데이터분석_기말_프로젝트.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "mount_file_id": "1c6HNjgmAiz_YkFJX6EVis5zFsvQec-qZ",
      "authorship_tag": "ABX9TyPREMGeqgFgmeMnv2QYakqS",
      "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/ChaewonYoon13/DataAnalysis/blob/main/%EB%8D%B0%EC%9D%B4%ED%84%B0%EB%B6%84%EC%84%9D_%EA%B8%B0%EB%A7%90_%ED%94%84%EB%A1%9C%EC%A0%9D%ED%8A%B8.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "당뇨병의 여부를 정확히 예측하는 모델을 만드는 것을 목표로 한다.knn를이용하여 예측해본다. 가장 정확도가 높은 k를 찾아보자"
      ],
      "metadata": {
        "id": "0oJCMBkha3Gy"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 47,
      "metadata": {
        "id": "GoAZcmJQry-S",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "outputId": "06d2a801-1bb0-4df0-8360-09dc7bf82466"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
              "0              6      148             72             35        0  33.6   \n",
              "1              1       85             66             29        0  26.6   \n",
              "2              8      183             64              0        0  23.3   \n",
              "3              1       89             66             23       94  28.1   \n",
              "4              0      137             40             35      168  43.1   \n",
              "..           ...      ...            ...            ...      ...   ...   \n",
              "763           10      101             76             48      180  32.9   \n",
              "764            2      122             70             27        0  36.8   \n",
              "765            5      121             72             23      112  26.2   \n",
              "766            1      126             60              0        0  30.1   \n",
              "767            1       93             70             31        0  30.4   \n",
              "\n",
              "     DiabetesPedigreeFunction  Age  Outcome  \n",
              "0                       0.627   50        1  \n",
              "1                       0.351   31        0  \n",
              "2                       0.672   32        1  \n",
              "3                       0.167   21        0  \n",
              "4                       2.288   33        1  \n",
              "..                        ...  ...      ...  \n",
              "763                     0.171   63        0  \n",
              "764                     0.340   27        0  \n",
              "765                     0.245   30        0  \n",
              "766                     0.349   47        1  \n",
              "767                     0.315   23        0  \n",
              "\n",
              "[768 rows x 9 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-01118909-957c-4125-855c-eaace4573953\" 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>Pregnancies</th>\n",
              "      <th>Glucose</th>\n",
              "      <th>BloodPressure</th>\n",
              "      <th>SkinThickness</th>\n",
              "      <th>Insulin</th>\n",
              "      <th>BMI</th>\n",
              "      <th>DiabetesPedigreeFunction</th>\n",
              "      <th>Age</th>\n",
              "      <th>Outcome</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>6</td>\n",
              "      <td>148</td>\n",
              "      <td>72</td>\n",
              "      <td>35</td>\n",
              "      <td>0</td>\n",
              "      <td>33.6</td>\n",
              "      <td>0.627</td>\n",
              "      <td>50</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>85</td>\n",
              "      <td>66</td>\n",
              "      <td>29</td>\n",
              "      <td>0</td>\n",
              "      <td>26.6</td>\n",
              "      <td>0.351</td>\n",
              "      <td>31</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>8</td>\n",
              "      <td>183</td>\n",
              "      <td>64</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>23.3</td>\n",
              "      <td>0.672</td>\n",
              "      <td>32</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>89</td>\n",
              "      <td>66</td>\n",
              "      <td>23</td>\n",
              "      <td>94</td>\n",
              "      <td>28.1</td>\n",
              "      <td>0.167</td>\n",
              "      <td>21</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>137</td>\n",
              "      <td>40</td>\n",
              "      <td>35</td>\n",
              "      <td>168</td>\n",
              "      <td>43.1</td>\n",
              "      <td>2.288</td>\n",
              "      <td>33</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>763</th>\n",
              "      <td>10</td>\n",
              "      <td>101</td>\n",
              "      <td>76</td>\n",
              "      <td>48</td>\n",
              "      <td>180</td>\n",
              "      <td>32.9</td>\n",
              "      <td>0.171</td>\n",
              "      <td>63</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>764</th>\n",
              "      <td>2</td>\n",
              "      <td>122</td>\n",
              "      <td>70</td>\n",
              "      <td>27</td>\n",
              "      <td>0</td>\n",
              "      <td>36.8</td>\n",
              "      <td>0.340</td>\n",
              "      <td>27</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>765</th>\n",
              "      <td>5</td>\n",
              "      <td>121</td>\n",
              "      <td>72</td>\n",
              "      <td>23</td>\n",
              "      <td>112</td>\n",
              "      <td>26.2</td>\n",
              "      <td>0.245</td>\n",
              "      <td>30</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>766</th>\n",
              "      <td>1</td>\n",
              "      <td>126</td>\n",
              "      <td>60</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>30.1</td>\n",
              "      <td>0.349</td>\n",
              "      <td>47</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>767</th>\n",
              "      <td>1</td>\n",
              "      <td>93</td>\n",
              "      <td>70</td>\n",
              "      <td>31</td>\n",
              "      <td>0</td>\n",
              "      <td>30.4</td>\n",
              "      <td>0.315</td>\n",
              "      <td>23</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>768 rows × 9 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-01118909-957c-4125-855c-eaace4573953')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-01118909-957c-4125-855c-eaace4573953 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-01118909-957c-4125-855c-eaace4573953');\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",
              "\n",
              "\n",
              "<div id=\"df-0525dfb6-1b31-414c-9c19-3e41523308c3\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-0525dfb6-1b31-414c-9c19-3e41523308c3')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-0525dfb6-1b31-414c-9c19-3e41523308c3 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_a0458934-ffea-491e-86cd-24947c81f87d\">\n",
              "    <style>\n",
              "      .colab-df-generate {\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-generate: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-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate: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",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_a0458934-ffea-491e-86cd-24947c81f87d button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ],
      "source": [
        "#데이터 불러오기\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "file_path = \"/content/diabetes.csv\"\n",
        "df = pd.read_csv(file_path)\n",
        "df\n",
        "#인도여성의 당뇨병 여부를 예측하기 위한 모델 만들기를 위한 데이터 파일"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#데이터 전처리-이상 데이터 제거\n",
        "#포도당 농도 0인거 삭제\n",
        "df = df[df.Glucose != 0]\n",
        "#혈압 0 삭제\n",
        "df = df[df.BloodPressure != 0]\n",
        "#당뇨병이란 인슐린 분비량 부족 이기 때문에 0이어도 삭제 하는 것은 유보\n",
        "\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "7EYXqP6yCnsW",
        "outputId": "23e25405-d298-4e3b-f8c3-979089d80dd9"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
              "0              6      148             72             35        0  33.6   \n",
              "1              1       85             66             29        0  26.6   \n",
              "2              8      183             64              0        0  23.3   \n",
              "3              1       89             66             23       94  28.1   \n",
              "4              0      137             40             35      168  43.1   \n",
              "..           ...      ...            ...            ...      ...   ...   \n",
              "763           10      101             76             48      180  32.9   \n",
              "764            2      122             70             27        0  36.8   \n",
              "765            5      121             72             23      112  26.2   \n",
              "766            1      126             60              0        0  30.1   \n",
              "767            1       93             70             31        0  30.4   \n",
              "\n",
              "     DiabetesPedigreeFunction  Age  Outcome  \n",
              "0                       0.627   50        1  \n",
              "1                       0.351   31        0  \n",
              "2                       0.672   32        1  \n",
              "3                       0.167   21        0  \n",
              "4                       2.288   33        1  \n",
              "..                        ...  ...      ...  \n",
              "763                     0.171   63        0  \n",
              "764                     0.340   27        0  \n",
              "765                     0.245   30        0  \n",
              "766                     0.349   47        1  \n",
              "767                     0.315   23        0  \n",
              "\n",
              "[728 rows x 9 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-b05aead3-2ab2-473b-abb1-f2855f467bb8\" 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>Pregnancies</th>\n",
              "      <th>Glucose</th>\n",
              "      <th>BloodPressure</th>\n",
              "      <th>SkinThickness</th>\n",
              "      <th>Insulin</th>\n",
              "      <th>BMI</th>\n",
              "      <th>DiabetesPedigreeFunction</th>\n",
              "      <th>Age</th>\n",
              "      <th>Outcome</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>6</td>\n",
              "      <td>148</td>\n",
              "      <td>72</td>\n",
              "      <td>35</td>\n",
              "      <td>0</td>\n",
              "      <td>33.6</td>\n",
              "      <td>0.627</td>\n",
              "      <td>50</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>85</td>\n",
              "      <td>66</td>\n",
              "      <td>29</td>\n",
              "      <td>0</td>\n",
              "      <td>26.6</td>\n",
              "      <td>0.351</td>\n",
              "      <td>31</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>8</td>\n",
              "      <td>183</td>\n",
              "      <td>64</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>23.3</td>\n",
              "      <td>0.672</td>\n",
              "      <td>32</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>89</td>\n",
              "      <td>66</td>\n",
              "      <td>23</td>\n",
              "      <td>94</td>\n",
              "      <td>28.1</td>\n",
              "      <td>0.167</td>\n",
              "      <td>21</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>137</td>\n",
              "      <td>40</td>\n",
              "      <td>35</td>\n",
              "      <td>168</td>\n",
              "      <td>43.1</td>\n",
              "      <td>2.288</td>\n",
              "      <td>33</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>763</th>\n",
              "      <td>10</td>\n",
              "      <td>101</td>\n",
              "      <td>76</td>\n",
              "      <td>48</td>\n",
              "      <td>180</td>\n",
              "      <td>32.9</td>\n",
              "      <td>0.171</td>\n",
              "      <td>63</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>764</th>\n",
              "      <td>2</td>\n",
              "      <td>122</td>\n",
              "      <td>70</td>\n",
              "      <td>27</td>\n",
              "      <td>0</td>\n",
              "      <td>36.8</td>\n",
              "      <td>0.340</td>\n",
              "      <td>27</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>765</th>\n",
              "      <td>5</td>\n",
              "      <td>121</td>\n",
              "      <td>72</td>\n",
              "      <td>23</td>\n",
              "      <td>112</td>\n",
              "      <td>26.2</td>\n",
              "      <td>0.245</td>\n",
              "      <td>30</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>766</th>\n",
              "      <td>1</td>\n",
              "      <td>126</td>\n",
              "      <td>60</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>30.1</td>\n",
              "      <td>0.349</td>\n",
              "      <td>47</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>767</th>\n",
              "      <td>1</td>\n",
              "      <td>93</td>\n",
              "      <td>70</td>\n",
              "      <td>31</td>\n",
              "      <td>0</td>\n",
              "      <td>30.4</td>\n",
              "      <td>0.315</td>\n",
              "      <td>23</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>728 rows × 9 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b05aead3-2ab2-473b-abb1-f2855f467bb8')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-b05aead3-2ab2-473b-abb1-f2855f467bb8 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-b05aead3-2ab2-473b-abb1-f2855f467bb8');\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",
              "\n",
              "\n",
              "<div id=\"df-99098b59-52bb-41dd-8751-29c8670cc6d1\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-99098b59-52bb-41dd-8751-29c8670cc6d1')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-99098b59-52bb-41dd-8751-29c8670cc6d1 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_7b37ca11-4f06-4b56-aecf-6d4e3fe87711\">\n",
              "    <style>\n",
              "      .colab-df-generate {\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-generate: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-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate: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",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_7b37ca11-4f06-4b56-aecf-6d4e3fe87711 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#knn 지도 학습 모델\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "\n",
        "#학습데이터 테스트 데이터 분리\n",
        "y = df.Outcome\n",
        "x = df.drop(['Outcome'], axis=1)\n",
        "#randomsedd 0\n",
        "train_input, test_input, train_label, test_label = train_test_split(x,y, test_size = 0.3,random_state=0)\n",
        "\n",
        ""
      ],
      "metadata": {
        "id": "pqaba8u3JsN4"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#knn 모델 생성 k=1\n",
        "knn1 = KNeighborsClassifier(n_neighbors = 1)\n",
        "knn1.fit(train_input, train_label)\n",
        "predict_label = knn1.predict(test_input)\n",
        "\n",
        "print('test accuracy {:.2f}'.format(np.mean(test_label == predict_label)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "r6NP6vqnThg_",
        "outputId": "99eb1517-efa7-4415-9a57-7da92e9b8977"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "test accuracy 0.66\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#knn 모델 생성 k=2\n",
        "knn2 = KNeighborsClassifier(n_neighbors = 2)\n",
        "knn2.fit(train_input, train_label)\n",
        "predict_label = knn2.predict(test_input)\n",
        "\n",
        "print('test accuracy {:.2f}'.format(np.mean(test_label == predict_label)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "11beec80-2661-40a3-8d84-68ff757c2a72",
        "id": "oAJECQR4TrbI"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "test accuracy 0.74\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#knn 모델 생성 k=3\n",
        "knn3 = KNeighborsClassifier(n_neighbors = 3)\n",
        "knn3.fit(train_input, train_label)\n",
        "predict_label = knn3.predict(test_input)\n",
        "\n",
        "print('test accuracy {:.2f}'.format(np.mean(test_label == predict_label)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6528a77c-e15a-4712-ffbc-9bcbae6d01ab",
        "id": "c-QRaV-8Tz-x"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "test accuracy 0.67\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#knn 모델 생성 k=4\n",
        "knn4 = KNeighborsClassifier(n_neighbors = 4)\n",
        "knn4.fit(train_input, train_label)\n",
        "predict_label = knn4.predict(test_input)\n",
        "\n",
        "print('test accuracy {:.2f}'.format(np.mean(test_label == predict_label)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ae5367a8-23af-4695-aa44-b87457b5f3fa",
