px12cybNozYBlZYCsXJ4oj/UUIIixAiGogRQkQKIaKqvlLQ\n4gtHRUrpA6YCPwLbgFlSyi1CiCeFEKOqrjFACJEFXA28K4TYUnX4NcBwYJIQYn3VV+8TeJ/18bkg\na3XdMb8HnIVHPsaRD7lbIH+HJoB3PNjjtDTW/pPh4AZY/gZYGvDWmcPg8wmQv50wo2RM1F7i/3s2\n0fPvIO7TCxi6/z1iDFo1c4HDzS3TV3PNuyu445O1XPjir2QVV9Ix3s6vD55Lp1ah/OPrzRRWeKjw\n+Hn9l3SW7a47f49fcnG3Vny8fF+d8d35Dg6Uugg1G3n52t5YjNqfjEEn+PtlXXj9l3S+3ZDD+v0l\njHlrKU9+u5XNWaVc1qMNn6/eX20cALbklLFxf/08gwibibgwC3oBT367FZfXz/TJA0mIaDimEG41\n8vb1fQm3am6ydjEhvDa+N7ULTJ8AACAASURBVAEpyS1zUVgr8NxUlDg97CtysjGrpFrmXCcgObru\nw0TvthE01rtUXKWSW5v8cjc7avUQVzSMlJL8chf7CivILXPVWdEqTpyjrSBuB+4F2lDVbrSKMuCN\nYE5eFdied9jYY7V+Xo3mejr8uE+AT4K5xnFjsELbs7Xag+oxc00B3OE4cmH6KMjfrr1uOwiu/eT4\nUl99Ls0wZK3Wiuxu+ha2fK1dAyC+mxagzloFCx6HUa+j++FBTZupCl3a+3DOfWCNID3Pwfr9NX0h\nHG4fLy3Yyb/H9aBVuJXZa+p79uZuOMAFXeKrK4dtJj1d4i1HrCQud/vo0zaCn+8fwZ78CkItBmav\nyWLOumzW7y9h6nmp5Je78fgCxIdbCLMa2JXnqHeeg2WuemMlTg8uX4AeieF8cssgpISoo8hnW4x6\nhnSI5qf7huPxBbCbDZRUernt41VsySmjW5swXp/Qh3YxIU3ioilxenj5511MX5YJ1Mich1sMvHh1\nL56Zt52NWSUMbBfF30d2wW42VLeI9QfAZBBHTQfWC4HVVP9Z7XiyzJqSEqcHty+ATmjpzI2N0ZwM\nMgud3PDBSrKKKwkx6Xnhml6c2ykWq7Fxac+nKwUON06PH5NeR6ilvlbbiXK0GMSrwKtCiD+fkQ2C\nrOFw+UswcyIcWK8ZhrHvauJ4hyOlVs18yDiAJluRuRS6j62/fyAAznzw+zSjc3iWkSUcul+lGQhP\nBXx3P9z6s1b74PdqsYqvbtf2dRYceWXjdUFpFv0jJaundiKjxMdDPxwgs9BJvsONxy+xAr3a1q9w\n7pccgakqEOr1+TFLFynGEh45vxW3zqqR0DgnNQqbAaxGHWajHpfXz5QZaXXiHzZTjbuntNLL3kIn\ndrOB0b3asHBbXvV+Jr2OEWfVbcuZW+bivi/Ws2x3Ia3CLLx4TS/6JR+7ENFk0BMfpt1AC8rd3Do9\njd35mkHaklPGLdPT6gXQj5dip7faOIAWJH/15508enlXYuwmHrmsM76AJKvISUyIGYtRz8asUv78\n+Tr2FTnp0zaC16/rQ2Jkw67LMKuRhy7tzLi3llVXiXdpHUpydMt19z1Y9XtZvruQ1uEWXry6F32T\nI08pKZLiCg8P/G9Dtdx9hcfPvTPX8+uD52ENP/MNxIGSSm767yp25jow6AR/ufgsrhuYTLjtCLI7\nx0EwTtt3hRDThBCzq76mCiGabgYtSXgiXP8l3LcF7lwG7c8FUwNujYC/JlZRm7yt9cf8XshZC+9f\nCC93hU+uhJK6bht0ek0yfNhfNMPkc2mGIrK9VhFtsMCV70LqBdDvZk2Lqc8NNccnDYbLXwVvJcwY\njWHOFGIpZuCqe/ns2iTCrAZuHpKCQSfYmFWCxahnZM+agG+/5EjG9knUAtVSoivLxrrsefTZaQzK\nn82CKZ352/ltmH5NO14530pbmwd7VdZTVIiZASlRNW9FwMOXduartTWrlH9+u4XOrUIZkBLFO9f3\npX9yJOd1juWbqUOJqXXDdri8PPX9Vpbt1ozfwTIXkz9aTWllLeHCIHD7A9XG4RAZBRV4fE3jbsgv\nr7vqSYy0cus57Xnkq82MfnMZby5Kp024lQu6xBMfbqG4wsNN/13FviJNCn7d/hL+MmsDJc76KceH\nOCvezi8PnMujI7vwzvV9+fiWQfUC+icLh8vLv77dyvKq38uBUhc3H8fvpbnxBgJsrtVREcDtC1Dh\nrl8EeabhdPt4YcHO6k6TvoDk3/N3UHyUv7HjIRgz+xZgrPoOcAOaDMatTTqTlqKBGoJ66A3Q90ZN\n5rs23cbU39dZBJ9drX03WGDAFM1A5G3VpMCt0WA0a/IdIx6CQbdpnelcZZC/EeZO0wyGJQImfK5l\nVxltcM5fNINmjULq9IjV/4Ftc+Gy52H9p7D0VWh3DrFrX+F/kx8lPjqCdftLuP79ldhMeh64uBO3\nndOeCJsRu8VAdJXLQzry0H90idYy9ZafsM/9M6nr/kNqmz6wK4dArwnoUqZWv72oEBOvju/N1pwy\ndhc4OLdTHHazgYHtIlm/v5gIm4m/X9aZzq3D0OkEIzrFMah9dHVWVZ2PyuNnWXrNyshuNjBxUBIu\nr58ChzvoG6RJL4gPM5NbVhN7iAs1N7qm4UgkR4dgM+lxejSDc/d5qTwxdwtr92nxlEU78jnwcRqf\n3KqV+VR4/JQ4695MVx4jy8pqNJAUZeCWc9o3yZxPBKfHXy9G5fYFKHC4T6l+HxaD5mpctKNG7dhu\nNhDayKr60xGn199gPC+jsIKUmKZbeQbzHzRASnmTlPKXqq+bgQFNNoPThVY9YPSbEN0BYjtr4ncN\nKaJ6KjTjAHDJ07BnEXw0Ej67Fl7rA/m1Vh1Gi1YgZ7CA3625mnxVT6uuEq2xjylE2yckBgbegQyN\nR3w1Bfavgt0L4bNrYNAdkL0W4rtjzN1IpygdAnh94S5A+4d/8rutjH5zKfuKnNXGAYCKPCjL0eIb\nS1+F8Z9q77FgJ7LLaHR9rtcE/moRbTdzzlmxTBrSjpToEGLsZqZd0JG0v1/I99OGMXd9DsP+vYhz\nn1/MzFX70FHfOIAWS+hRJfBnNer54Kb+5Dvc/OnV3xn/3grSMotwe/2Uu7zsK3Iyf/NBduc7KK2s\n+5QUFWLm7Yn9iKhaWkfYjLw1sS/RR6jEbixGneCjmwfQPzmSVmEWereNqDYOh9h+sLw6QGoz6bEd\nFj/o1iYMfROlrHp8/mYNxppr/V4OYdAJopro82wqwqxGnhnbg0HttBVt2ygrMyYPrP47OJMJtRg4\nv3Pd+KdOQKf4putkCcGtIPxCiA5Syt0AQoj2NEKT6YzBWiX73fFiQGg37Ib+4U02LQPJ74a4rvD9\nX2q2+Vzww0MwYWbdYLgtRuvt4DksqFu0h1KvoMIRIDY0gNHvRqz6T919/F7Y8yt0HwcFu6DLKMpE\nKA63n3+M7MLS9EJe+2VX9dPv4U/VwhSiGT9PBWz7FooyYMg0fElDMITGH7Nw0O8PkOdwM3d9Dh1i\nQ1i7r4Rfd2lPn5VeP//8divDO8Y26BcNsxr51+juTPrvKs5uH823Gw9Uu6rS8xxMfH8lvz54Lqsz\ni5k2c111XOSvl3bixsEp1QE5vU7QIzGcn+4djtPjx2bSExliQt8EKwi/P8CsNVl8uSaLCQOTiLGb\ncHv9xNhNFNSqUg+zGKo/2wibkTev68s9M9dRVtWO9dXxvU+4A53X7yenxMVbi3bjcHu5Y0QH2sXa\nGy1UeCzCrUaeHtODmz5cxZ6CCmwmPc+M7UF4A0a+pWkdYeWd6/vh9gfQC0GM3fSHqB0xG/RMGd6e\nnJJK5m0+SIzdxDNjezS5cQzmL+tBYJEQYg9aFXUy8MeUANfpj521ZI3Uspvm/lnr6XA45Qe14PUh\n/F4txhDwaS6k0ixtvPPlMOIhrM6DGK0+fI5QjCFhyPDE+kqsYW3grEtgwywKBz/Ms99vZ/Za7Twj\ne7Tmzev6cvNHqzm/cxztY0Oo9PiwmgxVQXQrctwHCBnQVgo/PIgvIglvSGsMhmP/eeSWu7n0ld8o\nc/l4+NLO1X7r2mzOKaVDnL2BozXxwKfGdCfGbuaW6Wl1trl9ATIKKnht4c46eksvLdjJ2D6JdTI2\njHodcQ24P8qqZDAkWlZQZCO1lpxeP7/vymdXnoMnv9NWf+d1iuP/ruzB1M/W4fYFMOoFz13Vs/rc\nZoOeoanRLLh/BG6vH6vJQHSIqc5cbCZ9o3Wf8ss9XPrK79UJAfM2H+TbqcPontD0Mutto2zMun0w\nTq9f092yGk+pAHVtjqTZdaYTYzfzzJU9ePTyrohmyjQLph/EQiFER6BT1dAOKWXTJ5qfKcgAxHeH\nKYvAV6mtNGrXTPSeqBmRQzjyoChT016aOBu+uQv0JmT/yYgPL8XkdWISAnnB48heE5BDpiG2fFVz\nzta98KeMQOqNGM77G2vTy/jfmqzq03+38QAjzorl+2nDqHD7+OvsTYSY9dx7YUeSzQ6MO75FLHhc\nk/yITkVOnI0htDWGY6QJFjrcSAlfrN5fXSy2M6+cvsmRrDvMN3q0G5jJoKfc5WNVRhEd4+3Vgd1D\ntA63sr+4bvc6r1/iCxy7GK2ows1z83fwRdp+pIQLOsfx3FU9j/kkX1rpxaQXWE0GfP4Ag9tHs7RW\nrGTRjjweHdmZ3x46j0KHm6gQM2FWQx2Z8tpZVofm8uwP2/nfmiykhIu6xPPsuB6NWlUs2JpbpzhQ\nSvjP73t47qqemJtB/ymmCTLAzmQcLi/5Dg+rMgrp1CqUpKiQk+6GC7UYG3TfNhXBVFIb0WoihlcN\nLRZCvCulPLVSGloaKbWn/9+eh8J06H299lR/y8/w8xNaHKHz5Zrb6ZDbJhCAoj2w+2foeAlkLkFe\n9C9ERBLik3HaTbvq3OKXp6DdCEREMv7blyAPbsats+EPT0YGTJhtkRhMBpamZ9Sb2qrMIga1i2bk\na0uqx37elsuW+7vD/IdremoXpiPmPahVexsbDkYGAprsx5QZa0iMtNYRyPt+4wE+vXUQ6XkOft2Z\nj82k56FLOh0z2DwgJYoDpS7uPi+VrTllHCh1oRNwzwUdsZv1XNA5nu831fRu6JkYHlT3uK055cxc\nXaMXuXB7Hj9uOciEgUkNuiFKnB5W7Clk+rK9tArX4iqfrdjHhV3juahrPD9vy8Wk1zHtglSi7GYi\nbKajBm19/gBFFR68/gA7ch3MSqsx3Au25XLhtniuHRB8Z78wa/1/11CLAd0Jd/dQNBZ/QLIkvYA7\nPqkpERs/oC1/+1PnJlE5DpZKj58yl3YrtjfQs/5ECeZsb3MmZzE1Br8HhAEaknRw5GnFb3GdNVfN\n/Ie0Bj+DbtcyjTZ/CQuf0AzEeY8AQjufxwEp58D0kZohsITDpB/qd6kL+LQViauEgN7Gbn0qrUKN\n2Bc/gaFsH/4+N0KnS7m0ezwRBg9JYTp+3ONhwbY8Lunais9W1e2rrRcCd2kutsBh4aSctTWB8sMo\nqvCwJbuUBdty2ZRdSkZBBR/fMpCPl+/F7Qvg9gWY+tk6vrxzMDqdQCcE4VYDlmOsRiJDTEwYmER5\npYcv7xyCy+vHatRjtxgItRj55+hutIsNYdH2PPolR3L3ealBPXmvyqzv7lqaXshV/dpiMtS/qS7Z\nVcDUz9dVv164La9KLmQVd56bypRz2hOQko6xdrz+AN9vPECx08P5neOIsZvqqLhKKdl6oIybPlzF\nwHZRDVaFL00vYFzfhKCF+YamxpAYaa3O+7ebDdw2vAPGozRYUjQPRRUe/vlt3TT3mav3M+2CjoQ3\nk1LP4RRXePhwaQbv/bYHKeH6s5OYen7HJl3FBGMgBkgpe9V6/YsQYkOTzeB0wFmsFdOt+1hzH/We\nCKHxdffxe7Sg9YaZ0KYP3DAHfnpUC2xnr9XiF+FttZTWvcu1YHVFPox8EXb+qK1ABtyqBZt3zNMy\nk+zx8NUUbb+wBPA4we/B+MFFdLZFwXl/B2sobFmJfv9K5MSvGBjdgYHON9Bl7eaiTldRcv4YbBER\n/L6rbtpihcevNRoyWOoahHbn1jFOeWUufttVQIzdxKasUox6XXXuucPt463Fu/lsytl8uyEHAUwa\nmkJsqLnRktcmg47oKt2hEqeHCrefQodWyRtjNzPt/I7cPEQLTAfjCy+u8DC6VwILt+WxJacmFnRp\n91YNdqzT/tky64yVuXzsynMQHWLm2R+0IslQs4EF9w9nwn9WklFQAYDFqGPetHNoH1sTZylwuPnz\n5+sodmoifGP7JNY7/5+6t2qUamtcqIU5dw1h+Z4iKtw+zu0US2wL1UqApt/ldPvR68QJB+BPNySS\nsgbqQrzHocN1vGw/WM7rv6RXv/5waSYD20VxafeGRS6PB5XFdCz8fq3e4Ntp2uvNX8LGmTD+c9g4\nSyuui+qguWq2f6ftc2CDptk09l0tUO33QtYaiOkIva6DtwZphgKg7AAIHST01ZRePxpZI6mR2B9G\nvQGr3tVqJtIXQcEOraraWQizJ8PkH2HD5xCeiIjpgPjgIm01A4TmrMNOJWLoPdw2vD0HyypJiQ5h\nT0EFe/Ic5HotJE+Yhe6bO6EsW3svlzwNZi1VLq/Mxeg3l3Kg1MVr43vz+ap99EmK5Iazk7n/ok4Y\nDYLs4kqemLuFDyf1JzrEjBCajtDP27KocPu5rGdrYg97uj4aRRVunv5+G19WZTOdFW/n41sGER9m\nqXcTyi/XBAoDAUnH+FBiQ82Uu7ys2VvMawvTMegFD1/amV935vHxin1MGJjEgJQoKty+ektxo14Q\nFVLfl9smwsLlPVvTPyUKh9tHVIiRzdll1cYBNNG+Vxfu4tkre1bLY/gDsLdQcxHuL6qkqMLD1PNT\n+XBJBv6A5IazkxnU/giyLkchKsTMsA7R+KREALoWytjJL3fz9Pdb+XFLLikxNp67qhedW9kx6oN/\nMNDkSDy4fZpURGSIqclqV5qbUIuRiWcn895ve6rHOrcKbXIXz9H4ZXtuvbEft+RycddWJ9T+tzYq\ni+lYVBbWV1vN36EVvy17DX59VqvC3vF93X2yVoP0w+wp2uuz79TSUMsP1BgHgK1z4LIXtG1LX6mj\nt0RWGjK0FZz7CEJXZUT+d1PNdhnQ2o/GnAV9b9LiGY4aaQsAsW4G9JmIyRDNsNQYFu3IZ0BKFI9f\n3pW7Pl3DfReexYhbfgYpCRitGEJqqqR/31VQrd5a7vIRZTcRYTVg0Ou469M1lLl8dG0dxhvX9cFm\n1KPTCfLKXFzxxpLqorUXF+zgx3uHBy0bkVngrDYOADtzHXy4NIO/XNSpzpN/XrmLa95ZTmbVTTgh\nwso3dw8lq6SSSf+tEWG8KXMV8+8dzoSBSazbV8KFLy7msVFdubxnG2ymmj9/u0WTu1iSXlAt3903\nOYLUODsLt+Vx+8driLQZefGaXpS76j85lrt8BGqlWpkMgj5tI6oD9v/4ehO3D2/Pz/ePqCoaNGiZ\nZI3A7w+wOaeM2z5OI7fMTUKElQ9u6k+nVqEnNbXT6fbx/I/b+Xp9DgDbDpQz4b0V/PKXEcSFBW8g\nMgudTP5oNRkFFUSHmHjjuj70S448LRouWY167hjRgeQoG99tOkDPxHBuGdrupFa/D+kQw39+rxtz\nHH5WTJMZB1BZTEEgQN+AT0+nr7mZVxZplc+VxTXbDWYt0+hQ/+gvb9XcTqYQrTmR36vVH4z4K4TE\naauQNR/Vu4z0ONDZYiFvC