Machine_Learning_2.ipynb

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      "name": "Machine Learning 2.ipynb",
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        "<a href=\"https://colab.research.google.com/github/Ohgangrae/ai/blob/main/Machine_Learning_2.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "# 머신러닝이란 !!!\n",
        "# 정답이 있는 입력데이터를 모델에 투입하여 입력 데이터와 정답 사이의 \n",
        "# 관계를 학습한다. 따라서 새로운 데이터를 입력했을 때 모델이 학습한\n",
        "# 결과를 적용하여 예측할 수 있다. (지도학습)\n",
        "# 정답이 없는 경우 데이터 속에 숨어 있는 패턴이나 규칙을 알아낼수도 \n",
        "# 있다.(비지도학습) \n",
        "\n",
        "# 이처럼 사람이 어떤 규칙을 정하는 것이 아니라 컴퓨터가 스스로 데이터를\n",
        "# 학습하여 문제를 해결하는 과정을 머신러닝이라한다. \n",
        "\n",
        "# 지도학습 supervised learning      문제와 답을 모두 투입\n",
        "# 비지도학습 unsupervised learning  문제만 투입\n",
        "\n",
        "# 회귀regression와 분류classification\n",
        "\n",
        "# 회귀 문제 : 설명 변수와 목표 변수 사이의 연속적인 회귀 관계식을 찾는다. \n",
        "#             금값의 변화, 주가의 변화, 코인의 변화, 날씨의 변화, 중고차 가격 변환..\n",
        "\n",
        "# 분류 문제 : 설명 변수와 목표 변수 사이의 관계를 찾지만 연속적이지 않은 이산적인 값을\n",
        "              # 찾는다. \n",
        "              # 개와 고양이를 분류, 0~9 중 숫자를 판독,\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 머신러닝 프로세스\n",
        "\n",
        "# 문제 파악  ->   데이터 탐색   ->  데이터 전처리  -> 모델학습   ->   예측"
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    {
      "cell_type": "code",
      "source": [
        "# 붓꽃의 품종 판별 \n",
        "import pandas as pd  # 데이터 분석 모듈\n",
        "import numpy as np   # 데이터 계산 모듈\n",
        "\n",
        "#!pip install sklearn # 설치 필요시에는 먼저 설치하세요\n",
        "from sklearn import datasets    # 머신러닝 관련 모듈\n",
        "iris = datasets.load_iris()     # 내장 데이터셋 읽어오기"
      ],
      "metadata": {
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      "execution_count": 2,
      "outputs": []
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      "source": [
        "# iris 데이터셋 확인 (기본적으로 딕셔너리로 구성되어 있다. )\n",
        "iris.keys()"
      ],
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        "print(iris['DESCR'])\n",
        "        # 설명 변수 feature\n",
        "        # - sepal length in cm 꽃받침 길이\n",
        "        # - sepal width in cm  꽃받침 너비\n",
        "        # - petal length in cm 꽃잎의 길이\n",
        "        # - petal width in cm  꽃잎의 너비\n",
        "\n",
        "        # 목표 변수 class\n",
        "        # - Iris-Setosa        0\n",
        "        # - Iris-Versicolour   1\n",
        "        # - Iris-Virginica     2"
      ],
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          "text": [
            ".. _iris_dataset:\n",
            "\n",
            "Iris plants dataset\n",
            "--------------------\n",
            "\n",
            "**Data Set Characteristics:**\n",
            "\n",
            "    :Number of Instances: 150 (50 in each of three classes)\n",
            "    :Number of Attributes: 4 numeric, predictive attributes and the class\n",
            "    :Attribute Information:\n",
            "        - sepal length in cm\n",
            "        - sepal width in cm\n",
            "        - petal length in cm\n",
            "        - petal width in cm\n",
            "        - class:\n",
            "                - Iris-Setosa\n",
            "                - Iris-Versicolour\n",
            "                - Iris-Virginica\n",
            "                \n",
            "    :Summary Statistics:\n",
            "\n",
            "    ============== ==== ==== ======= ===== ====================\n",
            "                    Min  Max   Mean    SD   Class Correlation\n",
            "    ============== ==== ==== ======= ===== ====================\n",
            "    sepal length:   4.3  7.9   5.84   0.83    0.7826\n",
            "    sepal width:    2.0  4.4   3.05   0.43   -0.4194\n",
            "    petal length:   1.0  6.9   3.76   1.76    0.9490  (high!)\n",
            "    petal width:    0.1  2.5   1.20   0.76    0.9565  (high!)\n",
            "    ============== ==== ==== ======= ===== ====================\n",
            "\n",
            "    :Missing Attribute Values: None\n",
            "    :Class Distribution: 33.3% for each of 3 classes.\n",
            "    :Creator: R.A. Fisher\n",
            "    :Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)\n",
            "    :Date: July, 1988\n",
            "\n",
            "The famous Iris database, first used by Sir R.A. Fisher. The dataset is taken\n",
            "from Fisher's paper. Note that it's the same as in R, but not as in the UCI\n",
            "Machine Learning Repository, which has two wrong data points.\n",
            "\n",
            "This is perhaps the best known database to be found in the\n",
            "pattern recognition literature.  Fisher's paper is a classic in the field and\n",
            "is referenced frequently to this day.  (See Duda & Hart, for example.)  The\n",
