Model_4.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Model 4.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyMts7AGzb8G85TAs58x3btN",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/khushi0111/LNB_DS_Internship/blob/main/Model_4.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "DS404 Khushi Agarwal"
      ],
      "metadata": {
        "id": "xhM0Y4ODn8ct"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "BREAST CANCER DATASET (Breast Cancer Prediction)"
      ],
      "metadata": {
        "id": "2oK1BkXfkZiI"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "s-x9X92qsN5a"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "url=\"https://drive.google.com/file/d/1F6E8y4nl4v61Hka1jMmgptgCN17z6iLy/view?usp=sharing\"\n",
        "url='https://drive.google.com/uc?id=' + url.split('/')[-2]\n",
        "df = pd.read_csv(url)"
      ],
      "metadata": {
        "id": "udRvUOQKhpY6"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "iYI2rA6FksEp",
        "outputId": "44a4310f-db9e-4e5a-d580-bb041c46feb5"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   mean_radius  mean_texture  mean_perimeter  mean_area  mean_smoothness  \\\n",
              "0        17.99         10.38          122.80     1001.0          0.11840   \n",
              "1        20.57         17.77          132.90     1326.0          0.08474   \n",
              "2        19.69         21.25          130.00     1203.0          0.10960   \n",
              "3        11.42         20.38           77.58      386.1          0.14250   \n",
              "4        20.29         14.34          135.10     1297.0          0.10030   \n",
              "5        12.45         15.70           82.57      477.1          0.12780   \n",
              "6        18.25         19.98          119.60     1040.0          0.09463   \n",
              "7        13.71         20.83           90.20      577.9          0.11890   \n",
              "8        13.00         21.82           87.50      519.8          0.12730   \n",
              "9        12.46         24.04           83.97      475.9          0.11860   \n",
              "\n",
              "   diagnosis  \n",
              "0          0  \n",
              "1          0  \n",
              "2          0  \n",
              "3          0  \n",
              "4          0  \n",
              "5          0  \n",
              "6          0  \n",
              "7          0  \n",
              "8          0  \n",
              "9          0  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-210a8552-0cab-4b42-8714-82a8c81636b6\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>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>diagnosis</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</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</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</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</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</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>12.45</td>\n",
              "      <td>15.70</td>\n",
              "      <td>82.57</td>\n",
              "      <td>477.1</td>\n",
              "      <td>0.12780</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>18.25</td>\n",
              "      <td>19.98</td>\n",
              "      <td>119.60</td>\n",
              "      <td>1040.0</td>\n",
              "      <td>0.09463</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>13.71</td>\n",
              "      <td>20.83</td>\n",
              "      <td>90.20</td>\n",
              "      <td>577.9</td>\n",
              "      <td>0.11890</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>13.00</td>\n",
              "      <td>21.82</td>\n",
              "      <td>87.50</td>\n",
              "      <td>519.8</td>\n",
              "      <td>0.12730</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>12.46</td>\n",
              "      <td>24.04</td>\n",
              "      <td>83.97</td>\n",
              "      <td>475.9</td>\n",
              "      <td>0.11860</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-210a8552-0cab-4b42-8714-82a8c81636b6')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"https://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-210a8552-0cab-4b42-8714-82a8c81636b6 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-210a8552-0cab-4b42-8714-82a8c81636b6');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IvX0Z-Ro0RzE",
        "outputId": "3baa7736-960f-454d-8be2-0677bc6b0594"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "mean_radius        0\n",
              "mean_texture       0\n",
              "mean_perimeter     0\n",
              "mean_area          0\n",
              "mean_smoothness    0\n",
              "diagnosis          0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.describe()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "mG0_pFTM0VIV",
        "outputId": "3c4b66ac-8214-4551-fa98-ee61ca3a10d9"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       mean_radius  mean_texture  mean_perimeter    mean_area  \\\n",
              "count   569.000000    569.000000      569.000000   569.000000   \n",
              "mean     14.127292     19.289649       91.969033   654.889104   \n",
              "std       3.524049      4.301036       24.298981   351.914129   \n",
              "min       6.981000      9.710000       43.790000   143.500000   \n",
              "25%      11.700000     16.170000       75.170000   420.300000   \n",
              "50%      13.370000     18.840000       86.240000   551.100000   \n",
              "75%      15.780000     21.800000      104.100000   782.700000   \n",
              "max      28.110000     39.280000      188.500000  2501.000000   \n",
              "\n",
              "       mean_smoothness   diagnosis  \n",
              "count       569.000000  569.000000  \n",
              "mean          0.096360    0.627417  \n",
              "std           0.014064    0.483918  \n",
              "min           0.052630    0.000000  \n",
              "25%           0.086370    0.000000  \n",
              "50%           0.095870    1.000000  \n",
              "75%           0.105300    1.000000  \n",
              "max           0.163400    1.000000  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-af9f91b4-47de-415c-88ca-1248c0312128\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>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>diagnosis</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>14.127292</td>\n",
              "      <td>19.289649</td>\n",
              "      <td>91.969033</td>\n",
              "      <td>654.889104</td>\n",
              "      <td>0.096360</td>\n",
              "      <td>0.627417</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>3.524049</td>\n",
              "      <td>4.301036</td>\n",
              "      <td>24.298981</td>\n",
              "      <td>351.914129</td>\n",
              "      <td>0.014064</td>\n",
              "      <td>0.483918</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>6.981000</td>\n",
              "      <td>9.710000</td>\n",
              "      <td>43.790000</td>\n",
              "      <td>143.500000</td>\n",
              "      <td>0.052630</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>11.700000</td>\n",
              "      <td>16.170000</td>\n",
              "      <td>75.170000</td>\n",
              "      <td>420.300000</td>\n",
              "      <td>0.086370</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>13.370000</td>\n",
              "      <td>18.840000</td>\n",
              "      <td>86.240000</td>\n",
              "      <td>551.100000</td>\n",
              "      <td>0.095870</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>15.780000</td>\n",
              "      <td>21.800000</td>\n",
              "      <td>104.100000</td>\n",
              "      <td>782.700000</td>\n",
              "      <td>0.105300</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>28.110000</td>\n",
              "      <td>39.280000</td>\n",
              "      <td>188.500000</td>\n",
              "      <td>2501.000000</td>\n",
              "      <td>0.163400</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-af9f91b4-47de-415c-88ca-1248c0312128')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"https://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-af9f91b4-47de-415c-88ca-1248c0312128 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-af9f91b4-47de-415c-88ca-1248c0312128');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.countplot(df['diagnosis']);"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "vXNJ4ZzC0brG",
        "outputId": "a4fed606-dee3-4362-eb72-1cbd0510c2e4"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(20,8))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "id": "2Wzl4Yz-3cMQ",
        "outputId": "9d182bbe-0fda-4fc9-f797-1bbcdf478578"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Figure size 1440x576 with 0 Axes>"
            ]
          },
          "metadata": {},
          "execution_count": 10
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x576 with 0 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.countplot(df[\"mean_radius\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "YkDGVkDu3e-k",
        "outputId": "70e51940-3723-4048-95f2-aa4a7bbc0861"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f694c76fcd0>"