+xeVD8+ENVeS50NS6huMFSHkFi8Pj/P/7K9Okj6y/Y8Vu4pZGzfRALo\n2OG0UOHW0hkTpJuoqj/yQ5IFMXYTRoOO1yf0RSB55KtNlLt9RNqM7Mwt55l523jpWk1s99ed+XUq\nml3eAO/+tod/XtEtKF95Qwqmm7NLcXn9mAw6Kj1aB7fSSg/3X3QWLy3YSWahk+ySSjZll9YJZgME\nJHy5JotKr5/YUDPPjuvJX/63geEd46oNRLnLy7YD5SzfXcA3dw9l28FyWoVZSI21s2DbQT6q0mGq\nLPVzw4erWPzAuYRZDHWkvu8Y0aHO02NUiJm3r+/LT1tz+WBJBnazgSv7JTbYiyNYCp0epsxII69c\n+3yzSyq5/ZM1zL5j8EmVBXe4fXU0tg6N5ZW7G0w1boiiCg/3zFxXvRIrrPBw24w1/PyXEXWyv05l\noqpiZ1f0aoPVqD/psaBebcMZP6Ats9L2I4HLe7RheMfYYx7XGILJYtIDlwApVftfKIRASvnSUQ88\nUwiJgQse1xoLRaZAh/OhTV/NLRQSC6Ne1/ruXfyUVvl8iOEPaT0darP9e0gZBuM+hN9egMv+DXPu\nhOIMLcPp7Ltgbo2sBRFJ6CKSNPmNnLWabtTeJdpKAbRYSHQHLRYR3R6KM6HbWNgyR9uuN8Elz1Dm\nFXWeygEW78zn7yO7EGkzMW3mOpbtLmRM7zbcd9FZLN6ZT1yomYu6xTN/8wGeGN2d/5u3nb9+uZGE\nCCtPju7GS6PaIYp2gr0183dX4vL4CbMYq5++a1Pp8SMJruHf2R2iEIf1mR7Vqw2hFgPlLi9fr8vm\nqe+3YdAJuieE8/K1vbl1ehqFFR5yip315LcBYkPN/LjlIDOWF/PIZV0Y3D66TiXytgNlPPX9NvwB\nyRuL0umZEM471/fHoBfVhXtGvaBHQjhOj5/0PAffTzuHd37drd3YzmlPx/ia+IPT4yOzwMlrv+wi\nEJC8PqEPrcMtJ3wT93gD1cbhEHsLnXW6BZ4MTAYdqXF2CjNqGkhpefjBB0d9/gAbs+rqKJW7fVR6\nTi/vtU4nCLO2TAFhVIiZRy7rwj0XdgSp1dY0dQZVMGvcbwEXsAk4eRGYUwUhIGUo3LtZuwGb7VC4\nG/athJvmwqwbNSG/offALT9pshURyVpAecbldc8V2hpiO0F4MrTuDXNu04wDaPGL9ufCxP/B2o+1\nlUGPcdqqYO9SLQj+9V1w6f+ByY4MS0BYIjRD1eF8CPgJxPVA9L0J0ecGzZWVPAQ2f4kvZTRmg666\nmjrEpOfNiX3ZklPG0vQCxvZJ4Lbh7UmKsPDSgh3M35yL2xege0IYH9zUn/+bt51FO7QnxqziSu78\nZC2/3d6R+FlXgBBce96TCH0yYOHCLnH8e76huuObTsCUc9oF7TaIsZt5Z2I//vX9VspcXq4flMxF\nXVshhKDE6eXf83fwzJU9aBVmweH2YdTrmHZ+Ko9/u5W+yZFE2c3MXLWf7BIt0yc1zk6vthE8PW8b\nAPM3H2BM7wRsZm0+FW4foRYjY/skYNDpGNIhGo8vQInLQ6zdzNTzUsktd9OlVSgrMooItRhIjbOT\nGGnj8Su64ZeyXsrtwVLNzeavUmb9eVsuP903/IQNhMmgq9e0qUNsSLUq78kiwqZV7V773nIKHB70\nOsHDf+rcKA0kg15H36S6kiVhVkOLS5yfboRZjc1qoISUR3/6EEJslFL2bLYZNBH9+/eXaWlpx97x\neMjdAh9cXCOF0fdGSByouZVmXV+zn9EG/SZBj2vAEqrpLxVWZRnEdIQbvgGdEbwVmlvokytrah0O\ncesv2gpg8FTYt0zrfNemNyx+BnYt0PYxhRK4axlSZ0QfEgd6PYUON7fPWMPMiSkYijM0mfAlL0PO\nOly3LGbGnlCeqeoU97c/dWZjVgnfbzpYfdkpw1K4t206utyNlHadyJQ52WzKLuPHe8/h+vdXkX9Y\nE565N6XS8+sLtHapOgPy3k2IsDYUOlxkFbtYsC2XHgnhdIyzU1zhoUvrsOr+1sfCH5AUVXiQSK2f\nddUNePnuAgodHmavzWJxlUBbjN3EF7cNpqjCTZfWYdgtRvLLXezJ11wXbl+AR+Zsqk4Nvbxnax7+\nU2fahFvR6QQ5JZWMfO336sZGrcIsvH19X5akF9Ah1s7cDTncdW4Hxr+3otrAdogNYeZtR5YS/795\n23i3VvASYNKQFJ4Y1S2o93+0z2XHwTJu/2QN+4sqaRcTwns39KNjE+vvBEMgICmscFPm8hFiMmC3\n6LGbj3yjcvv8FJS7WbRDW532SYrE4/Nzy/Q0tle59N6c2IeeieGNCnQrThwhxBopZf+GtgXzH/uD\nEOJiKeVPTTyv0wNnMcx7oK5O0toZWlD4wGHZvl4n5G3TCuYsXeC6WVoPCaHXqqd/fU7LRpoxWusq\nlzIMdtX6WENiNS2nwl1QsB1mV+UCGG1a/CJnvXbjv/BxdJvnwLJX4ca50Ko7Hl+AtH3FLMpszYWt\noxG7foK4LnDe37FkreDCs66hf8oQ0vYWc0m3eJ6dv73O1Ges2MetXdsTP+dGrBum8+Y1CxjxzlYc\nLh9dW4fy664aA6ETEBNi0PSbAAI+KiocuHRuyl0+npi7mafH9uDpedvYuL+UAe2ieHJ0t6ANhF4n\nGrz5to8JweH2VxsHgAKHh7cWp/PUmO7VQd/YUM2dU+x089DsjdXGISrExEOXdiI6xEyx04PdrOeL\n1fvqdL07WOZia04ZXVqHcev0NCYNSeHDJZnVxgFgd34FG/aXcGHXw1Kdq4huoNlRU/Sl0OsEXVqH\n8dWdQ/D6JSa9rsWqnXU6UfU5B7d/Rn4Fo95YWq1o2yk+lE9vHcSntw7C7Qtg0AuiT9GmRH9kgvmP\nXQHMEULoAC9aJpOUUjbQJ/MMxO+Bkv31xyvyIKG/lhLqrhVY7T0RlrwEuZvhz2s1V5IjVzMawx/Q\nMpiiU7WMpOQhmuHZu0xrQXr2XbDlK00Q0BajuaPyd2iGZ810uOFrrVhOb4ZPx2mprnNugxvnYtDb\n6ZMUQe9IF6IiX6vFKN6De99aCjpew00fb8DjC/D13UMbfJtaEkzVarIiH1veGvomtSbGbuJvl3Uh\n4+M17CtyYjboePxP7Qnb9llNkD4imT2l8P7irTxyWWduPac902auJ72qo9wv2/MocXr54Kb+J6Sb\nE2Y14nDXzyDKLHTi9gWwHnZqu9nIk6O68+jlErfXR6TNjF9K/vX9VtbuLWbEWbGM7ZPA9OV768hz\np8SE8PEKrbDQZtKzv/iwVR5QcpTeCKN7J/DBkozqYH2s3cy4vvUaJx4XQojTrk+1w+XlhZ921JE7\n35Fbzq68cgZ3CEJuX9FiBGMgXgIGA5vksfxRZyLWCOh5NfxeKyZvjdTcS+5SuHUh/P6ilrHU/2ZN\nCnzoPVqGkt+rpbrOf1jrQNd3kmYkzv+HdnNd+zF0G6fVOvg98M7QmhTYiCS48j348FLttbtMO9eB\njZpROZTOmrcNfG5iDDqm39CLkCVPwer3qBz1PstDRrIo18FX7+/QCuPQ9HzG9E7gyj4JdQLXt57d\nmrAdNf0uzGYzr43vQ26Zixd+2sk/RnYhwmZCr4NWFh+2VYUQloA/rhuFw5/i4Tm57Mgt52+Xdeas\n+NBq43CItfuKcQXZwCev3MW2A+XoBZrIn9SenqNCTAxqF43FoMNV1dEuMdLKbcPbV/enPkSJ08Pc\n9Tm8unAXvoDkrnM7d+Z7VAAAIABJREFUMKZPArdOT2NTVaHf9oPlZBRUcNs57Xnuxx0IAc+N60mI\nWU9MlSH7eVsu91/UqU7WTohJz7DUI9cwxIWamXv3MNbuKyYgJQNSouqsIEqdHsrdPircfqJCjMTY\nzWe0Aqk/ABXu+r97RwNjilOL/2fvvMOjrNI+fJ+pKZPeQwgQWui9NwEFLIggiljACupi11X3U9d1\n17LquvZVFOwVK6KC2JAmRSnSewklhPSezMz5/ngmmUwSIYgIyLmvKxcz75y3TcJ53vOU39MQA7Eb\nWPNbjINSagTwFGAFXtZaP1Lr84HAk0BH4CKt9Qc1PpsI3ON7+y+t9WtHev7fBZsTev9FJDbWfCCZ\nTKf/Q9qGWu0SI4hqClFNJDOpPF/cPsunS/C6zUi44BUJaleWwDNdxHDYnHDe/6TIzenyxSg+gV0/\nSl+GvF0y+Sd2hP2roctl6NAEVOY6WP6y//rSBsOaD1EbvyDs7MdRzYdASjdsW7/jl6AW5JYreqXF\nMH9zFpUeTfM4F06bhb+d1YYR7RNZuCWbM9JjaFvyE8Ezn5NjRqYSmtabMFcIdquFXTklTHrjp+pT\nupw25t9yP+saX8PmbDcvvpXBvvwyYl0OrBaFw2YhzGmjsEZnr/iwhrkPMgvKOM9XnAeQGh3Ck+M6\nc92bPzHt8h4y+d7QH69XY7UqMnJKKanwkFlQTqzLUV2ZvDWrmPtmrq0+7sNfbqB1YhjltYzU3PWZ\n/O2sNqzKyKdbk0hKKzxMmLaU16/syee/7GNTZhHr9uXz8oRuvLtsN5Ehdm4Y0rLeyuGicjf5JRXs\nySsl1GkjxGGlY0pkwKopr6SCp7/ZzPSFO2gcHczjF3Ri7d4Cyt1eOjeOJM7l/F3z2E8EIkLsTB6U\nxuJtfumTyBA7HVN+fxVaw+9LQ4LUrwJpwJdAtSP6cGmuvvTYTcAZQAawDBivtV5XY0xTIBy4HZhZ\nZSCUUtHAcqA74vf4Ceimta5RaBDIMQ1SA7grfHUOGnYsgkVPQpP+kNQRPp7sH3f+NNFk2uvX9KHX\ntdDrOpg6UPo+VBEaB5d+BDsWSBC6oghany1FdW+chz7zUVTuTnSzgaiIFDwOF5b8DNQXt0ngvNNF\nMOB2EQPc8Jm4s3pcJbGKZoPxJrZHVRajsVBmj6S8spLgsgPYw+OxBkXIveTsEAOlPfDLBxCWRF6r\nsezI97Ant5iWcSHY7A7OfnpBtZLooFZxPHZBR95dupsn5m4CxEX14qVd6ds8ltlr9+PVcM/Ha6jw\neAmyW3h5Qnf6pMUctkfDc99t4Z2luxjXozFJEUEs35FLs9hQlmzP5qahrbhtxiq2HCji9St78uic\nDazZIzIaLqeNWTf0r+6m9a9Z63h5QWAR0fldG+GwWninhoBfdKiDZ8Z3Yd6mLIamx3Pnh6vZkV1C\n19RI7jm7LZsOFJIUHkR4sJ2swnIGtIyl3O2lwuPFabVUpxWWVLiZtXofd324Gq8Gp83Ckxd1ZvHW\nbG4+vVV1CujWrCKG/mceAO9P7sPdH61mqy+gHhVi5/MbB5Bcj27TyU5BqUiOTF+wncSIICYPbE5S\nxG+vCTH8fhxtkHq778fh+2koPYEtWuttvot4FxgFVBsIrfUO32e102eHA3O11jm+z+cCI4B3juD8\nvy82h7ibfnhUVgogdQYRtXzLESmBxgFEw6n39YHGAcStZLHAnLv92zbMksroDuMobXoGe2PdJEdH\nEOIuwPrq2VJo1+taSYnN2QqLnoHGPaV16YwrIKqZbOt3M5ZZN8GWb1CNuhEy7EFC3horRkgpGHIv\nuu1o9MbPsSx5AVqegR74V7QznN05JSTZS0jf+wqOrVkUd7mGRbf25NXlB2nfKJyuqVHEuJxM7NOU\nszoksflAIT1TQonyZKG+v59zLEEUdL6Gj//Sl9ziSuLDnIQ6rRSUuQ8Zg9BaYwH+O64zz327hW0H\nixmSHs/AVnGEOKx8tGIPWw4U0SQmhJziimrjAPL0/tQ3m3hotMhddEmNQv5s/XRrEkVEsL3aQCgF\nfzurDW8v2cmXa/bTJy26unju5115nP/CIprHuXjiwk5cNm0JMyb3IbOwnNvfX8Uve/LplRbNw6M7\n4rRbqHB7ufeTNfgyWyl3e/nXrPXcc04bSirc1QZiny/9tlNKBOv3FVQbB4DckkqmL9jOXWemH5FG\n08lAeLCd3mkxdEqJwGaxGIHBk4SGVFL/o+q1L1Dt0lrX0wmnDo0Q91QVGUCvBl5Xffs2qj1IKTUJ\nmASQmprawEMfBcUHRYyvir0/S11CcJS/itpST4qewyXGIL6t9KauIv1s2LOi7vhdi/GOfpGiUi+r\n9xSxp8TBoIgcf83Epi8lhlGlDwWi23TOk3Id/W5B//Qqass38lnXCRIHqcrE0hq+/Re0HYVl+zzR\njLLaUTMmoIoO0K7zpVhang7Lngevm7Bf3sVzxRxuOSPw1xcRYicixE6LeJek8z7fBzwVBAFBP7+E\nZcI8Kp2RvPDDVrIKyxnUKo7h7RJIigiudwKscHs5p1Myo59fWN2t7dVFO9BaM6hVHHPWivZMWJCt\n3ubsWYWi6xPssNI7LZrBreOq+xX3ahZNy4QwGkcF893tp7E1q4j0xDBC7FYGtIjl3nPaEhZk529n\npXPZ9KVoLV9TfJgTheKj6/oR6rQybuqP1RpL6/YWsHZvPs1iQ7FbLdx1ZjqPz/HHe/bmlxId4gho\nNdoiXlx8YUH2eu9hf0EZHq0b9OR2MnKk8iKG40tDKqnfBq5FBPqWAeFKqae01o8d64s7HFrrqcBU\nEBfTMTtRZZmkqxZliqRFVdc3r0cUWyfNk5hDaY4Yi86XwMq3/PsPuhN+/B+MfEpSZJM6ySohojGU\nZNU5nU7tg2XBk8T/NJ1RzU/HPfI5oEYJfdeJ8MUdgTtlLIfoZjD7LvTQv6Nqnj8s0V99XX0SL6qi\nWBRkw5Ph5aEiKQ5YFj8j6bbpZ8O6T0FrrPMfh7HTq4X8AvC40YueQ3lqTHhl+UTtnM3HlcMY0S6R\nkgo3X67JpLjczbgejUmMqOtG2V9QRk5xRUArT4DPVu/j7I5JnN0hiQVbDrJhXyGdG0cSbLcGNNC5\nsHsK7y7dzbmdk4kPc3JV/zRuHNoSrWHbwSImv/ETd45ozdhujQlxWHl98Q6yCsu5ol8zUqNDCHXa\n6JIaxbe3ncY36zNJCA8i1Gnj0mlLKC53M+fmgdXGwaLguUu68vicjSzbIQ8Hw9sl8N9xnavjNQNa\nxFJUSxgwKsTBu5N6889Z6xjUKo6tB4oY0CqOwrJKZizP4Mp+zY5J8x+D4bfQEHPeVmtdoJS6BIlD\n3IXEBA5nIPYANbuhpPi2NYQ9wGm19v2+gfv+/hQfgGlnwJiXJAPp7XH+ArfoZlL8NvTvUhewaTb0\nvwU6XyyxgKSOsP0HWD5NMo8G3QGz74aDm6D1WZLVNPAOEf5zl6ObDUK1Gi61EoB169eoFdOhxzWS\nCtt0oKS/1ofXTU67iejgJkSnDUbtmC/bdy6WyX5VDQ9daCzaGYaa/7i4rLzuwGNtniuV3Os+BUDb\ngpAFZF1KKjw4UdSe1rxakRwRxMb9Bfxn7mYA5qzdz9z1mUyf2AOLT7SuakJ8d9luxnWv20CnUWQw\noQ4bjaMlY2nG8t28s3QXH13fl2e/3UJOSQVju6aQVVTOI7M38Mqi7Xw2pT+frNzDBzW661Vda0Fp\nJcOf/KE6tXXGTxl8+pd+dEyJJNRpo5nTxtkdkpgwfSmba2Rj5ZVWVgffB7aKY8m2nGrjIPeWydkd\nkujVLIrEiGBuH9YaV5AtoNLVaRf310sTuuP2ajqkRPK/77cSHergwdHtSYtrmKihwfBH0BADYfd1\nlTsPeFZrXamUasjT+jKgpVKqGTLhXwRc3MDrmgM8pJSq6s05DLj7EOOPLVu/FxeRIxSWviQSG7k7\npSdE1kapjLZYxJB8dDW0PhM6XyYSHW+M9rufek2S99ormVDxbSQu0fY8aHk6OMLE8LwzPkCUz7Jj\ngQTE+90iVShLX5ZA9uy7qsfolB6UOGK4ckkR4Su38trYcdLDwhEqxw+OkIK7jZ9DbCsqRjyGY8Xb\nYrSimta959gWkO+z51a7rIJqNTHyeDWZBWW8s3QXk7pNJmzVW/7rDo6iotXZdMLFPZ+uDdhvzZ4C\n8koquP7tFQxtE881A9KIdTlpkxjGzuwSJvZtyms+gbxgu5V7z2nDe8t2886yXUwZ3ILPbxyA3VdY\n9Y9z2/H5L3t5a8nOatmGzIJy9uaXce3AND5esada8iLEYWV4u0Tmb84KqHvQWoLj/x3XuToGkVNS\nEWAcAD76OYMnxnViytsraBwVUq+w4JasIl68rDs2qwXXIQoDo0IczPhpNw/5JEC2Hyxm/NQlfH/H\naUT+QQ1nDIbD0RAD8SKwA1gF/KCUagIcNgahtXYrpaYgk70VmK61XquUegBYrrWeqZTqAXwMRAEj\nlVL/0Fq301rnKKX+iRgZgAeqAtbHhUhfIDpjqUQ2v7oX2p4raawbv4T0kfJ5eRFc5ZPD8FRKKmxS\nZ3FPFWcBSorbJnwqqaxVMYQWp8uqQ9lQGt/YGqT2lviDK1aMVXInSYu9+D05f2wrVOuz0dt+oH1C\nK0alu1BvXwAD7oDQaJh2ujQG6nIpXPAansimlNsisXkqsZTmSkptl0thxZu++20Cp90NW76BAbej\nO49HuRLrfC0Hi8o586n55JdWsi0zhgevXEh49ipUYjtAUenRhDq9hDpsAdXKAA6rYsuBIrYcKOJA\nQRn/HNWeAS3j+O/Xm0iJCmHGtX04UFBOUkQQG/bnk11cQcv4MEZ1aRSQ5WO1KD5ZsbdOH+wQh5Xk\nyGC+uLE/ry/eSZDdwsS+zYgLc9bbcCjIbkXVaN0ZHxZUR/eoaWwofZvH8MNfB1Pp8fJLRl312NPb\nJBDZAMG0/LLKgBakABUeLyt35xERbKOkwktEsL3e5kbHkvJKDzklFazclUdyZDCNo4OJDj21mgEZ\n/Bw2zbXODlLRY9Vauw87+A/kmKW5luRAZSlsnyfZQePelKK3Ve9Ix7dBd8qEWl4An90s6aYgPakH\n3ipSHeWFItTnDJMOcW1GBjz9AxIoPrhZdJw2fCbV2JUl0OpMmawPbpLVSdXvq+UZ0q1u02wIipL3\nQZFk2xNxezwkvNxFDNGsW/x6UABK4b1xFVM+P8iTZyfgmDZEVjhD74PmQ9CeSqnE/vp+CAoXYcCE\njqgh/1en1eoHy3dx+we/yO02juSx0a1odHARITMniXE863F0bCsqtIUl+zzc/FkGOcUVjGgTy01D\n0jjzuaUA2CyKRXcPITrEwdasYs586gfsVgvhQXZySyuYfdNAwoKs2CyWeusPlu/M4aIXf8TtWyn0\naxHDX4enkxodjNsLWYVl2KwWgmwWVuzOo0OjCGat2ssTX4vby2G18PlN/WkZH0Z2UTleLXn6WYXl\nPPfdFrZlFTO2WwpD0uOJCnXg9npRQEGpm1cX7WDagu3YrYrbhrVmZKfkOkV7OcUVbD1QxMKtB+nX\nIpYWcS6C7Bb+9vEaPl4R6HX98Lo+vL1kFz/tzGVE+0SuGZBW7z2XVrgpKHPjtFnqNUgVbg9Wi+WI\npSvW7slnzP8WUe4rRDy9TTyPju30u7axNJxYHG2aawC+grkTyjgcM3K2w6fXS8vQ1D7SRS5zDXxy\nnX/M5rlww0+w7Xu/cQCpvv7gasl0AgkEX/YJ9LkeNtUja7V7iRiCgr3Qfix0vFDiApVl0kVu/uOB\nGtib58KA22D9LLjwNZh7H4REE3Pus3hztqPP+BcqvJEE1muiNZTl0zk1kgd/yOX/rp6HPrAObQuC\nkCT4+TWCvrs/YBevK5HVu7JpGhfhT1Mty8fh8UtQXD2gGUHuQkJmXQeecikO/Ope1J6fcAIDmvRj\n7jUvsG5/Ma1S4vnLjI3V+yaEB6FQ2KwWGkdL45+nv92MUoqbhrYkOTIooLlPbdolh/Pt7acxd+1+\nEiKCcFgtXP3aMt68ujfXvfkT23w9B3o0jeKGIS0Z+sQ8nr+4K69c0YP1ewuqJ/Ul27J5YNY6cosr\nuLR3Ey7umcp957SlzO0lPMhGWaWHLQcKmbZgOyF2G5f3a8rkQWlc0jsVhaic1u6IVlzuZuoPW3lh\nniQJPPn1Zq4/rTlTBrfg1jNaMX9zVnVQ/vQ2CWQVlldXuL8wbxsl5R7uPqtNgMppVmEZ//lqE9+s\nP0CLBBcPnteepjGhWCyK/JIK1u4t4J2lu2ge72J8z1QSGtijIa+kgn9+vo5yt5cz2iZwSa9UQhw2\nisorjYE4RTE5Z79G0QF4a6z/6Xvbd/DhlSKjUYXNCc0GirT3gfX+7YkdxG1TZRxAJv6f34B+N4A9\nFH6uURjeqBv0nCzH2PiFTwxwgtRUrHpXDEB5XX83VgdM/AyW/M+fPqs9WGbfKauHskJJfT2wHjbP\nEZdYZBOUI5Sr856mOLIFxeXn8d7eJny5Zj+bMpcyb9I5BM1/xB9LsFjJ6ziJ695Zzbjujbl+cHMJ\nKpcX0jtJkRgexP6CMuJcTmzefAnUp50Gu5f5myUBaudCYvYvpF+rEXyxsYDlOyUuY7UoHhrTnhjf\nBBTisNEhJZInx3UBCMgAcnu85BRXsDOnhFiXg6gQB5EhDoLtNixUMGv1Pg4Wl7M7p5SzOiTy8YqM\nauMAsGxHLnvzS2mTGM4Ds9Yxc0o/BreOByAjt4SLX15SHa94dM5GYlwOLuzeGKtFcbConNIKD4/O\n3sBX60R2491lu5h766BDFrYVlbmZVqtg7+X525nYtymNIoP58qYBvp4OXuLDgzj3mQUBYz9dtZcp\nQ1pUG4ii8koe/Hx9dTe3rKJyLpr6I7Nu7E9sqJPvNmZx83srq/f/6Oc9fHhd3waJBbq90gL0gu4p\n9E6L4db3V5FTXEH3JlE8e3FXEiNOLg0ow9FjDMSvUVka6JoBmfAifFk2cekw6jkpbPvuQZnQbU55\nHRIjPZ5rk7cDSvJk3BkPiL5Tm5GyYvjgCjEi7UbD+HfFOF0+SzKWSnIkg+mbB/zHik4T3abXR0kA\nHcRgWGySTrtzMSS0gy1fiwLswL9KcDyhHer9y1CZawkDWPMGw89+j0dmSxHfkz/m88Ck+VgXP40n\nPIWsjtexM6+S5y5OoaisUvT/FcRhIf7Lycy87CXmbi3BprwUeR2yUgpPFkXa2uxfjaXzeAakh/Ld\n7bHsyi6mZUIYkcH2OhW19fX23XawmDHPL6ruNXFxz1T+OqK1aERZFasy8qoL1eLDgtiVU1dkb1d2\nCfHhTl8nM/85l+3IqTYOVXz48x6GpMczZ20mz3+3BatVcc2ANDo3juJRX73Dl2v2cVX/tLr36kOj\n6xzX7fWitV8RtbTCw4in5vP21b0oqQyUAmkUGRzgJiqp8DB77f6AMQcKyykqc2NRiv99vzXwfnNK\n2JtX2iADERVs59JeqXRqHMmFLy6ubkS0fGcuD36xjofHdDikpLfhz0eDDIRSqi/+jnIAaK1fP0bX\ndGJgc4ofvar6ud1oqYZ2hMIVX0rQ9/0JkO+r51v9LlzwKjQfDJnrpMPcD49JrUQVPa6GeF+KaqNu\nckyvG57r5Z/kf3pFpC/ajxEjtep9KNoHfW+Es5+QIrn4dtBzksQOJn4mxXnb5oHXK8HxN8fCdQvh\nxQH+lcfmuTD+PTE2mTWyirI2ElqRRUSwnRZxIdzaOwzrj8+hEjuyu+kFjH1+KdnFcm0X9WhMh9xS\n7vl0Df8Z1ZLz2o4i/pVeXJLaFwpbsK/PfeSNnUHk4n9Dh7H+oHcVHcYCEBHsICLYQbPYhqd05pVU\n8PdP11YbB4C3l+7imoFpRIY4cDltTBqYVu3KWbojh1tPb8UXvp4XPZtFc37XFLo3icSx2kKHRuEB\nWUZN6+mZ3ad5DJsPFHHPJ2uqt9336VpeubxHdQDbWSuInF1UTnG5m4Vbs4lzOWibHMFlvZrwmk8d\nFkTttaphEUCQw0pUiIMfNh/k8r5NeWXhDkDkOh4c3T4gBmFRiibRoWzM9K8orRZFsMOKRal6g9oN\nrVq2Wi2c17kR6/cX1OlSt2RbDiXlHmMgTjEaUij3BtAcWIkUy4HoI/25DURwFIyeCjMmyqTf5lx4\n4zxxocS2grMe8xuHKhY/C2Nelif1jOVw+ZcitFewV57mo9Jkgg6JFgPkcEnswlOronbzVyIQGJUG\nQ/4P1s+UQrfmQ0TqO6a59JjYPBdCY+Qzr0eaDP38GsS1gl2L67qllk2F4f+uc6uRIQ6Gtonn/wZG\nE/P6aVCSTeGg+3lw9uZq4wBSp3BRj8aEOqzc9ulmWl89hrbXDkftXQWNupLkspEX3IrS4Y/jUB68\nZz6BfdEToCwU9bkDa1hTfqvKUKXHW90lriY5xRU0iw0lLMjOtYNEsXXjfimkC3XYeGRMB/bnl9Eq\nMYwnv97EU1+7uaR3E8Z0bURZpafaddMkJoSzOyRVZyUlRwQxsU8THvx8fZ1zztuURefGkVS4cxja\nJoFNmYXsyS2lXXIYGbllXPLykuoCvtYJYUyd0I1G0SGs2JXL4PR4Tk+PJzzIP9HGuZy8dXUv7v30\nF4amJzBzSj+KytykxbuIqhXwjnU5eXRsR8a/JA2MlII7R0g3N5fTzp1npnPZtCXV4arOjSNIOIKe\nEZGhDprGhGK1qICVT9fUKNPt7RSkISuI7kix3Kkl9W1zQtoguGmlCPVNHeRvkFOWJ0/3tbHYJdto\n1xLoNgG2fiuTfWJHcRW9ehak9ICzHpeJPSQaEtrWPU5sa9FXKs2B186B5kOlwVDWRkjtjcYC5Xmo\n/F2w8g05fp+/oEvzUa54kdQIiqx73KBIsPkmHGeYBKyjmuCIbMSDo6MJ2rtU0nCBclcqO7Lr3mNm\nYTn3ndOWv374C/fP3cO0Cd0J7+y/h0jgYFEk57y4mDYJHbh06Ado4LWVhdyUaqfNEXQRqcrUOVhU\nTrzLyXldknn6G7/bLzzIRkqU3+RE+mIS6Ylykgq3h9NaxVFY7mb4kz9Uu58em7ORiGA7fZvHsHZv\nPh4NbZPCeHh0B+4Y3pqSSg9xLifRoU46pkTW6efdNjmcnk2juW9kWz5dsYdHZm9kYp8mhAfZmL5w\nR0B198bMQtbuLWDD/gJSo4M5o20CUbWyjpRSNI0N5ZnxXalwewl12up1sVXRJimM724/jcyCMmJc\nTsJ9xgEkm2zuLYOYvWY/zeNC6dE0ut4sqEMRHmznvxd25v8+/oXCcjftksO5b6RIkRhOLRpiINYA\nicC+ww3802EPlp+8Xf5iN5AAtsUmhW5VwWllgUF3Sc1Dm7MlID3fJ+q3+SsJEg/7l8QaBv+fb9Wg\nZUXQ42pY5pPwDk+GwX+DTXOk9iG+DXQaJ4aiqlfEkHtQ6SOlQjt7q/wcWAejX4IWQyW2ERobeH2O\nUKnY9rjhxpXSs9rqixm44gguOij35CNi11zObXMtT9QoFguyW0iODCbIZiHO5aRv81iCf2Uis1oU\ns9ZkMWuNv6bj1uFtGvzVV3q8LN6Ww3Vv/kS5W2opZt3YD4Xik5V7SIkK5q4RbbAqaX9ZnyrogULp\nKNe/RSy1wgB8tXY/LqetOqCbGB7Ep1P6VavBVnF2hyQ++CmjuodEr2ZRDEmPJ9blJCO3hEdmSzbW\nGW0TKCx3U1BWt5FQYVklG/cXMiuz6JDxiobUTwA4bFYSwq31Zie5nNIze8qQFg06Vn2EOm0Mb59A\nr7Ro3B4vQXbrERsZw5+DhhiIWGCdUmopgXLf5x6zqzrRsAWJWye7RgBw6VS49GOZpHO2QqvhUhux\n9CVpNbrs5cBj7P8FwpMkq2jNB9Iv2lMObcfAGf+Q+Ia7TCbtz2+HYf+ARWuh+1Uw9+9+4wCo7x+W\n4rrRL8Lr58rKJmuj6CcteYnCS2cTcnAV1gvflGBxSTY06Se9JpoNgrfG+A1HUie4eIa4pBp1gytm\nw9IXsa/7gEvHX00ZzfhwxV6SI4K5+6w2HCwsJzE8iPE9Urisd5M6aZ0AMaEO7hjemmte92cxdUmN\nrM5UqqKozE1uSQXr9xXQPN5FbKijWj47t7iC22esqs7HL65wc+Wry3jnmt40iQkhs6CMG975mYIy\nN7Nu6F9vJtEXv+xjd24JqTF1S5ObxobSKsFFx5QIVmfks7+gjBnLdzNlSMuAcbFhTl69ogf5pZVY\nlCIs2EaMr3CsZhvS/NJK0uJcXNAthfmbD1ZvD3PaaJsUzvp9BUzo0xRX0MmRF+L0GSHDqU1D/lrv\nP9YXccLjiodLPoCPJknqauPecPZ/ZMLvNA6KsuD5XtXuGYIiJNBcFljdS0gsxKcHqrCu/VDcTLuX\nwNZvoMsEMRiLnoUul0k8I29X4HG8HnFxrf0I2o6ClW+LHIbNCYntceoSimM6EP7FDaIJ5YqXwrxm\np4m2Us2U3P2rZXW060fpa+EIhaH3Q8/JRFvhpvQCLm8dh1Y2Hl22iw9/3sPAlrE8dkGnX+2HrJSi\nd1oMs28awGer99I6IYy+zWMDnkIrPV7mbc5iyts/43LYuH5wc4a2SaCk0kNksAO3V5NTHBib6ZQS\nxdQftjHdF8St4qOfM7ikV5M6UuIup43dOaVYLYqhbeKru8I1iQlhTJdG3PHBaq4ZkFa9isjILa13\nNRLjctb7BB0V4qhO8521eh9jujaiRXwYL17WjXeX7ibG5eC6Qc35eEUGj43txOD0+EPWcxgMJxoN\nkfue90dcyAlDlc5zraphotMkC8jrlsk4JNr/mafcbxwAbMHizvmsRs1E2/Nkv63f1j3n7h8lM2nz\nV5LF1GuS1DCExorrqs258Mv7/vERKbJqyN4GjaReQA/+P9T+teAMw7HhMxQWykc8hnPXfKnABkm/\nrS24lzZYqsSX/E/eu8vgsxvg8i/gnYtwJnYkPrkLB5qdyzcbZIL9YfNByitrt/AIJCzITnqSnfSk\n+oMOucUV3PfMOI/BAAAgAElEQVTJGhTw4oRuvL54J4/O2YjDauGGIS0Y270xHRpFVLt2AJrEBNdR\negUoc3v5ftMBRnZMDpARP71NAk99s5k7Zqzm0yn9mDywORVuL5UeL7fNWMXWrGKSfLn9TWNCuHZQ\nGgeLyrFZVYPkJWJdDmZc24dHZ29gy4EiIoMdlFRUEmy3cPdZrYkLcxIR5OAvg1uaAK/hpKQhWUy9\ngWeANkjDICtQrLU+gnDjSYDXC0X7YfkrIrrXcxJENpWncq8b7EESWK4Ph0v0mKoqqcvzJXNp4mew\nc5HUTKAlgym5S939U3rCnhoyIfl74LS/Sj/qyFQ4/2VpVrRptsQVBtwmct+9rhUp7ykXoWxB4iZa\nPxMS2mPvfDFeZYMVNZLNdi6CEQ9JA6MqkrtIfKQ2e1dIwVvT/pQGJfDFDgIE7rxHmbOggdySCgan\nx7N8Ry6z10g6arnbS7DDSlZBGf+5sBOPfLmB5Ttz6No4imHtEnHaLLy3bHe1rEaw3crp6fG8MG8b\nw9omBhiI+PAgPpvSnwVbDuL2aG5+dwU5JRWU+Yxb77RoDhaVM7ZbCtcOas4t761ixe48OqZE8NRF\nXQ6bhquUonF0CA+f35HySg8RwXZsVguVHi82i6ruM22Mg+FkpSHr3WcRJdYZSEbTBKDVsbyo40Lx\nAakbKPb5j39+TYT3diwSt1LPSZKqGlxPdlBwJIz8L7QYAlu/k5VCZYnUSSS0l2ro+LYy4VqsUo29\n5AUJVHeZKIHl/atg7CuSXZTUWZ7qywtE2uPloVL81uUyMQJf3gnp50jMoGCPvzJ7nq/l9/rPYMPn\nWMa/A84afX9zt0PWJhgzVQLZFqtoOCmrCPPVJKY5uvWZ4HCx5gDc/5XfgHVoFEHYUfrSQxxWzuqQ\nRGp0CD/v9CcAjOqcTIXby8hnF5IQ7uTS3k2YPDCN5MhgQh1WQpw2vrhpAC/P347DqhjTNYUnv9nM\nmC6N8GrN6ow8Fm45SPem0TSPcxEfHsSYril4vJpXr+jJLe+vZN2+Avo2j+HxCzrhctro3jSax2Zv\npGlsKHmllazOyOfKV5cx49o+xDYgOOty2gJqKuqLyxgMJyMN6Um9XGvdXSm1Wmvd0bdthda6nkfh\n48dRi/Wt/RhmXB64rdUImdgX+NpvX/QOpJ9Vd1+toWAfVOkXOsPAq2HXQnmibzoQkjtLz+dFT8uT\nf9tzwZUo2US5OyG1l0h27P1ZUlzd5fDiwMDz9L1Rgtb7VkrB3C8zZPUxZiq8NNhv3KqYslxqIaYP\n99daNOomXfBC40RevDQfrT2oFW9AYnuJeZTkQrMB6PJClD0Eb2kehZGteej7g7RKDGNUp+RfjT80\nBLfXiwVFbkkF3288wN68Mv7j6209/fIe3Pr+yoDVCsDCu4bQyBeILiqrZGtWEbNW7+Pr9Zmc3zWF\n8T1TmbE8g0dmb6je56r+zbjl9Ja4aqRnZheV49Eah9Uvcrc/v5RZq/ex5UARA1vFcbConL/PXMv8\nvw4mJerotbdzSyqodHtxOW2EHCJ91WA4HhytWF+JUsoBrFRKPYqku/75HpEs9eR4W+2ga1RCL/yv\nSG/XjD9Ulskk/92Dkp2ktcQMzvmvyGi09hmU3B3+7m1bv5Gn9lm3iMYTSPbS+Hdh8XMSxL5ukRio\nTbPlc4cL2p0H3z8Cq2s0/tm1WP611zORKSWG4Mo5km0VlihxjfcuFeHAihJRpC3cj45rjZr3b4lR\nnHYXZCxHLXke+t6IpSyfiMXP8PCYqaiQmGrXSW1yistxezWRwY56K3pLKtzsyS1l2oLthDpsXNGv\nKWd1SKawrJK1ewuYs24/dovC7an70FL1IOP2eimt9NAkJpRJA9OYNDCNyGAHOSUVPPVNoLzHq4t2\ncM2AZgEGonawObu4nJveXcmS7aIm/+6y3dw5ojXjujfGUWslUFBaWV3JHeKwHjYt1ePxsvVgMX/7\n6Be2HSxmWNsEbhvWukGyF8ebSo+H7KJKft6VS6zLSVpcaINWU4Y/Fw0xEJchBmEKcAvSJe78Y3lR\nx4WUHhL8rWonarFJF7eZN/jH2ENkYq9JWYHoHv0yw79t/UwxDJ3H+3tUxzQXV9P6z6BJX5HEqDIO\nIE/4C56QrKO598G7l8iKpcfV0h8itY+k02bVyEByuODcpyV+Muhu+LSGymyrEVCYKdpRXrdoMhXt\nF0nxPlPkGuc9ClOWoSoKYOYU/74fXi1ZWxXFcv8XvQWbv6akwou2uusUTJVVeli/r4D7Z64lq7Cc\n8T1TuaR3kzoKoBm5pZz51PzqCt33lu9m7q0DSYoI5t/nd+Dv57bFZlU8MKotGoXNopi9Zj95JRWE\nOKQP9cyVe3lp/jaC7FbuGpFO77ToaimJSk9g4Nzj1XXqH2pTXOapNg5VTF+4g/cm9Q64/uyich7+\ncgMf/pyB1jC8bQIPjelQb3aT1pqsonLySioprfAwoFUcK3bn8e6y3Xi15v5z253w2Uy7sksZ+eyC\n6lTebk2iePGybsZInGI0JItpp1IqGEjSWv/jD7im40NYAlz9jYjvFWWJXPfPb0KeT0PHYpMucF43\n7F8jq4aEdoBFXD612f49dBwXmA1VWQY75kttQmKHuvuUZPt7PuftlH7VH0+SCmhXIox+QWIhn14P\nMS0kZuF1Q0GG1DNM/kEaCCV3kWC2xy0rk5AYOO9/kt4aFCaV3t8+IO6kXT+KgavN1m+h08Uw/3H0\nxi/ZM2wq98/ciduzgxuGtqB1ol/LKKe4gnEv/kiFb4L+z9xNhAfZuLRP02qhuUqPl6k/bAuQbygq\nd/PV2kwm9m1KRIiDCOQpPchu47E5G6n0eLmqfzPO6pBEdKiDbzdk8veZfh2pq19fzte3DKRFkJ1Q\nh5Xzu6Xw3jK//MnQNvGE/IYAsUKKxWoGvFdn5Ae0L52zLpMz2iYwtp4WqbtzS7nwhcXsLyhDKbi8\nb1PuHJHOQ1+sZ87aTG4f1vqENhBF5ZU8OmdDQJ3HTztz2ZldbAzEKUZDsphGAo8jGUzNlFKdkQ5v\nf75CubBEeWKvos/1kkaanyE9ne0h8PltsO5j+dweAtd8K6uFpS8GHqv12XVTZVsNg343Q7MBoufU\nZhSs/9T/eafxkuoK8tn2+ZL5FJYEPa4U909sKynEC0uUuoUD6+Q6hj8s28rypYI7f4+My90GH1wJ\njbrLPbz/t4B2pmgk9lCbqKbQuBds/RYd35775uziW1+a67zNWXxx4wDa+FJYV2fkVxuHKj5euZfh\n7RNJjPAXsNUWtgNw2gO37ckr5fq3/DLp//hsHS3iXUQG2+t0YAOYuz6TFglhuILs3DmiNd2aRPH1\nukz6No9lZKekw7qBHDZFn7RoFm/zryJuHNqyTlHfj9uya+/Kgi3ZjO7aCGuN33NhWSUPfr6O/QXy\nHWsNryzcwXuTexNkt9AsNjTA8JyIVHp0venEOcV1q8QNf24a8pd6P9ATyAPQWq8Emh3Dazr+VKmi\nuuKlEK3PX2TCLM31GweQTKWdiyGqCVy7UFp8BkdBr8kSp6gKGpfkSCBae2Vyfn8ivDZSXFDnPitV\nzqNflABySR6MnS5yGxs+h6b9YcQj0s3u2W7ihopLF/dQZanoOl0yQ4xRRCMxVLuXSnbTvEdl1VKW\nLyuapE6BWVhBEbLSSD8ncEWT0l22f3glnHYnBc3PYd4mv2SG1vDaoh143TJhNI6uW8WcGh3Cl2v2\nk1UoE6XdamHSwDSCahiEOJezuh9DFVXprjWZsTwDBdUGqSatE8KqX0eHOrmgWwpPXdSFCX2aHFYe\n4mBROXd+tJobhrbk7yPbcnHPVN6f3IdzOibVmcSHtkmos/+ZHRIDjAOIu21zZlGdsfvzy2gcFczD\nYzqc8M13okIcXN63acC2MKeNjikR9e9g+NPSkHVupdY6v1Zg8s8r3FewT1JGc7ZBj6sgNh2Cff8x\nSmq1xe4zReojpg8X49FpPEyaB8teEi2m2FZiYOY9IpN5YgdJbwUR1Hv3YnELleZAVDM595gXZcym\nr3zZRvHw6pn+c2/8AnpeI+/HTIWv7pEq7KTO0p8ita+4sUA60bl9gntej9ROTPxcxlcUSVxjzt3S\na+LCN0SdVikxbB9cKbGP2NY4sdAmKYw1e/ytyBNDNJaKQrBFkxQRzKhOyXy6SprYxIc5ubxvU655\nfTm5xRXcMLQldquFpIggvrntNGat2kuIw8qwdonE1wrYpieFUZt2yeHYbRbG92jMzJV72ZolE3D/\nFrF0ahyYdqyUanDdwbIdOczbeJB5Gw/SvUkUjaKC2bi/gO5NouqMbRnv4vZhrXj++614vJqJfZvS\no2l0nXGRwQ5GtE/k+Rp9GZw2C11To3jr6t4nvHGoYkDLWJ6/pCuvLdpBfJiTW4e1rrOqMvz5aYiB\nWKuUuhiwKqVaAjcCixpycKXUCOAppLjuZa31I7U+dyKy4d2AbGCc1nqHUsoOvAx09V3j61rrhxt4\nT7+dokyYdkaNHg/vSWZR6zPlfXSarBBKc0WSos058MqZ/lagP78msYy8DFl1fH6bPIn/MkPiFxs/\nDzyf9krx2ur3JYB98ftSi1ElDPjLe1LRHBoXaJx2LICBt8uEXxX/2LdSDM6Ih6sNhG5zLmrzXPk8\nKArOew7QklJbkiMxj9A46aV93UKp/C7Y63dBxbeBjKUEf3UPL46bS7/nxP8fF+bkog5hslJpPYLo\nUAf3j2rHdac1Z39BGQ6bhb/PXEt2cQXLduRSWuHBHmzBYbPSKDKYyYOa/+qvoEfTaHqnRfOjz+WT\nnhjGmK4pKKWIDw/ivcm9ySmuwGZRRIbYG1Tx/GtsqfGkv3xnLst35uL1asb1SMVRS24jKtTB1QPS\nuNAXcwgLshHsiyPklVSwI7uEOWv20yU1kol9mlLu9jBjeQZJEcE8NKY98eFO6cR3khAZ4uCsDkn0\nax6D3WY5oWMmhmNHQ37rNwD/hwj1vQPMAf55uJ2UUlbgOeAMIANYppSaqbVeV2PYVUCu1rqFUuoi\n4N/AOOACwKm17qCUCkHEAt/RWu9o+K39BrK31u3x8MNjUmsQGiMpold/A3PvBWuQ9KyuXUeybR6c\n+4wovrYcJjEAED2luHSZVGsS01wC3vFtJGW1pmqs1rDwKUmbzXrMv333Uug6sW5wPG+nXGNQJLrj\nhajuV6AXPYVSFpjwCXz/b1j3iawS2l8gbqy+N0l7UFsIXPg6vHeZFNTFpUuDoi9uh6IDJBWt5b2J\n7SiotNApwU7cx+Ng5JPVp44KcZBdVMGUt1cENPU5vW38IaWraxPrcvLcJV0pKHXj8XqJDHEEBEZj\nXc7fLVB6Zocknvh6U8Cv8KKeqfWm6AIE2a0E2QMn+UqPl1mr9wU0FRrWNoGHxrRn8sDmWC3qpFZC\njWigwqzhz8lhYxBa6xKt9f9prXtorbv7Xpcdbj8kbrFFa71Na10BvAuMqjVmFFDVnPkDYKgSX5YG\nQpVSNiAYqAAKONZY66mFsDn9+kUWq0zoo1+UOofYegrKEzuKouubY6D1CAkOK4usIrpOhNgaaqFd\nJ0gwuSQbmp9e/zXZgqDpAP+1hUTD8IfEZRWZGjg2OEpcUqP/J7UKz/dBtRsLN64SGfJ1n8g4rUXb\nKWOZuMiumiMGMKE9XPGFBN4H3CbGwdd9zlJZTK/lN3PGgguJL9qAim9DeWRzDhSUcaCgjOJyN/Fh\nTu49p41ITlgUF3RP4bzOjQJaZjaEmFAnzWJDaREfVr8x8HpktVe4X9KMfyMJ4U5eubwHbZPCaR7n\n4vGxHWmXfGQKMnklFTzhK/Kr4qt1mZRVSo/pk9k4GAwNyWLqDvyNui1HOx5m10ZAzcfxDKDXr43R\nWruVUvlADGIsRiFFeSHALVrrWgEAUEpNAiYBpKam1v74yIlsIpXTB3yLHGWBIfdBSC2ftDMMSvMk\nENzjalg+XdxFiR1E3fX186SuofgAFGfD+HektmH2XdKlzh4CNodIgC98GgbdKRlGpdkSDM/dIeex\nOkT0b8MscTV5KsAZLuf+/kFRlP3I13rUGSbd7L57KFDY7+0L4PLPRbqjNruXiPzHl3fA6Q9IsD08\nWSbfT66TFFoQ11pYosiIAKz5iJIRT/D2T5k8+fVmKtxeLurZmJtPb8XoLikMTo9Hawh1Wn//FpXl\nxbBzIcy6Sa6z7Whxq7niD79vLcKC7JzWOp4OjSLQGqJDHfX2lTgcbm9d4cJTrL2W4U9KQ9b+bwF3\nAL8Ah5bw/P3oibQ3TQaigPlKqa+11ttqDtJaTwWmgkhtHPVZXfEw4VNpA5qzXfpChyXVP9bqkOKz\n8GS4brEEg3O2wwdXSQDYYhVjs+kr+Ok1qVmoKBEZ8HmPSaDZXQGD/goxLWHnAvjxfyLwt2GWTPrp\nIyXTqOtE0WWy2MWwLHhC6hd+fEHcQsril86oaRxAJMe9ldDqTJEFr0mTfvIEvvYTyNkBl34oLqq4\n1jBlmUiDhMbKfXxSowgvLImsEs2/arTjfH3xTjqlRHJ+txTiw+o2svndKMuFd8aJQQapXo9IEXeZ\n7bc9rR/NU35EsIPJA5vz2JyN1dt6NYs+IreawXCi0pC/4iyt9czfcOw9SNV1FSm+bfWNyfC5kyKQ\nYPXFwGytdSVwQCm1EBEK3MaxxhUPHS88/LjiLJm43xknBuDC12HevyUO4AgVF5QtSMTw2o6Efavh\n7QvFddP/Zmlh6vHlldtDJLZxwWvw1X2y8jjjnz6VVSVyGHPvFaPR4gzpF7H5K5Hs2OoT2Rv/nsiO\np/QQ11EVrUbA1u/FGPT+Cyx/WQxKj2vE3fXdgzJu30qfom2mvI9oIsarJAemDoa8Hf7vp/vlzF6R\nWecr+Wrdfs7qkHRs1Usz1/mNQxWb50hSwG9YRRwtDpuFi3um0iYpnE9X7qFH02hGtE88abKVDIZD\n0RAD8Xel1MvANwR2lPvoMPstA1oqpZohhuAiZOKvyUxgIrAYGAt8q7XWSqldwBDgDaVUKNAbeJIT\nhYoS+OafEoC+8A0JSGuv1CNoLauL4EhpV1rFomfl3/wMkRT31Cg6qiyR5j9dL5faiFDfRLd9Pgy5\nB6YP8/sstsyVOEi3K2Cx75iNe4t76Ms7RRp88XNS7d3pIqmjWP6KVIJ3vwp6XysrF+0V2e+1vl/j\nuc9ICu38x/16TK3PlJjH1XNh70pZiTTqDq54Ojeu4/Gjd1pMvcVwvysx9bTsTOoisiPHiahQB0PS\n4xnYKhZb7eJIg+EkpiEG4gogHbDjdzFp4JAGwhdTmIJkPVmB6VrrtUqpB4DlvlXJNMQIbAFyECMC\nkv30ilJqLaJ88IrWevWR3doxxFMu8hZ7fpI017BECRqPfFoE9eqj8yXSlKfDheL7t9pl0m46QCZr\nW7C4qT67UYKvYUlSMJdbX6bU93Dxe9B8qHw7FcUS4zjzMXFDnXa3rAQ2fgH7Vomh2PKNuMM6XiDG\naedCiZ0AJHeV+MvrNYrjP7lOgtWNusmTeathAZfQMsHFpb1TeXvJLrwaTmsdx8hOyYf14R8sKqe4\n3I3dammQ4F0dQmJgyL3w/cMSI4lrLUbUcfSqq0eLMQ6GPxsNkfveqLVu/Qddz2/mqOW+jwStpUbi\n48n+bbYguOFnqWauj/JC0XjyVIgf3REGVpv4+S1WMSBf3RdYqR3TEi56E57zxfbtIVIN3aS/TPrf\nPiATPcC4t+RYGUtFDLDdaElZLcoU91L3KyX7p9M4GV9ZLgV6lSUy6X51T2AjIYBe18OZv15+UlBW\nSXG5G62lKU7UYSb7AwVlXDptCZt89QcXdGvE3X1dRBduFNdYQ11E5UUSk/FUyHdyHFxLBsOfhaOV\n+16klGpbq37h1EYpqXEY+TQse1kCuWf8U/79NZxhUp396tny/rJPYPooCWiDBJwv+wg2fSG9IEBE\n/bQXhv5d9u06QWolYlvL6mPnQql6HvGwuJviWsvYwr1SeDfwDklV3bkQhv1LgrlV2J1grxGAT+5S\n10Akdz7k1xAeZCc8qGFZSpVuL9MWbK82DgAzftrDxe1aED37dnGrXfqBBNsPh9MlPz5yisvZdrCY\nzfuL6N08hrgwZ0ADH4PB8NtoyP+i3kgviO1IDEIBugFprn9uQqKlw1v62eLWCT6MTk1xtlRWlxdC\n+/Ml+6bCP1lSlgeb5kja6SZfC9DoNDl2x3GSUvvKmf7U08a9RKH1rQsguZu4iZa8KPGH6DQ44wEJ\nbleh9aGftNuMlCynqtanqX2g+ZAj/VZ+lVK3J6C/dBUbs8roEp4MGctFr6ohBqIGeSUV3D9zHTN9\nMh9KwcsTujMkPf5X+1YYDIaG0RADMeKYX8XJisVy6FVDTbyV/voGZfFP9DVRFgj3uahiWsD50