            "data set contains 3 classes of 50 instances each, where each class refers to a\n",
            "type of iris plant.  One class is linearly separable from the other 2; the\n",
            "latter are NOT linearly separable from each other.\n",
            "\n",
            ".. topic:: References\n",
            "\n",
            "   - Fisher, R.A. \"The use of multiple measurements in taxonomic problems\"\n",
            "     Annual Eugenics, 7, Part II, 179-188 (1936); also in \"Contributions to\n",
            "     Mathematical Statistics\" (John Wiley, NY, 1950).\n",
            "   - Duda, R.O., & Hart, P.E. (1973) Pattern Classification and Scene Analysis.\n",
            "     (Q327.D83) John Wiley & Sons.  ISBN 0-471-22361-1.  See page 218.\n",
            "   - Dasarathy, B.V. (1980) \"Nosing Around the Neighborhood: A New System\n",
            "     Structure and Classification Rule for Recognition in Partially Exposed\n",
            "     Environments\".  IEEE Transactions on Pattern Analysis and Machine\n",
            "     Intelligence, Vol. PAMI-2, No. 1, 67-71.\n",
            "   - Gates, G.W. (1972) \"The Reduced Nearest Neighbor Rule\".  IEEE Transactions\n",
            "     on Information Theory, May 1972, 431-433.\n",
            "   - See also: 1988 MLC Proceedings, 54-64.  Cheeseman et al\"s AUTOCLASS II\n",
            "     conceptual clustering system finds 3 classes in the data.\n",
            "   - Many, many more ...\n"
          ]
        }
      ]
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        "iris['target']"
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    {
      "cell_type": "code",
      "source": [
        "# 데이터 프레임으로 변환 (pandas의 자료형)\n",
        "\n",
        "df = pd.DataFrame(iris['data'], columns = iris['feature_names'])\n",
        "df"
      ],
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>sepal length (cm)</th>\n",
              "      <th>sepal width (cm)</th>\n",
              "      <th>petal length (cm)</th>\n",
              "      <th>petal width (cm)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5.1</td>\n",
              "      <td>3.5</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4.9</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>4.7</td>\n",
              "      <td>3.2</td>\n",
              "      <td>1.3</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4.6</td>\n",
              "      <td>3.1</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5.0</td>\n",
              "      <td>3.6</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>145</th>\n",
              "      <td>6.7</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.2</td>\n",
              "      <td>2.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>146</th>\n",
              "      <td>6.3</td>\n",
              "      <td>2.5</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>147</th>\n",
              "      <td>6.5</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.2</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>148</th>\n",
              "      <td>6.2</td>\n",
              "      <td>3.4</td>\n",
              "      <td>5.4</td>\n",
              "      <td>2.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>149</th>\n",
              "      <td>5.9</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.1</td>\n",
              "      <td>1.8</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>150 rows × 4 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-17274099-7c88-480d-b727-cb7edd0adad2')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-17274099-7c88-480d-b727-cb7edd0adad2 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-17274099-7c88-480d-b727-cb7edd0adad2');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "     sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)\n",
              "0                  5.1               3.5                1.4               0.2\n",
              "1                  4.9               3.0                1.4               0.2\n",
              "2                  4.7               3.2                1.3               0.2\n",
              "3                  4.6               3.1                1.5               0.2\n",
              "4                  5.0               3.6                1.4               0.2\n",
              "..                 ...               ...                ...               ...\n",
              "145                6.7               3.0                5.2               2.3\n",
              "146                6.3               2.5                5.0               1.9\n",
              "147                6.5               3.0                5.2               2.0\n",
              "148                6.2               3.4                5.4               2.3\n",
              "149                5.9               3.0                5.1               1.8\n",
              "\n",
              "[150 rows x 4 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#  열 이름 변환\n",