            ]
          },
          "metadata": {},
          "execution_count": 11
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.countplot(df[\"mean_perimeter\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "marOJgPh3mfR",
        "outputId": "943ff2d6-1696-4f48-a0c2-808b59285465"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f694bb9c650>"
            ]
          },
          "metadata": {},
          "execution_count": 12
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.countplot(df[\"mean_texture\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "U93D4TG_3yP5",
        "outputId": "3e0172f3-c140-4877-b0a1-26368babc4e5"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f694af4cb10>"
            ]
          },
          "metadata": {},
          "execution_count": 13
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(15,6))\n",
        "sns.heatmap(df)"
      ],
      "metadata": {
        "id": "7UFO0H400gFM",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 397
        },
        "outputId": "34f57fee-f9b4-4b95-eb34-cd3799ae16f9"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f694a4457d0>"
            ]
          },
          "metadata": {},
          "execution_count": 14
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x432 with 2 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAyAAAAFrCAYAAAApcO3uAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nOzdeZxcdZnv8c9T1d3phIREtggJXBhWFUcYA3hVLntEBmRwlO2KBFBcQFwYNnXAEfQGQRYHBo2yXhBE9gsIZBgQcNj3HQIEyCIhkARCSLqr+rl/nNNYNFXd1XW68qvfqe/79corVad+derp9Cvd53d+v+d5zN0RERERERFZGQqhAxARERERkfahCYiIiIiIiKw0moCIiIiIiMhKowmIiIiIiIisNJqAiIiIiIjISqMJiIiIiIiIrDRNm4CY2a5m9qyZzTKzY5v1OSIiIiIiEo+mTEDMrAicDXwe+Ciwn5l9tBmfJSIiIiIiw2Nm65rZbWb2lJk9aWbfTY//xMzmmtkj6Z/dKt5zXLq48KyZfa7i+LAWHjqa8yWxNTDL3V9Mg7oM2BN4qkmfJyIiIiIi9SsBR7r7Q2Y2DnjQzGamr53u7qdWDk4XE/YFPgasA/ynmW2Svnw2sAswB7jfzK5z95rX/c2agEwCXq14PgfYpkmfJSIiIiIiw+Du84H56eO3zexpkmv4WvYELnP3FcBLZjaLZNEBhrnwoCR0EREREZE2ZmbrA1sC96aHDjezx8zsPDP7UHqs2gLDpEGO19SsFZC5wLoVzyenx95jZocChwIUixM+WSiu0qRQRKSW8d36fxeza0Z/PHQI0qDt3rw7dAgibavUM9dCx1CP3oUvepb3d6254TdIr7VTM9x9xsBxZjYWuBL4nru/ZWbnACcCnv79S+DgLLEM1KwJyP3Axma2AcnEY19g/8oB6T/ADIDOrknununfWAIxi+L/sNTwbm9P6BAkg6PHvhk6BBERaVGV19q1mFknyeTjEne/Kn3faxWv/xa4Pn062ALDoAsPAzVlAuLuJTM7HLgZKALnufuTzfgsCUsTx7gVNIGM2qqFUaFDEBGRZukrN/X0ltxFPhd42t1Pqzi+dpofArAX8ET6+Drg92Z2GkkS+sbAfYAxxMLDQM1aAcHdbwRubNb5pTVoBSRu+v7F7UOF7tAhiIhIs3hfsz/hM8ABwONm9kh67Ick7TO2INmCNRv4BoC7P2lml5Mkl5eAw9y9DDDchQdrhTvYnV2TwgchDdEFbNy6O7pChyAZfGa1TUOHIA2a+dpjoUMQaVvR5IDMfzrT9XHn2h9p2a+zaSsgItL6iqZCeDHbqrha6BCkQTOHHiIibc6bvwISTKYJiJmdB+wOLHD3zdNjWwC/BrpJlme+7e73ZQ1UREZeR7EYOgTJ4FPvNnd/sIiISDNkXQG5ADgLuKji2C+Af3P3P6Wt238BbJ/xc0SkCXrKpdAhSAYXd68IHYKIiDRLn1ZAqnL3O9LGJe87DKyaPh4PzMvyGSIiUt1qphweEZHc0hasYfkecLOZnUrSaf3TTfgMERkBygGJ22pK4xMRya8ml+ENqRm/vb4FfN/drzSzvUnqC+88cFBlJ/RCcTyFgjoyx6gVqqhJ40o5/uHWDp71d0KHICIizZLjFZDMZXjTLVjXVyShLwEmuLunDU6WuPuqg5yCDpXhjVbL1neTunR1dIYOQTJYf9zE0CFIg55dNCd0CCJtK5YyvD2zH8h0fdy1/pSW/TqbsQIyD9gOuB3YEXh+qDeoG7NIGGM71cguZtd/eNB7O9LCNl4UOgIRaXlKQq/OzC4lqXC1hpnNAU4Avg6caWYdwHLSbVaD0TaeeKkRYdx6tQUratct0gpIvJ4KHYCItDj1AanB3fer8dIns5xXRFaOco5/uLWDJwoqwysikltaARGRPCooiydq41AjSRERiU/DExAz6wbuAEal57nC3U9IE89PAr4MlIFz3P1XIxGsiIysYkFleGO23XJ9/2KlX4oiMqQc71LIsgKyAtjR3ZeaWSdwl5n9CfgIsC6wmbv3mdlaIxGotCbl78Rteak3dAiSwb93vhE6BBERaZYc52k2PAHx5Mpzafq0M/3jJH1A9vc0c8bdF2QNUlqXktDjNrpTnbRjdlzPhNAhSINuDx2AiLQ+rYBUZ2ZF4EFgI+Bsd7/XzDYE9jGzvYDXgSPcfchSvCKy8pVznODWDh4epT4uIiK5lePf0VmrYJWBLcxsAnC1mW1OkhOy3N2nmNkXgfOAbbOHKq1IW7Dipk7ocfsLS0KHICIiMmwjUgXL3Reb2W3ArsAc4Kr0pauB86u9x8wOJe0RUiiOp1BYZSRCkZVMW7Dipu9f3NYtjAkdgoiINIu2YH2Qma0J9KaTj9HALsDJwDXADsBLJB3Rn6v2fnefAcwA6OyapNvoIgFoBStur7n6gIiI5Ja2YFW1NnBhmgdSAC539+vN7C7gEjP7PkmS+tdGIE4RaYJRHcohiNlXl48OHYI06MrQAYhIy0syHfIpSxWsx4AtqxxfDPxjlqAkHu6ubTwRW6EyvFE7t/ud0CGIiEizaAuWSG3axhOvrqJ+BMTsqyvGhg5BGnRd6ABERALKfPVhZrOBt0m6npfcfUrFa0cCpwJruvvCrJ8lIiOrp1wKHYJkcNGopUMPEhGROCkHZEg7DJxgmNm6wFTglRH6DGlB2n4lEk4nhdAhiIhIs2gLVkNOB44Grm3iZ0hg2n4lEo6mHyIiOZbjXl0jMQFx4BYzc+A37j7DzPYE5rr7o7pDLtK6+jSBjNqC8rLQIYiIiAzbSExAPuvuc81sLWCmmT0D/JBk+5XknCaYcSsWdA89Zpt3rBY6BGnQn0MHICKtT1uwanP3uenfC8zsapLmgxsA/asfk4GHzGxrd/9r//vUCT0ftAUrbp2FYugQJIPd39X/v1idHToAEWl9SkKvzsxWAQru/nb6eCrwU3dfq2LMbGDKwCR1dUIXCU9VsOL2H6O0BUtEJLe0AlLTRODqdKWjA/i9u9803JNo9hEvbcCKW2+OE9zawaPL5oQOQUREmkUrINW5+4vAJ4YYs36Wz5DWphwQERERERkOtUGWTJQDEreCJpBR6yroR7iISG5pBeSDzGxT4A8Vh/4OOB6YBOwB9AAvAAe5++JBz9VoENIStAoSr95yidGdo0KHIQ3aYcwGoUOQBu0wZgN+O+8vocMQkRbmnt9t0g1PQNz9WWALADMrAnOBq4FNgePcvWRmJwPHAccMeq5Gg5DgdAc9bp3FDkrKA4nW7ctmhw5BGvT84rmhQxCRVqcVkCHtBLzg7i8DL1ccvwf40gh9hrQgbcESCaec4wopIiJtL8c/40dqArIvcGmV4wfz/m1aVekeery0/Spuozu6QocgGfyqsFHoEKRBuzM/dAgiIsFknoCYWRfwBZKtVpXHfwSUgEuGOofuoUdMKyBRe7fUEzoEyeA7fc+HDkFERJpFW7AG9XngIXd/rf+AmU0Ddgd28hp7dNQJXSS8DnVCj9pWY9YLHYI06KUlfw0dgoi0Om3BGtR+VGy/MrNdgaOB7dy9ZptedULPB23BiltBGyCjthljQocgIiLNohWQ6sxsFWAX4BsVh88CRgEz04vTe9z9m1k+R1qXktDjViwUQocgGaxf0gRSRETik7UT+jvA6gOODTsrUnfRRcIomiYgMfsI74QOQUREmkVbsJpLd9Hjpclj3N7pXRE6BMngSN4KHYKIiDSLtmA1ly5iRcIYP0o5BDG76Qglocdq1X99JnQIItLqNAGpzsy+C3ydpJXHb939DDNbjaT3x/rAbGBvd1802Hm0AiISxpIVNetESAS+cNa80CGIiEizaAvWB5nZ5iSTj62BHuAmM7uepLTure4+3cyOBY4FjhmJYEVkZHV3dIYOQTI4vHf1oQdJS7o9dAAiIgFlWQH5CHBvf6ldM/sz8EVgT2D7dMyFJD9nNQHJKW2fExEREWkCbcGq6gngZ2a2OvAusBvwADDR3eenY/4KTMwWorQybZ+LW29fOXQIksEN3b2hQxARkWbRFqwPcvenzexk4BbgHeARoDxgjJuZrlBzTCsgcVMjwriNQ53sRURySysg1bn7ucC5AGb2c2AO8JqZre3u881sbWBBtfea2aEk+SIUiuMpFFbJEooEohWQuJVzfHelHbzq74YOQUREmiXHv6OzVsFay90XmNl6JPkfnwI2AA4Epqd/X1vtve4+A5gB0Nk1SVexkdIKSNzUiDBu69jo0CGIiIgMW9Y+IFemOSC9wGHuvtjMpgOXm9khwMvA3lmDlNalFZC4KQckbk+XF4cOQUREmkVbsKpz922rHHsD2CnLeUVEZGg9XgodgoiINIsmICLVaQtW3IoFbcGK2UYdE0KHIA26K3QAItL6crzLpK4JiJmdB+wOLHD3zdNjVTuem9l44GJgvfT8p7r7+YOdP7//vG0gx/852sGKksq4xuz/LXoidAgiIiLDVu8KyAXAWcBFFceOpXrH88OAp9x9DzNbE3jWzC5x955aJ9c9dJEwtAISt4mjPxQ6BGnQG+++HToEEWl17b4Fy93vMLP1Bxyu1fHcgXGW7M0ZC7wJaKOySAtSFay4bTRqzdAhSIOe4pXQIYhIq2v3CUgNtTqenwVcB8wDxgH7uOe4kLFIxPq0hS5qb5aXhQ5BRESaJceXzyOShD6g4/nnSLqi7whsCMw0szvd/a1a71cis0gYY7u6Q4cgGfxhY+XwxGpS1Ra9IiIVmrwCYmbrkqRXTCTZwTTD3c8cJM/bgDOB3YBlwDR3fyg914HAj9NTn+TuFw722VkmILU6nh8ETPekQcQsM3sJ2Ay4b8AXrU7oIoG93aNO2jHb6akVoUMQEZF4lYAj3f0hMxsHPGhmM4FpVM/z/jywcfpnG+AcYJt0wnICMIVkIvOgmV3n7otqfXCWCch1VO94/gpJH5A7zWwisCnw4sA3qxN6Pmj1Km5dBVXijtnWoyeHDkEa9MyiV0OHICKtrsnbpNNUivnp47fN7GlgErXzvPcELkoXGe4xswnpIsT2wEx3fxMgncTsClxa67PrLcN7aXryNcxsDsksp1bH8xOBC8zscZICV8e4+8J6PkdEVi51Qo/bYyteCx2CiIg0y0pMQk+LTW0J3EvtPO9JQOXdkznpsVrHa6q3CtZ+NV76QMdzd58HTK3nvCIi0rjxxdGhQxARkWbJOAGpTHdIzUh3IA0cNxa4Evieu79VubtlQJ73iNH+C8nEVUUpap2FYugQJINN1Qk9Wn8OHYCItL6MVbAq0x1qMbNOksnHJe5+VXq4Vp73XGDdirdPTo/N5W9btvqP3z7Y5w45AanRBf0UYA+gB3gBOMjdF6ev/T3wG2BVoA/Yyt2XD/U5EiflgMRN37+4bdXbFToEadCgVwQiIitBWtXqXOBpdz+t4qVaed7XAYeb2WUkSehL0knKzcDPzay/O+5U4LjBPrueFZAL+GAX9JnAce5eMrOT0w85xsw6gIuBA9z9UTNbHVCdyBzTCkjcesrqERqz85k/9CAREYmS9zX9GuszwAHA42b2SHrsh9TO876RpATvLJIyvAcBuPubZnYicH867qf9Cem1DDkBqdYF3d1vqXh6D/Cl9PFU4DF3fzQd98ZQ5wfdhRUJZfyoMaFDkAxu3G9s6BCkQRN+FToCEWl5TU5Cd/e7SApGVVMtz9uBw2qc6zzgvHo/eyRyQA4maVYCsAng6VLMmsBl7v6LoU6gu+giYagPSNwO+mPoCEREpGnUCb06M/sRSROTSyrO91lgK5KlmVvN7EF3vzVTlNLStIIlEkZHzRtXIiIiravhCYiZTSNJTt/J/7aEMQe4o7/vh5ndCPwD8IEJiDqh54dWsOLVVVQhvJgdtLw7dAjSIC1eiciQmp8DEkxDVx9mtitwNLCduy+reOlm4GgzG0NSIWs74PRq56gsDdY1anJ+/4VFWphWr+I22lREQEQkt1ZiI8KVrZ4yvNW6oB8HjAJmphcw97j7N919kZmdRpIF78CN7n7DUJ+hO+giYZTK6oQes//X3Rk6BBERaZZ2noDU6IJ+7iDjLyYpxSsiIk2k9SsRkRzL8Q16bQCXTLSFJ27FQiF0CJLB/1yu/38iIhIfTUAkE22fi9vykvqExuzMjtdDhyAiIs3SzluwAMzsPJKKVwvcffMBrx0JnAqs2V/9Kj2+FXA3sK+7XzFyIYvISNEKSNw+0vGh0CFIg+4MHYCItD5VweIC4CzgosqDZrYuSffzVwYcLwInA5Ud02vK7z9ve9AmkHiV+pSEHrM73n1l6EEiIhKndm9E6O53mNn6VV46naQc77UDjn8HuJKkIeGQdAErEkZXQbswY7Z394ahQ5AG/ZRXQ4cgIq1OKyAfZGZ7AnPd/dHKRGQzmwTsBexAnRMQEQmjnOO7K+3gQV8SOgQREZFha7QR4RjghyTbrwY6AzjG3ftUIUmktfWpiEDUlvb1hA5BRESaxNs9Cb2KDYENgP7Vj8nAQ2a2NTAFuCw9vgawm5mV3P2ayhOY2aHAoQCF4ngKhVUaDEVEGlUwo2hKRI+ZEtHj9OfQAYhI69MWrPdz98eBtfqfm9lsYEpaBWuDiuMXANcPnHyk55gBzADo7JqU339hkRZmZvSpDES0NuhYleWokICISC7leJt0vWV4LwW2B9YwsznACe5esxu6tA9ts4ub+rjE7Y2+FaFDEBERGbZ6q2DtN8Tr69c4Pq2u89czSFqTLmCjpiT0uL3Qs3DoQSIiEidtwWou3UMXCcP0vy9q44qjQ4cgIiLN0s5J6LW6oJvZd4DDgDJwg7sfbWa7ANOBLqAHOMrd/6spkYtIZgVtoYva2MKo0CGIiEiztPkKyAUM6IJuZjsAewKfcPcVZtafkL4Q2MPd55nZ5sDNwKSRDVlERkpHoRg6BMlgqq0eOgRp0K2hAxCR1pfjbdJDTkBqdEH/FjDd3VekYxakfz9cMeZJYLSZjeofJyKtpadcCh2CZPDH0pzQIYiIiAxbozkgmwDbmtnPgOXAv7j7/QPG/DPwkCYf+aYqWCLhdBdaIo1PRESaoc23YNV632rAp4CtgMvN7O88relpZh8DTqZ6p/QP0EVsvFTGNW6jO7tChyAZ/Lw8PnQI0qD/FToAEWl56oT+QXOAq9IJx31m1kfS9fx1M5sMXA181d1fqHWCyk7oxeIECkV1Qo+RJo9xUxf0uG2++9uhQ5BGXRg6ABFpeVoB+YBrgB2A28xsE5KqVwvNbAJwA3Csu/9lsBMM7ISuO+lx0nctbkt7locOQTLY+QbtcBURkfjUU4b3A13QgfOA88zsCZJyuwe6u5vZ4cBGwPFmdnx6iqn9SeqSP1r/iFtXUTkEMdunY93QIUiDHmJW6BBEpNW18wrIIF3Qv1Jl7EnASVmDEpGVQ53Q4/ZSoTd0CCIi0iw5/h2t25+SiXJA4qatj3F7XUUGRUTyq51XQKB6N3Qz2wL4NdANlIBvu/t9llyRngnsBiwDprn7Q80IXsLTBWzc+vT9i9orpSWhQxARkSbxdp+AUKUbOvAL4N/c/U9mtlv6fHvg88DG6Z9tgHPSv0WkxbjKCESt5OXQIYiIiAxbXROQGt3QHVg1fTwemJc+3hO4KC1rdY+ZTTCztd19/gjEKyIiqRV9ygEREcktrYBU9T3gZjM7FSgAn06PTwJerRg3Jz2mCUgOKQckbuoDErcNu9YIHYI06CleCR2CiLQ6NSKs6lvA9939SjPbGzgX2LmRE+kiViSMVUeNCR2CZHDpH/YPHYI0aOx2So0UkSFoBaSqA4Hvpo//CPwufTwXqCxOPzk99j7qhC4SXm+5FDoEyWDW/74kdAgiItIsmoBUNQ/YDrgd2BF4Pj1+HXC4mV1Gkny+pFr+hzqhi4T3Tq/KuMbs2yveDR2CiIjIsNVbhrdaN/SvA2eaWQewnHQ1A7iRpATvLJIyvAeNcMwiMkJUBStub5eXhw5BRESaJM835+utglWrG/onq4x14LAsQYnIymEo/ypmEzqUwyMiklvagiUiIiIiIiuNJiAi1amCWdyKBZXhjdl6HasOPUhERKTFDDkBMbPzgN2BBe6+eXrsE8CvgbHAbOB/u/tbZtZJUg3rH9JzX+Tu/2eoz8jv/K4N5Hh/Yjso9amTdswefPcDBQZFRCQnvM1XQC4AzgIuqjj2O+Bf3P3PZnYwcBTwr8CXgVHu/nEzGwM8ZWaXuvvswT5A99DjpRWQuHUVtAgas21Hrxc6BGnQM4teHXqQiLS3dp6AuPsdZrb+gMObAHekj2cCN5NMQBxYJa2MNRroAd4aqWCl9eS5QkM7KHt+u6y2g3l9y0KHICIizZLjX9GN3v58EtgTuIZk1aO/8eAV6fH5wBiSTulvZg1SWpdWQOLWVdQKSMz2Lk0IHYI06IbQAYhIy2v3LVjVHAz8ysz+laTxYE96fGugDKwDfAi408z+091fzBypiIy4XuWARO22LvUBERGR+DQ0AXH3Z4CpAGa2CfCP6Uv7Aze5ey+wwMz+AkwBPjABMbNDSZsXForjKRRWaSQUCUxbsOJWNFXBitlWpe7QIUiDLggdgIi0Pq2AvJ+ZreXuC8ysAPyYpCIWwCvAjsD/NbNVgE8BZ1Q7h7vPAGYAdI2anN9/YZEWNqrYGToEyWC/zy8IHYI06LALQ0cgIi2vnXNAzOxSYHtgDTObA5wAjDWz/m7nVwHnp4/PBs43sydJilud7+6PDfUZuosuEsY7vdrCE7N9b1YOj4hIXrV1Doi771fjpTOrjF1KkpQuIhFQFay4zelZFDoEERFplhz/itbtM8lMlbDipRyQuG00as3QIUiDnuKV0CGIiARTzxasdUmaEE4k6fMxw93PNLMvAz8BPgJs7e4PVLzn74HfAKuSzN+2cnft9cgpbaGLlyYgcVvfxoQOQUREmqStt2ABJeBId3/IzMYBD5rZTOAJ4IskE433pE0ILwYOcPdHzWx1oHeE4xaREaAyvHF7uPRG6BBERKRZ2nkLlrvPJ2ksiLu/bWZPA5PcfSZU3X4zFXjM3R9N36PfkDmm7VdxUyPCuO3LxNAhSIPu5KnQIYhIi8tzmuawrj7MbH1gS+DeQYZtAriZ3QysCVzm7r9oNEBpbdp+FTclocftz8V3QocgIiIybHVPQMxsLHAl8D13f2uIc34W2ApYBtxqZg+6+62ZIhWREVfu0wQkZq+VNQEREcmtHP+KrmsCYmadJJOPS9z9qiGGzwHucPeF6XtvBP4BeN8EpLITerE4gUJRndBFVrbRnV2hQ5AMftY3NnQI0qDPhg5ARFpenjcp1FMFy4Bzgafd/bQ6znkzcLSZjQF6gO2A0wcOquyE3tk1ybWVR2TlW1FSfYiYndmZ499OIiLtLsc/4utZAfkMcADwuJk9kh77ITAK+HeSPI8bzOwRd/+cuy8ys9OA+0nK9t7o7jc0IXYRyUg5IHF7ZsXroUOQBn18tfV5/M3ZocMQkRbW7F/RZnYesDuwwN03T4/9BPg60P8L5ofufmP62nHAIUAZOMLdb06P70rSoLwI/M7dpw/12fVUwboLqFXq6Ooa77mYpBSv5JyqYMWtq6AqWDGb2r1e6BCkQb+cd0foEERELgDOIun3V+l0dz+18oCZfRTYF/gYsA7wn2a2Sfry2cAuJGkY95vZde4+aKk/XX1IJto6F7c+9P2L2RtqsSQiklvNXgFx9zvSCrf12JOksu0K4CUzmwVsnb42y91fBDCzy9Kx2SYgg3RCPzH9gD5gATDN3edVvG8r4G5gX3e/os4vTkRE6tRNMXQIIiLSJAF3SR9uZl8FHiBpRr4ImATcUzFmTnoM4NUBx7cZ6gOydEI/xd3/FcDMjgCOB76ZPi8CJwO31HF+iZi2YMVNK1hxm923NHQIIiLSLJ7tGquy4mxqRloEajDnACeSLDqcCPwSODhTIFVk6YReubSyShpov++QlO3dagRjlRakC9i4dRR0Bz1mO/Gh0CFIg24KHYCItLysKyCVFWeH8Z7X+h+b2W+B69Onc4F1K4ZOTo8xyPGaMnVCN7OfAV8FlgA7pMcmAXulzzUByblCoRA6BMmgp1wKHYJkcEV53tCDRERE6mRma6eLD5Bczz+RPr4O+H1a6XYdYGPgPpJCVRub2QYkE499gf2H+pxMndDd/UfAj9KyXIcDJwBnAMe4e1+923N0Dz1efeqkHTUv6H9fzBaX1AldRCSvvK+529zN7FJge2ANM5tDch2/vZltQXJ5Phv4BoC7P2lml5Mkl5eAw9y9nJ7ncJI+gEXgPHd/csjPrmcLTdoJ/Xrg5mrNCM1sPZJ+H5ub2Uv8rWzvGsAy4FB3v2bAe97bl1Yojv9koaBO6DFSDkjcRneOCh2CZPCF1T8ROgRp0KXz7w0dgkjbKvXMjeLiZd6nd8h0l3Cd/76tZb/Ohjuhm9nG7v58+nRP4BkAd9+gYswFwPUDJx/puPf2pXWNmqzbsCIBdBVViTtmRxdUhjdWl4YOQERanmdMQm9lWTqhH2Jmm5KU4X2ZtAJWI5TIHK+ikpijVtYWuqj9uTQ+dAgiIiLDlqUT+o11vHdaAzFJRMp95dAhSAYlff+idgNvhA5BRESaJGAfkKbT/guRNtan1ceovdW3InQIIiLSJM1OQg9JExDJREnocRvd2RU6BMngp6U1Q4cgDfocz4YOQURaXJ7vEdaThL4ucBEwkaQk1wx3P9PMfgJ8HXg9HfpDd7/RzHYBpgNdQA9wlLv/VzOCl/CUvxO3FSUlMcfsnO53Q4cgIiJN0u4rICXgSHd/yMzGAQ+a2cz0tdPd/dQB4xcCe7j7PDPbnKQu8KSRC1laiVZA4lbO8wbTNvBy7+LQIYiIiAxbPUno84H56eO3zexpBplQuPvDFU+fBEab2Sh312blHNIKSNysan0JicVo6wwdgoiINEmeV0AKwxlsZusDWwL9HZQON7PHzOw8M/tQlbf8M/CQJh8iIiIiIvVzz/anldWdhG5mY4Erge+5+1tmdg5wIkleyInAL4GDK8Z/DDgZmFrjfJWd0FEndJGVT40I47ZvYe3QIUiD/jvp3SsiUlOeV0Dquvows06Syccl7n4VgLu/VvitXdsAACAASURBVPH6b4HrK55PBq4GvuruL1Q7Z2Un9M6uSS0+TxPJJ+WAxO3uwrLQIYiISJO0dSd0S7KMzwWedvfTKo6vneaHAOwFPJEenwDcABzr7n+pJwglMouEoRWQuH1zxbB20UoLuTR0ACIiAdVz9fEZ4ADgcTN7JD32Q2A/M9uCZAvWbOAb6WuHAxsBx5vZ8emxqe6+oNYHKJE5Xpo8xq3cpxWQmN3YrT4uIiJ5ledNCvVUwboLqpbKubHG+JOAk4YThC5iRcIoFnQHPWZf9qWhQ5AGnRw6ABFpeX3tvAVrZdAKSLw0eYybVkDidm951dAhiIhIk7R7DkitTuh/ADZNh00AFrv7Ful7jgMOAcrAEe5+czOCl/A0eYybvn9xe7GjHDoEERGRYWu4E7q779M/wMx+CSxJH38U2Bf4GLAO8J9mtom71/xNqbvo8dL3Lm4dxWLoECSDb6/2eugQpEG/nBc6AhFpdW1dhneQTuhPwXtVsvYGdkzfsidwWdp88CUzmwVsDdw9yGdk+RpEpEErSr2hQ5AMjn5jbOgQRESkSfJ8eTysHJAqndABtgVec/fn0+eTgHsqXp+THhORFtOX559ubeDF3jdDhyAiIk3S1isg/QZ2Qq94aT9U0rxtafUqbqqCFbePd60VOgRp0MNU7dErIvKetq+CVa0Tenq8A/gi8MmK4XOBdSueT06PDTznocChAMXiBArFVYYdvIhkM7pDfSRi9vP1FoYOQRp0kXJARKSNNdwJPbUz8Iy7z6k4dh3wezM7jSQJfWPgvoHndfcZwAyAzq5JrjvpcVISetx6yqXQIUgGt7wwOXQI0rDnhx4iIm2trcvwUqMTurvfSFLt6n3br9z9STO7nCRJvQQcNlgFLImbJo5x0wQkbr9N6oOIiEgO5fkSK0sndNx9Wo3jPwN+likyiYJWQOLW3dEZOgTJ4OiyckBitRfPhA5BRFpc2+eAiNSiFZC49fZpcTJmfxi1InQIIiLSJG29BWuQTuhbAL8Gukm2Wn3b3e8zs/HAxcB66flPdffzm/UFiEjjyn19oUOQDOaW3w4dgoiIyLA13Akd+AXwb+7+JzPbLX2+PXAY8JS772FmawLPmtkl7t7TpK9BRBrUVdQiaMy2K2oLVqzuTHr5iojUlOdNJlk6oTuwajpsPNBfVNCBcWn1rLHAmySTGBFpMSVtwYraw74kdAgiItIkygFJDeiE/j3gZjM7FSgAn06HnUVSinceMA7Yx921zyOnlIQeN3VCj9s73hs6BBERaZK2zgHpN7ATupmdBHzf3a80s71JeoXsDHwOeATYEdgQmGlmdw7oni4iIhmt6NPisoiIxCdLJ/QDge+mj/8I/C59fBAwPe0sOMvMXgI2Y0AzwspO6IXieAoFdUKPkbtrFSRixUIhdAiSwWadq4UOQRp0d+gARKTltfUWrEE6oc8DtgNuJ1nt6G/r+gqwE3CnmU0ENgVeHHjegZ3QG/8SJDSV4hUJYxSaQIqI5FWer64a7oQOfB0408w6gOWkqxnAicAFZvY4SQPDY9x94ciGLSIjQWV44/Z0aVHoEEREpEnaegVksE7owCerjJ8HTM0Yl4isBNqCFbeJRW1dFRHJKyWhi0guaQUkbq+V3wkdgoiIyLBpAiLSxrQCErf/0bHq0INERCRKeb5FWE8SejdwBzAqHX+Fu59gZhsAlwGrAw8CB/R3O0/L8v6EJH/mUXffvznhi0gW6oQet6+tyHOKYr5dFDoAEWl5XjMDIn71XH2sAHZ096VpOd67zOxPwA+A0939MjP7NXAIcI6ZbQwcB3zG3ReZ2VpDfYDKuIqIDF9nIc/3x0RE2ltfju8x1ZOE7sDS9Gln+sdJSu/2r2xcSLLicQ5Jdayz3X1R+v4FdXzGcOMWkRGw+N2ljOroCh2GNOhI1N81Vp9eczP++/VnQochIhJEvY0IiyTbrDYCzgZeABa7e38b3jnApPTxJul7/gIUgZ+4+00jGbS0Dq1exa2r2Bk6BMmgoP9/0bprwdOhQxCRFtfX5luwcPcysIWZTQCuJulsPtg5Nwa2ByYDd5jZx919ceWgyk7opk7o8dLqVdTKri08MZu3YvHQg0REJErtngPyHndfbGa3Af8TmGBmHekqyGRgbjpsDnCvu/cCL5nZcyQTkvsHnEud0EUC6ywUQ4cgGWw+Zp3QIUiDXlwyP3QIItLi8nyLsJ4qWGsCvenkYzSwC3AycBvwJZJKWAcC16ZvuQbYDzjfzNYg2ZL1YhNiF5GM+rSCFbU3y8tChyAiIk3S7isgawMXpnkgBeByd7/ezJ4CLjOzk4CHgXPT8TcDU9PXy8BR7v5GE2IXkYyUQxC3cYXu0CGIiIgMWz1VsB4Dtqxy/EVg6yrHnaRE7w/qDUL3YOOly9e49faVQ4cgGTy5bO7Qg0REJEptvQVrZdBFrEgYRVMn9JhtPPrDoUOQBr3y1pAV6kWkzbX1BGSQTuiXAFOAXuA+4Btp4nn/+7YC7gb2dfcrmhG8iGTT3aEyvDGbVl4tdAjSoFtDByAiLa/dc0BqdUK/BPhKOub3wNdIGhH29w05Gbhl5EMWkZHSUy4NPUha1kXFN0OHICIiTdKX3/lH453Q3f3G/jFmdh9JKd5+3wGuBLaqJwg1sxMJY5RWQKL2c1MX+1jNDB2AiEhADXVCd/d7K17rBA4Avps+nwTsBexAnRMQEREZPlVRFhHJL3VCH9AJ3cw2d/cn0pf/A7jD3e9Mn58BHOPuffWubLh+i4oE8W5vT+gQJINjWRE6BBERaZI8Xx032gl9V+AJMzsBWBP4RsWwKST9QQDWAHYzs5K7X1N5LjM7FDgUoFAcT6GwSuNfhYg0pFhQFayYTSrq56aISF61exWsqp3QzexrwOeAndz9vX8jd9+g4r0XANcPnHyk42YAMwA6uybleZKXa8rfiVshx8u77WADVyNCERGJT5ZO6CXgZeDu9CL0Knf/afNClVak7XMi4ZQ0fxQRya2+HN/kzdIJvZ73TmssLBFZGcqe5wXe/Hu0b0noEEREpEnyfIu3JTqhi0gYfVrBitrC8rLQIYiISJPk+RZhw53QK17/FXCwu49Nn/+ApClhCXg9fe3lJsQuLUA5IHHrKuoeRMz2K6wTOgRp0L08GzoEEWlxbd2IkBqd0N39HjObAnxowPiHgSnuvszMvgX8AthnZMMWkZFQ0AQyauv25vn+mIiINJOZnQfsDixw983TY6sBfwDWB2YDe7v7IkvuOJ8J7AYsA6a5+0Ppew4Efpye9iR3v3Coz264E3qalH4KsD9J48H+8bdVvP0e4CtDfYbES0nocXu3pD4gMTue2aFDEBGRJlkJjQgvAM4CLqo4dixwq7tPN7Nj0+fHAJ8HNk7/bAOcA2yTTlhOIGnD4cCDZnaduy8a7IMb7oRuZt8FrnP3+YNswzkE+FM9nyEiK19HoRg6BMlgq25twYrVk2hnsogMrtm3eN39DjNbf8DhPYHt08cXAreTTED2BC5KFybuMbMJZrZ2Onamu78JYGYzSfoFXjrYZzfaCf1/AV+uCPADzOwrJLOh7er5DBFZ+cp92sITsyd73wgdgoiINEnWHJDKpt+pGWkfvsFMdPf56eO/AhPTx5OAVyvGzUmP1To+qEY7oe9AshoyK139GGNms9x9IwAz2xn4EbCdu6+odi51QhcRyabbVERARCSvst4irGz63eD73cyashDTcCd0d/9wxZilFZOPLYHfALu6+4Ja51UndJHwPNdVxvNvcUlleEVEZES9ZmZrpykWawP91/JzgXUrxk1Oj83l/TuiJpNs2xpUw53QBxl/CjAW+GO6OvKKu3+hjs8RkZVMOSBx+1T3kKvc0qIeVwEBERlCoFuE1wEHAtPTv6+tOH64mV1GkoS+JJ2k3Az83Mz6q+JOBY4b6kMa7oQ+YMzYisc7D3VOEWkNygGJ2zOlQYuMiIhIxJrdB8TMLiVZvVjDzOaQVLOaDlxuZocALwN7p8NvJCnBO4ukDO9BAO7+ppmdCNyfjvtpf0L6YLSBWKSNdWoFJGpbd6wROgRp0J2hAxCRltfsW4Tuvl+Nl3aqMtaBw2qc5zzgvOF8dktMQNRNO17qAxK3MZ2jQocgGfzkhHWHHiQt6ZffCB2BiEg49SShdwN3AKPS8Ve4+wlpR8STSMrxloFz3P1Xg3VKrEUXsSJhLFmhJOaY7fzjB0KHICIiTZLnTdL1rICsAHZ096Vm1gncZWZ/Aj5Ckg2/mbv3mdla6fiqnRJHPnQRyaqg1ceorVEcEzoEERFpEs/xr+h6ktAdWJo+7Uz/OPAtYH9370vH9ZfpqtopsaKpyQc/I8MXIGHl+P9GW+jtK4cOQTJ46t2aP1ZFRCRy7b4CQlqC90GS5oNnu/u9ZrYhsI+Z7QW8Dhzh7s9TuyOiflOKtBitgMStq9ASaXwiItIEbT8BcfcysIWZTQCuNrPNSXJClrv7FDP7Ikn2+7b1frA6oeeDCgjErWiF0CFIBh/pnhg6BGnQs8wJHYKISDDDun2WdkO/DdiVZGXjqvSlq4Hz08e1OiUOPJc6oeeACgjETROQuG3JuNAhSIOuCR2AiLS8PF9h1VMFa02gN518jAZ2AU4m+fm5A/ASsB3wXPqWqp0Sh/iMxr8CEWlYR1F9QGK2h70VOgRp0AmhAxCRltfsRoQh1bMCsjZwYZoHUgAud/frzewu4BIz+z5JkvrX0vFVOyWKiMjIerenM3QIIiLSJG2dA+LujwFbVjm+GPjHKsdrdkqU/NEWrLgVVMcsaht+4o3QIUijbg0dgIi0uraegKwMuoiNl7bPxU1leOP226fUCT1es0IHICISTJZO6HfCexmQawH3ufs/mdl44GJgvXT8qe5+fpVTV35Ghi9BQtMEMl6dBeWAxGzvca+HDkEa9K8qTC8iQ8jz1VXDndDd/b2Su2Z2JXBt+vQw4Cl33yNNYH/WzC5x954Rj16C0+Qjbn25/vGWfz0rNIEUEcmrtk5CH6QTOgBmtiqwI39LNndgnCXLGmOBN4HSEJ8x7MBFJLtSWVuwYnZJ74TQIYiISJO0fQ5ItU7oFS//E3Cru/fXgzyLpBTvPJItWvu4e57/Dduats/Fraz/mlF73peFDkFERGTYGu6E7u5PpC/vB/yuYvjngEdIVkU2BGaa2Z0VExTJEa1exa1P37+ovdy7OHQIIiLSJHn+DZ2lE/oTZrYGsDWwV8Wwg4Dp6datWWb2ErAZcF/luczsUOBQgEJxPIXCKo1/FSIibUid7EVE8ivPeZpZOqEDfAm43t2XV7zlFWAn4E4zmwhsCrw48LzuPgOYAdDZNSm//8I5py1YcSvo+xe10QU1IhQRyas8b5JuuBN6+tq+wPQB408ELjCzxwEDjnH3hSMVsLQWbcESCWdCYVToEEREpEnyfIXVcCf09LXtqxybB0zNHJlEwcw0CYmYvn9xW9O6ebH8dugwREREhqUlOqFLvHTxGj9to4vXUi+xVmF06DBERKQJ2noL1iCd0HcETgW6SEr0HuLupfQ92wNnkPQMWeju2w3xGVm+BhFpUFdR9yBidshyNSKM1aWhAxCRltfWjQip3gn9ZuBCYCd3f87MfgocCJyblur9D2BXd3/FzNYa6gNUCjReOf6/0RaW9iwfepC0rEM6XwgdgoiINElbV8Gq0Qm9DPS4+3Pp8ZnAccC5wP7AVe7+Svr+BUN9hi5i46XVq7h1F1VFKWb/PHbT0CFIg05767XQIYhIi8vv9KPBTugkPT06zGyKuz9AUo533XT4JkCnmd1O0gn9THe/aKQDl9agHJC49faVQ4cgGTxUfjN0CCIiIsPWUCd04GMkJXhPN7NRwC0kqyL95/wkSS+Q0cDdZnZPxWqJiLSIYkGN7GK2uhLQRURyq62T0CtVdkJ391OBbQHMbCrJygfAHOANd38HeMfM7gA+AbxvAqJO6CLhlfvy/OMt/14pLQkdgoiINElb54DU6oRuZmu5+4J0BeQY4GfpW64FzjKzDpIKWdsApw88rzqhi4hkM8aUwyMikld5vjhuuBO6mZ1iZrunx85x9/8CcPenzewm4DGS1aPfufsTTYpfRKRtqQiEiIjEqOFO6O5+FHBUjfecApySOToRaaqCLmCjNlorICIiuZXnTdLqQibSxtSDJ26vl5YOPUhERKLU1jkgIpJfnQV10o7Z1K5JoUOQBj3A86FDEJEWl9/pxzAmIGkOyAPAXHff3cwOB74HbAis6e4LB4zfCrgb2NfdrxjBmKWFaA963FSGN25/16v/fyIieaUtWInvAk8Dq6bP/wJcD9w+cGA6WTmZpD+I5JgaEcZteak3dAiSwbnMDx2CiIjIsNXbCX0y8I8kpXZ/AODuD6evVXvLd4Arga3qOb8uYeOlJOa4uf73RW1h79uhQxARkSbJ8+/oeldAzgCOBsYNNdDMJgF7ATtQ5wREl7AiYXQoByRqu4zZIHQI0qDnF88NHYKItLi23oKV9vpY4O4Pmtn2dZzzDOAYd+8bLD9AndBFwlMn9Lg9WVoUOgQREWmSdq+C9RngC2a2G9ANrGpmF7v7V2qMnwJclk4+1gB2M7OSu19TOUid0EXCUxWsuG1bXDN0CNKgP4cOQERaXp4vjutpRHgccBxAugLyL4NMPnD39/YEmNkFwPUDJx8i0hp6+8qhQ5AMbi7NCx2CiIjIsDXcB8TMjiDJC/kw8JiZ3ejuXxuxyCQKqoIlEk4nKqMsIpJX7b4F6z3ufjtp2V13