        "id": "3OFKhGo8T90z"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "test accuracy 0.70\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#knn 모델 생성 k=5\n",
        "knn5 = KNeighborsClassifier(n_neighbors = 5)\n",
        "knn5.fit(train_input, train_label)\n",
        "predict_label = knn5.predict(test_input)\n",
        "\n",
        "print('test accuracy {:.2f}'.format(np.mean(test_label == predict_label)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "acf5248a-0f02-40f7-cc3d-1c2cec382e2a",
        "id": "W_V9D_OuUMx_"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "test accuracy 0.68\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "k=2, 3, 4 일때 가장 정확도가 높은 것 같다.\n",
        "가장 적합한 모델을 찾기 위해 데이터 값들을 바꿔서 다시 유사도 테스트를 해본다"
      ],
      "metadata": {
        "id": "LA5msMC4UWOQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#랜덤 시드를 바꾼다 randomsedd=2\n",
        "train_input, test_input, train_label, test_label = train_test_split(x,y, test_size = 0.3,random_state=2)"
      ],
      "metadata": {
        "id": "sxLv06f7UWtj"
      },
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#knn 모델 생성 k=2\n",
        "knn2 = KNeighborsClassifier(n_neighbors = 2)\n",
        "knn2.fit(train_input, train_label)\n",
        "predict_label = knn2.predict(test_input)\n",
        "\n",
        "print('test accuracy {:.2f}'.format(np.mean(test_label == predict_label)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0aeb2cc7-5c7e-4cf9-859e-e415f07cdbb5",
        "id": "FidC8vP3VQ-L"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "test accuracy 0.67\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#knn 모델 생성 k=3\n",
        "knn3 = KNeighborsClassifier(n_neighbors = 3)\n",
        "knn3.fit(train_input, train_label)\n",
        "predict_label = knn3.predict(test_input)\n",
        "\n",
        "print('test accuracy {:.2f}'.format(np.mean(test_label == predict_label)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f2ced37a-652e-4d3a-87b6-1fe028150b59",
        "id": "6DcNBXO9Vg_U"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "test accuracy 0.68\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#knn 모델 생성 k=4\n",
        "knn4 = KNeighborsClassifier(n_neighbors = 4)\n",
        "knn4.fit(train_input, train_label)\n",
        "predict_label = knn4.predict(test_input)\n",
        "\n",
        "print('test accuracy {:.2f}'.format(np.mean(test_label == predict_label)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a9516dc3-1b3f-4cb7-9bf4-c71eae5bbd57",
        "id": "Jz0-D7dnVlJv"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "test accuracy 0.68\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "k=1,2,3,4예측 유사도가 비슷한 것 같다. k의 값을 늘려보겠다"
      ],
      "metadata": {
        "id": "-rSyOgo_WX5z"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#knn 모델 생성 k=5\n",
        "knn5 = KNeighborsClassifier(n_neighbors = 5)\n",
        "knn5.fit(train_input, train_label)\n",
        "predict_label = knn5.predict(test_input)\n",
        "\n",
        "print('test accuracy {:.2f}'.format(np.mean(test_label == predict_label)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "cb6b1537-6143-4a65-c82d-12da7f14b8b2",
        "id": "l0TlPEY2WttC"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "test accuracy 0.70\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Ie1yeDHOW3IE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#knn 모델 생성 k=6\n",
        "knn6 = KNeighborsClassifier(n_neighbors = 6)\n",
        "knn6.fit(train_input, train_label)\n",
        "predict_label = knn6.predict(test_input)\n",
        "\n",
        "print('test accuracy {:.2f}'.format(np.mean(test_label == predict_label)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "07fa3ae0-1a07-4258-9f56-8e52c0c0eaa5",
        "id": "iXDMrk3qW3Wk"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "test accuracy 0.70\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#knn 모델 생성 k=7\n",
        "knn7 = KNeighborsClassifier(n_neighbors = 7)\n",
        "knn7.fit(train_input, train_label)\n",
        "predict_label = knn7.predict(test_input)\n",
        "\n",
        "print('test accuracy {:.2f}'.format(np.mean(test_label == predict_label)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "35ac8bb2-b7f2-4d1f-b255-9e58eaa6870e",
        "id": "jIIsNUvbW-sk"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "test accuracy 0.72\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#knn 모델 생성 k=8\n",
        "knn8 = KNeighborsClassifier(n_neighbors = 8)\n",
        "knn8.fit(train_input, train_label)\n",
        "predict_label = knn8.predict(test_input)\n",
        "\n",
        "print('test accuracy {:.2f}'.format(np.mean(test_label == predict_label)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e7483f00-a538-4825-dc50-e86d87e90cf9",
        "id": "M0XPgtrXXFbN"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "test accuracy 0.71\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#knn 모델 생성 k=9\n",
        "knn9 = KNeighborsClassifier(n_neighbors = 9)\n",
        "knn9.fit(train_input, train_label)\n",
        "predict_label = knn9.predict(test_input)\n",
        "\n",
        "print('test accuracy {:.2f}'.format(np.mean(test_label == predict_label)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7f0858e1-739a-4688-8468-5f015600b27d",
        "id": "QfRpuE97XL4s"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "test accuracy 0.72\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "k의 값을 1-200까지 늘려서 위 작업을 반복하여 유사도 값을 확인해 보겠다"
      ],
      "metadata": {
        "id": "UkXYG14_XT9H"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def knn_kValue_print_test_accuracy(k):\n",
        "  knn = KNeighborsClassifier(n_neighbors = k)\n",
        "  knn.fit(train_input, train_label)\n",
        "  predict_label = knn.predict(test_input)\n",
        "  print('k=', k, end=\" \")\n",
        "  print('test accuracy {:.2f}'.format(np.mean(test_label == predict_label)))\n",
        "\n",
        "for i in range(1,201):\n",
        "  knn_kValue_print_test_accuracy(i)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BHVM0PfkXBkq",
        "outputId": "c7a02af0-def7-451d-c479-00795471ac2b"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "k= 1 test accuracy 0.65\n",
            "k= 2 test accuracy 0.67\n",
            "k= 3 test accuracy 0.68\n",
            "k= 4 test accuracy 0.68\n",
            "k= 5 test accuracy 0.70\n",
            "k= 6 test accuracy 0.70\n",
            "k= 7 test accuracy 0.72\n",
            "k= 8 test accuracy 0.71\n",
            "k= 9 test accuracy 0.72\n",
            "k= 10 test accuracy 0.71\n",
            "k= 11 test accuracy 0.70\n",
            "k= 12 test accuracy 0.71\n",
            "k= 13 test accuracy 0.73\n",
            "k= 14 test accuracy 0.71\n",
            "k= 15 test accuracy 0.71\n",
            "k= 16 test accuracy 0.72\n",
            "k= 17 test accuracy 0.73\n",
            "k= 18 test accuracy 0.72\n",
            "k= 19 test accuracy 0.74\n",
            "k= 20 test accuracy 0.73\n",
            "k= 21 test accuracy 0.74\n",
            "k= 22 test accuracy 0.73\n",
            "k= 23 test accuracy 0.74\n",
            "k= 24 test accuracy 0.73\n",
            "k= 25 test accuracy 0.72\n",
            "k= 26 test accuracy 0.73\n",
            "k= 27 test accuracy 0.73\n",
            "k= 28 test accuracy 0.73\n",
            "k= 29 test accuracy 0.73\n",
            "k= 30 test accuracy 0.74\n",
            "k= 31 test accuracy 0.74\n",
            "k= 32 test accuracy 0.73\n",
            "k= 33 test accuracy 0.74\n",
            "k= 34 test accuracy 0.74\n",
            "k= 35 test accuracy 0.74\n",
            "k= 36 test accuracy 0.73\n",
            "k= 37 test accuracy 0.73\n",
            "k= 38 test accuracy 0.73\n",
            "k= 39 test accuracy 0.73\n",
            "k= 40 test accuracy 0.72\n",
            "k= 41 test accuracy 0.72\n",
            "k= 42 test accuracy 0.71\n",
            "k= 43 test accuracy 0.72\n",
            "k= 44 test accuracy 0.71\n",
            "k= 45 test accuracy 0.72\n",
            "k= 46 test accuracy 0.71\n",
            "k= 47 test accuracy 0.72\n",
            "k= 48 test accuracy 0.72\n",
            "k= 49 test accuracy 0.72\n",
            "k= 50 test accuracy 0.72\n",
            "k= 51 test accuracy 0.72\n",
            "k= 52 test accuracy 0.72\n",
            "k= 53 test accuracy 0.71\n",
            "k= 54 test accuracy 0.71\n",
            "k= 55 test accuracy 0.70\n",
            "k= 56 test accuracy 0.70\n",
            "k= 57 test accuracy 0.71\n",
            "k= 58 test accuracy 0.71\n",
            "k= 59 test accuracy 0.70\n",
            "k= 60 test accuracy 0.71\n",
            "k= 61 test accuracy 0.70\n",
            "k= 62 test accuracy 0.71\n",
            "k= 63 test accuracy 0.70\n",
            "k= 64 test accuracy 0.70\n",
            "k= 65 test accuracy 0.69\n",
            "k= 66 test accuracy 0.70\n",
            "k= 67 test accuracy 0.70\n",
            "k= 68 test accuracy 0.68\n",
            "k= 69 test accuracy 0.70\n",
            "k= 70 test accuracy 0.69\n",
            "k= 71 test accuracy 0.69\n",
            "k= 72 test accuracy 0.68\n",
            "k= 73 test accuracy 0.68\n",
            "k= 74 test accuracy 0.68\n",
            "k= 75 test accuracy 0.69\n",
            "k= 76 test accuracy 0.69\n",
            "k= 77 test accuracy 0.69\n",
            "k= 78 test accuracy 0.68\n",
            "k= 79 test accuracy 0.70\n",
            "k= 80 test accuracy 0.68\n",
            "k= 81 test accuracy 0.68\n",
            "k= 82 test accuracy 0.68\n",
            "k= 83 test accuracy 0.68\n",
            "k= 84 test accuracy 0.68\n",
            "k= 85 test accuracy 0.68\n",
            "k= 86 test accuracy 0.68\n",
            "k= 87 test accuracy 0.68\n",
            "k= 88 test accuracy 0.68\n",
            "k= 89 test accuracy 0.68\n",
            "k= 90 test accuracy 0.68\n",
            "k= 91 test accuracy 0.68\n",
            "k= 92 test accuracy 0.66\n",
            "k= 93 test accuracy 0.66\n",
            "k= 94 test accuracy 0.66\n",
            "k= 95 test accuracy 0.66\n",
            "k= 96 test accuracy 0.63\n",
            "k= 97 test accuracy 0.64\n",
            "k= 98 test accuracy 0.64\n",
            "k= 99 test accuracy 0.64\n",
            "k= 100 test accuracy 0.64\n",
            "k= 101 test accuracy 0.64\n",
            "k= 102 test accuracy 0.63\n",
            "k= 103 test accuracy 0.63\n",
            "k= 104 test accuracy 0.63\n",
            "k= 105 test accuracy 0.63\n",
            "k= 106 test accuracy 0.63\n",
            "k= 107 test accuracy 0.63\n",
            "k= 108 test accuracy 0.63\n",
            "k= 109 test accuracy 0.63\n",
            "k= 110 test accuracy 0.62\n",
            "k= 111 test accuracy 0.62\n",
            "k= 112 test accuracy 0.63\n",
            "k= 113 test accuracy 0.62\n",
            "k= 114 test accuracy 0.63\n",
            "k= 115 test accuracy 0.62\n",
            "k= 116 test accuracy 0.62\n",
            "k= 117 test accuracy 0.63\n",
            "k= 118 test accuracy 0.63\n",
            "k= 119 test accuracy 0.63\n",
            "k= 120 test accuracy 0.62\n",
            "k= 121 test accuracy 0.63\n",
            "k= 122 test accuracy 0.63\n",
            "k= 123 test accuracy 0.62\n",
            "k= 124 test accuracy 0.62\n",
            "k= 125 test accuracy 0.62\n",
            "k= 126 test accuracy 0.62\n",
            "k= 127 test accuracy 0.63\n",
            "k= 128 test accuracy 0.63\n",
            "k= 129 test accuracy 0.63\n",
            "k= 130 test accuracy 0.62\n",
            "k= 131 test accuracy 0.63\n",
            "k= 132 test accuracy 0.62\n",
            "k= 133 test accuracy 0.62\n",
            "k= 134 test accuracy 0.62\n",
            "k= 135 test accuracy 0.62\n",
            "k= 136 test accuracy 0.62\n",
            "k= 137 test accuracy 0.62\n",
            "k= 138 test accuracy 0.62\n",
            "k= 139 test accuracy 0.62\n",
            "k= 140 test accuracy 0.62\n",
            "k= 141 test accuracy 0.62\n",
            "k= 142 test accuracy 0.62\n",
            "k= 143 test accuracy 0.62\n",
            "k= 144 test accuracy 0.62\n",
            "k= 145 test accuracy 0.62\n",
            "k= 146 test accuracy 0.62\n",
            "k= 147 test accuracy 0.62\n",
            "k= 148 test accuracy 0.62\n",
            "k= 149 test accuracy 0.62\n",
            "k= 150 test accuracy 0.62\n",
            "k= 151 test accuracy 0.62\n",
            "k= 152 test accuracy 0.62\n",
            "k= 153 test accuracy 0.62\n",
            "k= 154 test accuracy 0.62\n",
            "k= 155 test accuracy 0.62\n",
            "k= 156 test accuracy 0.62\n",
            "k= 157 test accuracy 0.62\n",
            "k= 158 test accuracy 0.62\n",
            "k= 159 test accuracy 0.62\n",
            "k= 160 test accuracy 0.62\n",
            "k= 161 test accuracy 0.62\n",
            "k= 162 test accuracy 0.62\n",
            "k= 163 test accuracy 0.62\n",
            "k= 164 test accuracy 0.62\n",
            "k= 165 test accuracy 0.62\n",
            "k= 166 test accuracy 0.62\n",
            "k= 167 test accuracy 0.62\n",
            "k= 168 test accuracy 0.62\n",
            "k= 169 test accuracy 0.62\n",
            "k= 170 test accuracy 0.62\n",
            "k= 171 test accuracy 0.62\n",
            "k= 172 test accuracy 0.62\n",
            "k= 173 test accuracy 0.62\n",
            "k= 174 test accuracy 0.62\n",
            "k= 175 test accuracy 0.62\n",
            "k= 176 test accuracy 0.62\n",
            "k= 177 test accuracy 0.62\n",
            "k= 178 test accuracy 0.62\n",
            "k= 179 test accuracy 0.62\n",
            "k= 180 test accuracy 0.62\n",
            "k= 181 test accuracy 0.62\n",
            "k= 182 test accuracy 0.62\n",
            "k= 183 test accuracy 0.62\n",
            "k= 184 test accuracy 0.62\n",
            "k= 185 test accuracy 0.62\n",
            "k= 186 test accuracy 0.62\n",
            "k= 187 test accuracy 0.62\n",
            "k= 188 test accuracy 0.62\n",
            "k= 189 test accuracy 0.62\n",
            "k= 190 test accuracy 0.62\n",
            "k= 191 test accuracy 0.62\n",
            "k= 192 test accuracy 0.62\n",
            "k= 193 test accuracy 0.62\n",
            "k= 194 test accuracy 0.62\n",
            "k= 195 test accuracy 0.62\n",
            "k= 196 test accuracy 0.62\n",
            "k= 197 test accuracy 0.62\n",
            "k= 198 test accuracy 0.62\n",
            "k= 199 test accuracy 0.62\n",
            "k= 200 test accuracy 0.62\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "아무리 k값을 변경하여도 정확도가 0.7정도가 최대인듯하다. 객관적 사물이 아니라 사람이기 때문에 정확도가 이정도 정확도 나오는 것이 최대인지 알아보기 위해 당뇨병여부와 독립변수들 사이의 상관 분석을 해보자. 또한 상관여부가 낮은 변수는 삭제해서 모델 예측을 다시진행해보자\n",
        "\n"
      ],
      "metadata": {
        "id": "6Y8T2YC2Z3PI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#상관관계 분석\n",
        "import pandas as pd\n",
        "import matplotlib as mpl\n",
        "import matplotlib.pylab as plt\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "\n",
        "#다른 변수들사이의 상관을 점검해 본다\n",
        "corr = df.corr()\n",
        "corr"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 331
        },
        "id": "4SmFiZ6iafUB",
        "outputId": "eb0a96d4-e159-4dfc-959a-615bd474550e"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                          Pregnancies   Glucose  BloodPressure  SkinThickness  \\\n",
              "Pregnancies                  1.000000  0.129459       0.141282      -0.081672   \n",
              "Glucose                      0.129459  1.000000       0.152590       0.057328   \n",
              "BloodPressure                0.141282  0.152590       1.000000       0.207371   \n",
              "SkinThickness               -0.081672  0.057328       0.207371       1.000000   \n",
              "Insulin                     -0.073535  0.331357       0.088933       0.436783   \n",
              "BMI                          0.017683  0.221071       0.281805       0.392573   \n",
              "DiabetesPedigreeFunction    -0.033523  0.137337       0.041265       0.183928   \n",
              "Age                          0.544341  0.263514       0.239528      -0.113970   \n",
              "Outcome                      0.221898  0.466581       0.065068       0.074752   \n",
              "\n",
              "                           Insulin       BMI  DiabetesPedigreeFunction  \\\n",
              "Pregnancies              -0.073535  0.017683                 -0.033523   \n",
              "Glucose                   0.331357  0.221071                  0.137337   \n",
              "BloodPressure             0.088933  0.281805                  0.041265   \n",
              "SkinThickness             0.436783  0.392573                  0.183928   \n",
              "Insulin                   1.000000  0.197859                  0.185071   \n",
              "BMI                       0.197859  1.000000                  0.140647   \n",
              "DiabetesPedigreeFunction  0.185071  0.140647                  1.000000   \n",
              "Age                      -0.042163  0.036242                  0.033561   \n",