        "df.columns = ['sepal_length','sepal_width','petal_length','petal_width']\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "jCqM6lhLCDK5",
        "outputId": "ce02d5ea-b3b0-4862-ffb5-4f3a805730e6"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "\n",
              "  <div id=\"df-7ca21d9c-7ffa-4d7e-a7bb-1dfc78c44ce7\">\n",
              "    <div class=\"colab-df-container\">\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>sepal_length</th>\n",
              "      <th>sepal_width</th>\n",
              "      <th>petal_length</th>\n",
              "      <th>petal_width</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5.1</td>\n",
              "      <td>3.5</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4.9</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>4.7</td>\n",
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              "      <td>1.3</td>\n",
              "      <td>0.2</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4.6</td>\n",
              "      <td>3.1</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.2</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5.0</td>\n",
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              "      <td>1.4</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>145</th>\n",
              "      <td>6.7</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.2</td>\n",
              "      <td>2.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>146</th>\n",
              "      <td>6.3</td>\n",
              "      <td>2.5</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>147</th>\n",
              "      <td>6.5</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.2</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>148</th>\n",
              "      <td>6.2</td>\n",
              "      <td>3.4</td>\n",
              "      <td>5.4</td>\n",
              "      <td>2.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>149</th>\n",
              "      <td>5.9</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.1</td>\n",
              "      <td>1.8</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>150 rows × 4 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7ca21d9c-7ffa-4d7e-a7bb-1dfc78c44ce7')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "\n",
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              "      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",
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              "    }\n",
              "\n",
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              "      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",
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              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-7ca21d9c-7ffa-4d7e-a7bb-1dfc78c44ce7 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-7ca21d9c-7ffa-4d7e-a7bb-1dfc78c44ce7');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "     sepal_length  sepal_width  petal_length  petal_width\n",
              "0             5.1          3.5           1.4          0.2\n",
              "1             4.9          3.0           1.4          0.2\n",
              "2             4.7          3.2           1.3          0.2\n",
              "3             4.6          3.1           1.5          0.2\n",
              "4             5.0          3.6           1.4          0.2\n",
              "..            ...          ...           ...          ...\n",
              "145           6.7          3.0           5.2          2.3\n",
              "146           6.3          2.5           5.0          1.9\n",
              "147           6.5          3.0           5.2          2.0\n",
              "148           6.2          3.4           5.4          2.3\n",
              "149           5.9          3.0           5.1          1.8\n",
              "\n",
              "[150 rows x 4 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['target'] = iris['target']\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "xNZQ1eATCDIY",
        "outputId": "6cbb1140-8dd3-4735-a4ef-8993d96ca6c4"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-4839875c-f298-4403-85d5-4b9da8330343\">\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>sepal_length</th>\n",
              "      <th>sepal_width</th>\n",
              "      <th>petal_length</th>\n",
              "      <th>petal_width</th>\n",
              "      <th>target</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5.1</td>\n",
              "      <td>3.5</td>\n",
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              "      <th>1</th>\n",
              "      <td>4.9</td>\n",
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              "      <td>0.2</td>\n",
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              "      <th>2</th>\n",
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              "      <th>3</th>\n",