/xXwqyHGT2swLhERERGRtpXnLM2W6ISe3/ld/qmCWdzyXOKvHbxZeid0CCIi0iR5/h2dpRP6uSQJ5wY8B0xz96Vm9gPga0AJeB042N1fHvTcjUYvItLGugudoUMQEREZtiyd0L/v7m8BmNlpwOHAdOBhYIq7LzOzbwG/APYZuZCllQxWallan2n6H7WCKQdERCSv2n4LVo1O6P2TDwNGk+6kcvfbKt56D1CzYpbET0nocVMn+7hN7BiyN6yIiERKW7BqdEI3s/OB3YCngCOrvO8Q4E9ZAhQRkep6XGWURUTyqq1XQAbrhO7uB6W5If9Oss3q/Ir3fYUkR2S7EY1YWoq2YMWtq9gSdSikQUf0TAgdgjTo1tABiEjL68vxLpPMndDdvWxml5GskJwPYGY7Az8CtnP3FdVOamaHAocCFIrjKRRWyfzFyMqnLVhxUyPCuF0/Wt+/aC0KHYCISDgNdUIHDjCzjdx9VpoD8gXgmXTMlsBvgF3dfcEg550BzADo7Jqkq9hIaQUkbppAxm1e37LQIYiISJPk+Td0o/svDLjQzFZNHz8KfCt97RRgLPDH9OL0FXf/QtZARWTkaQIZt3HWFToEERFpEnVCT1V2QifZmlVtzM7ZQpKY6A563Mp9eU5xy78XStrHIyKSV6qCJVKD7qDHTUnocTugMCl0CNKg+3kudAgi0uLyfIswSyf0C0gqXC1Jh0xz90fSnJAzScrzLkuPPzSyYUur0ApI3FaUe0OHIBmcV3o5dAgiIiLDlqUTOsBR7n7FgHGfBzZO/2wDnJP+LTmkFZC4qRN63CZ0jAkdgoiINEnb54BU64Q+iD2Bizy5NX6PmU0ws7XdfX62UKUVaQUkbsVCIXQIksEmxfGhQ5AG3R46ABFpecoBqdEJHfiZmR1P0lPp2LTnxyTg1Yoxc9JjmoCIiIygco5/OYmItLu2zgEZpBP6ccBfgS6Sfh7HAD9tRpDSurQFK26qghW3WaUlQw8SERGpwcxmA28DZaDk7lPMbDXgD8D6wGxgb3dfNJJ53pk7oQMrzOx8kgaFAHOBdSvePzk99j7qhC4iko1uAIiI5NdK3Oa+g7svrHh+LHCru083s2PT58cwgnneDXVCd/ev9Od1pLOhfwKeSN9yHXC4mV2WBrWkWv6HOqHng3JA4lbQBWzUuk1llEVE8ipgEvqewPbp4wtJ0taOYQTzvLP89rrEzNYk6YT+CPDN9PiNJEszs0iWZw7K8BnS4gpKYhYJZhXrDB2CiIg0yUraJO3ALWbmwG/SBYKJFZOKvwIT08cjlufdcCd0d9+xxhgHDhtuIBInrYCIhFPOdYqiiEh7y1oFqzLdITUjnWBU+qy7zzWztYCZZvbM+2Jw93RyMqJaYv1el7Dx0gaeuJVdF7Axe2b5gtAhiIhIi6pMdxhkzNz07wVmdjWwNfBaRarF2kD/L5u68rzrkaUTugEnAV8myZw/x91/ZWbjgYuB9dLzn+ru5w92bu1DFwlj9e6BlbUlJg8/+fvQIUgGo9fZNnQIItLCmp0DYmarAAV3fzt9PJWkou11wIHA9PTva9O31JXnXY8sndCnkcyCNnP3vnTpBpLtV0+5+x5pjsizZnaJu/fUOrG28cRLVXjitqRnWegQJINvTzkmdAjSoPPn/XfoEESkxa2E6+OJwNXptVwH8Ht3v8nM7gcuN7NDgJeBvdPxI5bnnaUT+reA/d2TPRzu3r8848C4dIVkLPAmUGo0QGltmjzGTd+/uL3hK0KHICIiTdLsTdLu/iLwiSrH3wB2qnJ8xPK8s3RC3xDYx8z2Al4HjnD354GzSJZo5qXj9+mfpEj+aAVERERERIYjSyf0UcDytGPiF4HzgG2Bz5GU5d2RZJIy08zudPe3an2G7sFGTHfQo6Yk9Lg9pyR0EZHcyloFq5U13AmdpPbvVemYq4H+RPODgOnpMs0sM3sJ2Ay4r/Kk6oQuEp6pjlnUVuvQz00RkbwK2Iiw6bJ0Qp8O7AC8BGwHPJe+5RWSfWN3mtlEYFPgxSrnVSf0HNAWLJFwugtqRCgikld5ztPM0gdkOkk39O8DS4GvpcdPBC4ws8dJ2kQc4+4Ls4UprUoTEJFwtIIlIpJfbb0CUmlAJ/TFJJWxBo6ZR1JHWNpAX59yCKJWCB2AZLGorDLKIiISn5bohC7x0gpI3IoFzUBi9rGuNUKHIA16iFmhQxCRFtfuSegiNeV5f2I7KGsFK2ov9C4OHYKIiDRJX46vseqegJhZEXgAmOvuu5vZTsApJJs4lgLT3H1WOnZv4CckFXYfdff9RzpwEcmuoBWsqCkJXUQkv/I7/RjeCsh3gaeBVdPn5wB7uvvTZvZt4MfANDPbmKRq1mfcfZGZrTWiEYvIiNEWurhNLIwJHYKIiDRJ2yehm9lkkoTznwE/SA87f5uMjCfpfA7wdeBsd18E4O7qlCXSorSFLm4ltIVORETiU+8KyBnA0cC4imNfA240s3eBt4BPpcc3ATCzvwBF4CfuftPIhCsiIv0W960IHYKIiDRJW6+AmNnuwAJ3fzBtRNjv+8Bu7n6vmR0FnEYyKekANga2ByYDd5jZx9OyvSLSQkZ1KIcgZkf1jBt6kLSkmaEDEJGWl+ddCvWsgHwG+IKZ7QZ0A6ua2Q3AZu5+bzrmD0D/Kscc4F537wVeMrPnSCYk91ee1MwOBQ4FKBTHUyiskvmLEZHhebe3J3QIksGJ4xaFDkFERJqkrVdA3P04kqRy0hWQfwH+CfirmW3i7s8Bu5AkqANcA+wHnG9ma5BsyXqxynlnADMAOrsm5fdfOOeUxBw3VcGKW5epkrqIiMSnod9e7l4ys68DV5pZH7AIODh9+WZgqpk9BZSBo9z9jRGJVkRGVFdRF7AxO6C8eugQpEG3hg5ARFqeGhGm3P124Pb08dXA1VXGOEmlrB8MfE1EWktPuRQ6BMngms63QocgIiJN0u45ICKSU9pCF7e1rDt0CCIi0iRtnQMCYGazgbdJtlSV3H2Kma1Gkny+PjAb2Lu/90f6nq2Au4F93f2KkQ1bWkWeZ+ftQN+/uM3uWxo6BBERaZI8/44ezgrIDu6+sOL5scCt7j7dzI5Nnx8DYGZF4GTglnpOrLuwImGM7ugKHYJkcGqnfnbG6u9DByAiElCWLVh7kvT6ALiQJDfkmPT5d4Arga3qOVGeZ3h5p8lj3JaXe0OHIBn8vDQqdAgiItIkbb8FC3DgFjNz4DdpCd2J7j4/ff2vwEQAM5sE7AXsQJ0TEImXJo9x0/cvbm/0vRs6BBERaRJVwYLPuvtcM1sLmGlmz1S+6O6eTk4AzgCOcfe+eu+O5/efN/+0/hG3sveFDkEyeGH566FDEBGRJunL8U3CuiYg7j43/XuBmV0NbA28ZmZru/t8M1sbWJAOnwJclk4+1gB2M7OSu19TeU51Qs8HbcGKW9EKoUOQDNbvXiN0CNKgF5fMH3qQiLS1tl4BMbNVgIK7v50+ngr8FLgOOBCYnv59LYC7b1Dx3guA6wdOPtJx6oSeA9rCEzdNQOL22YIaEcbqv0IHICISUD0rIBOBq9M73R3A7939JjO7H7jczA4BXgb2bl6Y0qq0AiISzmIrhw5BRESapK23YLn7i8Anqhx/A9hpiPdOazgyiYJWQOKW5wob7WA5yuEREcmrtt6CJSIiramc419OIiLtrq1XQKBmJ/RTgD2AHuAF4CB3X2xmu5DkhXSlrx3l7truKtKCyn26gx6zp3rfDB2CiIjIsGXphD4TOM7dS2Z2MnAcSSPChcAe7j7PzDYHbgYmjVjE0lKUAxK37o7O0CFIBkf0fTh0CNKgu3lm6EEi0ta0BasKd7+l4uk9wJfS4w9XHH8SGG1mo9x9RaOfJa1LOSBx6ymXQocgGVzYuTh0CCIi0iRtvwWL6p3QKx0M/KHK+/4ZeGioyUd+/3nzT+sfcVMjwrjNWr5g6EEiIhIlrYBU6YTu7ncAmNmPgBJwSeUbzOxjwMkkfUMGpYvYuGkbVrxM//uitnrnuNAhSINmhQ5ARFqe5/gmYZZO6HeY2TRgd2Anr9iLY2aTgauBr7r7C9XOqU7o+aFtWPEqFtSIMGbrdawaOgRp0L2hAxARCciGunis0gl9JkkndIDTgO3c/fWK8ROAPwP/5u5X1RNEhzqhR0v3z+PWUVQl7pitvcpqoUOQBr3y+R3GigAAEN5JREFUlrbPiYRS6pkbxeXL/1j97zNdH7/8xmMt+3Vm6YQ+CxhFsiUL4B53/yZwOLARcLyZHZ+eY6q71/xp27L/OiI511kohg5BMth4tKpgxUoTEBEZSp53mGTphL5RjfEnASdlD01Emi3PFTbaQVmd0EVEcqtPSegiItJqVMVMRCS/2noFRETyq6AKZlH7cFHFO0REJD51TUDMbDbwNlAGSu4+peK1I4FTgTXdfaElCSFnArsBy4Bp7v7QSAcuItlpC1bcXupVI0IRkbzK8+/o4ayA7ODuCysPmNm6JH0+Xqk4/Hlg4/TPNsA56d+SU+oDEi+V4Y3bR7vWCB2CNOgBng8dgoi0ODUirO104Gjg2opjewIXpX1B7jGzCWa2trvPz/hZ0oI0+YhbuU85BDGb1bsodAgiItIkygEBB24xMwd+4+4zzGxPYK67PzrgInQS8GrF8znpsZoTkPz+8+afph9xK/WVQ4cgGbyw7K+hQxARERm2eicgn3X3uWa2Fknfj2eAH5Jsv8pMF7EiYYz6/+3df5hcVX3H8fdnNwkJIQIC7QMBDVVsiwQChAhFbLBIfRQF+VEKVgwoYDVoaeWHSCmPQCVKKwhCTRBSMSAKIimkCVKIYIwkEELYRBGLoRKpRIuRX5KEfPvHOUNuJjO77G52Zu7M5/U88+ydM/eec+bePXPPr3vvsOHNzoINwhHb7tnsLNgAfe2FHzY7C2bW4jr+NrwRsSr/fUbSbcCfA7sDldGPXYElkiYBq4DdCpvvmsM2Iek04DSAru5t6ery3VzKKCI8DavE2nl4txP8Jl5udhZsgI7aeX+++/RDzc6GmbWwdj5H99kAkTQa6IqI5/Ly4cDnIuIPCuusBCbmu2DNBqZK+ibp4vM1ta7/iIjpwHSA4SPGtu8ebnNufJSbj1+5jcBPsi+rbz29qNlZMLMW1+l3wfpD4LZcURkG3BgRc3tZfw7pFrw/I92G9+TBZtJaVzu3zjuBrwEpt5+sXd3sLJiZ2RBp5zpWnw2QiHgC2KePdcYVlgP4RH8y0b67t/25/9ysedZuWN/sLJiZmfVbSzwJ3ZXY8vIUnnKTS1+pbdM9stlZMDOzIdLxF6HXehK6pAuBU4HKHIDzImKOpOHAtcB+Of6vR8Tnt3TGzcw6nTsAzMzaV0dPwSrY7EnowJci4rKqsOOArSJivKStgRWSboqIlfUibt/d2/5c/Sm3dn7Kaif47boXmp0FMzMbIp1+EXp/BTBa0jBgFLAW+F1vG7gSa2bWfyO7/BwXM7N21c6dhAN+EnoOnyrpJOBB4B8i4lngFuBI0pPPtwbOjIj/28L5NjPreO08P9jMzNrXYJ6Efg1wEalxchHwL8ApwCTStSK7ANsD90u6O99Ny8xayLAuP0eizA4cObbZWbABWs6Tzc6CmbW4jp+CVeNJ6JMi4r7K55JmAHfktycCcyNiHfCMpAXARGCTBoifhG5mNjgvhp/jYmbWrjr6IvR6T0KXtHPhCecfAHry8v8A7wRuyOsfCFxeHa+fhN4efBeecuvyFViltqNGNDsLZmY2RDr9GpCaT0KXdIOkCaQpWCuB0/P6XwGul7ScdH359RGxrLcEXIktr3ZunXeCEd0t8SggG6DTR/R6fw9rYVc2OwNmZoCkdwNXAN3AtRFxaSPSHfCT0CPiQ3XWf550K14za3G+iLncXnzJd8EyM2tXQ93JK6mbNHDwLuApYLGk2RGxYkgTpkWehG7l5dGrcvMULDMzs9bUgFkmk4CfVW4UJembpDvZdkYDpJ2v8m93Xc3OgA3KmpdfbHYWbBDe/8J/NzsLZmY2RBpQOx4L/KLw/ingbUOfbIs0QNavXdXW3bCSTis8O8VKxsevvHzsys3Hr9x8/MrLx641DLZ+XLzjbDa9VY6rO7Ab47S+V7EW5uNXXj525ebjV24+fuXlY9cGImJ6REwsvKobH6uA3Qrvd81hQ84NEDMzMzOzzrMY2EPS7pJGAH8NzG5Ewi0xBcvMzMzMzBonItZLmgrMI92G97qIWN6ItN0AaYyWmG9nA+bjV14+duXm41duPn7l5WPXISJiDjCn0enKD5IzMzMzM7NG8TUgZr2Q9HeSth7gtttJ+viWzpOZmZlZmXkExKwXklYCEyPi1wPYdhxwR0Ts1c/tuiPilf6mZ2YbSRoWEeubnQ+zRpF0IfA88Drgvoi4u4l5+Vyz82CtzSMgJSDp+fx3F0m3NDs/jSJpnKSfSJop6aeSZkk6TNICSY9LmiRptKTrJC2S9LCkIwvb3i9pSX79WQ6fLGm+pFty3LNU53Hukj4J7ALcK+neHHa4pIU5zm9L2kbSG3N+dpTUldM9HLgUeJOkpZK+mNO+oxD/VZKm5OWVkqZJWgIcVyudodzXnUzSRElf3kJxTZG0y5aIq1U1u1zm9S+QtFhSj6TplXVzHJdLehD4lKT9JX1f0kOS5knaOa93at7+EUm3DnSU04aGpPMKy+Mk9TQzP2UTERc0u+LfCnmwFhcRfjXhBXT3Y93nm53fJu2jccB6YDypsfwQcB0g4Ejgu8A/A3+T198O+CkwGtgaGJnD9wAezMuTgTWke113AQuBt/eSh5XAjnl5R+A+YHR+fw5wQV7+KPBt4Czgq4X89xTimkwaEam8vwqYUkjn7L7S8WuL/48N28LxzSeNmDUtDw3YZ61QLl9fWL4BeF9h/1+dl4cDPwR2yu+PJ93hBWCHwvYXA2c0e7/6tcnxfb6wvMnvqF+b7avP5vL1A+Am4NPATODY/PkFpFut9pAuLK/MfDkAWAYsBb5Y2cfAFOA7wFzgceALhbROAB7NcU3LYd05vZ782Zk5vJiHS4EVOb3Lmr3P/GqNV8eMgLRIr111L3fNXjil+zEvlPSopIurvkNPXp4i6arCZ3fk/HTn79iTtz9ziHZpo/w8Ih6NiA3AcuC/IiJIP3TjgMOBcyUtJVU+RgJvIFU+Zkh6lNQw2LMQ56KIeCrHuTTH81ocmONZkNP7MPBGgIi4ljTs/THSCWAgbu4rnVbTQuXqC/n/fZGkN+fwnXK5WpxfB+fwCyXdIGkBcIMKI1P5s3/P+XpS0tGFuOdKGp7X26xnXdKxwERgltKo16ha6+XtN+mpH7ojNGSaXS4PlfRAjuedwFsLn1XK0R8DewHfy/k4n9TAAdgrH+NHgQ9Wbd9yWqScXSpphaRlki7LYTMlXSPpR5KeyHFeJ+nHkmYWtj0hl6EeSdN6C5d0KTAql6FZedVuSTMkLZd0l6RRed35SufURXm/HJLDu5VGnRfn/J6ew3eWdF+Ou0fSISrxOVPS/qTnNkwA3kNqVFS7KiIOiDQVeBRwRA6/Hjg9IiYA1VN+J5Aa7OOB4yXtpjSyO41U3iYAB0g6Ki+PjYi9ImJ8jreYxx2ADwBvjYi9SQ1+s467De+bgeOAU0g9AicCbwfeD5xHaqHfExGnSNoOWCTpbuAZ4F0R8XtJe5B6GSbmOPclnbx+CSwADib1RNTzm4jYD1LBjIgZefli4CPAlcAVwDUR8XVJn+jnd3z1xyDHu10/t281LxeWNxTebyD9/74CHBMRjxU3UpoL+ytgH1KP6u/rxPkKr70cCPheRJyw2Qep8Vip3GwDPFdj+/VsOu1xZNXnL/SVTotqhXK1JiLGSzoJuJx0kr0C+FJE/EDSG0j3Of/TvP6epB72lyRNrorrTcCheZ2FpP+vsyXdBrxX0p2kcnpkRKyWdDxwSf5+U4FPR8SDubGy2Xp5PwGMiIiJlFPTyqWkkcDVpJGmX+Q4i2WpWI6WR8RBNaKZCRwVEY8oTYOcXCutFtO0claoRP5JRETVeWV74KCcj9k5jo8CiyVNyOlPA/YHngXuyhXXRbXCI+JcSVNzxbhyLd0ewAkRcaqkbwHHAN/I6Q+LiEmS3gP8E3AY6Vy6JiIOkLQVqTPnLuBoYF5EXCKpmzQiV+Zz5iHAbRHxIoCkWg+QO1TS2aTv+npguaT7gTERsTCvcyMbGyaQOhTW5DhXkDrAdgDmR8TqHD4LeAdwEfBHkq4E7gTuqkp/Damcf02po+cOzOi8a0Ca3WsHG3vnoH4v3MGkkwSk6QX98QT5x0DSu4Hf9XP7spkHnFHpuZO0bw7fFng6H5cPkYaJB+I5YExe/hFwsDb2sI+W9Jb82TRgFmm4e0aNbQGeBPaUtFU+yf1FnTR7S6cVtUK5uqnwt1LhPAy4Kqc7G3idNl5LMzsiXqoT139GxLqc/27SVAQK36e3nvWivta7ucY27WIoy2WlsfHrfDyPrbPeY8BOkg7KeRguqfIbOwZ4OjcSPziAPDRDM8tZsRJ5NPBi4bP/KOTjV1V5HEfqlZ8fEasj3RSgUnGtF17vuy/Nyw9V5fM7NcIPB07K++IBUuV5D1LD7eTcaB0fEc/RxufMQmP92Dw6MYPNO75qec2ddBHxLKlDYT5pBsC1VZ+vByYBt5AaOXMxo/NGQFqhN/2FwvJM6vfC9XV7spq96RHxrKR9gL8k/Rj8FRt7XNvRRaQe72WSuoCfk37krgZuzT3ic9l0v/fHdGCupF9GxKH5ON2Ue9UAzleaVnMAcHBEvCLpGEknR8T1SlMkekiV2rNy711PzufDtRLMveWbpUOa59uKWqFcRY3lLuDAiCjGS64T9/b/8DJARGyQtC5Xrorfp7ee9U2S6mO9gf5PlsGQlcuI+K2kGaRy9L+kSmWt9dYqTYv7sqRtScfuclLF+B9JFdPV+e+YWnG0mKaVs0hPS55E6jQ5FphKmopTjKOYp2K+1vX91fpUnc9RNT4r5l+k63rmVUck6R3Ae4GZkv41zzQo6znzPtL3+Dzpu78P+Grh81qN9VtyGXpO0tsi4gHSNK6+LCKVpR1JI1YnAFfm92sj4lZJj7FxZAqAnO7WETFHadrrEwP/utZOOq0B0pdKr90ZeZh534h4mNRr91SukHyYgfemV6vuhVuVwxeQfhC+Qf3euZXAx/PJfSyph4G+fgzKJCJWknqQK++n1Pns9BrbPg7sXQg6J4fPJ/XUVNab2kceriRNo6m8v4fa82wPLKxzdGH5xKr4zgbOrpHOuKr39dIpo0aUq+NJFzoeT5o2BWkqwBmkCyyRNKHQizoYr/asR8TCXH7fEhHL2XTUq7f1SqtFyuX5pEZ5dfjkqvdLqdGrHhHXANf0lkYJDVk5G2QlsmbFtZdwgHWShueRyIGYB/ytpHsiYl0eQV5FusHHUxExI3fu7CdpDiU9Z0bEEkk3A4+Qprotrvq8t8b6R0gjYxuA75NGuXpL62lJ5wL3khp4d0bE7bnxdn2uiwB8pmrTMcDteTRGwN8P4KtaG3IDZFND3ZterV4v3KeAGyWdA9xeZ9sFOX8rgB8DS3L4WHr/MTBrtEaUq+0lLSP1hlaunfkk8JUcPozUW/ixQaQB9NmzPhP4N0kvkaaC1VvPbEsbynI24EpkvYorQL1w0sjzMqUbtnx2APm9ljQda0meBrgaOIo0y+AsSetIz8s4iZKfMyPiEtK1ZfU+r9lYJ43O7g2vHocH8/ozSb9jle2PKCzfxMbprpWwR4D9aqQ7pfB2Up9fxDqOH0RoBihdYLx7VfA5tYbwrbVoEA+LtNbmcmk2NJRuivEZUsfIk6Rbwq9ubq6sk7gBYmal5gaImZlZuXgK1hBwr53ZltdLuRrXhOyYtSWfv8ysETwCYmZmZmZmDdNpzwExMzMzM7MmcgPEzMzMzMwaxg0QMzMzMzNrGDdAzMzMzMysYdwAMTMzMzOzhvl/SOjKKkoty64AAAAASUVORK5CYII=\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.corr()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "id": "qk2kiHKy39Na",