+Tf\nsjz4+v7AfXYvERnwqGbyNL3oaX/AOXOtCAJe63M/RadJ3KQkJ7DZUU1c8RLXKM2TWTYoouH31gBc\nDhvndExm0Va/KqpS0KtJGCzypYhWlhzxcYvK3dXGAcQO/nPWOjqlRB5V1zuDwdDAfhB/xIX86XGG\nS1xg6VSpn7j4Pfj5db87yR4CHS6Qng3tz5fAc2SqzKJej6w8alOWD+PelAB37Z7SFUXiahpwm8iR\nL3gyQBqjXkJjf1ejUBOLRTGifSK7cop568ddRITYuXdoMrEb35J7cyWI6+wIqXDXLc0pKHOj/8R6\nkgbDH4Vx1P5ROEKkrsBiF8mLzV/D5PniFrJYxI1UuF96M7grIC7a/7QfEiPKsR9P8h/PlSCrjYOb\nRD8qLl1qM6pQSlYYuTsl7nH+NAiOllqH45RtEx3q4ObTW3Fl/2Yor4fo4i1Yt26EPjdA7+t+czV0\n8zhXtcIrwIQ+TYgINnUIBsPRctgsppOFPzSLqShTFFutdghPOjK/eWWZFLxlroXv/iUTu/ZK7+lJ\n8yD6V1ptlOSKW2n5NHEr9Z0CIbGivLriLenhMGOiXJvFCqffD9EtYc0MMS6uBFmxFOyFXpNE0sMZ\nJimvJTmy4nCEiJ6T7RhWQtf5PkrEaNang9VAMgvKmL5gO2v3FjCqczJD2ySYQjWDoYEcbRaToSaF\n+2H6CKlPAMn+ufh9eWLf9gPsXSESHdFpgc15qrAHSfaQxSKT8ap3ZKIe9mDd+EDRAZHvsNqlt3Tr\nEVL4ZrWLrERpnvSYDomSp++rv4GSgxKr2PAFJLlEJsOVAC8N9ldJr3wDrpgtge79v4iwYJWE+QWv\nQrNBv1m24oixH339QkJ4ELcPa01ppYewIJsJThsMvxPGQBwJXi/89KrfOIAYhO3zRUupqvBs8TOS\nYdRhHFh/RXbClSBun4oi0UPKWAYHt0BsCwkQF+6XRkRVge2EdpIaW+WG0VoE+N6f4D9mdJpURH9y\nndQ95G6XwHVcusQ6+vxFjnNws+g+RaZKY6AqifGKYvjwarh+iayMTiLsNgv2Y13FbTCcYpj/UUeC\ndovPvzbZmyUNtSbfPSRP84fC64ZXz4EX+sGsm+HlISLCp7V0pKsyDiAuqa3f+t+XHIQfHg88Xs42\ncdcMf1ikMT67Sfz7jjCpMSjJljakebuk4RCWQGMHEvh2N0TN3WAw/NkxK4gjweqAblfCmg/925SC\ntuf6W4lWoT1SMXIocraJuF9NfnhUtJWyt9Qdn11Tq1BJJXZtbA6/PMawf8pqpLwYPpkM23+QMbk7\n5NwXvSOxi4wasZuIxr+L28dgMJz8mBXEkZLYXjSSEtpDo67i9gmOEfdOTfrfKtsPharH/WSxST3E\ngFulQjq5i2+sgg7n+8eFxkqvipr+9sQOEJ4i7qHGPcWFFBQhhqrKOFSxd4WsYMa+IlIcIDIel354\nxMVqBoPhz4lZQRwpwZHQbgw0GwhYpBMbSO/q9TNFwK/TeJms63vCr0lUE4kPZG2Q90qJfMemL6XV\nqD1YmgkdWC9aReG1dJ5SusN1P8qKJqYFNB8Mrnomd2WRAHjNvtaOUDlfSR6Mek6C0lbHMauDMBgM\nJx8mzfX35kjrDIoOSG+H7G3Q9TJx/bw5xv+5ssBflga2Kj1SPJWw8Uv44HIpulMKRj4jnfN+fF7G\nNBsEY6eZ1YPBcIph0lz/SI60CM0VD10uldeVpSLbXZOqGomjMRBWO7QYCjetlvhDZCpk/OQ3DiAZ\nUZnrIG3Qbz+PwWD4U2FiECcSVru4impTO77xW3CEippr0/5Sf7Hyzbpj6guMGwyGUxZjIE4kLDbo\nOUkyiapo1B1Seh5+3/JCfz3DYc9jh/Zja22zmtWDwWAIwLiYTjTCk6STW+5OCRwfTsrDXQbZW+Gb\nB6Tort/NYlAOJT9eUSzV22f8U3pABIVL1lXRwfpXMAaD4ZTEGIgTEVd8w4Xrig7A1NOkuxrAjgUw\n8XNo1v/X9wmKlGK5XYuh7w1iMOb/By547agv3WAw/HkwBuJkZ9Mcv3GoYsn/pEbj1/o0W62iHlt8\nUFYernjpZx16mLoNg8FwSnFMDYRSagTwFGAFXtZaP1LrcyfwOtANyAbGaa13+D7rCLwIhANeoIfW\n2mhA1CasHs2k8GSJMxyK0Bg47U7oNdnX+MiktxoMhkCOWZBaKWUFngPOBNoC45VSbWsNuwrI1Vq3\nAP4L/Nu3rw14E7hWa90OOA2oPFbXelLTuKcotlYREg19bwRbA+SzbU4ISzDGwWAw1MuxXEH0BLZo\nrbcBKKXeBUYB62qMGQXc73v9AfCsEq3mYcBqrfUqAK11Nob6ccXDhJlwcKNkMiV3gdAjb7xjMBgM\ntTmWBqIRsLvG+wyg16+N0Vq7lVL5QAzQCtBKqTlAHPCu1vrR2idQSk0CJgGkpqb+7jdw0uCKq19i\nw2AwGI6CE7UOwgb0By7x/TtaKTW09iCt9VStdXetdfe4ODNBGgwGw+/JsTQQe4AaFV+k+LbVO8YX\nd4hAgtUZwA9a64Na6xLgC6DrMbxWg8FgMNTiWBqIZUBLpVQzpZQDuAiYWWvMTGCi7/VY4Fst6oFz\ngA5KqQM3/T8AAAkMSURBVBCf4RhEYOzCYDAYDMeYYxaD8MUUpiCTvRWYrrVeq5R6AFiutZ4JTAPe\nUEptAXIQI4LWOlcp9QRiZDTwhdb682N1rQaDwWCoi5H7NhgMhlOYQ8l9n6hBaoPBYDAcZ4zURn2U\n5okIns0p0tgGg8FwCmJWELXJ2w0fXAnPdIMZV4ioncFgMJyCGANRk+KD8N6lsPUbkc7e9h28Mx6K\nso73lRkMBsMfjjEQNXGXwb6Vgdsy18h2g8FgOMUwBqImFltd4brgKGkFajAYDKcYxkDUJCQWRr8I\ntiB5b3PK+xDTJ8FgMJx6mCymmlht0KQf3LRSMpmCIuXHrCAMBsMpiDEQtbEHgT2p/kY8BoPBcAph\nXEwGg8FgqBdjIAwGg8FQL8ZAGAwGg6FejIEwGAwGQ70YA2EwGAyGejEGwmAwGAz1YgyEwWAwGOrF\nGAiDwWAw1IsxEAaDwWCoF2MgDAaDwVAvx9RAKKVGKKU2KqW2KKXuqudzp1LqPd/nS5RSTWt9nqqU\nKlJK3X4sr9NgMBgMdTlmBkIpZQWeA84E2gLjlVJtaw27CsjVWrcA/gv8u9bnTwBfHqtrNBgMBsOv\ncyxXED2BLVrrbVrrCuBdYFStMaOA13yvPwCGKqUUgFLqPGA7sPYYXqPBYDAYfoVjaSAaAbtrvM/w\nbat3jNbaDeQDMUopF3An8I9DnUApNUkptVwptTwry7QFNRgMht+TEzVIfT/wX6110aEGaa2naq27\na627x8XFHWqowWAwGI6QY9kPYg/QuMb7FN+2+sZkKKVsQASQDfQCxiqlHgUiAa9Sqkxr/ewxvF6D\nwWAw1OBYGohlQEulVDPEEFwEXFxrzExgIrAYGAt8q7XWwICqAUqp+4EiYxwMBoPhj+WYGQittVsp\nNQWYA1iB6VrrtUqpB4DlWuuZwDTgDaXUFiAHMSIGg8FgOAFQ8sB+8tO9e3e9fPny430ZBoPBcFKh\nlPpJa929vs9O1CC1wWAwGI4zxkAYDAaDoV6MgTAYDAZDvRgDYTAYDIZ6MQbCYDAYDPViDITBYDAY\n6sUYCIPBYDDUizEQBoPBYKiXP02hnFIqC9h5vK/jOBALHDzeF3EcOdXvH8x3cKrfPxzdd9BEa12v\n2umfxkCcqiillv9aFeSpwKl+/2C+g1P9/uHYfQfGxWQwGAyGejEGwmAwGAz1YgzEyc/U430Bx5lT\n/f7BfAen+v3DMfoOTAzCYDAYDPViVhAGg8FgqBdjIAwGg8FQL8ZAnOAopXYopX5RSq1USi33bYtW\nSs1VSm32/Rvl266UUk8rpbYopVYrpboe36v/bSilpiulDiil1tTYdsT3rJSa6Bu/WSk18Xjcy2/h\nV+7/fqXUHt/fwUql1Fk1Prvbd/8blVLDa2wf4du2RSl11x99H0eDUqqxUuo7pdQ6pdRapdRNvu2n\nxN/BIe7/j/070FqbnxP4B9gBxNba9ihwl+/1XcC/fa/PAr4EFNAbWHK8r/833vNAoCuw5rfeMxAN\nbPP9G+V7HXW87+0o7v9+4PZ6xrYFVgFOoBmwFWnxa/W9TgMcvjFtj/e9HcF3kAR09b0OAzb57vWU\n+Ds4xP3/oX8HZgVxcjIK+P/27ia0jioM4/j/AatiW4xWKKUqqRIRFGykFJEiWUi0XTQRNwXR+oFu\nVHChqyIIFReCbvwEUVI1tKBV7K7WgqgFm2JtEmNbjXZha0xBRUVoleR1cc7FSZjcehOTm2SeHww5\n99yZOfNezr1v5uvMjlzeAXQX6t+M5HOgRdKqZmzgTETEJ6RnlBc1GvNtwL6I+CUifgX2AbfP/tbP\n3BTxT6UL2BURZyPiBDAMrM/TcER8HxF/AbvyvAtCRIxExOFc/gM4CqymIv2gTvxTmZV+4AQx/wXw\noaQvJD2U61ZGxEgu/wSszOXVwA+FZU9Sv1MtJI3GvBg/i0fy4ZM3aodWqED8klqBduAgFewHk+KH\nOewHThDz34aIuBHYCDws6Zbim5H2Lyt1rXIVYwZeAa4G1gIjwHPN3Zy5IWkZsBt4LCJ+L75XhX5Q\nEv+c9gMniHkuIk7lv6eB90m7jKO1Q0f57+k8+yngisLil+e6xaDRmBfVZxERoxExFhHjwGukfgCL\nOH5JS0g/jr0R8V6urkw/KIt/rvuBE8Q8JmmppOW1MtAJfAXsAWpXY2wFPsjlPcA9+YqOm4DfCrvj\nC12jMe8FOiVdknfDO3PdgjTpXNIdpH4AKf4tki6QtAZoA/qAQ0CbpDWSzge25HkXBEkCXgeORsTz\nhbcq0Q+min/O+0Gzz9Z7qnslw1Wkqw76gSFgW65fAewHvgU+Ai7N9QJeIl21MAisa3YM04x7J2n3\n+W/SMdMHphMzcD/pZN0wcF+z45ph/G/l+AbyF3xVYf5tOf7jwMZC/SbS1S/f1frOQpmADaTDRwPA\nkTxtqko/qBP/nPYDD7VhZmalfIjJzMxKOUGYmVkpJwgzMyvlBGFmZqWcIMzMrJQThJmZlXKCMJvH\nJJ3X7G2w6nKCsMqS1CrpmKQeSd9I6pV0q6QD+dkB6/N8S/PAaH2SvpTUVVj+U0mH83Rzru+Q9LGk\nd/P6e/OdsZPbf1DSIUn9knZLuijX90h6VdJB4NlG2zf7v/hGOausPErmMGmkzCHSsAT9pDuXN5Pu\nuu2W9AzwdUS8LamFNIRBO+lO1/GIOCOpDdgZEeskdZCGgLgO+BE4ADwREZ9Nan9FRPycy08DoxHx\ngqQe4DKgKyLGGm1/dj4tqyLvvlrVnYiIQQBJQ8D+iAhJg0BrnqcT2Czp8fz6QuBK0o//i5LWAmPA\nNYX19kXEybzeI3ldExIEcH1ODC3AMiaOEfRORIzNoH2zGXOCsKo7WyiPF16P8+/3Q8CdEXG8uKCk\np4BR4AbS4dozU6x3jPLvWg/QHRH9ku4FOgrv/Vlsahrtm82Yz0GYndte4NHaeQRJ7bn+YmAk0tDL\nd5Me79iI5cBIHtb5ria0b1aXE4TZuW0HlgAD+TDU9lz/MrBVUj9wLRP/6/8vniQ9JewAcKwJ7ZvV\n5ZPUZmZWynsQZmZWygnCzMxKOUGYmVkpJwgzMyvlBGFmZqWcIMzMrJQThJmZlfoH1teJ/rLdmIAA\nAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FFVliwUrGYUT",
        "colab_type": "code",
        "outputId": "cfe4b729-8cd4-4ee8-f8af-8f6733a23a82",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 72
        }
      },
      "source": [
        "import pandas_profiling \n",
        "pfr = pandas_profiling.ProfileReport(df,correlation_threshold = 0.5,\n",
        "                                     bins = 20)\n",
        "pfr.to_file(\"Analysis_report.html\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/pandas_profiling/describe.py:392: FutureWarning: The join_axes-keyword is deprecated. Use .reindex or .reindex_like on the result to achieve the same functionality.\n",
            "  variable_stats = pd.concat(ldesc, join_axes=pd.Index([names]), axis=1)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aDYDSbH0E7Oy",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X = df.drop(['target'],axis=1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NcM100R6FdkS",
        "colab_type": "code",
        "outputId": "b3d4bfab-ab41-4633-a430-25a394833866",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 261
        }
      },
      "source": [
        "X.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>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",
              "    </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>0.3001</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>25.38</td>\n",
              "      <td>17.33</td>\n",
              "      <td>184.60</td>\n",
              "      <td>2019.0</td>\n",
              "      <td>0.1622</td>\n",
              "      <td>0.6656</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>0.0869</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>24.99</td>\n",
              "      <td>23.41</td>\n",
              "      <td>158.80</td>\n",
              "      <td>1956.0</td>\n",
              "      <td>0.1238</td>\n",