              "Outcome                   0.130548  0.292695                  0.173844   \n",
              "\n",
              "                               Age   Outcome  \n",
              "Pregnancies               0.544341  0.221898  \n",
              "Glucose                   0.263514  0.466581  \n",
              "BloodPressure             0.239528  0.065068  \n",
              "SkinThickness            -0.113970  0.074752  \n",
              "Insulin                  -0.042163  0.130548  \n",
              "BMI                       0.036242  0.292695  \n",
              "DiabetesPedigreeFunction  0.033561  0.173844  \n",
              "Age                       1.000000  0.238356  \n",
              "Outcome                   0.238356  1.000000  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-08bd94c2-db46-423c-9c08-312359d1a788\" 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>Pregnancies</th>\n",
              "      <th>Glucose</th>\n",
              "      <th>BloodPressure</th>\n",
              "      <th>SkinThickness</th>\n",
              "      <th>Insulin</th>\n",
              "      <th>BMI</th>\n",
              "      <th>DiabetesPedigreeFunction</th>\n",
              "      <th>Age</th>\n",
              "      <th>Outcome</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Pregnancies</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.129459</td>\n",
              "      <td>0.141282</td>\n",
              "      <td>-0.081672</td>\n",
              "      <td>-0.073535</td>\n",
              "      <td>0.017683</td>\n",
              "      <td>-0.033523</td>\n",
              "      <td>0.544341</td>\n",
              "      <td>0.221898</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Glucose</th>\n",
              "      <td>0.129459</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.152590</td>\n",
              "      <td>0.057328</td>\n",
              "      <td>0.331357</td>\n",
              "      <td>0.221071</td>\n",
              "      <td>0.137337</td>\n",
              "      <td>0.263514</td>\n",
              "      <td>0.466581</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BloodPressure</th>\n",
              "      <td>0.141282</td>\n",
              "      <td>0.152590</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.207371</td>\n",
              "      <td>0.088933</td>\n",
              "      <td>0.281805</td>\n",
              "      <td>0.041265</td>\n",
              "      <td>0.239528</td>\n",
              "      <td>0.065068</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SkinThickness</th>\n",
              "      <td>-0.081672</td>\n",
              "      <td>0.057328</td>\n",
              "      <td>0.207371</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.436783</td>\n",
              "      <td>0.392573</td>\n",
              "      <td>0.183928</td>\n",
              "      <td>-0.113970</td>\n",
              "      <td>0.074752</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Insulin</th>\n",
              "      <td>-0.073535</td>\n",
              "      <td>0.331357</td>\n",
              "      <td>0.088933</td>\n",
              "      <td>0.436783</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.197859</td>\n",
              "      <td>0.185071</td>\n",
              "      <td>-0.042163</td>\n",
              "      <td>0.130548</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BMI</th>\n",
              "      <td>0.017683</td>\n",
              "      <td>0.221071</td>\n",
              "      <td>0.281805</td>\n",
              "      <td>0.392573</td>\n",
              "      <td>0.197859</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.140647</td>\n",
              "      <td>0.036242</td>\n",
              "      <td>0.292695</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DiabetesPedigreeFunction</th>\n",
              "      <td>-0.033523</td>\n",
              "      <td>0.137337</td>\n",
              "      <td>0.041265</td>\n",
              "      <td>0.183928</td>\n",
              "      <td>0.185071</td>\n",
              "      <td>0.140647</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.033561</td>\n",
              "      <td>0.173844</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Age</th>\n",
              "      <td>0.544341</td>\n",
              "      <td>0.263514</td>\n",
              "      <td>0.239528</td>\n",
              "      <td>-0.113970</td>\n",
              "      <td>-0.042163</td>\n",
              "      <td>0.036242</td>\n",
              "      <td>0.033561</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.238356</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Outcome</th>\n",
              "      <td>0.221898</td>\n",
              "      <td>0.466581</td>\n",
              "      <td>0.065068</td>\n",
              "      <td>0.074752</td>\n",
              "      <td>0.130548</td>\n",
              "      <td>0.292695</td>\n",
              "      <td>0.173844</td>\n",
              "      <td>0.238356</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-08bd94c2-db46-423c-9c08-312359d1a788')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-08bd94c2-db46-423c-9c08-312359d1a788 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-08bd94c2-db46-423c-9c08-312359d1a788');\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",
              "\n",
              "\n",
              "<div id=\"df-f95ee693-254f-4e64-a784-09e05b23bedc\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-f95ee693-254f-4e64-a784-09e05b23bedc')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-f95ee693-254f-4e64-a784-09e05b23bedc button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_a05c5413-a883-4095-86ee-e6517c8c38b3\">\n",
              "    <style>\n",
              "      .colab-df-generate {\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-generate: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-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate: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",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('corr')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_a05c5413-a883-4095-86ee-e6517c8c38b3 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('corr');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.heatmap(data = df.corr() , annot=True, fmt = '.2f', linewidths=.5, cmap='Blues')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 621
        },
        "id": "TpEMxUUClN1H",
        "outputId": "5e67a596-0cdb-4b19-9d17-8e4d7d5bf167"
      },
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 59
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#당뇨병을 기준으로 상관계수를 확인한다\n",
        "outComeCorr = corr.Outcome\n",
        "outComeCorr.sort_values(ascending=False) #상관계수가 큰 값이 연관성이 높기때문에 내림차순으로 정렬해본다"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6ua-57kTheqP",
        "outputId": "48b3184c-d25c-4861-d4dd-8d6084975f99"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Outcome                     1.000000\n",
              "Glucose                     0.466581\n",
              "BMI                         0.292695\n",
              "Age                         0.238356\n",
              "Pregnancies                 0.221898\n",
              "DiabetesPedigreeFunction    0.173844\n",
              "Insulin                     0.130548\n",
              "SkinThickness               0.074752\n",
              "BloodPressure               0.065068\n",
              "Name: Outcome, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "OutCome을 기준으로 뚜렷한 상관관계(0.466581)를 가지는 변수는 혈중 포도당 농도이고 무상관에 해당하는 변수는 피부 두께와 혈압이었다. 대부분의 변수들은 약한 상관관계를 보이기때문에 애초에 수식적으로 아주 깔끔한 예측은 힘들 것으로 예상된다.\n",
        "일단 무상관을 가지는 변수 피부 두께와 혈압을 제거한 후 다시 모델예측을 해보겠다."
      ],
      "metadata": {
        "id": "v9t8pSYhkSKe"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df2 = df.drop(['SkinThickness','BloodPressure' ], axis=1)\n",
        "y = df2.Outcome\n",
        "x = df2.drop(['Outcome'], axis=1)\n",
        "\n",
        "#위의 1-200사이의 k값을 반복한 동일한 랜덤 시드의 데이터로 진행한다\n",
        "train_input, test_input, train_label, test_label = train_test_split(x,y, test_size = 0.3,random_state=2)\n",
        "\n",
        "def knn_kValue_print_test_accuracy(k):\n",
        "  knn = KNeighborsClassifier(n_neighbors = k)\n",
        "  knn.fit(train_input, train_label)\n",
        "  predict_label = knn.predict(test_input)\n",
        "  print('k=', k, end=\" \")\n",
        "  print('test accuracy {:.2f}'.format(np.mean(test_label == predict_label)))\n",
        "\n",
        "for i in range(1,201):\n",
        "  knn_kValue_print_test_accuracy(i)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fjmLONNHhEuv",
        "outputId": "3421684f-2c7b-40d5-c1c6-146b5865823a"
      },
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "k= 1 test accuracy 0.67\n",
            "k= 2 test accuracy 0.72\n",
            "k= 3 test accuracy 0.73\n",
            "k= 4 test accuracy 0.73\n",
            "k= 5 test accuracy 0.76\n",
            "k= 6 test accuracy 0.75\n",
            "k= 7 test accuracy 0.77\n",
            "k= 8 test accuracy 0.75\n",
            "k= 9 test accuracy 0.76\n",
            "k= 10 test accuracy 0.76\n",
            "k= 11 test accuracy 0.76\n",
            "k= 12 test accuracy 0.77\n",
            "k= 13 test accuracy 0.76\n",
            "k= 14 test accuracy 0.74\n",
            "k= 15 test accuracy 0.76\n",
            "k= 16 test accuracy 0.77\n",
            "k= 17 test accuracy 0.76\n",
            "k= 18 test accuracy 0.77\n",
            "k= 19 test accuracy 0.76\n",
            "k= 20 test accuracy 0.76\n",
            "k= 21 test accuracy 0.76\n",
            "k= 22 test accuracy 0.77\n",
            "k= 23 test accuracy 0.76\n",
            "k= 24 test accuracy 0.77\n",
            "k= 25 test accuracy 0.78\n",
            "k= 26 test accuracy 0.78\n",
            "k= 27 test accuracy 0.77\n",
            "k= 28 test accuracy 0.76\n",
            "k= 29 test accuracy 0.77\n",
            "k= 30 test accuracy 0.77\n",
            "k= 31 test accuracy 0.77\n",
            "k= 32 test accuracy 0.78\n",
            "k= 33 test accuracy 0.77\n",
            "k= 34 test accuracy 0.77\n",
            "k= 35 test accuracy 0.77\n",
            "k= 36 test accuracy 0.77\n",
            "k= 37 test accuracy 0.77\n",
            "k= 38 test accuracy 0.77\n",
            "k= 39 test accuracy 0.77\n",
            "k= 40 test accuracy 0.77\n",
            "k= 41 test accuracy 0.76\n",
            "k= 42 test accuracy 0.75\n",
            "k= 43 test accuracy 0.75\n",
            "k= 44 test accuracy 0.76\n",
            "k= 45 test accuracy 0.77\n",
            "k= 46 test accuracy 0.75\n",
            "k= 47 test accuracy 0.76\n",
            "k= 48 test accuracy 0.75\n",
            "k= 49 test accuracy 0.75\n",
            "k= 50 test accuracy 0.75\n",
            "k= 51 test accuracy 0.75\n",
            "k= 52 test accuracy 0.75\n",
            "k= 53 test accuracy 0.75\n",
            "k= 54 test accuracy 0.76\n",
            "k= 55 test accuracy 0.75\n",
            "k= 56 test accuracy 0.75\n",
            "k= 57 test accuracy 0.75\n",
            "k= 58 test accuracy 0.75\n",
            "k= 59 test accuracy 0.75\n",
            "k= 60 test accuracy 0.75\n",
            "k= 61 test accuracy 0.75\n",
            "k= 62 test accuracy 0.75\n",
            "k= 63 test accuracy 0.75\n",
            "k= 64 test accuracy 0.75\n",
            "k= 65 test accuracy 0.75\n",
            "k= 66 test accuracy 0.74\n",
            "k= 67 test accuracy 0.75\n",
            "k= 68 test accuracy 0.74\n",
            "k= 69 test accuracy 0.76\n",
            "k= 70 test accuracy 0.76\n",
            "k= 71 test accuracy 0.76\n",
            "k= 72 test accuracy 0.76\n",
            "k= 73 test accuracy 0.75\n",
            "k= 74 test accuracy 0.75\n",
            "k= 75 test accuracy 0.76\n",
            "k= 76 test accuracy 0.75\n",
            "k= 77 test accuracy 0.75\n",
            "k= 78 test accuracy 0.75\n",
            "k= 79 test accuracy 0.75\n",
            "k= 80 test accuracy 0.75\n",
            "k= 81 test accuracy 0.75\n",
            "k= 82 test accuracy 0.74\n",
            "k= 83 test accuracy 0.76\n",
            "k= 84 test accuracy 0.74\n",
            "k= 85 test accuracy 0.76\n",
            "k= 86 test accuracy 0.75\n",
            "k= 87 test accuracy 0.76\n",
            "k= 88 test accuracy 0.75\n",
            "k= 89 test accuracy 0.75\n",
            "k= 90 test accuracy 0.75\n",
            "k= 91 test accuracy 0.75\n",
            "k= 92 test accuracy 0.74\n",
            "k= 93 test accuracy 0.75\n",
            "k= 94 test accuracy 0.75\n",
            "k= 95 test accuracy 0.76\n",
            "k= 96 test accuracy 0.76\n",
            "k= 97 test accuracy 0.76\n",
            "k= 98 test accuracy 0.76\n",
            "k= 99 test accuracy 0.76\n",
            "k= 100 test accuracy 0.76\n",
            "k= 101 test accuracy 0.76\n",
            "k= 102 test accuracy 0.75\n",
            "k= 103 test accuracy 0.76\n",
            "k= 104 test accuracy 0.75\n",
            "k= 105 test accuracy 0.76\n",
            "k= 106 test accuracy 0.75\n",
            "k= 107 test accuracy 0.75\n",
            "k= 108 test accuracy 0.75\n",
            "k= 109 test accuracy 0.75\n",
            "k= 110 test accuracy 0.75\n",
            "k= 111 test accuracy 0.75\n",
            "k= 112 test accuracy 0.74\n",
            "k= 113 test accuracy 0.75\n",
            "k= 114 test accuracy 0.75\n",
            "k= 115 test accuracy 0.75\n",
            "k= 116 test accuracy 0.74\n",
            "k= 117 test accuracy 0.76\n",
            "k= 118 test accuracy 0.75\n",
            "k= 119 test accuracy 0.76\n",
            "k= 120 test accuracy 0.75\n",
            "k= 121 test accuracy 0.76\n",
            "k= 122 test accuracy 0.75\n",
            "k= 123 test accuracy 0.76\n",
            "k= 124 test accuracy 0.76\n",
            "k= 125 test accuracy 0.75\n",
            "k= 126 test accuracy 0.75\n",
            "k= 127 test accuracy 0.75\n",
            "k= 128 test accuracy 0.74\n",
            "k= 129 test accuracy 0.75\n",
            "k= 130 test accuracy 0.76\n",
            "k= 131 test accuracy 0.76\n",
            "k= 132 test accuracy 0.76\n",
            "k= 133 test accuracy 0.76\n",
            "k= 134 test accuracy 0.75\n",
            "k= 135 test accuracy 0.75\n",
            "k= 136 test accuracy 0.74\n",
            "k= 137 test accuracy 0.74\n",
            "k= 138 test accuracy 0.74\n",
            "k= 139 test accuracy 0.74\n",
            "k= 140 test accuracy 0.74\n",
            "k= 141 test accuracy 0.74\n",
            "k= 142 test accuracy 0.74\n",
            "k= 143 test accuracy 0.74\n",
            "k= 144 test accuracy 0.74\n",
            "k= 145 test accuracy 0.74\n",
            "k= 146 test accuracy 0.74\n",
            "k= 147 test accuracy 0.74\n",
            "k= 148 test accuracy 0.74\n",
            "k= 149 test accuracy 0.73\n",
            "k= 150 test accuracy 0.74\n",
            "k= 151 test accuracy 0.74\n",
            "k= 152 test accuracy 0.74\n",
            "k= 153 test accuracy 0.74\n",
            "k= 154 test accuracy 0.73\n",
            "k= 155 test accuracy 0.74\n",
            "k= 156 test accuracy 0.75\n",
            "k= 157 test accuracy 0.74\n",
            "k= 158 test accuracy 0.72\n",
            "k= 159 test accuracy 0.74\n",
            "k= 160 test accuracy 0.73\n",
            "k= 161 test accuracy 0.73\n",
            "k= 162 test accuracy 0.72\n",
            "k= 163 test accuracy 0.73\n",
            "k= 164 test accuracy 0.70\n",
            "k= 165 test accuracy 0.71\n",
            "k= 166 test accuracy 0.69\n",
            "k= 167 test accuracy 0.71\n",
            "k= 168 test accuracy 0.68\n",
            "k= 169 test accuracy 0.69\n",
            "k= 170 test accuracy 0.68\n",
            "k= 171 test accuracy 0.67\n",
            "k= 172 test accuracy 0.67\n",
            "k= 173 test accuracy 0.67\n",
            "k= 174 test accuracy 0.67\n",
            "k= 175 test accuracy 0.67\n",
            "k= 176 test accuracy 0.67\n",
            "k= 177 test accuracy 0.67\n",
            "k= 178 test accuracy 0.67\n",
            "k= 179 test accuracy 0.67\n",
            "k= 180 test accuracy 0.67\n",
            "k= 181 test accuracy 0.67\n",
            "k= 182 test accuracy 0.67\n",
            "k= 183 test accuracy 0.67\n",
            "k= 184 test accuracy 0.67\n",
            "k= 185 test accuracy 0.67\n",
            "k= 186 test accuracy 0.67\n",
            "k= 187 test accuracy 0.67\n",
            "k= 188 test accuracy 0.67\n",
            "k= 189 test accuracy 0.67\n",
            "k= 190 test accuracy 0.67\n",
            "k= 191 test accuracy 0.67\n",
            "k= 192 test accuracy 0.67\n",
            "k= 193 test accuracy 0.67\n",
            "k= 194 test accuracy 0.67\n",
            "k= 195 test accuracy 0.67\n",
            "k= 196 test accuracy 0.67\n",
            "k= 197 test accuracy 0.67\n",
            "k= 198 test accuracy 0.67\n",
            "k= 199 test accuracy 0.67\n",
            "k= 200 test accuracy 0.67\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "무상관의 변수를 제거 하였더니 육안상으로도 유사도 분포가 평균적으로 오른 것을 볼수 있다."