        "outputId": "ba6bec8e-f467-421f-8f43-cc8859dc8880"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                 mean_radius  mean_texture  mean_perimeter  mean_area  \\\n",
              "mean_radius         1.000000      0.323782        0.997855   0.987357   \n",
              "mean_texture        0.323782      1.000000        0.329533   0.321086   \n",
              "mean_perimeter      0.997855      0.329533        1.000000   0.986507   \n",
              "mean_area           0.987357      0.321086        0.986507   1.000000   \n",
              "mean_smoothness     0.170581     -0.023389        0.207278   0.177028   \n",
              "diagnosis          -0.730029     -0.415185       -0.742636  -0.708984   \n",
              "\n",
              "                 mean_smoothness  diagnosis  \n",
              "mean_radius             0.170581  -0.730029  \n",
              "mean_texture           -0.023389  -0.415185  \n",
              "mean_perimeter          0.207278  -0.742636  \n",
              "mean_area               0.177028  -0.708984  \n",
              "mean_smoothness         1.000000  -0.358560  \n",
              "diagnosis              -0.358560   1.000000  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-257ef944-6758-4663-bb95-83db369128bb\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>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>diagnosis</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>mean_radius</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.323782</td>\n",
              "      <td>0.997855</td>\n",
              "      <td>0.987357</td>\n",
              "      <td>0.170581</td>\n",
              "      <td>-0.730029</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean_texture</th>\n",
              "      <td>0.323782</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.329533</td>\n",
              "      <td>0.321086</td>\n",
              "      <td>-0.023389</td>\n",
              "      <td>-0.415185</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean_perimeter</th>\n",
              "      <td>0.997855</td>\n",
              "      <td>0.329533</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.986507</td>\n",
              "      <td>0.207278</td>\n",
              "      <td>-0.742636</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean_area</th>\n",
              "      <td>0.987357</td>\n",
              "      <td>0.321086</td>\n",
              "      <td>0.986507</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.177028</td>\n",
              "      <td>-0.708984</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean_smoothness</th>\n",
              "      <td>0.170581</td>\n",
              "      <td>-0.023389</td>\n",
              "      <td>0.207278</td>\n",
              "      <td>0.177028</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.358560</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>diagnosis</th>\n",
              "      <td>-0.730029</td>\n",
              "      <td>-0.415185</td>\n",
              "      <td>-0.742636</td>\n",
              "      <td>-0.708984</td>\n",
              "      <td>-0.358560</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-257ef944-6758-4663-bb95-83db369128bb')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"https://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-257ef944-6758-4663-bb95-83db369128bb button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-257ef944-6758-4663-bb95-83db369128bb');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(15,15))\n",
        "sns.heatmap(df.corr(),annot=True,cmap=\"coolwarm\",linewidths=2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 885
        },
        "id": "qJoTALmI4CiM",
        "outputId": "53140204-f412-4446-d2aa-bef032490d74"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f6947b92cd0>"
            ]
          },
          "metadata": {},
          "execution_count": 16
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x1080 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data=df.drop([\"diagnosis\"],axis=1)\n",
        "print(\"The shape of f{data} is: \",data.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8OI19U6Z4MgT",
        "outputId": "7ac97a8e-c5a5-4cdc-ec0b-f2e79dc67613"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The shape of f{data} is:  (569, 5)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(16,5))\n",
        "ax=sns.barplot(data.corrwith(df.diagnosis).index,data.corrwith(df.diagnosis))\n",
        "ax.tick_params(labelrotation=90)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 407
        },
        "id": "PmfIOGFu4SEw",
        "outputId": "4a1ac836-ed39-4793-a48b-ec16d5eb16fe"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1152x360 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.corrwith(df.diagnosis).index"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2tiAl9dr4bP7",
        "outputId": "f5cf8cb6-3951-4d6f-864d-36fedcd4c7f5"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['mean_radius', 'mean_texture', 'mean_perimeter', 'mean_area',\n",
              "       'mean_smoothness'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x=df.drop([\"diagnosis\"],axis=1)\n",
        "y=df[\"diagnosis\"]"
      ],
      "metadata": {
        "id": "xs98YNP14ejE"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.3,random_state=5)"
      ],
      "metadata": {
        "id": "hsQEVsWk0jBT"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_test"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "doq3opVT0mMZ",
        "outputId": "8d06523f-2611-4c56-accd-3967684b43b3"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     mean_radius  mean_texture  mean_perimeter  mean_area  mean_smoothness\n",
              "28        15.300         25.27          102.40      732.4          0.10820\n",
              "163       12.340         22.22           79.85      464.5          0.10120\n",
              "123       14.500         10.89           94.28      640.7          0.11010\n",
              "361       13.300         21.57           85.24      546.1          0.08582\n",
              "549       10.820         24.21           68.89      361.6          0.08192\n",
              "..           ...           ...             ...        ...              ...\n",
              "447       14.800         17.66           95.88      674.8          0.09179\n",
              "98        11.600         12.84           74.34      412.6          0.08983\n",
              "448       14.530         19.34           94.25      659.7          0.08388\n",
              "381       11.040         14.93           70.67      372.7          0.07987\n",
              "467        9.668         18.10           61.06      286.3          0.08311\n",
              "\n",
              "[171 rows x 5 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-a9d7ea99-08bd-46a0-ab7f-7432e649bd35\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>mean_radius</th>\n",
              "      <th>mean_texture</th>\n",
              "      <th>mean_perimeter</th>\n",
              "      <th>mean_area</th>\n",
              "      <th>mean_smoothness</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>15.300</td>\n",
              "      <td>25.27</td>\n",
              "      <td>102.40</td>\n",
              "      <td>732.4</td>\n",
              "      <td>0.10820</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>163</th>\n",
              "      <td>12.340</td>\n",
              "      <td>22.22</td>\n",
              "      <td>79.85</td>\n",
              "      <td>464.5</td>\n",
              "      <td>0.10120</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>123</th>\n",
              "      <td>14.500</td>\n",
              "      <td>10.89</td>\n",
              "      <td>94.28</td>\n",
              "      <td>640.7</td>\n",
              "      <td>0.11010</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>361</th>\n",
              "      <td>13.300</td>\n",
              "      <td>21.57</td>\n",
              "      <td>85.24</td>\n",
              "      <td>546.1</td>\n",
              "      <td>0.08582</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>549</th>\n",
              "      <td>10.820</td>\n",
              "      <td>24.21</td>\n",
              "      <td>68.89</td>\n",
              "      <td>361.6</td>\n",
              "      <td>0.08192</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>447</th>\n",
              "      <td>14.800</td>\n",
              "      <td>17.66</td>\n",
              "      <td>95.88</td>\n",
              "      <td>674.8</td>\n",
              "      <td>0.09179</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>98</th>\n",
              "      <td>11.600</td>\n",
              "      <td>12.84</td>\n",
              "      <td>74.34</td>\n",
              "      <td>412.6</td>\n",