              "      <td>0.1866</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>0.1974</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>23.57</td>\n",
              "      <td>25.53</td>\n",
              "      <td>152.50</td>\n",
              "      <td>1709.0</td>\n",
              "      <td>0.1444</td>\n",
              "      <td>0.4245</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>0.2414</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>14.91</td>\n",
              "      <td>26.50</td>\n",
              "      <td>98.87</td>\n",
              "      <td>567.7</td>\n",
              "      <td>0.2098</td>\n",
              "      <td>0.8663</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>0.1980</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>22.54</td>\n",
              "      <td>16.67</td>\n",
              "      <td>152.20</td>\n",
              "      <td>1575.0</td>\n",
              "      <td>0.1374</td>\n",
              "      <td>0.2050</td>\n",
              "      <td>0.4000</td>\n",
              "      <td>0.1625</td>\n",
              "      <td>0.2364</td>\n",
              "      <td>0.07678</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   mean radius  mean texture  ...  worst symmetry  worst fractal dimension\n",
              "0        17.99         10.38  ...          0.4601                  0.11890\n",
              "1        20.57         17.77  ...          0.2750                  0.08902\n",
              "2        19.69         21.25  ...          0.3613                  0.08758\n",
              "3        11.42         20.38  ...          0.6638                  0.17300\n",
              "4        20.29         14.34  ...          0.2364                  0.07678\n",
              "\n",
              "[5 rows x 30 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Kx6am_FBFguR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "y = df['target']"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WJ_Qks9LMp2S",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.model_selection import train_test_split"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ksr49O9AM6iz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30, random_state=1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i_TkrpIhNL76",
        "colab_type": "code",
        "outputId": "af4edfac-7666-407e-c636-c2e1dbe2a4b1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 86
        }
      },
      "source": [
        "from sklearn.svm import SVC \n",
        "from sklearn.metrics import classification_report, confusion_matrix\n",
        "svc_model = SVC( kernel='rbf',gamma='auto')\n",
        "svc_model.fit(X_train, y_train)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
              "    decision_function_shape='ovr', degree=3, gamma='auto', kernel='rbf',\n",
              "    max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
              "    tol=0.001, verbose=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oaMjMyyMNdth",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "predict_y = svc_model.predict(X_test)\n",
        "cm = confusion_matrix(y_test, predict_y)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KHTlb7l1cR3N",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#!pip install seaborn "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pfuGIGN8NykR",
        "colab_type": "code",
        "outputId": "8cd4731b-5559-4d66-a267-a7003cf1dc70",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 478
        }
      },
      "source": [
        "#%matplotlib inline\n",
        "plt.figure(figsize=(6,5))\n",
        "sn.heatmap(cm, annot=True,  fmt='.2f', xticklabels = [\"No\", \"Yes\"] , yticklabels = [\"No\", \"Yes\"],)\n",
        "plt.ylabel('True label',fontsize=12)\n",
        "plt.xlabel('Predicted label',fontsize=12)\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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TAACAGTMJBkICAABmXAJpoN0AAAAcYiYBAAAz2g0GQgIAACZWQoKBdgMAAHCImQQAAMyY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\n",
            "text/plain": [
              "<Figure size 600x500 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1JpflrdKOAD5",
        "colab_type": "code",
        "outputId": "a79f61c7-848f-4d94-90f6-7b7d18c19685",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 228
        }
      },
      "source": [
        "print(classification_report(y_test, predict_y))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "         0.0       0.00      0.00      0.00        63\n",
            "         1.0       0.63      1.00      0.77       108\n",
            "\n",
            "    accuracy                           0.63       171\n",
            "   macro avg       0.32      0.50      0.39       171\n",
            "weighted avg       0.40      0.63      0.49       171\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.\n",
            "  'precision', 'predicted', average, warn_for)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "q1-PrufVgX22",
        "colab_type": "text"
      },
      "source": [
        "# Let's improve the model\n",
        "\n",
        "*   Scaled the x_train and X_test data\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eBZWqPOgdBIu",
        "colab_type": "code",
        "outputId": "9816c295-ae22-4731-b60a-0557d733d161",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 555
        }
      },
      "source": [
        "min_train = X_train.min()\n",
        "min_train"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "mean radius                  6.981000\n",
              "mean texture                 9.710000\n",
              "mean perimeter              43.790000\n",
              "mean area                  143.500000\n",
              "mean smoothness              0.052630\n",
              "mean compactness             0.019380\n",
              "mean concavity               0.000000\n",
              "mean concave points          0.000000\n",
              "mean symmetry                0.106000\n",
              "mean fractal dimension       0.050240\n",
              "radius error                 0.115300\n",
              "texture error                0.360200\n",
              "perimeter error              0.757000\n",
              "area error                   6.802000\n",
              "smoothness error             0.001713\n",
              "compactness error            0.002252\n",
              "concavity error              0.000000\n",
              "concave points error         0.000000\n",
              "symmetry error               0.009539\n",
              "fractal dimension error      0.000895\n",
              "worst radius                 7.930000\n",
              "worst texture               12.020000\n",
              "worst perimeter             50.410000\n",
              "worst area                 185.200000\n",
              "worst smoothness             0.071170\n",
              "worst compactness            0.027290\n",
              "worst concavity              0.000000\n",
              "worst concave points         0.000000\n",
              "worst symmetry               0.156600\n",
              "worst fractal dimension      0.055210\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cjMJAT8hdH11",
        "colab_type": "code",
        "outputId": "803f9d31-b7e1-4566-ea1a-650aaf211aa2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 555
        }
      },
      "source": [
        "range_train = (X_train - min_train).max()\n",
        "range_train"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "mean radius                  21.129000\n",
              "mean texture                 29.570000\n",
              "mean perimeter              144.710000\n",
              "mean area                  2357.500000\n",
              "mean smoothness               0.110770\n",