      ],
      "metadata": {
        "id": "QMikFKxbpzxJ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "df2 데이터를 기반으로 랜덤 시드를 계속 바꿔 보면서 이제 적당한 k값의 범위가 어떤 것인지 알아보자"
      ],
      "metadata": {
        "id": "AeIlTHTGqOWt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#랜덤 시드를 0-100까지 반복하여 k값의 평균 정확도를 구한다\n",
        "#x축은 k값, y축은 평균 정확도로 그래프를 그린다\n",
        "#k값은 1-200사이다\n",
        "\n",
        "def knn_mean_test_accuracy(k, randomRepeat):\n",
        "  total = 0\n",
        "  for i in range(randomRepeat+1):\n",
        "    train_input, test_input, train_label, test_label = train_test_split(x,y, test_size = 0.3,random_state=i)\n",
        "    knn = KNeighborsClassifier(n_neighbors = k)\n",
        "    knn.fit(train_input, train_label)\n",
        "    predict_label = knn.predict(test_input)\n",
        "    total = total + np.mean(test_label == predict_label)\n",
        "  return total/(randomRepeat+1)\n",
        "\n",
        "graphX = []\n",
        "graphY = []\n",
        "for i in range(1, 201):\n",
        "  graphX.append(i)\n",
        "  graphY.append(knn_mean_test_accuracy(i, 100))"
      ],
      "metadata": {
        "id": "YaiM8YxUqNXy"
      },
      "execution_count": 78,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from matplotlib import pyplot as plt\n",
        "\n",
        "plt.plot(graphX,graphY)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 433
        },
        "id": "BNyUgPVqxI9p",
        "outputId": "314d5662-3861-4a2c-ad1b-a1de27b72d5f"
      },
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAiwAAAGgCAYAAACJ7TzXAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAABe20lEQVR4nO3deXhU5fk38O+ZmUz2fd9IQiCsCUuAGFFAiSLFBa2IFgWpK2Jd8GeRtkK1Flp9VaqlUi1UW6uiFncKQlhkCSAJAcKShYTs+zbZJzNz3j9m5iQjgUzWM5l8P9c1l8mZM8N9Mphz8zz3cz+CKIoiiIiIiGyYQu4AiIiIiLrDhIWIiIhsHhMWIiIisnlMWIiIiMjmMWEhIiIim8eEhYiIiGweExYiIiKyeUxYiIiIyOYxYSEiIiKbx4SFiIiIbF6vEpZNmzYhMjISTk5OSEhIwPHjx6947pw5cyAIwmWPBQsWdHn+448/DkEQsHHjxt6ERkRERHZI1dMXbNu2DatWrcLmzZuRkJCAjRs3Yt68ecjMzERAQMBl52/fvh1arVb6vrq6GpMmTcKiRYsuO/eLL77A0aNHERIS0tOwYDAYUFJSAnd3dwiC0OPXExER0eATRRENDQ0ICQmBQnGVcRSxh2bMmCGuXLlS+l6v14shISHihg0brHr9m2++Kbq7u4uNjY0Wx4uKisTQ0FAxIyNDjIiIEN98880exVVYWCgC4IMPPvjggw8+huCjsLDwqvf5Ho2waLVapKamYs2aNdIxhUKBpKQkpKSkWPUeW7Zswb333gtXV1fpmMFgwAMPPIDnn38eEyZMsOp92tra0NbWJn0vmjadLiwshIeHh1XvQURERPLSaDQIDw+Hu7v7Vc/rUcJSVVUFvV6PwMBAi+OBgYG4cOFCt68/fvw4MjIysGXLFovjf/7zn6FSqfDUU09ZHcuGDRvw0ksvXXbcw8ODCQsREdEQ0105x6CuEtqyZQtiY2MxY8YM6Vhqair+8pe/4P333+9R7cmaNWtQX18vPQoLCwciZCIiIrIBPUpY/Pz8oFQqUV5ebnG8vLwcQUFBV31tU1MTPvnkEzz00EMWxw8ePIiKigqMGDECKpUKKpUK+fn5eO655xAZGXnF93N0dJRGUziqQkREZN96lLCo1WrEx8cjOTlZOmYwGJCcnIzExMSrvvazzz5DW1sb7r//fovjDzzwAE6fPo309HTpERISgueffx67du3qSXhERERkp3q8rHnVqlVYtmwZpk2bhhkzZmDjxo1oamrC8uXLAQBLly5FaGgoNmzYYPG6LVu2YOHChfD19bU47uvre9kxBwcHBAUFYcyYMT0Nj4iIiOxQjxOWxYsXo7KyEmvXrkVZWRkmT56MnTt3SoW4BQUFl62jzszMxKFDh/D999/3T9REREQ0rAiieT3wEKfRaODp6Yn6+nrWsxAREQ0R1t6/uZcQERER2TwmLERERGTzmLAQERGRzWPCQkRERDaPCQsRERHZPCYsREREZPOYsFCPHM2txhcni+QOg4iIhpkeN46j4UtvEPHYv1NR39KOET6uiI/wljskIiIaJjjCQla7UKZBfUs7AODr9GKZoyEiouGECYuMcioaUdeslTsMq524VCt9/d2ZUuj0BhmjISKi4YQJi0wOZlfi5jcP4PEPU+UOxWon8jsSlqpGLVJyqwEAbTq9XCEREdEwwYRFBm06PdZ9dRYG0ThqMVRu+KmXagAAowPcAABfp5fggyOXMHHdLrz+faacoRERkZ1jwiKDfxzMQ25VEwBAZxCRVdYoc0Rd0xtE7L1QjhatHsV1LSipb4VSIeA3C8YBAL44WYx1X59Fu17Ex8cLoDfYxT6aRERkg5iwDLLiuhb8dW8OAMDd0bhIK6OkXs6Qruifh/Pwy/dPYMV/UnHCNLoyIcQDs0f7I9jTCTpTgqJSCKhq1OJkQe3V3o6IiKjXmLAMss37L6KlXY/pkd64L2EEAOCsDSYsoihi24+FAID9mZXYuCcbABAf4Q2FQsBD10XBVa3EH++ciFvjggEA358r7/K9DAYRGcX1aGht73NcyefLcaFM0+f3ISKioYV9WAZRs1aHL08alwM/PTcG1U1tAICzJbZ3A84o1iC7omOqKs80hTUtwgcA8PD1I/HLmVFQKAR4u6jxZXoJdp0tw5r5Y/H856eRcrEa8ycGITbME38/kItzpRosiAvGpl9M7XVM6YV1eOiDE4j0dcH+52/o2wUSEdGQwoRlEH17qhQNbTqM8HHBtdG+Uh3L+VIN9AYRSoUgc4Qd/ptm7GZ78/hA5FY1IceUvEyL7GgWpzDFOzvGH2qVAvnVzfjtlxn4PNX42n8cyrN4zwOZlX26zr3njSM4l6qbUdukhberulfvQ0REQw+nhAbRR8cLAAD3zRgBhUJAlJ8rXNRKtLYbkFtpO4W37XoDvjlVAsAY6/o7Y6FUCBgf7IFAD6fLznd1VOH6UX4AgI+OGa/xwWsjMW9CIEK9nPGwafqosU2HrPKGXsd1IKtS+vp8qe2NShER0cDhCMsgOVeiQXphHVQKAXfHhwEAlAoB44I9kJpfi7MlGowOdJc5SqMfsipR3aSFn5sa14/2g0qpwK5nZsHLxeGKr7l5QiCSL1QAAG4aH4h1t42HIHSMpJwv0+BwTjXSCmoxLtgD/0q5hF1ny7DpF1Ph5dL9SEl1YxtOF3fU+pwr1eBaU5I0UERRtLgGIiKSD0dYBsm/j+YDMN7Y/d0dpeMTQjwA2E7hbW2TFm/uyQIA3D4pFCql8a/IqAA3+Lk5XvF1N40PgqezA8J9nPHqz+Muu9FPHWGcSkrLr0Nrux6v7szE4ZxqaSSnO4dyqiB2WjV9vrT3IzXWqGnSYuHfjuCOvx5COzv6EhHJjiMsg+BwThU++dE4VfLANZEWz00M8QRgLHKVQ4tWj+c+S4dCEDArxh/v/pCLnIpGeDo7YNm1EVa/j4+rGgeenwMHpQKujpf/tTInLCcLanEgqxKNbToAwOGcajyQGNnt+x/INE4HjfR3RW5lkzQlJIoiWtsNcFYrrY61K41tOnydXoL4CG8Eezlh6dZj0mdyqrAO0yJ9+vT+RETUN0xYBlhVYxue2ZYOUTTWgyRG+1o8P77TCIscUxDfnyvDjjNlAIBvT5cCAII8nPCvh2Ygwte1R+91tamdKSO8AAC5VU340DTaBABHLlZBbxBR3diG5e//iJvHB+HppNEWrzUYRPyQbUxYHps1Eqv/ewY5FY1o1xvw9t4c/HVvNj58OAHXRvd+iuiVb8/hE9Mybk9nB2mTRwBIuVhtkbCIooiLlU3wd3OE51WmyYiIqP9wSmgAiaKI5z87hcqGNsQEumHtreMvOycm0B0OSgGaVh2KalsGPcbjecaGcLGhnhgb5I7J4V747xPXIqaf62m8XNSI9jcmQAezqwAACgHQtOpwtqQe/0rJx9kSDbYcyoXB1JDuw6P5mPXqPjyw9RiqGrVwUSuxcEoo3BxV0OoNOFeiwT8P58EgAp8cL7Q6FlEU8dquC3hzdxZEUURVYxu2m5abqxQC6lva4e6kwi9MfXKO5hn3TNLpDfj7gYuY+8YBJL1xADP/vBf/OJjLTSCJiAYBR1gG0PG8GuzLrIRapcDb903tctpCrVJgdIA7zpVqcLakHuE+LoMeIwA8eeMozJsQNKB/1tQR3rhYaVzKHerljHHB7thzvgIHMivx6QljwqFp1SG3qhGjAtzx7g+5KKhpRkFNMwDg2mg/OKqUGBvkjhP5tXh7bzYaWo1TS/suVECrM0CtUqC6sQ1qlQLuTl2Pfpwt0WDTvosAgEg/FxRUt0CrM2BSuBe2LpuG5PMVmBrhBVE0rnoy7/e0ad9FvJVsbKCnEIzTSK98dx7fni7FJ49eAyeHvk1LERHRlXGEZQC9+0MuAGBRfBjGBF15xGJiqHlaaHDrWGqatFJzuOmDUKMxNaKjh8utccG4zrTK592DuahoaJOeS82vRXFdCwpqmqFUCHhh/lj8ImEEVt8yBgAwLtj489pzvkJ6TUObDim51Sisacac1/bjzr8dkUY+Wtv1OJhdKY3c7O7Ukff3X5/Dv49eAgA8dF0UfN0ccc/0cIwKcJcKjdt0BhzPq5GmslbdFIP0dTfjT3fFwt1RhfTCOuw533WXXyIi6h9MWAZITkUDki9UQBCMN8KrmWAqvB3shOXHTrsv+wxCE7Z4i4QlBNeNNiYs5lESV9MIVFp+HY7lGqdhJoZ64vHZ0Vh/Z6y07NucsJhdb3qf78+W4c3dWWho0yGnolFKTH77RQYe2HIc7xwwjqqYkwtnByXqW9pR1ahFsKcT5k+0HGESBAHXjDQmci99cw41TVqEeDrhiTnR8HBywL0zRmCpqTB5e1pxf/yIiIjoCpiwDJD3fjB2eb1pXCBG+rtd9Vzz0uaM4sFd2vyjaTpoetTgrIAZ5e+Gu6aGYvG0cEwM9UC0vxsCOi3xfvamGABAWkEtjuUaY7umi9jGBXeMVs2I8sHD148EAHx9qgRfpHckDu8fuYScigZ8cdLYeXfroTxcrGzE2RINFALwj2XToDJ13V2aGAkH5eX/O5iLpM2dfpdcEyEt9QaAO6cYe+ocyKpEVWPbZa8nIqL+wYRlAFQ2tOELUxHnY7NHdnv+uGAPCAJQ0dCGyobBu+mZR1gSBilhUSgEvHHPZPz5bmOfFkEQpGmh60b5YeGUUABAdkUj9mUap3uuGel72fuMCXKHeTHV3fFhSBzpC3dHFRpadRBF4/UoFQKO5dXguc9OwzQThOomLZ75JB2AcbRn5ig/vLYoDndNDcUDiV0v4U7s9OerVQrcN2OExfOjAtwwKdwLeoOIr9NLcK5Eg0f+dQIHsyt/+lZERNQHTFgGwPG8Gmj1BowL9kB8RPfJgKujClF+xhU0g9VArqlNhwzTFNRg1K9cyRM3RCNpXCB+u2Ac/NwcEelrLDquaGiDQrDcu8jMRa3C/QkRuGakD26NC4ZapcCcsQEAjMWwf7xzIuZNCARg7KECAIunhQMAzphGsZLGGZ+/c0oY3rhnMty66B0DAFF+rgj0MI4C3T4ppMups59PNSZaWw/nYfHfU7D7XDn+3/dZvfp5EBFR15iwDIByTSsAY5Mzaw1WHUu73oC0glp8eDQfeoOIMG9nhHg5D+ifeTWjAtzxj2XTpLoUc4M5wFi/cqWVPn9YOBGfPJoIF7Ux0bh3ejgEwTi1MyrAHcs6NaP7WWwQ1t42Hp7OHe910/hAq+ITBAEPXReFCF8XrJgT3eU5t8aFQKUQUFTbggZTQ7xThXWoaGi16s8gIqLuMWEZAOWmG1Wg++UbBV7JYLXof+aTdNz1tyPY8L8LAIAZNtbBdUqnwtyupoOuZOYoP5x88Sasu83Y62ZGlA+mR3rDRa3EM0kxcHVU4f5rjNM5I/1du60r6uzRWdE48PwNiL7Ca3xc1dKS8JvHB2K8Kfnaf8E4LfTm7iws3XqcCQwRUR+wD8sAqNAY61DMUwnWmDgIIyw6vQH7TbUhE0I8EOLlfMVRA7lMNXXEBSCt0LFW5067giDg3w8loFmrl6ZxHp8dDU2L7rLVQP1h/V2xWDw9HDNH+eGve3NwrlSDPefLMXmEF/5i6t3ywD+OY9tj11i12SMREVliwjIAzFNCQZ49H2HJr26GprUdHleYCumLC2UNaNLq4e6owjdPXgeFwvZ2Ih4T6I4RPi5o1ur6XFvj5KC0aObm7uSAPyyc2NcQu+Tp7IBZMf4AgLnjAvDmniwczK6CotNWC5nlDfjFe8cwNsgdJfUtWJYYifmxwQMSDxGRvWHCMgDMCUtAD6aEvF3VCPF0Qkl9K86VaHo0HWKttIJaAMZpF1tMVgBApVTgq5UzoRfFK9av2LoJIR4I8nBCmaYVO88a92nauHgyfv/NWZwr1eCcaePGvKom3DQ+0GKZNBERdY2/KQdAb6aEAGBSuBcA4+7OAyE135iwxI+4fOWNLfF2VcPPrWc/O1siCAJuHBcgfZ80LgALp4Ti40euwZKEEXh+3hh4uzigXNMmbepIRERXx4SlnzW16aSVIgEe1o+wAJAKN787XQpRFLs8p1zTiq2H8tDaru9xbFLCEmHbCYs9SOqUsPzqRuPu0+OCPfDHO2Ox8oZRuGuqseHcNtMO0W8lZ+Ped1NQ26Qd/GCJiIYAJiz9zLwnjpuj6oq9Pa4kaXwg1CoFcquacL60AQBwvlSDsvqO1SXPbkvHy9+ew/tHLl3xfXIqGvDrz0/hb/tzcDyvBnqDiHJNK4pqW6AQgMmdCltpYFw3yh8L4oKxYk60NHLW2eLpxr4wyecr8N4PuXhjdxaO5tbgo+MFgxwpEdHQwBqWfibVr/RwOggwJjk3jPHHrrPl+PZ0Caqb2rB063EEujsh+bnZyK9uxpGLxj129l2owOOzL1/hI4oinvv0FE4VdSyPnh3jj7vjjf+iHxvk0eNEinpOrVJg0y+mXvH5mEB3TA73QnphHf6447x0/OPjBVgxO9pma4yIiOTCEZZ+Zk5YetKDpbNb40IAAF+ll+D5z05DFIEyTSve2X8RWw/nSeelFdSi0TT11Nmus2U4VVQPF7US8ycGwclBgQNZlXjpm7MAOB1kS+41jbIAxq0JPJxUKKptwaEBqmEiIhrKmLD0s94W3JrNHRcAJwcFiutaUKZplbqzvnswF1+nlwAw7mrcrhelHY3N9AZRagn/0HVReOf+eLx69yQAQFWjsTaCCYvtuHVSCEI8nTDSzxVv3zdFqmv56BinhYiIfooJSz+TRlh6WHBr5qJWYe5YY9t4pULA+8unI3GkL7Q6A7R6A6aM8JI2Cfwhy3KFyX9Ti5BT0QgvFwc8Msu46eLtk0Lw4LWR0jlMWGyHm6MK+5+/Af975np4u6qljRX3nC9HhYZdcYmIOmPC0s/KTUW3PV0h1Nkvr4uEl4sD1swfiykjvLH2tvEwlzT8cmYUrh9tbFB2MNs4dVBc14L/++wUXth+GgCwYna0ReO53/xsHBbFh+HBayMR5i3fvkF0ObVKAUeVsbndmCB3xEd4Q2cQ8fbeHJkjIyKyLay+7GcdIyy97yMSH+GD9LU3S9+PC/bAn34eh0tVTZg/MQjN7XooFQJyq5rwz8N5eG1XJpq1xmXOt8YFY1mnERXAeFN8bdGkXsdDg+fJG0Zh+fs/4t9H8zEx1AOLp4+QOyQiIpvAhKWfVfRxSuhK7pnWUaDpoVRgSrgXTuTX4qVvzgEApkV447cLxmGKjTeFo6u7YWwAnk2KwZt7svC7LzMQ5eeGGVG2tUElEZEcOCXUj0RRRLm56LaXq4SsZZ4WAoBliRH45NFrmKzYiafmjsKC2GC060W8sP30FZsIEhENJxxh6UcNbTq0mDrQ9qYPS0/cNyMcp4rqMG9CIKcN7IwgCPjTz2OxP7MCuZVNOHKxGjNH+ckdFhGRrDjC0o/M00Gezg4WuwQPhAAPJ2x9cDqTFTvl7uSAO6caV4N9eDRf5miIiOTHhKUflfexBwtRZ/dfEwEA+P5cuVTMTUQ0XDFh6Ud97cFC1NnYIA/MiPSB3iDiP0fzUd3YBk1ru9xhERHJgglLPzKPsAQMcMEtDR9LrjFO+b21Nwfxr+zB1Jd342xJfTevIiKyP0xY+lFZfQuAgS+4peHjlolBGOnnKn2vM4j49nSpjBEREcmDCUs/yq1qAgBE+rrIHAnZC0eVEruenYUTv0vCa3fHAQAOZld28yoiIvvDhKUf5VYaE5aR/m4yR0L2xEGpgJ+bI2aPMfbeySjWoKqxTeaoiIgGFxOWftLarkeJaUqo8xA+UX8JcHfCuGAPAMDhnCqZoyEiGlxMWPpJXlUTRBHwcFLBx1Utdzhkp2bFGBvIHcjitBARDS9MWPpJXlXHdJAgCDJHQ/ZqVqedutmyn4iGE7bm7ye5lY0AgJH+nA6igTMt0htODgpUNrThv2nFKKtvQWK0L+IjuEEiEdk3Jiz9xFxwG82CWxpAjiolrhnpi/2Zlfi/z04BAMK8nXFo9Y0yR0ZENLA4JdRPLpqnhFhwSwNs4WTjHkPuTioIAlBU28JVQ0Rk95iw9ANRFKUpoShOCdEAu2NyCFLW3Ii0F29ClClBPlPM7rdEZN+YsPSDqkYtGlp1EAQg0pcJCw0sQRAQ7OkMB6UCsaGeAICMIiYsRGTfmLD0A/MKoVAvZzg5KGWOhoYTc8JymiMsRGTnmLD0g44VQiy4pcEljbAwYSEiO8eEpR/ksuCWZDIh1BOCAJTWt7LwlojsGhOWfsAeLCQXN0cVC2+JaFhgwtIP8qubAbDgluTBwlsiGg6YsPSD2uZ2AICvG/cQosFnTlg4wkJE9owJSx+JoghNizFh8XR2kDkaGo6klUJF9civbkKzVidzRERE/Y8JSx+1thug1RsAMGEheZgLb8s0rZj92n5Mfmk3DudUyR0WEVG/6lXCsmnTJkRGRsLJyQkJCQk4fvz4Fc+dM2cOBEG47LFgwQIAQHt7O1avXo3Y2Fi4uroiJCQES5cuRUlJSe+uaJDVm0ZXlAoBbo7cmokGn5ujCo/Pjjb1AVJAqzfgt1+cQZtOj9omLX75/o/4f7sy5Q6TiKhPepywbNu2DatWrcK6deuQlpaGSZMmYd68eaioqOjy/O3bt6O0tFR6ZGRkQKlUYtGiRQCA5uZmpKWl4cUXX0RaWhq2b9+OzMxM3H777X27skFiTlg8nFQQBEHmaGi4Wn3LWBx+4Ub8+Nsk+Ls74lJ1MzbtzcEj/zqBvRcq8Lf9Oahp0gIA2nR6pBXUQhRFmaMmIrJejxOWN954A4888giWL1+O8ePHY/PmzXBxccHWrVu7PN/HxwdBQUHSY/fu3XBxcZESFk9PT+zevRv33HMPxowZg2uuuQZ//etfkZqaioKCgr5d3SCoZ/0K2RB3Jwf8bsE4AMBbe3NwIr8WAGAQgf2Zxn9UvPLtedz1tyP49EShbHESEfVUjxIWrVaL1NRUJCUldbyBQoGkpCSkpKRY9R5btmzBvffeC1fXKy8Brq+vhyAI8PLyuuI5bW1t0Gg0Fg85MGEhW3P7pBAkRPkAAByUAmbF+AMAks9XoKlNh/+mFQEAvjhZLFuMREQ91aOEpaqqCnq9HoGBgRbHAwMDUVZW1u3rjx8/joyMDDz88MNXPKe1tRWrV6/GfffdBw8Pjyuet2HDBnh6ekqP8PBw6y+kH0lTQkxYyEYIgoA//zwOs2P8sekXU/Fs0mgAwA9Zlfj6VAmatXoAwPG8GmmaiIjI1g3qKqEtW7YgNjYWM2bM6PL59vZ23HPPPRBFEe+8885V32vNmjWor6+XHoWF8gxvc4SFbFGknys++OUM3DwhCJPCvODn5oiGNh1e3XlBOscgAsnny2WMkojIej1KWPz8/KBUKlFebvlLrry8HEFBQVd9bVNTEz755BM89NBDXT5vTlby8/Oxe/fuq46uAICjoyM8PDwsHnJgwkK2TqEQcONY47RQbXM7BAG4d7pxRPL7c0xYiGho6FHColarER8fj+TkZOmYwWBAcnIyEhMTr/razz77DG1tbbj//vsve86crGRnZ2PPnj3w9fXtSViyYtM4GgpuHNsxjXvdKD8suzYSgHGaqLqxDW8nZ+OzTkW4pfUtWL/jPDdUJCKb0ePGIatWrcKyZcswbdo0zJgxAxs3bkRTUxOWL18OAFi6dClCQ0OxYcMGi9dt2bIFCxcuvCwZaW9vx9133420tDR8++230Ov1Uj2Mj48P1GrbbnfPERYaCq4f7Qe10tij5e74MIwNcke4jzMKa1pw4+sHUN/SDqVCwPzYYLg5qvDm7ix8eqIIOr2ItbeNlzt8IqKeJyyLFy9GZWUl1q5di7KyMkyePBk7d+6UCnELCgqgUFgO3GRmZuLQoUP4/vvvL3u/4uJifP311wCAyZMnWzy3b98+zJkzp6chDiomLDQUuDqqsO728ThfqsH8icEQBAE3jw/ClkN50t9hvUFEekEdrhvthxOXjMuhTxYa/yuKIlZ9egoGUcTGxZPZc4iIBl2vWrM++eSTePLJJ7t8bv/+/ZcdGzNmzBWbVEVGRg7pBlZMWGioWJIQYfH9/ddE4EBWJaaEe6GmSYvkCxVIza/F+BAP5FY1AQDOlmig1RlwqbpJWga96qYYRHBnciIaZOwl30dMWGioivJzxZ5VswEA/0q5ZExYCmoxIaSjgF2rM+BCmQYnC+qkY+dKNExYiGjQcfPDPmIfFrIHU0d4AwBO5tfix/wai+dOFdYh5WK19P35UnmaNBLR8MaEpY84wkL2YGyQO1zUSjS06bA9zTj1E+rlDABIK6jD0byOhOWcKWH58VINZv5pL3u5ENGgYMLSB63temh1BgCApwsTFhq6VEoFpozwAgBUNhiXMi+fGQkA2HW2DHXN7dK550qMCcv7hy+huK4F/0rJH9RYiWh4YsLSB+bRFYUAuKlZDkRDW7xpWggAvFwcsHBKKABIrfzjI4zPl9S3orqxDYdyqgAAqfm10Bu6LpyvaGjF05+cxMmC2oEMnYiGASYsfaDpVL+iUHCZJw1tUyM6Epb4Ed7wc3NEuI+zdOym8YHS99tOFEoJe2ObTqpruVTVJI3AAMCWQ3n4Kr0Ev/0iY0ivBiQi+TFh6QPWr5A9mdJphMWcvEwK85KOJY70xfhg4wqifx6+ZPHa43k1aNbqcNc7R7Dwb4dRXNcCAPghyzgKc65Ug9NF9QCMU6kNre0gIuoJJix9wISF7Imns4NUx3LdKD8AwORw4/fujipMCPHAOFPCYq5zifR1AWAswN1xpgw1TVpodQb870wpKhpaLVYUfXy8AE1tOizcdBjX/mkvKhpaB+nKiMgesPCiD5iwkL15Z0k8imqbMcmUqMybEIS39+bg7vgwqJQKaYTF7JmkGDyzLR3H82qkJAYAdpwphY+rcVsNdycVGlp1+PpUCRrbdLhQ1gAA2H2u/LJmdkREV8IRlj5gDxayN0GeTpgW6SN9H+7jglPrbsaLtxr3ExrXKWGJ8nPF/NggOKoUqG7S4kR+LRQCIAjGpdCfmjZTvP+aCET7u6JZq8e3p0ul1++7UDHg1/PRsQLcsvEHFFQ3D/ifRUQDiwlLH3CEhYabMG9nuDsZB2ZnjfaDo0opTRsBwJwxAdJqo6O5Nabz/HHfjBHSOQtigwEAh3Oq0dquH7BYdXoD3tidiQtlDfj30UsD9ucQ0eBgwtIHTFhouBEEAYkjjTuuz5sYBACYEdUxInPPtHD8zJSQAICLWon4CG8sig9HTKAbbh4fiL/cOxnBnk5oadcjJbcaA+VQThWqGrUAgJ1ny7hKiWiIY8LSB0xYaDh69e44fPPkdbg22liYmxhtTGD83Bwxd1wAbjElMoBxZZFapYCniwO+f3Y23l06DSqlAnPGBAAwTgsZDCJ+vFRjsXKoXW9As1bXpzi/Si+Rvi6sacHZEg0qGlox9/X9eP6zU316byIafExY+kDDhIWGIS8XNWLDPKXvE0f64tW747D1wWlwUCoQ4uWMqabVRrPH+Hf5HjeONSYsyecrsPKjNCzanILffJEhPf+rj05ixh+Te1R7otMb8P92ZWJnRhmatTrsOlsGAIgwrWTadbYMG/dk42JlE/6bVnTFpdWiKOL9w3n4rlO9DRHJj6uE+oAjLETGaaJ7poVbHHtt0STsPleOe6eP6PI1M0cZR16K61qkni37LlSgXW9AY6sO358rg0EEvj1TgifmjAIA5FU1IdDDES5X6Cq9I6MMf92XAwCYM8YfzVo9Inxd8PTc0Vj16Sls+7EQ1U3GKSKDaOzQax7p6exEfi1+/805qBQCZo7yhZeL2qqfQ05FA17dmYnnbh6DMUHuVr2GiKzHEZY+YMJC1LVofzc8PjsaalXXv2Jc1CqpFsbHVQ13JxUa23Q4WVCHQzlVMHf6Tz5vXEmUcrEaN76+H09/km7xPp3rUr473TEFtD+zEgBwx+RQzB0XCAelgIqGNostBI7nGYuC65vbsetsmfTc5yeKAAA6gyj9+dZ4dWcmvj9Xjr8fuGj1a4jIekxY+oAJC1Hv/eZn4/DQdVH4auVMaaTjYHYlDmRVSuekFdSiurEN/zycB1EE9pwvR4XG2HBu7VcZmPTS9zhXokFDazv2mZKUJQnGUR1BABZODoGnswNmmhrhKRUCHps9EoCx2R0A/OaLM3js36l4/ftMNGt1+LZT4vO/jDKrrqWmSYt9mcbkJr2orrc/EiK6CiYsfcCEhaj3xgS548VbxyPcxwWzRhsTih+yOhIWJwcFRBH45MdCJJt6togi8O3pUpTUteA/xwqgadXhD9+ew57z5dDqDBjp74pXFk7Ehw8l4N0HpmGkvxsASFNTD10XJX19qrAeRbXNUq3Lewdz8bd9F9Gk1cPDtHT7h+xKNLXpsPdCOe746yGkmTZxFEURm/bl4O8HLkIURXx7ugTteuMITW5lE+pNu1v/8btzeOG/p2G4wuaQRGQ91rD0UptOj9Z2AwA2jiPqq+tHG4tzT5n2G3J2UGLptRH4+4Fc/GVPNvQGEQ5KAe16Ed+cLkFVY8f0TkpuNfKrmwAAt8aFQBAEXGdKgMxumRiE47+ZC393RwCAv7sjKhvasO6rs9CZ3qddL0o1MA9fPxLb04pwqboZHx8vwFvJ2dC06rBqWzp2PjML350uxWu7MgEA3i5qbE8rtvjzThfXYYSPC947mAcAWD4zinUtRH3EEZZeMv8LShCM+6wQUe8FeTphTGDHDf3aaF/Mn2js56LVG/9h8Py8MVAIwMmCOvw7JR8AMDHU2Hm3pN44TXRrXDCuJMDDCYIgQBAEzDB18zWP3Dw+OxpqpfHXoSAAP48Pk/rMvPLdeWhajUusL1U346VvzuGlb85K7/u7rzKQXlgHpULAtaYl3qcK67C3UyffM8X1vfq5EFEHJiy9ZF7ZEOjuBIVCkDkaoqHv+k6jIrNi/BEX6imNiHg6O2BpYqTU86WhTYcQTyf865cJ0pRsTKAbYgKtG8WYHtmxM7WLWoknbxyFx021LdeN8kOolzNumdDRT8bJQYFf3zIGgHETR02rDrGhnrhulB+0OmNCNTvGH3PHBQIA0n+SsGQwYSHqMyYsvVRYa0xYwn2cZY6EyD7Miuno2TI7xh8KhYCbxxsTgEXxYXByUOL2SSHSOfcnRsDHVY0188cCAB5IjLT6z5oR5St9vSA2GG6OKjydFIO37puC1xdNAgBMCvNCqJfx/+/f/mwcVsyORtI4Y3Gwg1LAa4vi8MbiSfBzMy57vjs+TNqmIDW/FsdMWxMAwNkSJixEfcW5jF4qrDE2tAr3cZE5EiL7kDDSBzMifeDv7ohIP1cAwOr5YxEX5ok7JocCAG6ZEIxXvjsPiB2FtPfOGIEFccakw1pjgtzh66pGdZMW984w9pBRKgSLhEihELD1wenIqWjEz2KDIAgCXlkYi3b9afwsNghjg4zTUdseS8TpojrMnxiENp0BKoWAWtOUsYtaiWatHmdLNNAbRCg5GkvUa0xYeqmo1pSweDNhIeoPjiolPn080eKYh5MDFndqPufp4oBvnrwOgmDs32Lm7tSzwnelQsCWB6ejXNOK+AifK543Jsjdolg2yNMJH/xyhsU50f5uiDatRnJyUGJssDsyijUAgJ9PDcNnqYVo1uqRV9WEqsY2PP5hKn77s3FY9JNme0R0dZwS6qXCGvOUEBMWosEU6eeKCF/XPr/P5HAvzOtUp9JfOu9enTQ+EOODjSMxGcX1eHtvNuqa27HlUF6//7lE9o4JSy8VSiMsrGEhog6TwrwAGJdmJ0T5IDbUuO/St6dLcTjHuDv1hbIGaSk2EVmHCUsv6A0iims5wkJEl7tpfCAmhXthxZxoODkoMcGUsOw5X25x3vdny7t6OQBjjZx59RERGTFh6YXS+hboTI2sAj2c5A6HiGyIl4saX62ciafmjgYAaYTFzLx829xhN6+qSeqgCwD7Mitw/av78NquC4MUMdHQwISlF8z1K6Fezqz6J6KrGhXgJm0CGejhiPV3xgIAUgtqsT+zAgveOohFm1OQV2WcIvo63biXkXkDRyIyYsLSC1L9CqeDiKgbDkqFVHi7eFo4wn1cEBfmCVEEfvn+j2jW6qE3iPhfRikMBhE/mPZSyq1qQmu7Xs7QiWwKE5ZeKDL1YAnjkmYissLqW8biFwkj8ND1xm665oZ4BtHYRRcw1rScLdGgukkLwFgrl1PRKE/ARDaICUsvsMstEfVEYrQv1t8ZK20jsCAuBA5KAcGeTtj2qLH3THphHT49UWjxunOlmkGPlchWsXFcL0hdbjnCQkS9EOXnit3Pzoa3qxqezg6YMsILJwvq8J9jxk0dvV0cUNvcjvNMWIgkHGHpBdawEFFfRfq5SiMu5gZ2BtH43LJrIwGACQtRJ0xYeqi1XY9yTRsANo0jov5hrmkBgGh/VySZdn0+X9oAURTlCovIpjBh6aHiOmP9iqtaabGXCRFRb430d8OoAON+RLNjAjA60A0qhYD6lnaU1rfiQpkGOzNKZY6SSF5MWHqo8y7NgsAeLETUP55NisHEUA/cf80IOKqU0oaKB7Mrcc/mFDz+YRrOFNXLHCWRfFh020Ol9a0AjE3jiIj6y4K4YCyIC5a+HxvsjszyBrz0zTk0a439WH68VIPYMM8rvQWRXeMISw9VNhjrVwI8HGWOhIjs2ThTszlzsgIAZ4o5wkLDFxOWHjInLH5uTFiIaOCYExYAmBhq/PpUUZ1M0RDJjwlLD5kTFn93JixENHAmh3nB09kBUX6ueGdJPAAgt7IJmtZ2tGj1+ON353DiUo3MURINHtaw9FBloylh4QgLEQ0gTxcH/PD8DVApBbg6qhDm7Yyi2hZkFNXjTHE93juYh+TzFUh+bjYXANCwwBGWHqpq5AgLEQ0OTxcHuDoa/105KcwLAHCqqB7b04oBGDdINLfvNxhE6PQGWeIkGgxMWHqINSxEJAfz6qDPUwuRWd4gHf/utLE/yzPb0jH55d3cMJHsFhOWHmhq00kV+xxhIaLBFGdKWC5WNgEAfE2NK789XYpD2VX4+lQJGtt0+MfBXACATm/Ah0fzkcGVRWQnmLD0gHl0xUWtlIZpiYgGQ2yoJzqXqqy7fQKcHZQoqGnGc5+lS8e/TC9GXbMWWw7l4XdfZuCZbemXvRfRUMSEpQcqWb9CRDJxd3LASD9XAICPqxrzJwbhxnEBAIByTRvcHVWI9ndFa7sBf0nOxl+SswEAORWNKNe0yhY3UX9hwtIDVQ1cIURE8pkywhsAcPukEDgoFbg1tqMz7uNzovHYrGgAwD8PX7JoOJdysXpwAyUaAExYesA8wsKCWyKSw3M3x+C5m2Lw3M0xAIAbxgYg0tcFMYFueOi6KNw+OQTeLg4AAIUAXD/aDwATFrIPLMToATaNIyI5BXs641dzR0vfOzkosfe5OdCLIhyUxn9/PpAYibeSs7E0MRKzYvxwMLsKKblMWGjoY8LSA0xYiMjWKBQCFOioxn167mjMjvHD5HBvNGt1UCoEFNQ0o7iuBTkVjdiZUYrfLRjPhQM05PBvbA8wYSEiW6dUCIiP8AFgLNSdGOqJU4V1+HdKPt4/kofWdgPGBXtgaWKkvIES9RBrWHqgim35iWiISRzpCwDYfOAiWtuNnXAP51TJGRJRrzBh6QGpyy1HWIhoiEiM9pW+dlQZf+Ufza2B3iDKFRJRrzBhsZIoiuzDQkRDzvRIb7iolQCAd+6fCjdHFepb2nHetAdRysVq5Fc3yRkikVVYw2Kl+pZ2tOuN/yLxc1PLHA0RkXVc1Cp8+HACWtv1uDbaDwlRPki+UIHDOVWobdbigS3HEebtjAPP3wClQkBjmw4nLtVgdow/d4Emm8IRFiuZp4M8nR3gqFLKHA0RkfWmjvDGtdHGnizmKaLDF6vx+vdZAICi2hYcMtW1PPPJSTz4zx/xjWlTRSJbwYTFSpwOIiJ7MHOUMXE5mF2J9MI66fjnqUXILGvAnvMVAIBj7N1CNoZTQlaSCm45HUREQ9iYQHf4uKpR06QFYOyGezC7CrvOlkGr62jnz12eydZwhMVKHT1YnGSOhIio9xQKQVrq7KJWYuPiyRgT6A6tzoBdZ8ul886XNaBdb5ArTKLLMGGxUiV7sBCRnbhzSigA4Km5o+Hr5ohF08Kk56ZFeMPdSQWtzoDs8ka5QiS6DBMWK1U3GodPfTklRERDXNL4QJx7eR4en23c3XnhlFCoFMYVQY/NjsaEEA8AQEYJp4XIdjBhsZJWZxwadXbgCiEiGvpc1B0ljH5ujnhtURyenzcGc8cGYGKIJwDgbHE9DAYRbydn46v0YrlCJQLAolurmbtCqpTsS0BE9ufOKR3TQrFhxoTlTHE9dp4tw+u7s6BWKjBvQhCc+I82kglHWKykMxhHWJQKJixEZN8mmEZYzpVqsGlfDgBAqzfgZEGdjFHRcMeExUo6U5dbFRMWIrJzI/1c4apWorXdgLMlGun48bwaGaOi4Y4Ji5V0pikhpYI/MiKybwqFgPGmwlsACDA1zDyWx2ZyJB/efa0k1bBwhIWIhoGJocZpIZVCwKt3xwEA0gpqpQUIRIONCYuVWMNCRMPJjWMDAABLEkZgdow/fFzVaG034ExxHT48mo9JL33P9v00qHqVsGzatAmRkZFwcnJCQkICjh8/fsVz58yZA0EQLnssWLBAOkcURaxduxbBwcFwdnZGUlISsrOzexPagOEICxENJ9eP9sfRNXOx7rYJEAQB0yO9AQCf/liEP3x7DvUt7fj30XyZo6ThpMcJy7Zt27Bq1SqsW7cOaWlpmDRpEubNm4eKioouz9++fTtKS0ulR0ZGBpRKJRYtWiSd8+qrr+Ktt97C5s2bcezYMbi6umLevHlobW3t/ZX1s44aFiYsRDQ8BHk6QWH6nZcQZWznv+1EIdpM00IHsirZvp8GTY8TljfeeAOPPPIIli9fjvHjx2Pz5s1wcXHB1q1buzzfx8cHQUFB0mP37t1wcXGREhZRFLFx40b87ne/wx133IG4uDj861//QklJCb788ss+XVx/Yh8WIhrOEkb6SF87qhTwdHZAQ6sOP3LlEA2SHiUsWq0WqampSEpK6ngDhQJJSUlISUmx6j22bNmCe++9F66urgCAvLw8lJWVWbynp6cnEhISrvqebW1t0Gg0Fo+B1LGsmWU/RDT8jA3ygKezAwDjHkQ3jQ8EAOw5XwGDQcQbu7PwwZFLMkZI9q5Hd9+qqiro9XoEBgZaHA8MDERZWVm3rz9+/DgyMjLw8MMPS8fMr+vpe27YsAGenp7SIzw8vCeX0mOsYSGi4UypEPDa3XF4au5oPDprJJLGGYtyky+UY+vhPLyVnI3ff3MWTW06mSMlezWowwVbtmxBbGwsZsyY0ef3WrNmDerr66VHYWFhP0R4ZVwlRETD3c0TgrDqphg4KBW4brQ/1EoF8qub8af/XQAAiCJwqbpJ5ijJXvUoYfHz84NSqUR5ebnF8fLycgQFBV31tU1NTfjkk0/w0EMPWRw3v66n7+no6AgPDw+Lx0BiDQsRUQc3R5VU12JelAAAeVVMWGhg9ChhUavViI+PR3JysnTMYDAgOTkZiYmJV33tZ599hra2Ntx///0Wx6OiohAUFGTxnhqNBseOHev2PQcTO90SEVky17F4uThgVow/ACCvkgkLDYwe79a8atUqLFu2DNOmTcOMGTOwceNGNDU1Yfny5QCApUuXIjQ0FBs2bLB43ZYtW7Bw4UL4+vpaHBcEAc888wxeeeUVjB49GlFRUXjxxRcREhKChQsX9v7K+hlrWIiILN0zLRwVmjYkjQ/E4Zwq/JBVyREWGjA9TlgWL16MyspKrF27FmVlZZg8eTJ27twpFc0WFBRA8ZNRiMzMTBw6dAjff/99l+/561//Gk1NTXj00UdRV1eH6667Djt37oSTk1MvLmlgsA8LEZElJwcl/m/eGABASV0LACCXCQsNEEEURbH702yfRqOBp6cn6uvrB6SeZcrL36O2uR27n52F0YHu/f7+RERD2flSDeb/5SA8nR1wat3NcodDQ4i1928WZFiJIyxERFcW6WvsrVXf0o7aJq3M0ZA9YsJipY4aFv7IiIh+ylmtRIincRqf00I0EHj3tZI0wsJlzUREXYryN3UwZ8JCA4AJi5W4SoiI6Oqi/MwJSyNEUURhTTPspEySbAATFiuIoiglLKxhISLqWpSfGwDjCMtfkrNx/av78J9jBTJHRfaCCYsV9J26ODqwhoWIqEsjTSMsP16qxaZ9OQCAHWdK5QyJ7Ajvvlbo3HaaNSxERF0zTwlVNrSh3bTD/Yn8WrS26+UMi+wEExYrdB5hYQ0LEVHXwrydpd+RapUCXi4O0OoMSMuvlTkysgdMWKxgMcLChIWIqEsqpUIaZXl8djRuGBMAADhysRoAcKFMg4zietnio6GNCYsVOo+wKAUmLEREV/LSHRPwTNJoPDEnGonRxr3jjlysQkF1M+7462Es2pyC+pZ2maOkoajHewkNRzqDAQCgEAAFR1iIiK7o2mg/XBvtZ/ramLCcKqrH777KQJvO+Lv0bHE9rh3lJ1uMNDRxhMUK7HJLRNRzYd4uiPB1gd4g4oesSun4aU4LUS/wDmwFnZ49WIiIesM8ygIAXi4OAIAzTFioF5iwWEHHLrdERL0y0zT14+Gkwst3TAQAFt5Sr7CGxQp6Uw0Le7AQEfXMLROC8GxSDK4Z6YOYQHcAQH51M+pb2uHp7CBzdDSUcITFChxhISLqHZVSgaeTRiNhpC+8XdUI83YGYCy8JeoJJixWYA0LEVH/iAvzBMA6Fuo5JixW4CohIqL+MTGUCQv1Du/AVpCmhFjDQkTUJ7GmhCWjuB71ze3YnlaEumatzFHRUMCExQrmERZOCRER9c3EEGPCcqm6GTe8vh+rPj2FvyRnyxwVDQVMWKxg7nTLolsior7pXHhb02QcWeEyZ7IGExYrdIyw8MdFRNRXd8eHwcvFAfdfMwIAkFnWAFEUu3kVDXfsw2IFLmsmIuo/zyTF4Om5o9GmM+Dj44XQtOpQ0dCGQA8nuUMjG8YhAyvouayZiKhfCYIAJwclInxdAABZ5Q0yR0S2jgmLFTjCQkQ0MGICjN1vM8uYsNDVMWGxgrnoliMsRET9KybQDQCQXd4ocyRk65iwWEHPPixERAMiJsg4wpJVwREWujomLFboaM3PHxcRUX8yb4iYXd7IlUJ0VVwlZAU9a1iIiAZEpK8rVAoBjW06lNS34uNjBahsaMP6u2I5DU8WOGRgBR073RIRDQi1SoEoP1cAwDv7c/DXfTnYdqIQ6YV18gZGNocJixX07HRLRDRgzHUsHx4tkI6duFQjVzhko5iwWKFj80P+uIiI+pt5aXNnPzJhoZ/gHdgKrGEhIho45qXNAPDwdVEAgBP5tTAYWIRLHZiwWIE1LEREA+eakb4I9XLGbZNC8OtbxsLJQYG65nZcrGRvFurAVUJW4AgLEdHA8XZV49DqGwAYW/ZPCfdGSm41jl+qwejAy6eLaHjiCIsVdNxLiIhoQAmCAEEw/o6dHukNADhxqVbOkMjGMGGxAlcJERENnulRPgCMhbeiKKKwphnteoPMUZHcmLBYoaOGhT8uIqKBNmWENxQCUFTbgqQ3DuD6V/dh454sucMimfEObAXuJURENHjcHFUYH+IBALhY2QQASD5fIWdIZAOYsFihnTUsRESD6skbRmHqCC+svCEaAJBd0YjWdr3MUZGcuErICqxhISIaXLdMDMYtE4MhiiK2/ViIqkYtzpVqMHWEt9yhkUw4wmIF9mEhIpKHIAiIDfUEAGQU18scDcmJCYsV2IeFiEg+5oTldBETluGMCYsVuEqIiEg+EznCQmDCYhXzCIsDVwkREQ262DBjwmIuvP3udCk27smCKHKvoeGERbdWYA0LEZF8gjyc4OemRlWjFl+nl+A3X5yBziDi+tH+iI9gEe5wwREWK3CVEBGRfARBkKaFXvwqQ/pH5IUyjZxh0SBjwmKFjr2E+OMiIpKDufC2TdfRoj+rrEGucEgGvANbgauEiIjkZU5YAGBskHEH5wtMWIYVJixWYA0LEZG8pkf6wMNJhbFB7lh/VywAIKu8gYW3wwiLbq3AvYSIiOTl7arG4RduhEqhgCAACgGobW5HZUMbAjyc5A6PBgFHWKygMxXdcoSFiEg+7k4OcFYr4eSgRKSfKwAgs5zTQsMFExYrmItuWcNCRGQbzHUsmaxjGTaYsFiBnW6JiGxLTGBHwlLd2IZff34KR3KqZI6KBhJrWKzAVUJERLZljClhySpvwIb/XcDnqUXIq2rCtaP8ZI6MBgoTFitwlRARkW0ZY5oSOleqwWnTHkMXK5vkDIkGGOc4rMBOt0REtiXC1xVqlQLtehHmlc01TVrUNmnlDYwGDBMWK+ikZc38cRER2QKlQsDoADcAxo1pPZyMEwa5VY1yhkUDiHdgK+g5JUREZHMmh3sBAO6/JgJxYcavOS1kv5iwWIHLmomIbM9zN4/BxsWTsWb+OIz0N/ZluVjJERZ7xaJbK3CEhYjI9vi4qrFwSigAYKSpkVwuR1jsFkdYrKBja34iIpsWbapnyTWNsHx0rAArPkxFa7tezrCoHzFhsQJXCRER2baR/saEpaCmGY1tOrzy3Tn8L6MMh7LZTM5eMGGxAjvdEhHZtmAPJzg5GJc5f3g0H81a48hKYW2zzJFRf+Ed2ArsdEtEZNsUCgFRfsZRln8czJWOF9a0yBUS9TMmLFYwrxJi0S0Rke2KNq0UqmrsaB7HERb7wYTFCjrWsBAR2TxzHUtnRbUcYbEXTFi6YTCIMM0IcYSFiMiGmUdYAOD60cZNEItqmiGae/fTkMaEpRv6Tn/RVSy6JSKyWSP9OkZYHp01EgDQ0KZDfUu7XCFRP+rVHXjTpk2IjIyEk5MTEhIScPz48aueX1dXh5UrVyI4OBiOjo6IiYnBjh07pOf1ej1efPFFREVFwdnZGdHR0fjDH/5gE1mxueAWAJTsw0JEZLNGB7oh0tcFk8K9MDPaD/7ujgBYeGsvetzpdtu2bVi1ahU2b96MhIQEbNy4EfPmzUNmZiYCAgIuO1+r1eKmm25CQEAAPv/8c4SGhiI/Px9eXl7SOX/+85/xzjvv4IMPPsCECRNw4sQJLF++HJ6ennjqqaf6dIF9pTN0HmFhwkJEZKucHJTY939zYBCNq4bCvJ1R2dCGwtpmxIZ5yh0e9VGPE5Y33ngDjzzyCJYvXw4A2Lx5M7777jts3boVL7zwwmXnb926FTU1NThy5AgcHBwAAJGRkRbnHDlyBHfccQcWLFggPf/xxx93O3IzGPR6JixEREOFIAgwD4aHe7vgZEEdCmuaUdukxTPb0rEgLhj3TAuXN0jqlR5NCWm1WqSmpiIpKanjDRQKJCUlISUlpcvXfP3110hMTMTKlSsRGBiIiRMnYv369dDrO9olX3vttUhOTkZWVhYA4NSpUzh06BDmz5/fm2vqV+YVQgCLbomIhpJwH2cAxqXN/00rwoGsSmw5mCdzVNRbPRphqaqqgl6vR2BgoMXxwMBAXLhwocvX5ObmYu/evViyZAl27NiBnJwcPPHEE2hvb8e6desAAC+88AI0Gg3Gjh0LpVIJvV6PP/7xj1iyZMkVY2lra0NbW5v0vUaj6cmlWK3zxoeCwISFiGioCPN2AWBc2pxXZdwUsaqx7WovIRs24Ls1GwwGBAQE4N1334VSqUR8fDyKi4vx2muvSQnLp59+iv/85z/46KOPMGHCBKSnp+OZZ55BSEgIli1b1uX7btiwAS+99NJAh9+pLT+TFSKioSTclLBklTWg0pSo1DRrodMboFJy1edQ06OExc/PD0qlEuXl5RbHy8vLERQU1OVrgoOD4eDgAKVSKR0bN24cysrKoNVqoVar8fzzz+OFF17AvffeCwCIjY1Ffn4+NmzYcMWEZc2aNVi1apX0vUajQXh4/89Lsi0/EdHQZJ4SKqlvlY6JIlDTpEWAh5NcYVEv9SjFVKvViI+PR3JysnTMYDAgOTkZiYmJXb5m5syZyMnJgaFTLUhWVhaCg4OhVqsBAM3NzVD8pMeJUqm0eM1POTo6wsPDw+IxEDjCQkQ0NAV7OqOrX92VnBYakno8JrZq1Sq89957+OCDD3D+/HmsWLECTU1N0qqhpUuXYs2aNdL5K1asQE1NDZ5++mlkZWXhu+++w/r167Fy5UrpnNtuuw1//OMf8d133+HSpUv44osv8MYbb+DOO+/sh0vsGz3b8hMRDUlqlQJBnUZSzP/wrGxgwjIU9biGZfHixaisrMTatWtRVlaGyZMnY+fOnVIhbkFBgcVoSXh4OHbt2oVnn30WcXFxCA0NxdNPP43Vq1dL57z99tt48cUX8cQTT6CiogIhISF47LHHsHbt2n64xL7pGGHhfCcR0VAT5uOCkvpWuKiViA31xLG8GovNEWnoEERbaCfbDzQaDTw9PVFfX9+v00MZxfW49e1DCPJwwtHfzO239yUiooH33Ken8N+0Itw8PhBujipsP1mM1beMxYo50XKHRibW3r85bNAN1rAQEQ1dd8eHYUygOx6bHS216ufS5qFpwJc1D3VSDQv3ESIiGnISo32x69lZAIC0/FoArGEZqjjC0g2dniMsRET2gCMsQxsTlm6Y+7A4sOiWiGhI83NjwjKU8S7cDdawEBHZB/MIC6eEhiYmLN2QOt2yhoWIaEjzczM2K61tbke7/sqNSck2MWHpBkdYiIjsg7eLWvpdXs1eLEMOE5ZusNMtEZF9UCgE+LoaR1lYxzL0MGHpBkdYiIjsh1THwoRlyGHC0o2O3Zr5oyIiGurMK4VYeDv08C7cDfZhISKyH517sTRrdcgub5A5IrIWE5ZudIywMGEhIhrqOo+wrPgwDTe9+QNOFdbJGxRZhQlLN9pNRbccYSEiGvrMIywHs6twIKsSAHCqqE7GiMhaTFi6wT4sRET2w9yLJaeiUTpWVNsiVzjUA0xYutFRw8IfFRHRUGceYemsmAnLkMC7cDdYw0JEZD/83ToSFkeV8RZYVMeEZShgwtINHRMWIiK70XmE5eHrowBwhGWoYMLSDanTLWtYiIiGPE9nB9w5JRTzJgRi+UxjwlLV2IbWdr3MkVF3VHIHYOvY6ZaIyH4IgoA3F08GAIiiCFe1Ek1aPYrrWhDt7yZvcHRVHGHpBjvdEhHZJ0EQEOrtDIDTQkMB78Ld4AgLEZH9CvUyJSwsvLV5TFi6wVVCRET2K8zbBQBHWIYCJizd4F5CRET2yzwlVFTbLHMk1B0mLN2QVgkxYSEisjs/nRISRVHOcOgqmLB0o6OGhT8qIiJ7E9ap6La4rgWJG/ZizfbTMkdFXeFduBvmKSH2YSEisj/mKaEyTSve+yEXZZpW7DhTJnNU1BUmLN3gKiEiIvvl5+oItUoBgwj851g+AKC+pR31ze0yR0Y/xYSlG6xhISKyXwqFgDBTHUu7vqN+pZBFuDaHCUs3OMJCRGTfzNNCnRXUMGGxNUxYuiH1YVHyR0VEZI/MK4WcHZS4cWwAACYstoh34W5wt2YiIvsWF+YFALh3RjgmhnoCYMJii7j5YTf0nBIiIrJr90wLw+hAN0wJ98KX6SUAgIJqJiy2hglLNzjCQkRk31RKBaZH+gAARvgYW/VzhMX2cEqoG+ZVQhxhISKyf+aEpbiuBTq9QeZoqDMmLN2QGsex0y0Rkd0LcDf2ZdEbRJTWt8odDnXCu3A3WMNCRDR8KBQCwk3LnAtqmlFQ3Yx/HMxFO0dbZMcalm6whoWIaHgZ4eOCi5VNKKhpxp93XsDponp4ODngnunhcoc2rHGEpRvSCAv3EiIiGhbMdSzfni7B6aJ6AEB6UZ2MERHAhKVbHGEhIhpewk0Jy+GcaunY2RKNXOGQCROWbpirxFnDQkQ0PJhHWDq7UKrhqiGZMWHphtSan6uEiIiGhRG+HQnL2CB3uKqVaNMZkFvVJGNUxLtwN7j5IRHR8BLu3ZGwLE2MxLhgDwDA2ZJ6uUIiMGHplnmExYFFt0REw4KrowoLYoMRF+aJOyaHYEKIKWEpZh2LnLisuRs6drolIhp2Ni2ZKn09IcS4IWJGST1qmrRY8o9jiAv1xJ/vjpMrvGGJIyzd8HZRw8dVDbWSPyoiouFovGmE5VyJBn/bl4PzpRp8llqI1na9zJENLxxh6cbOZ2bJHQIREckoJtAdDkoBmlYd/nnkEgDAIAKZZQ2YFO4la2zDCYcNiIiIrkKtUmB0gDuAjrpGADhXypqWwcSEhYiIqBvmwlsAmBHlA4CrhgYbExYiIqJuJIz0BQDcND4QSxJGADDWtNDgYQ0LERFRN+6aEgp/d0fMiPRBcV0zAOBCWQP0BpGrSAcJR1iIiIi6oVAImB3jD2e1ElF+bnByUKBZq0d+NbvfDhYmLERERD2gVAgYG2TufqvBV+nFuOmNA7hQximigcSEhYiIqIfMvVkOZVfhd19kILuiEZ/+WCRzVPaNNSxEREQ9NN60v9C2E4XSsdSCWrnCGRY4wkJERNRD4zstczY7W1yPFi273w4UJixEREQ9NC7IA+bFQQtigxHg7gidQcTpojpZ47JnTFiIiIh6yFmtxA1jAuDv7ogX5o/FtEhvAMZpIVEU8emJQuw+Vy5zlPaFNSxERES98I9l09CuF6FWKTB1hDd2nClDWn4tUnKr8evPT0OtUuD0upvh5KCUO1S7wISFiIioFwRBgFplnBeKjzCNsOTXorFNBwDQ6gw4V6rB1BHessVoTzglRERE1EcTQjzhqFKgtrkdR3NrpOOnC+vkC8rOMGEhIiLqI7VKgUlhXtL3zqZpoNPF3CCxvzBhISIi6gdTTdNCSoWA1beMAQCcLmLC0l+YsBAREfWDBbHBUCkELL82EgviQgAAFysb0dimQ0VDK/6yJxt1zVqZoxy6WHRLRETUD2LDPHH25XlQKxUQBAEhnk4oqW/FmaJ6/ONgLpIvVECr1+P5eWPlDnVI4ggLERFRP3FUKSEIxpVDcaaalu1pRUi+UAEAOFPMDRJ7iwkLERHRAIgL9wQAfJbasSni+VImLL3FhIWIiGgAdF41ZFbZ0IaqxrbBD8YOMGEhIiIaABNDPaWvp47wQpSfKwCOsvQWExYiIqIB4OnsgLFB7gCAx2ZHY1yw8esLpQ1yhjVkcZUQERHRAPnrL6biYmUj5k0IQlZZA3acKeMISy/1aoRl06ZNiIyMhJOTExISEnD8+PGrnl9XV4eVK1ciODgYjo6OiImJwY4dOyzOKS4uxv333w9fX184OzsjNjYWJ06c6E14RERENmFUgBvmTQgCAIwL9gAAnDMlLJeqmnCpqkm22IaaHo+wbNu2DatWrcLmzZuRkJCAjRs3Yt68ecjMzERAQMBl52u1Wtx0000ICAjA559/jtDQUOTn58PLy0s6p7a2FjNnzsQNN9yA//3vf/D390d2dja8vblhFBER2YdxIcaE5WJlIy5VNeFnbx2ESiEgZc1cuDpywqM7giiKYk9ekJCQgOnTp+Ovf/0rAMBgMCA8PBy/+tWv8MILL1x2/ubNm/Haa6/hwoULcHBw6PI9X3jhBRw+fBgHDx7sxSUYaTQaeHp6or6+Hh4eHr1+HyIiooEgiiImvfQ9NK06xIV5Sm37P/jlDMyO8Zc5OvlYe//u0ZSQVqtFamoqkpKSOt5AoUBSUhJSUlK6fM3XX3+NxMRErFy5EoGBgZg4cSLWr18PvV5vcc60adOwaNEiBAQEYMqUKXjvvfeuGktbWxs0Go3Fg4iIyFYJgoCxpmmhznsMpVysliukIaVHCUtVVRX0ej0CAwMtjgcGBqKsrKzL1+Tm5uLzzz+HXq/Hjh078OKLL+L111/HK6+8YnHOO++8g9GjR2PXrl1YsWIFnnrqKXzwwQdXjGXDhg3w9PSUHuHh4T25FCIiokE3PrhjBMHPzREAkJLLhMUaA76s2WAwICAgAO+++y7i4+OxePFi/Pa3v8XmzZstzpk6dSrWr1+PKVOm4NFHH8Ujjzxicc5PrVmzBvX19dKjsLBwoC+FiIioT8xLm9VKBf62ZCoAIKO4Hg2t7XKGNST0KGHx8/ODUqlEeXm5xfHy8nIEBQV1+Zrg4GDExMRAqVRKx8aNG4eysjJotVrpnPHjx1u8bty4cSgoKLhiLI6OjvDw8LB4EBER2bJbJgRj5ihfrLt9PGZE+SDC1wV6g4gfL9VA09qOf6VcQm0Td3TuSo8SFrVajfj4eCQnJ0vHDAYDkpOTkZiY2OVrZs6ciZycHBgMBulYVlYWgoODoVarpXMyMzMtXpeVlYWIiIiehEdERGTTPF0c8J+Hr8GSBOP9LXGkLwDgSE41nvgwDWu/OouXvjkrZ4g2q8dTQqtWrcJ7772HDz74AOfPn8eKFSvQ1NSE5cuXAwCWLl2KNWvWSOevWLECNTU1ePrpp5GVlYXvvvsO69evx8qVK6Vznn32WRw9ehTr169HTk4OPvroI7z77rsW5xAREdmbxGhjwvJByiUcyqkCAOw4U8b9hrrQ44XfixcvRmVlJdauXYuysjJMnjwZO3fulApxCwoKoFB05EHh4eHYtWsXnn32WcTFxSE0NBRPP/00Vq9eLZ0zffp0fPHFF1izZg1efvllREVFYePGjViyZEk/XCIREZFtMo+wtOuNHUY8nFTQtOrw6YlCPDFnFAwG43GFQpAtRlvR4z4stop9WIiIaCia+/p+XKxswm2TQnD9aD/8+vPTCPN2xkcPX4NH/30CLe167HjqerttLjcgfViIiIiof/1h4UQ8Omsk1t85EbdPCoGnswOKalsw/y8/4EJZA/Krm7HjTKncYcqOCQsREZGMro32w29+Ng7uTg5wclBiUXwYAKBJq4ejynib/jy1CABQ0dCK3399FhfKhl+zVCYsRERENmTZtZHwcFLhmpE++HLlTAgCcCyvBgXVzXj+s9N4/8glrPtq+K0kss8JMSIioiEq3McFqS/eBJVCgCAIuG6UHw5mV+FXn5zEqcI6AMYEpqi2GWHeLtiXWYHi2hbcf419twLhCAsREZGNcVAqIAjGlUF3m6aIzMmKeZroy5PFKKxpxmP/SsXvvsxAWkGtLLEOFiYsRERENmzehCC4OxknRKL9XbHutgkAgO1pxXhtVya0emNj1v2ZlbLFOBiYsBAREdkwJwclHp8dDR9XNV69Ow63Tw6Bk4MCuVVN+PpUiXTegSwmLERERCSjlTeMQtqLNyE+wgdujircMqFj/77rR/sBAE4X1aGmSYui2mYs23ocOzPK5Ap3QDBhISIiGmLummqsa1GrFNhwVyzGBrlDFIGD2ZVYv+M8DmRVYu1XGdDqDN2809DBVUJERERDzPWj/fD728Yjws8VYd4umB3jjwtlDfjHwTycKa4HAFQ0tOG7MyW4c0qYzNH2D46wEBERDTGCIODBmVG4YUwAAGB2jD8ASMmKp7MDAGDLoTyIoojiuhakm1YZDVVMWIiIiIa4+EhvuKiVAAAHpYB/PzQDjioFMoo1eOW785j7+n7c+bfDSM2vkTnS3mPCQkRENMQ5qpS4bpSx+Pa+GSMQF+Yl1blsOZSH1nYDRBH4+HihnGH2CRMWIiIiO7D2tvH47c/G4YX5YwEAv5wZCZVCgEIA7poSCgDYcaYUTW06OcPsNRbdEhER2YEwbxc8Mmuk9P3oQHdsf+JaOKqUiAl0w8nCOuRVNWHHmVIsmhYuY6S9wxEWIiIiOxUX5oUxQe4QBEFq8f95ahFEUURuZSNa2/UyR2g9JixERETDwJ1TQqWdn+e+fgA3vn4Ay7Yeh94gyh2aVZiwEBERDQMhXs5SYW5uVRMAY/Ly9x8uyhmW1VjDQkRENEysvXU8Nh/IRUKUD1p1eqz96ize3J2FCSGe0LS0wyCKmD8xGGqV7Y1nCKIoDo2xoG5oNBp4enqivr4eHh4ecodDRERk00RRxOMfpmLX2XKL45PCvbDpF1Pg6+qInIpGRPq5wN3JYcDisPb+zREWIiKiYUgQBKy/MxYZxRqUaVoxPtgDBTXNOFVYh5ve+AHtegN0BhF+bmq8de8UXGuaTpItXo6wEBERDV9anQEGUYSTgxKFNc148qM0nCoytvh3clCgtd0AQQCemRuDJ28cBaVC6Nc/39r7NxMWIiIikmh1BpwprkOghxN8XR3x+6/PYtsJY4fcDx9KwHWj+3ekhVNCRERE1GNqlQLxET7S93++Ow4zonxwoUzT78lKTzBhISIioqv6uanpnJxsb90SERER0U8wYSEiIiKbx4SFiIiIbB4TFiIiIrJ5TFiIiIjI5jFhISIiIpvHhIWIiIhsHhMWIiIisnlMWIiIiMjmMWEhIiIim8eEhYiIiGweExYiIiKyeUxYiIiIyObZzW7NoigCADQajcyREBERkbXM923zffxK7CZhaWhoAACEh4fLHAkRERH1VENDAzw9Pa/4vCB2l9IMEQaDASUlJXB3d4cgCH1+P41Gg/DwcBQWFsLDw6MfIrQ99n6N9n59gP1fo71fH8BrtAf2fn3AwF6jKIpoaGhASEgIFIorV6rYzQiLQqFAWFhYv7+vh4eH3f4FNLP3a7T36wPs/xrt/foAXqM9sPfrAwbuGq82smLGolsiIiKyeUxYiIiIyOYxYbkCR0dHrFu3Do6OjnKHMmDs/Rrt/foA+79Ge78+gNdoD+z9+gDbuEa7KbolIiIi+8URFiIiIrJ5TFiIiIjI5jFhISIiIpvHhIWIiIhsHhOWLmzatAmRkZFwcnJCQkICjh8/LndIvbZhwwZMnz4d7u7uCAgIwMKFC5GZmWlxzpw5cyAIgsXj8ccflyninvn9739/Wexjx46Vnm9tbcXKlSvh6+sLNzc3/PznP0d5ebmMEfdcZGTkZdcoCAJWrlwJYGh+fj/88ANuu+02hISEQBAEfPnllxbPi6KItWvXIjg4GM7OzkhKSkJ2drbFOTU1NViyZAk8PDzg5eWFhx56CI2NjYN4FVd2tetrb2/H6tWrERsbC1dXV4SEhGDp0qUoKSmxeI+uPvc//elPg3wlV9bdZ/jggw9eFv8tt9xicY4tf4ZA99fY1f+XgiDgtddek86x5c/RmvuDNb9DCwoKsGDBAri4uCAgIADPP/88dDpdv8fLhOUntm3bhlWrVmHdunVIS0vDpEmTMG/ePFRUVMgdWq8cOHAAK1euxNGjR7F79260t7fj5ptvRlNTk8V5jzzyCEpLS6XHq6++KlPEPTdhwgSL2A8dOiQ99+yzz+Kbb77BZ599hgMHDqCkpAR33XWXjNH23I8//mhxfbt37wYALFq0SDpnqH1+TU1NmDRpEjZt2tTl86+++ireeustbN68GceOHYOrqyvmzZuH1tZW6ZwlS5bg7Nmz2L17N7799lv88MMPePTRRwfrEq7qatfX3NyMtLQ0vPjii0hLS8P27duRmZmJ22+//bJzX375ZYvP9Ve/+tVghG+V7j5DALjlllss4v/4448tnrflzxDo/ho7X1tpaSm2bt0KQRDw85//3OI8W/0crbk/dPc7VK/XY8GCBdBqtThy5Ag++OADvP/++1i7dm3/ByyShRkzZogrV66Uvtfr9WJISIi4YcMGGaPqPxUVFSIA8cCBA9Kx2bNni08//bR8QfXBunXrxEmTJnX5XF1dnejg4CB+9tln0rHz58+LAMSUlJRBirD/Pf3002J0dLRoMBhEURzan58oiiIA8YsvvpC+NxgMYlBQkPjaa69Jx+rq6kRHR0fx448/FkVRFM+dOycCEH/88UfpnP/973+iIAhicXHxoMVujZ9eX1eOHz8uAhDz8/OlYxEREeKbb745sMH1k66ucdmyZeIdd9xxxdcMpc9QFK37HO+44w7xxhtvtDg2lD7Hn94frPkdumPHDlGhUIhlZWXSOe+8847o4eEhtrW19Wt8HGHpRKvVIjU1FUlJSdIxhUKBpKQkpKSkyBhZ/6mvrwcA+Pj4WBz/z3/+Az8/P0ycOBFr1qxBc3OzHOH1SnZ2NkJCQjBy5EgsWbIEBQUFAIDU1FS0t7dbfJ5jx47FiBEjhuznqdVq8eGHH+KXv/ylxSafQ/nz+6m8vDyUlZVZfG6enp5ISEiQPreUlBR4eXlh2rRp0jlJSUlQKBQ4duzYoMfcV/X19RAEAV5eXhbH//SnP8HX1xdTpkzBa6+9NiDD7ANp//79CAgIwJgxY7BixQpUV1dLz9nbZ1heXo7vvvsODz300GXPDZXP8af3B2t+h6akpCA2NhaBgYHSOfPmzYNGo8HZs2f7NT672fywP1RVVUGv11v84AEgMDAQFy5ckCmq/mMwGPDMM89g5syZmDhxonT8F7/4BSIiIhASEoLTp09j9erVyMzMxPbt22WM1joJCQl4//33MWbMGJSWluKll17C9ddfj4yMDJSVlUGtVl92EwgMDERZWZk8AffRl19+ibq6Ojz44IPSsaH8+XXF/Nl09f+h+bmysjIEBARYPK9SqeDj4zPkPtvW1lasXr0a9913n8Wmck899RSmTp0KHx8fHDlyBGvWrEFpaSneeOMNGaO13i233IK77roLUVFRuHjxIn7zm99g/vz5SElJgVKptKvPEAA++OADuLu7XzblPFQ+x67uD9b8Di0rK+vy/1Xzc/2JCcswsnLlSmRkZFjUeACwmDOOjY1FcHAw5s6di4sXLyI6Onqww+yR+fPnS1/HxcUhISEBERER+PTTT+Hs7CxjZANjy5YtmD9/PkJCQqRjQ/nzG+7a29txzz33QBRFvPPOOxbPrVq1Svo6Li4OarUajz32GDZs2DAkWsDfe++90texsbGIi4tDdHQ09u/fj7lz58oY2cDYunUrlixZAicnJ4vjQ+VzvNL9wZZwSqgTPz8/KJXKyyqgy8vLERQUJFNU/ePJJ5/Et99+i3379iEsLOyq5yYkJAAAcnJyBiO0fuXl5YWYmBjk5OQgKCgIWq0WdXV1FucM1c8zPz8fe/bswcMPP3zV84by5wdA+myu9v9hUFDQZYXwOp0ONTU1Q+azNScr+fn52L17t8XoSlcSEhKg0+lw6dKlwQmwn40cORJ+fn7S30t7+AzNDh48iMzMzG7/3wRs83O80v3Bmt+hQUFBXf6/an6uPzFh6UStViM+Ph7JycnSMYPBgOTkZCQmJsoYWe+Joognn3wSX3zxBfbu3YuoqKhuX5Oeng4ACA4OHuDo+l9jYyMuXryI4OBgxMfHw8HBweLzzMzMREFBwZD8PP/5z38iICAACxYsuOp5Q/nzA4CoqCgEBQVZfG4ajQbHjh2TPrfExETU1dUhNTVVOmfv3r0wGAxSwmbLzMlKdnY29uzZA19f325fk56eDoVCcdk0ylBRVFSE6upq6e/lUP8MO9uyZQvi4+MxadKkbs+1pc+xu/uDNb9DExMTcebMGYvk05yAjx8/vt8Dpk4++eQT0dHRUXz//ffFc+fOiY8++qjo5eVlUQE9lKxYsUL09PQU9+/fL5aWlkqP5uZmURRFMScnR3z55ZfFEydOiHl5eeJXX30ljhw5Upw1a5bMkVvnueeeE/fv3y/m5eWJhw8fFpOSkkQ/Pz+xoqJCFEVRfPzxx8URI0aIe/fuFU+cOCEmJiaKiYmJMkfdc3q9XhwxYoS4evVqi+ND9fNraGgQT548KZ48eVIEIL7xxhviyZMnpVUyf/rTn0QvLy/xq6++Ek+fPi3ecccdYlRUlNjS0iK9xy233CJOmTJFPHbsmHjo0CFx9OjR4n333SfXJVm42vVptVrx9ttvF8PCwsT09HSL/y/NqyqOHDkivvnmm2J6erp48eJF8cMPPxT9/f3FpUuXynxlHa52jQ0NDeL//d//iSkpKWJeXp64Z88ecerUqeLo0aPF1tZW6T1s+TMUxe7/noqiKNbX14suLi7iO++8c9nrbf1z7O7+IIrd/w7V6XTixIkTxZtvvllMT08Xd+7cKfr7+4tr1qzp93iZsHTh7bffFkeMGCGq1WpxxowZ4tGjR+UOqdcAdPn45z//KYqiKBYUFIizZs0SfXx8REdHR3HUqFHi888/L9bX18sbuJUWL14sBgcHi2q1WgwNDRUXL14s5uTkSM+3tLSITzzxhOjt7S26uLiId955p1haWipjxL2za9cuEYCYmZlpcXyofn779u3r8u/lsmXLRFE0Lm1+8cUXxcDAQNHR0VGcO3fuZddeXV0t3nfffaKbm5vo4eEhLl++XGxoaJDhai53tevLy8u74v+X+/btE0VRFFNTU8WEhATR09NTdHJyEseNGyeuX7/e4mYvt6tdY3Nzs3jzzTeL/v7+ooODgxgRESE+8sgjl/3Dz5Y/Q1Hs/u+pKIri3//+d9HZ2Vmsq6u77PW2/jl2d38QRet+h166dEmcP3++6OzsLPr5+YnPPfec2N7e3u/xCqagiYiIiGwWa1iIiIjI5jFhISIiIpvHhIWIiIhsHhMWIiIisnlMWIiIiMjmMWEhIiIim8eEhYiIiGweExYiIiKyeUxYiIiIyOYxYSEiIiKbx4SFiIiIbB4TFiIiIrJ5/x+edqH86azjcQAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "가능한 k의 범위가 매우 넓은 것을 확인 할 수 있다. 6-50 사이의 값이면 정확도 0.73정도를 가지는 것을 확인 할 수 있다."
      ],
      "metadata": {
        "id": "JWoU_hKn3mNj"
      }
    }
  ]
}