              "      <td>0.08983</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>448</th>\n",
              "      <td>14.530</td>\n",
              "      <td>19.34</td>\n",
              "      <td>94.25</td>\n",
              "      <td>659.7</td>\n",
              "      <td>0.08388</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>381</th>\n",
              "      <td>11.040</td>\n",
              "      <td>14.93</td>\n",
              "      <td>70.67</td>\n",
              "      <td>372.7</td>\n",
              "      <td>0.07987</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>467</th>\n",
              "      <td>9.668</td>\n",
              "      <td>18.10</td>\n",
              "      <td>61.06</td>\n",
              "      <td>286.3</td>\n",
              "      <td>0.08311</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>171 rows × 5 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a9d7ea99-08bd-46a0-ab7f-7432e649bd35')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"https://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-a9d7ea99-08bd-46a0-ab7f-7432e649bd35 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-a9d7ea99-08bd-46a0-ab7f-7432e649bd35');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "sc=StandardScaler()\n",
        "x_train_sc=sc.fit_transform(x_train)\n",
        "x_test_sc=sc.transform(x_test)"
      ],
      "metadata": {
        "id": "uHp6Q0LL0oIT"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_test_sc"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0Y7t6MBu48a0",
        "outputId": "ecce0718-25dd-4ace-c88c-93b357fada1f"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 3.45321408e-01,  1.46339385e+00,  4.48815430e-01,\n",
              "         2.42940781e-01,  8.33644906e-01],\n",
              "       [-5.24643017e-01,  7.38714477e-01, -5.13174621e-01,\n",
              "        -5.64472944e-01,  3.33652503e-01],\n",
              "       [ 1.10195888e-01, -1.95329120e+00,  1.02413691e-01,\n",
              "        -3.34303973e-02,  9.69357129e-01],\n",
              "       [-2.42492393e-01,  5.84274611e-01, -2.83235536e-01,\n",
              "        -3.18541776e-01, -7.64902232e-01],\n",
              "       [-9.71381506e-01,  1.21153807e+00, -9.80731648e-01,\n",
              "        -8.74599381e-01, -1.04346943e+00],\n",
              "       [ 2.75829706e+00,  1.22579406e+00,  2.69701391e+00,\n",
              "         3.30080553e+00,  7.40789174e-01],\n",
              "       [-6.42205778e-01,  3.91818777e-01, -5.97642040e-01,\n",
              "        -6.35298710e-01, -7.48473910e-01],\n",
              "       [-8.77331298e-01, -1.19772385e+00, -8.80479913e-01,\n",
              "        -8.05280546e-01, -1.69917375e+00],\n",
              "       [ 1.66038199e-01, -1.21910783e+00,  2.70495323e-01,\n",
              "         1.29830829e-02,  4.69364727e-01],\n",
              "       [-2.21918910e-01, -6.46492326e-01, -2.46547667e-01,\n",
              "        -2.96239195e-01, -1.81274345e+00],\n",
              "       [-2.68944014e-01,  2.12629728e+00, -2.98593248e-01,\n",
              "        -3.41748516e-01, -7.01331769e-01],\n",
              "       [-7.15682503e-01,  1.26618602e+00, -6.61632509e-01,\n",
              "        -7.39578347e-01,  1.93362819e+00],\n",
              "       [ 1.90302798e+00,  2.42804902e+00,  2.05710922e+00,\n",
              "         1.84812387e+00,  1.51934877e+00],\n",
              "       [-1.12715216e+00,  2.01937737e+00, -1.11809785e+00,\n",
              "        -9.95756647e-01, -4.44907094e-01],\n",
              "       [ 2.01307027e-01, -1.04803598e+00,  1.18624610e-01,\n",
              "         8.71240969e-02, -8.43472466e-01],\n",
              "       [-8.45001539e-01, -1.02902800e+00, -8.74507469e-01,\n",
              "        -7.89005690e-01, -9.62042093e-01],\n",
              "       [-1.49776877e+00, -4.23148519e-01, -1.41160081e+00,\n",
              "        -1.20943949e+00,  7.19360928e-01],\n",
              "       [-5.36399294e-01, -7.62916225e-01, -5.56688140e-01,\n",
              "        -5.66884034e-01, -1.12346821e+00],\n",
              "       [-1.38196945e+00,  2.08352932e+00, -1.39155046e+00,\n",
              "        -1.14675115e+00, -1.09275439e+00],\n",
              "       [-3.68872360e-01,  1.01947027e-01, -3.92872539e-01,\n",
              "        -4.29753297e-01, -3.69193959e-01],\n",
              "       [ 6.24532964e-01,  9.48190336e-02,  7.64501743e-01,\n",
              "         4.94296901e-01,  4.33650984e-01],\n",
              "       [-6.42205778e-01, -2.06932706e-01, -6.89788315e-01,\n",
              "        -6.45545842e-01, -9.94184462e-01],\n",
              "       [-6.45144847e-01, -1.94853920e+00, -6.71444381e-01,\n",
              "        -6.30175144e-01, -5.57762522e-01],\n",
              "       [-4.59983499e-01, -9.72487602e-03, -4.24867774e-01,\n",
              "        -5.00277676e-01, -6.37761306e-01],\n",
              "       [-1.49394798e+00, -5.01168411e-02, -1.35699561e+00,\n",
              "        -1.22752266e+00,  2.25082725e-01],\n",
              "       [-1.37814866e+00, -1.87924722e-01, -1.36766068e+00,\n",
              "        -1.12474996e+00,  3.12224258e-01],\n",
              "       [-2.01345427e-01,  5.86650609e-01, -2.43134842e-01,\n",
              "        -2.67607502e-01, -1.06489767e+00],\n",
              "       [ 1.82661219e+00,  5.70018623e-01,  1.87793590e+00,\n",
              "         1.84511001e+00,  1.46220678e+00],\n",
              "       [ 2.18941441e-01,  2.61138890e-01,  1.80908666e-01,\n",
              "         8.77268693e-02, -4.70559971e-02],\n",
              "       [ 1.32990953e+00, -3.58996574e-01,  1.35748009e+00,\n",
              "         1.27850389e+00,  9.55071632e-01],\n",
              "       [ 3.41077038e+00, -3.92260545e-01,  3.51182588e+00,\n",
              "         4.09345133e+00,  1.31220906e+00],\n",
              "       [ 9.24318002e-01,  1.49903382e+00,  8.75418556e-01,\n",
              "         8.34863347e-01, -9.44185222e-01],\n",
              "       [ 8.66833357e-02,  1.56594980e-01,  1.10945753e-01,\n",
              "        -2.80054451e-02,  7.07993549e-02],\n",
              "       [ 8.59658484e-01, -7.34887807e-03,  9.18078869e-01,\n",
              "         7.32994800e-01,  1.25506707e+00],\n",
              "       [-9.00843850e-01, -4.73044476e-01, -8.80053310e-01,\n",
              "        -8.59831455e-01,  1.63363275e+00],\n",
              "       [-1.40842107e+00,  6.72186535e-01, -1.40221554e+00,\n",
              "        -1.16875235e+00, -2.94909374e-01],\n",
              "       [ 1.20352956e+00, -9.76368001e-02,  1.21243503e+00,\n",
              "         1.14890780e+00,  1.30506631e+00],\n",
              "       [-5.18764879e-01, -1.35652767e-01, -5.49009283e-01,\n",
              "        -5.57842447e-01, -8.39901092e-01],\n",
              "       [ 2.16002372e-01, -5.15812439e-01,  1.28436482e-01,\n",
              "         6.60270604e-02, -5.20620229e-01],\n",
              "       [ 2.22926464e+00, -4.42156502e-01,  2.09123747e+00,\n",
              "         2.69501919e+00, -1.92053794e-01],\n",
              "       [-6.24571364e-01,  2.16668924e+00, -6.44568384e-01,\n",
              "        -6.30777916e-01, -8.68472086e-01],\n",
              "       [-1.13173357e-01, -2.85340638e-01, -1.60373835e-01,\n",
              "        -2.01302530e-01, -1.22060959e+00],\n",
              "       [-1.33964685e+00, -7.60540227e-01, -1.35102316e+00,\n",
              "        -1.12173609e+00, -8.49186665e-01],\n",
              "       [ 1.55033970e+00, -2.21188693e-01,  1.50679118e+00,\n",
              "         1.48646039e+00,  5.12221218e-01],\n",
              "       [-6.68657399e-01, -3.92260545e-01, -6.97040568e-01,\n",
              "        -6.60313767e-01, -9.14899952e-01],\n",
              "       [-4.39410016e-01,  3.91818777e-01, -4.15055902e-01,\n",
              "        -5.16552533e-01,  2.00083105e-01],\n",
              "       [ 3.21808856e-01,  2.73455275e+00,  4.91475742e-01,\n",
              "         1.96225914e-01,  5.90791453e-01],\n",
              "       [ 1.27112815e+00,  6.65058541e-01,  1.20816899e+00,\n",
              "         1.27549002e+00, -1.59197150e-01],\n",
              "       [-6.09876019e-01,  8.57514375e-01, -5.60100965e-01,\n",
              "        -6.14804446e-01, -1.94131293e+00],\n",
              "       [ 3.90747304e+00,  1.70099365e+00,  4.05361185e+00,\n",
              "         5.57325774e+00,  8.47930403e-01],\n",
              "       [ 1.42525647e-01, -8.12812182e-01,  3.41571905e-02,\n",
              "         3.95050717e-02, -1.45346320e+00],\n",
              "       [-4.89374189e-01, -1.49234759e+00, -5.56688140e-01,\n",
              "        -5.25895506e-01, -1.62917481e+00],\n",
              "       [-6.06936950e-01, -5.01168411e-02, -6.49261018e-01,\n",
              "        -6.22339101e-01, -9.04900104e-01],\n",
              "       [-1.36685909e-01,  4.53111166e-03, -1.03209016e-01,\n",
              "        -2.30536995e-01, -4.25621673e-01],\n",
              "       [-1.48442185e-01,  9.78690270e-01, -2.00047926e-01,\n",
              "        -2.36866106e-01, -2.90623724e-01],\n",
              "       [ 1.99707819e+00,  1.42062589e+00,  2.18082412e+00,\n",
              "         2.09526058e+00,  9.55071632e-01],\n",
              "       [ 3.92346512e-01,  1.14976212e+00,  5.08539867e-01,\n",