              "mean compactness              0.267320\n",
              "mean concavity                0.426800\n",
              "mean concave points           0.201200\n",
              "mean symmetry                 0.198000\n",
              "mean fractal dimension        0.045510\n",
              "radius error                  2.757700\n",
              "texture error                 4.524800\n",
              "perimeter error              21.223000\n",
              "area error                  535.398000\n",
              "smoothness error              0.029417\n",
              "compactness error             0.133148\n",
              "concavity error               0.396000\n",
              "concave points error          0.052790\n",
              "symmetry error                0.051921\n",
              "fractal dimension error       0.028945\n",
              "worst radius                 28.110000\n",
              "worst texture                37.520000\n",
              "worst perimeter             200.790000\n",
              "worst area                 4068.800000\n",
              "worst smoothness              0.151430\n",
              "worst compactness             0.910610\n",
              "worst concavity               0.960800\n",
              "worst concave points          0.291000\n",
              "worst symmetry                0.399200\n",
              "worst fractal dimension       0.093390\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QT44vX9odNtO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_train_scaled = (X_train - min_train)/range_train"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9uuW3-wudqcd",
        "colab_type": "code",
        "outputId": "5c9141b6-db3e-47e0-acac-be32b822ab01",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 508
        }
      },
      "source": [
        "sn.scatterplot(x = X_train_scaled['mean area'], y = X_train_scaled['mean smoothness'], hue = y_train)\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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5k1qpRg3ZfvBOZQq1IiIiIq3AiJY0slxpC9ekftnZOZimSWlp7TqUlJSQn1+QpFo1jkKt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\nAABU7w//mQRLWXtj2QAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 800x550 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LIxUMmZQdxzs",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "min_test = X_test.min()\n",
        "range_test = (X_test - min_test).max()\n",
        "X_test_scaled = (X_test - min_test)/range_test"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "01Gzk9KIgrht",
        "colab_type": "text"
      },
      "source": [
        "* For parameter optimization used Grid Search algorithms "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FLbV_VeUKOTQ",
        "colab_type": "code",
        "outputId": "63bf64bf-1eec-4a13-eab7-25022f0e4bc8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 86
        }
      },
      "source": [
        "#param_grid = {'C': [0.1, 1, 10, 100], 'gamma': [1, 0.1, 0.01, 0.001], 'kernel': ['rbf']}\n",
        "#from sklearn.model_selection import GridSearchCV\n",
        "#grid = GridSearchCV(SVC(),param_grid,refit=True,verbose=4)\n",
        "#grid.fit(X_train_scaled,y_train)\n",
        "svc_model = SVC(C=10, kernel='rbf',gamma=1)\n",
        "svc_model.fit(X_train_scaled,y_train)\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SVC(C=10, cache_size=200, class_weight=None, coef0=0.0,\n",
              "    decision_function_shape='ovr', degree=3, gamma=1, kernel='rbf', max_iter=-1,\n",
              "    probability=False, random_state=None, shrinking=True, tol=0.001,\n",
              "    verbose=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0OyguyIjnO6U",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#grid.best_params_"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sOr0_D2enQni",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#grid.best_estimator_"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V6lvEJPJKgbD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "svm1_predictions = svc_model.predict(X_test_scaled)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LLlxw18ee5or",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "CM = confusion_matrix(y_test, svm1_predictions)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iVQMnfRHfB8b",
        "colab_type": "code",
        "outputId": "86e004e1-35a9-4016-b70d-ad311ef7838e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 478
        }
      },
      "source": [
        "#%matplotlib inline\n",
        "plt.figure(figsize=(5,5))\n",
        "sn.heatmap(CM, annot=True,  fmt='d', xticklabels = [\"No\", \"Yes\"] , yticklabels = [\"No\", \"Yes\"],)\n",
        "plt.ylabel('True label',fontsize=12)\n",
        "plt.xlabel('Predicted label',fontsize=12)\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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evem9MmXKaM6cOXm6uSgAAAUtz+f8xo0bp1KlSunjjz/W7t27deDAAe3atUuLFy+Wv7+/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\nxA6rYk6YDNcAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 500x500 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SBl8kL2xiTau",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.metrics import accuracy_score,average_precision_score,f1_score"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1kG7xWGZfC_L",
        "colab_type": "code",
        "outputId": "044c20f8-c022-4218-a68a-419ec1d5c9e5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 191
        }
      },
      "source": [
        "print(\"classification report:\\n\",classification_report(y_test, svm1_predictions))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "classification report:\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "         0.0       0.87      0.98      0.93        63\n",
            "         1.0       0.99      0.92      0.95       108\n",
            "\n",
            "    accuracy                           0.94       171\n",
            "   macro avg       0.93      0.95      0.94       171\n",
            "weighted avg       0.95      0.94      0.94       171\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l7T6PipSiMJo",
        "colab_type": "code",
        "outputId": "8387a875-2ea4-4243-c640-0cf3a267f95f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "print(\"Accuracy score: {0:.2f}\".format(accuracy_score(y_test, svm1_predictions)*100))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy score: 94.15\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aOoQHIHAicCA",
        "colab_type": "code",
        "outputId": "f2c705c7-95ed-419d-e0e6-0efb6744faa0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "print(\"Average precision score:{0:.2f}\".format(average_precision_score(y_test, svm1_predictions)*100))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Average precision score:96.01\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IVgwgu-tCaxb",
        "colab_type": "code",
        "outputId": "706bfd54-bfe8-4955-ca22-eaf390e44ab9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "print(\"f1_score:{0:.2f}\".format(f1_score(y_test,svm1_predictions)*100))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "f1_score:95.19\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IJpahDFu9sBG",
        "colab_type": "text"
      },
      "source": [
        "## ====================== Knn Model ======================"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2cOppZB2iheF",
        "colab_type": "code",
        "outputId": "eff64d5d-233d-4cd5-852a-ba2c3dedb93f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 69
        }
      },
      "source": [
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "neigh = KNeighborsClassifier(n_neighbors=12)\n",