              "      <td>4.6</td>\n",
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              "      <td>0</td>\n",
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              "    <tr>\n",
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              "      <th>...</th>\n",
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              "    <tr>\n",
              "      <th>145</th>\n",
              "      <td>6.7</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.2</td>\n",
              "      <td>2.3</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>146</th>\n",
              "      <td>6.3</td>\n",
              "      <td>2.5</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1.9</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>147</th>\n",
              "      <td>6.5</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.2</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>148</th>\n",
              "      <td>6.2</td>\n",
              "      <td>3.4</td>\n",
              "      <td>5.4</td>\n",
              "      <td>2.3</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>149</th>\n",
              "      <td>5.9</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.1</td>\n",
              "      <td>1.8</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>150 rows × 5 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4839875c-f298-4403-85d5-4b9da8330343')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-4839875c-f298-4403-85d5-4b9da8330343 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-4839875c-f298-4403-85d5-4b9da8330343');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "     sepal_length  sepal_width  petal_length  petal_width  target\n",
              "0             5.1          3.5           1.4          0.2       0\n",
              "1             4.9          3.0           1.4          0.2       0\n",
              "2             4.7          3.2           1.3          0.2       0\n",
              "3             4.6          3.1           1.5          0.2       0\n",
              "4             5.0          3.6           1.4          0.2       0\n",
              "..            ...          ...           ...          ...     ...\n",
              "145           6.7          3.0           5.2          2.3       2\n",
              "146           6.3          2.5           5.0          1.9       2\n",
              "147           6.5          3.0           5.2          2.0       2\n",
              "148           6.2          3.4           5.4          2.3       2\n",
              "149           5.9          3.0           5.1          1.8       2\n",
              "\n",
              "[150 rows x 5 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 데이터 프레임 확인하기 \n",
        "df.info() # 기본 정보 확인"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7Jcz644ZCDDZ",
        "outputId": "4f303e3f-e245-4347-e655-436ee6abdbfb"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 150 entries, 0 to 149\n",
            "Data columns (total 5 columns):\n",
            " #   Column        Non-Null Count  Dtype  \n",
            "---  ------        --------------  -----  \n",
            " 0   sepal_length  150 non-null    float64\n",
            " 1   sepal_width   150 non-null    float64\n",
            " 2   petal_length  150 non-null    float64\n",
            " 3   petal_width   150 non-null    float64\n",
            " 4   target        150 non-null    int64  \n",
            "dtypes: float64(4), int64(1)\n",
            "memory usage: 6.0 KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.describe()  # 통계 정보"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "uVEGOe-QCDBR",
        "outputId": "6cda5daf-10dd-413f-b984-57d61e564f17"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-73379660-a587-4ceb-83bd-9c132f31b640\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>sepal_length</th>\n",
              "      <th>sepal_width</th>\n",
              "      <th>petal_length</th>\n",
              "      <th>petal_width</th>\n",
              "      <th>target</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>150.000000</td>\n",
              "      <td>150.000000</td>\n",
              "      <td>150.000000</td>\n",
              "      <td>150.000000</td>\n",
              "      <td>150.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>5.843333</td>\n",
              "      <td>3.057333</td>\n",
              "      <td>3.758000</td>\n",
              "      <td>1.199333</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.828066</td>\n",
              "      <td>0.435866</td>\n",
              "      <td>1.765298</td>\n",
              "      <td>0.762238</td>\n",
              "      <td>0.819232</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>4.300000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.100000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>5.100000</td>\n",