              "         2.39625532e-01,  1.55506251e+00],\n",
              "       [ 4.01163719e-01,  2.04114939e-01,  4.48815430e-01,\n",
              "         2.80011288e-01,  1.39077930e+00],\n",
              "       [-7.09804365e-01, -4.56412490e-01, -7.33728437e-01,\n",
              "        -6.97685660e-01,  8.65134018e-02],\n",
              "       [ 5.71629722e-01,  1.28083005e-01,  5.21337961e-01,\n",
              "         5.00023239e-01, -3.46337164e-01],\n",
              "       [ 7.27400379e-01,  2.13104927e+00,  7.00511274e-01,\n",
              "         6.21783279e-01, -8.55615139e-01],\n",
              "       [-8.96608046e-02,  1.27806601e+00,  1.92260811e-02,\n",
              "        -1.68451431e-01,  1.40506479e+00],\n",
              "       [-1.36962536e+00,  4.51218726e-01, -1.35528919e+00,\n",
              "        -1.15247749e+00,  5.62220459e-01],\n",
              "       [-5.30521156e-01, -1.59689150e+00, -5.55834933e-01,\n",
              "        -5.65678489e-01,  4.47936481e-01],\n",
              "       [-6.24571364e-01, -8.22316174e-01, -6.36889528e-01,\n",
              "        -6.28366826e-01,  5.00853839e-02],\n",
              "       [ 3.96582316e-02, -2.23564691e-01, -2.81268659e-02,\n",
              "        -5.63357512e-02, -2.19773760e+00],\n",
              "       [-7.21560641e-01,  2.15994929e-01, -7.11118472e-01,\n",
              "        -7.10042496e-01,  3.62223498e-01],\n",
              "       [-1.00371127e+00,  3.03906853e-01, -9.54708857e-01,\n",
              "        -9.24930882e-01,  8.33644906e-01],\n",
              "       [-6.95109020e-01, -2.30692685e-01, -7.20077137e-01,\n",
              "        -6.65135947e-01,  2.25082725e-01],\n",
              "       [ 1.77958708e+00,  1.01947027e-01,  1.78834925e+00,\n",
              "         1.80291594e+00,  1.19792508e+00],\n",
              "       [-1.04485823e+00, -1.87924722e-01, -1.06903849e+00,\n",
              "        -9.36986331e-01, -1.07918317e+00],\n",
              "       [ 1.48274111e+00,  1.35172195e+00,  1.72862481e+00,\n",
              "         1.42015542e+00,  6.22280565e-02],\n",
              "       [-7.00987158e-01, -1.05041198e+00, -6.64192128e-01,\n",
              "        -6.80506645e-01, -1.10989699e+00],\n",
              "       [-2.68944014e-01,  1.45864186e+00, -3.31868292e-01,\n",
              "        -3.43255447e-01, -6.15618786e-01],\n",
              "       [-4.42349085e-01,  1.15451412e+00, -4.49184152e-01,\n",
              "        -4.68330735e-01, -1.24989486e+00],\n",
              "       [-5.77546260e-01, -2.56828662e-01, -5.79724709e-01,\n",
              "        -5.92200478e-01, -4.04193427e-01],\n",
              "       [-8.94965712e-01, -6.67488267e-02, -7.92599669e-01,\n",
              "        -8.74599381e-01,  1.79077322e+00],\n",
              "       [-7.00987158e-01, -1.20960384e+00, -7.52498975e-01,\n",
              "        -6.76588624e-01, -1.31417960e+00],\n",
              "       [ 1.66496339e+00,  1.42775388e+00,  1.64330418e+00,\n",
              "         1.62811192e+00,  3.55080749e-01],\n",
              "       [ 6.27472033e-01,  6.57930547e-01,  6.66383024e-01,\n",
              "         5.27449387e-01,  1.42649304e+00],\n",
              "       [ 2.67012499e+00,  1.86731350e+00,  2.62875741e+00,\n",
              "         3.06873812e+00, -1.02769436e-01],\n",
              "       [-1.42564047e-01, -6.58372316e-01, -1.99194720e-01,\n",
              "        -2.41085514e-01, -1.40560678e+00],\n",
              "       [ 4.77579513e-01,  2.70642882e-01,  4.57347492e-01,\n",
              "         3.30041403e-01,  4.26508235e-01],\n",
              "       [ 5.98081343e-01,  5.77146617e-01,  6.10924618e-01,\n",
              "         4.76213727e-01,  3.05081509e-01],\n",
              "       [-1.21990564e-01,  4.08450763e-01, -7.16403848e-02,\n",
              "        -2.22700953e-01,  1.59791901e+00],\n",
              "       [-5.51094639e-01, -2.78212644e-01, -5.80577915e-01,\n",
              "        -5.77131166e-01, -7.13474442e-01],\n",
              "       [ 1.49449739e+00, -1.28524773e-01,  1.43426865e+00,\n",
              "         1.50755742e+00,  6.26505196e-01],\n",
              "       [-1.73583335e+00,  3.77562789e-01, -1.64708573e+00,\n",
              "        -1.34988547e+00, -1.77054022e-01],\n",
              "       [-9.25998736e-02,  1.10224216e+00, -1.36223596e-02,\n",
              "        -1.62725092e-01,  2.55504732e+00],\n",
              "       [ 5.89264136e-01, -2.92468632e-01,  6.45052868e-01,\n",
              "         4.68377685e-01,  5.33649464e-01],\n",
              "       [ 4.04102788e-01,  4.67850711e-01,  4.70145586e-01,\n",
              "         4.56020849e-01,  1.10506935e+00],\n",
              "       [ 1.45335042e+00,  1.35409795e+00,  1.55371753e+00,\n",
              "         1.36289203e+00, -4.08479076e-01],\n",
              "       [ 1.19013095e-01, -1.21910783e+00,  8.44963595e-02,\n",
              "        -2.28818791e-02,  9.55071632e-01],\n",
              "       [ 2.68905614e-01, -5.63332398e-01,  2.92252082e-01,\n",
              "         1.13344699e-01,  1.64369362e-01],\n",
              "       [ 4.51127892e-01,  9.71562276e-01,  7.81565868e-01,\n",
              "         3.66810523e-01,  1.02649912e+00],\n",
              "       [ 2.31743671e+00,  6.62682543e-01,  2.35999744e+00,\n",
              "         2.50213200e+00,  6.97932682e-01],\n",
              "       [-7.59768538e-01, -1.99368316e+00, -7.74255734e-01,\n",
              "        -7.31440919e-01, -7.54188109e-01],\n",
              "       [-1.07130985e+00, -9.81508036e-01, -1.04045609e+00,\n",
              "        -9.58987526e-01,  1.16512946e-01],\n",
              "       [-1.28938877e+00, -1.69668342e+00, -1.30708304e+00,\n",
              "        -1.09491272e+00, -2.87766625e-01],\n",
              "       [-8.39123401e-01, -8.57956143e-01, -7.89186844e-01,\n",
              "        -7.82977965e-01, -9.19899876e-01],\n",
              "       [-1.52098741e+00, -7.96180196e-01, -1.41373382e+00,\n",
              "        -1.22541296e+00, -1.36340355e-01],\n",
              "       [ 1.48568018e+00,  8.02866422e-01,  1.51958928e+00,\n",
              "         1.49550198e+00, -8.07044448e-01],\n",
              "       [-5.51094639e-01,  7.90986432e-01, -5.84417343e-01,\n",
              "        -5.58445220e-01, -1.04346943e+00],\n",
              "       [-6.95109020e-01,  5.91402605e-01, -7.32022025e-01,\n",
              "        -6.74780306e-01, -7.25617114e-01],\n",
              "       [ 9.55005427e-02,  2.63514888e-01,  1.11372356e-01,\n",
              "        -2.74026726e-02,  1.56512338e-01],\n",
              "       [ 1.78546522e+00,  1.83404953e+00,  1.78408322e+00,\n",
              "         1.75469414e+00,  1.80083408e-01],\n",
              "       [-2.18979841e-01,  2.75831273e+00, -2.36309192e-01,\n",
              "        -2.85087904e-01, -2.91337999e-01],\n",
              "       [ 1.60618201e+00, -2.28316687e-01,  1.65610228e+00,\n",
              "         1.69441689e+00,  1.10506935e+00],\n",
              "       [-3.48298877e-01, -6.98764281e-01, -3.71115780e-01,\n",
              "        -4.34575477e-01,  1.61512262e-01],\n",
              "       [ 2.95357235e-01,  2.54209692e+00,  2.06078251e-01,\n",
              "         2.04061956e-01, -9.52042245e-01],\n",
              "       [-8.18549918e-01, -1.19020782e-01, -8.15636238e-01,\n",
              "        -7.85389055e-01,  5.97934202e-01],\n",
              "       [ 1.22998118e+00, -1.26148775e-01,  1.13991249e+00,\n",
              "         1.17301870e+00, -7.60616582e-01],\n",
              "       [-7.95037366e-01,  3.01530855e-01, -6.10013531e-01,\n",
              "        -8.00759753e-01,  3.28360768e+00],\n",
              "       [ 9.47830554e-01,  9.38298305e-01,  8.15694118e-01,\n",
              "         8.47822955e-01, -7.07760243e-01],\n",
              "       [-2.71883083e-01, -7.81924209e-01, -3.33148101e-01,\n",
              "        -3.45063765e-01, -8.15615747e-01],\n",
              "       [-8.12671780e-01, -3.66124568e-01, -8.27154522e-01,\n",
              "        -7.59469839e-01, -5.67762370e-01],\n",
              "       [ 1.16238259e+00,  6.48426555e-01,  1.08872012e+00,\n",
              "         1.12178304e+00, -1.62988909e+00],\n",
              "       [-1.04485823e+00,  2.63514888e-01, -9.26979654e-01,\n",
              "        -9.44822373e-01, -4.14193275e-01],\n",
              "       [ 1.55033970e+00,  1.04284221e+00,  1.58784578e+00,\n",
              "         1.51659901e+00,  4.40793732e-01],\n",
              "       [-1.06249265e+00,  9.45426299e-01, -1.06775869e+00,\n",
              "        -9.57179209e-01,  3.55080749e-01],\n",
              "       [-4.01202119e-01, -1.36404371e+00, -4.09083458e-01,\n",
              "        -4.45123995e-01, -6.13475961e-01],\n",
              "       [-3.36542601e-01,  5.68030665e-02, -3.13950961e-01,\n",
              "        -4.15286758e-01, -5.27701960e-02],\n",
              "       [ 1.20352956e+00, -5.72448350e-02,  1.14417853e+00,\n",
              "         1.13082463e+00,  6.65137057e-02],\n",
              "       [-1.32818448e+00, -5.39572418e-01, -1.29001891e+00,\n",
              "        -1.11902362e+00, -8.37043992e-01],\n",
              "       [ 1.04481983e+00,  3.87066781e-01,  1.08872012e+00,\n",
              "         9.40047143e-01,  1.06935561e+00],\n",