        "neigh.fit(X_train_scaled, y_train)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
              "                     metric_params=None, n_jobs=None, n_neighbors=12, p=2,\n",
              "                     weights='uniform')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-vyCZ2ZyAMmh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "y_pred_knn = neigh.predict(X_test_scaled)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WystaSUIADHp",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "cm1 = confusion_matrix(y_test,y_pred_knn)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5rL9LdWAAbfd",
        "colab_type": "code",
        "outputId": "bdfec363-a12b-453d-dd45-cc6437bdcd52",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 478
        }
      },
      "source": [
        "plt.figure(figsize=(6,5))\n",
        "sn.heatmap(cm1, annot=True,  fmt='d', xticklabels = [\"No\", \"Yes\"] , yticklabels = [\"No\", \"Yes\"])\n",
        "plt.ylabel('True label',fontsize=12)\n",
        "plt.xlabel('Predicted label',fontsize=12)\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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OHe2uEQCAEsO4waXAVzckJyerS5cumj9/vn7++Wft3LlT2dnZOnz4sIYOHaoNGzYUR50A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\nEiEBAABYIiQAAABL/x+789t6SlW57gAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 600x500 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a6gqc4KSDAs1",
        "colab_type": "code",
        "outputId": "ff703e78-7a39-42fd-dc39-a0524580ad76",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 191
        }
      },
      "source": [
        "print(\"classification report:\\n\",classification_report(y_test, y_pred_knn))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "classification report:\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "         0.0       0.94      0.94      0.94        63\n",
            "         1.0       0.96      0.96      0.96       108\n",
            "\n",
            "    accuracy                           0.95       171\n",
            "   macro avg       0.95      0.95      0.95       171\n",
            "weighted avg       0.95      0.95      0.95       171\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bg7v7joWAyJP",
        "colab_type": "code",
        "outputId": "7d9259b7-03b4-43ad-8d0f-ab24dd08f163",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "print(\"Accuracy score: {0:.2f}\".format(accuracy_score(y_test,y_pred_knn)*100))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy score: 95.32\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ycCJisVsBT_b",
        "colab_type": "code",
        "outputId": "54717d3d-8333-452b-8f6b-6216693c5197",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "print(\"Average precision score:{0:.2f}\".format(average_precision_score(y_test,y_pred_knn)*100))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Average precision score:95.07\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "45xhM4ERByy7",
        "colab_type": "code",
        "outputId": "911d856f-02b1-4213-a83b-5043745dcff6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "print(\"f1_score:{0:.2f}\".format(f1_score(y_test,y_pred_knn)*100))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "f1_score:96.30\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HiC2byG9D5oL",
        "colab_type": "text"
      },
      "source": [
        "## ============= SVM-Knn-Ensemble(Voting)============\n",
        "\n",
        "---\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b34BRgMtCI28",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.ensemble import  VotingClassifier"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "za6lMscfGeR2",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "Svc_Model = SVC(C=3, kernel='rbf',gamma='auto')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YDe8VeBsFRbF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "eclf1 = VotingClassifier(estimators=[('SVM', Svc_Model), ('Knn', neigh), ], voting='hard')\n",
        "eclf1 = eclf1.fit(X_train_scaled, y_train)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2ONrcuogFoDm",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "y_pred_Enseble =eclf1.predict(X_test_scaled)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dm3lPyIdG_r_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "cm3 = confusion_matrix(y_test,y_pred_Enseble)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Nq-zKv4q-Kaj",
        "colab_type": "code",
        "outputId": "5364d48c-eb51-4ae0-e41a-dcda1cf2d938",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        }
      },
      "source": [
        "cm3"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 60,   3],\n",
              "       [  4, 104]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lcvJwUxqHZ57",
        "colab_type": "code",
        "outputId": "9ba323e1-c023-4206-8f5e-3c18ae78eea9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 401
        }
      },
      "source": [
        "plt.figure(figsize=(5,4))\n",
        "sn.heatmap(cm3, annot=True,  fmt='d', xticklabels = [\"No\", \"Yes\"] , yticklabels = [\"No\", \"Yes\"])\n",
        "plt.ylabel('True label',fontsize=12)\n",
        "plt.xlabel('Predicted label',fontsize=12)\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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27dPQoUN17ty5W/bdcccdWrhwoerVq+eU4gAA5rFs2TKNHTtWtWrVkiS9+uqrkqR33nlH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\nYII=\n",
            "text/plain": [
              "<Figure size 500x400 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9G1C1MQTHutj",
        "colab_type": "code",
        "outputId": "633dbe01-6d6a-4801-d51d-8b81d04ecf18",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 191
        }
      },
      "source": [
        "print(\"classification report:\\n\",classification_report(y_test,y_pred_Enseble))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "classification report:\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "         0.0       0.94      0.95      0.94        63\n",
            "         1.0       0.97      0.96      0.97       108\n",
            "\n",
            "    accuracy                           0.96       171\n",
            "   macro avg       0.95      0.96      0.96       171\n",
            "weighted avg       0.96      0.96      0.96       171\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7CbQts90IGiW",
        "colab_type": "code",
        "outputId": "156227ee-89f4-42c3-bd0a-9c873230ff5f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "print(\"Accuracy score: {0:.2f}\".format(accuracy_score(y_test,y_pred_Enseble)*100))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy score: 95.91\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PktsdvUvIaml",
        "colab_type": "code",
        "outputId": "d117ca44-7ed9-4339-ba96-17d1cf108e22",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "print(\"Average precision score:{0:.2f}\".format(average_precision_score(y_test,y_pred_Enseble)*100))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Average precision score:95.94\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FvvefcTbIu0W",
        "colab_type": "code",
        "outputId": "007f4754-5023-4aea-f84c-eba40dc32473",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "print(\"f1_score:{0:.2f}\".format(f1_score(y_test,y_pred_Enseble)*100))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "f1_score:96.74\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9mzNgLzYUu3r",
        "colab_type": "text"
      },
      "source": [
        "# Voting classifier perforem better than Knn & SVM in my case."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DAcrleg_XhC3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}