              "      <td>2.800000</td>\n",
              "      <td>1.600000</td>\n",
              "      <td>0.300000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>5.800000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>4.350000</td>\n",
              "      <td>1.300000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>6.400000</td>\n",
              "      <td>3.300000</td>\n",
              "      <td>5.100000</td>\n",
              "      <td>1.800000</td>\n",
              "      <td>2.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>7.900000</td>\n",
              "      <td>4.400000</td>\n",
              "      <td>6.900000</td>\n",
              "      <td>2.500000</td>\n",
              "      <td>2.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-73379660-a587-4ceb-83bd-9c132f31b640')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
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              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
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              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "    }\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",
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              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-73379660-a587-4ceb-83bd-9c132f31b640 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-73379660-a587-4ceb-83bd-9c132f31b640');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "       sepal_length  sepal_width  petal_length  petal_width      target\n",
              "count    150.000000   150.000000    150.000000   150.000000  150.000000\n",
              "mean       5.843333     3.057333      3.758000     1.199333    1.000000\n",
              "std        0.828066     0.435866      1.765298     0.762238    0.819232\n",
              "min        4.300000     2.000000      1.000000     0.100000    0.000000\n",
              "25%        5.100000     2.800000      1.600000     0.300000    0.000000\n",
              "50%        5.800000     3.000000      4.350000     1.300000    1.000000\n",
              "75%        6.400000     3.300000      5.100000     1.800000    2.000000\n",
              "max        7.900000     4.400000      6.900000     2.500000    2.000000"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.isnull().sum()  # 결측치 확인 <- 없음 \n",
        "\n",
        "# 결측치를 발견할 경우 일반적으로는 평균값이나 최빈값으로 결측치를 대체한다. \n",
        "# 결측치는 자료를 수집하거나 정리하는 과정에서 누락되어 유효한 데이터가 없는 경우이다. \n",
        "# 대부분의 머신러닝 알고리즘은 결측값이 있으면 오류가 발생된다.따라서 결측치를\n",
        "# 유효한 값으로 대체하거나 결측값을 삭제하는 처리가 반드시 필요하다. "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lZZiPo0rCjqy",
        "outputId": "2ab19b47-1538-49e2-8701-a0427be624c1"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "sepal_length    0\n",
              "sepal_width     0\n",
              "petal_length    0\n",
              "petal_width     0\n",
              "target          0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 상관 관계 분석\n",
        "df.corr()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "LtjmP_qqCjop",
        "outputId": "c5d79aa5-0a2d-4576-83ce-d994c2585d16"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-0424ceab-f744-4d23-bbe1-20e3db831b5f\">\n",
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              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "    }\n",
              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>sepal_length</th>\n",
              "      <th>sepal_width</th>\n",
              "      <th>petal_length</th>\n",
              "      <th>petal_width</th>\n",
              "      <th>target</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>sepal_length</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.117570</td>\n",
              "      <td>0.871754</td>\n",
              "      <td>0.817941</td>\n",
              "      <td>0.782561</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sepal_width</th>\n",
              "      <td>-0.117570</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.428440</td>\n",
              "      <td>-0.366126</td>\n",
              "      <td>-0.426658</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>petal_length</th>\n",
              "      <td>0.871754</td>\n",
              "      <td>-0.428440</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.962865</td>\n",
              "      <td>0.949035</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>petal_width</th>\n",
              "      <td>0.817941</td>\n",
              "      <td>-0.366126</td>\n",
              "      <td>0.962865</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.956547</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>target</th>\n",
              "      <td>0.782561</td>\n",
              "      <td>-0.426658</td>\n",
              "      <td>0.949035</td>\n",