              "       [-1.53920964e+00, -1.06229197e+00, -1.41160081e+00,\n",
              "        -1.22902959e+00,  9.29418755e-02],\n",
              "       [ 1.66790246e+00,  5.81898613e-01,  1.61344197e+00,\n",
              "         1.74263869e+00, -1.92768069e-01],\n",
              "       [-7.09804365e-01, -8.69836133e-01, -7.18797328e-01,\n",
              "        -6.97685660e-01,  1.31935181e+00],\n",
              "       [-3.62994222e-01, -1.18821986e+00, -2.95607026e-01,\n",
              "        -4.20711710e-01, -6.37761306e-01],\n",
              "       [ 4.11026880e+00, -1.52284753e-01,  4.12186835e+00,\n",
              "         5.56723002e+00,  1.26220982e+00],\n",
              "       [-7.68585745e-01,  1.14501013e+00, -7.40554087e-01,\n",
              "        -7.48318548e-01, -2.79909602e-01],\n",
              "       [-1.24060023e+00, -2.49700669e-01, -1.16374439e+00,\n",
              "        -1.05302003e+00,  3.29427873e-02],\n",
              "       [-7.39195055e-01, -7.34404250e-01, -7.00453393e-01,\n",
              "        -7.34153395e-01,  8.76501397e-01],\n",
              "       [-1.02134568e+00,  1.45151386e+00, -1.01827272e+00,\n",
              "        -9.18601771e-01,  2.94324314e-03],\n",
              "       [-8.53818746e-01,  3.49249610e+00, -8.99677053e-01,\n",
              "        -7.98650049e-01, -1.33775067e+00],\n",
              "       [ 1.14768725e+00, -5.37196420e-01,  1.09298615e+00,\n",
              "         1.01931172e+00, -5.04191908e-01],\n",
              "       [-5.09947672e-01, -3.87508550e-01, -4.79472974e-01,\n",
              "        -5.69295124e-01,  5.47934962e-01],\n",
              "       [-8.08435976e-02, -9.33988077e-01, -1.23259363e-01,\n",
              "        -1.94069261e-01, -9.77695122e-02],\n",
              "       [-2.83639359e-01,  3.39546822e-01, -3.33574705e-01,\n",
              "        -3.40844358e-01, -1.65560298e+00],\n",
              "       [-5.71668122e-01, -1.19534785e+00, -6.24091434e-01,\n",
              "        -5.73815918e-01, -1.37060731e+00],\n",
              "       [-1.21990564e-01, -1.02388796e-01, -1.34351044e-01,\n",
              "        -2.43496603e-01,  1.87940432e-01],\n",
              "       [ 1.45464716e-01,  1.16639411e+00,  1.14785181e-01,\n",
              "         3.28745745e-02, -4.84906486e-01],\n",
              "       [-7.36255986e-01, -2.21188693e-01, -6.61205906e-01,\n",
              "        -7.32345077e-01,  1.49792053e+00],\n",
              "       [ 1.91478426e+00, -4.18396523e-01,  1.83100956e+00,\n",
              "         2.05909424e+00, -1.47768752e-01],\n",
              "       [-8.09732711e-01, -5.24928391e-02, -8.40805822e-01,\n",
              "        -7.70922516e-01, -6.71332225e-01],\n",
              "       [-2.98334704e-01,  8.14746412e-01, -2.07300179e-01,\n",
              "        -3.68873277e-01,  2.62225017e-01],\n",
              "       [-1.42752502e+00, -1.48284360e+00, -1.29641796e+00,\n",
              "        -1.21486444e+00,  4.77644213e+00],\n",
              "       [ 1.90008891e+00,  5.05866678e-01,  1.95899050e+00,\n",
              "         2.01388630e+00,  8.55073151e-01],\n",
              "       [-9.77259644e-01,  6.81690527e-01, -9.84997679e-01,\n",
              "        -8.79722947e-01, -6.08476037e-01],\n",
              "       [-3.86506774e-01, -1.43769964e+00, -4.51743771e-01,\n",
              "        -4.30958842e-01, -6.52761079e-01],\n",
              "       [-8.42062470e-01,  2.01738941e-01, -7.74682337e-01,\n",
              "        -7.76648854e-01, -1.16632470e+00],\n",
              "       [-2.48370531e-01,  2.77770876e-01, -1.94502085e-01,\n",
              "        -3.21254252e-01,  5.40792213e-01],\n",
              "       [-3.57116084e-01, -6.60748313e-01, -3.98844983e-01,\n",
              "        -4.08053488e-01, -1.22275242e+00],\n",
              "       [-3.30664463e-01,  1.43012988e+00, -3.95432158e-01,\n",
              "        -3.96600811e-01, -9.17042777e-01],\n",
              "       [-6.15754157e-01, -2.80588642e-01, -6.73577396e-01,\n",
              "        -6.20229398e-01, -1.40703533e+00],\n",
              "       [ 1.58854760e+00,  3.17411237e+00,  1.54091943e+00,\n",
              "         1.72154165e+00, -8.80614759e-01],\n",
              "       [-1.11833496e+00, -6.55996318e-01, -1.13345557e+00,\n",
              "        -9.85208129e-01, -1.56340051e-01],\n",
              "       [-1.86650082e-01,  7.57722461e-01, -2.11992813e-01,\n",
              "        -2.73635227e-01, -6.43475505e-01],\n",
              "       [ 1.51342854e-01, -9.19732090e-01,  1.65977557e-01,\n",
              "         3.24861043e-04,  1.19078234e+00],\n",
              "       [ 1.33708440e-01, -1.29513977e+00,  1.02840294e-01,\n",
              "         2.11205113e-02,  1.27941344e-01],\n",
              "       [-6.53962054e-01, -1.05991597e+00, -5.87403565e-01,\n",
              "        -6.60012381e-01,  1.33363731e+00],\n",
              "       [-1.25735292e+00, -8.15188180e-01, -1.23200089e+00,\n",
              "        -1.08074757e+00, -1.14912109e-01],\n",
              "       [-1.13596937e+00, -1.04565998e+00, -1.09548789e+00,\n",
              "        -9.95153875e-01,  1.63655087e-01],\n",
              "       [-1.13596937e+00, -1.63728347e+00, -1.11468503e+00,\n",
              "        -9.95153875e-01,  2.45082421e-01],\n",
              "       [ 1.51213180e+00,  1.74851360e+00,  1.53665340e+00,\n",
              "         1.53769605e+00, -1.79911121e-01],\n",
              "       [ 1.75313546e+00,  1.12837814e+00,  1.82674353e+00,\n",
              "         1.79387435e+00,  8.19359408e-01],\n",
              "       [ 1.98367958e-01, -3.44740587e-01,  1.70670191e-01,\n",
              "         6.93423090e-02, -3.38480140e-01],\n",
              "       [-7.42134124e-01, -1.48997160e+00, -7.48232944e-01,\n",
              "        -7.20892400e-01, -4.78478013e-01],\n",
              "       [ 1.19013095e-01,  5.44270685e-02,  1.01133881e-01,\n",
              "         2.38329874e-02, -9.03471554e-01],\n",
              "       [-9.06721988e-01, -9.93388026e-01, -9.04796291e-01,\n",
              "        -8.41145508e-01, -1.18989577e+00],\n",
              "       [-1.30996226e+00, -2.40196677e-01, -1.31476190e+00,\n",
              "        -1.10154322e+00, -9.58470719e-01]])"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import confusion_matrix,classification_report,accuracy_score\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "\n",
        "forest=RandomForestClassifier()\n",
        "forest.fit(x_train,y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jjQ12J1o0tWr",
        "outputId": "0fb66def-614a-4370-9e2b-1d5b79bc6e97"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier()"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "prediction_rf=forest.predict(x_test)\n",
        "prediction_rf"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7MIcHV9402ik",
        "outputId": "a81c1cfa-6aed-483f-e4b5-8f17fbaa62eb"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1,\n",
              "       1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0,\n",
              "       1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 1,\n",
              "       1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1,\n",
              "       0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0,\n",
              "       1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 0, 1, 1,\n",
              "       1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1,\n",
              "       1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1])"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "accuracy_score(prediction_rf,y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "__UCGcIX09Wr",
        "outputId": "68aab13f-89d4-4d71-8229-a3227ca0c0df"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9298245614035088"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Saving Trained Model"
      ],
      "metadata": {
        "id": "sw4Ppdhj1EAE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# saving model\n",
        "import pickle\n",
        "\n",
        "with open(\"model4\",\"wb\") as f:\n",
        "    pickle.dump(forest,f)"
      ],
      "metadata": {
        "id": "FaoH2Gym1GI4"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# loading saved model\n",
        "with open(\"model4\",\"rb\") as f:\n",
        "    m4=pickle.load(f)"
      ],
      "metadata": {
        "id": "ACFwffo61NAI"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "m4.predict(x_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d-0XCl4q1WuZ",
        "outputId": "d7d536a5-7747-4781-c275-0747c7a03436"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1,\n",
              "       1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0,\n",
              "       1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 1,\n",
              "       1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1,\n",
              "       0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0,\n",
              "       1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 0, 1, 1,\n",
              "       1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1,\n",
              "       1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1])"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    }
  ]
}