              "      <td>0.956547</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0424ceab-f744-4d23-bbe1-20e3db831b5f')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-0424ceab-f744-4d23-bbe1-20e3db831b5f 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-0424ceab-f744-4d23-bbe1-20e3db831b5f');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "              sepal_length  sepal_width  petal_length  petal_width    target\n",
              "sepal_length      1.000000    -0.117570      0.871754     0.817941  0.782561\n",
              "sepal_width      -0.117570     1.000000     -0.428440    -0.366126 -0.426658\n",
              "petal_length      0.871754    -0.428440      1.000000     0.962865  0.949035\n",
              "petal_width       0.817941    -0.366126      0.962865     1.000000  0.956547\n",
              "target            0.782561    -0.426658      0.949035     0.956547  1.000000"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 상관 관계 시각화\n",
        "import seaborn as sns   # 시각화 관련 모듈\n",
        "\n",
        "sns.heatmap(data=df.corr(), annot=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 341
        },
        "id": "46Ju9KCyCjiz",
        "outputId": "2898635f-1baa-471e-8978-94a25a7f300d"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f969adbe590>"
            ]
          },
          "metadata": {},
          "execution_count": 13
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 정규분포 확인\n",
        "sns.displot(x='petal_width', data=df, kind='kde')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 387
        },
        "id": "ZDeEPYy7CjgC",
        "outputId": "c634cfef-5039-4f8a-fe50-d029b1826f88"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7f969a8e7110>"
            ]
          },
          "metadata": {},
          "execution_count": 14
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.displot(x='petal_length', data=df, kind='kde')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 387
        },
        "id": "7fcoz5SwCjd6",
        "outputId": "024b3c4b-47cf-4bf1-c858-e512f87ac075"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7f9697874890>"
            ]
          },
          "metadata": {},
          "execution_count": 15
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.displot(x='sepal_width', data=df, kind='kde')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 387
        },
        "id": "trU70VsNCjbq",
        "outputId": "be82318f-dc0e-4e18-f1e7-22dab0ff1292"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7f96957c49d0>"
            ]
          },
          "metadata": {},
          "execution_count": 16
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.displot(x='sepal_length', data=df, kind='kde')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 387
        },
        "id": "sQ_c_SiZCjW6",
        "outputId": "dda42109-fa43-4dbb-8b2a-1185d19b2b62"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7f9697838b50>"
            ]
          },
          "metadata": {},
          "execution_count": 17
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.pairplot(df, hue='target')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 744
        },
        "id": "89U-e_sbCjRx",
        "outputId": "e39ea92d-36e4-4c72-8302-3c020d677b40"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.PairGrid at 0x7f96956b2cd0>"
            ]
          },
          "metadata": {},
          "execution_count": 18
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 762.375x720 with 20 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 훈련용 train 과 검증용 test 데이터 셋 분할\n",
        "# 모델 학습에 사용할 훈련용 데이터와 모델 성능 평가로 사용할 검증용 test 데이터로 분리하자. \n",
        "# 일반적으로 8:2로 분할 비율을 사용한다. \n",
        "\n",
        "# 먼저 df 을 문제지와 답안지 형태로 다시 분할한다. \n",
        "# 문제지\n",
        "X_data = df.loc[:, 'sepal_length':'petal_width']\n",
        "\n",
        "# 답안지\n",
        "y_data = df.loc[:,'target']"
      ],
      "metadata": {
        "id": "nzlx2RWxCjPS"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X_data, y_data, test_size=0.2,shuffle=True, random_state=20)"
      ],
      "metadata": {
        "id": "-VKmaze4C2WH"
      },
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_test.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Vat5RPHqQng9",
        "outputId": "216d25a8-7cca-4815-a04c-7ac4f60fca50"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(30,)"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn import neighbors\n",
        "####################################################################################\n",
        "\n",
        "# KNN Algorithm (K-Nearest-Neighbors) \n",
        "# 예측하려는 데이터 X가 주어지면 기존 데이터 중 속성이 비슷한 K개의 이웃을 찾는다. \n",
        "# 다시 말해 X를 둘러싼 주변의 K개의 가장 가까운 이웃을 찾아 그 이웃들이 가장 많이 속해있는\n",
        "# 클래스를 예측 값으로 결정한다. \n",
        "\n",
        "from sklearn.neighbors import KNeighborsClassifier  # KNN 불러오기\n",
        "knn = KNeighborsClassifier(n_neighbors = 100)           # KNN 객체생성, k값 지정\n",
        "\n",
        "# 학습\n",
        "knn.fit(X_train,y_train)    \n",
        "                        \n",
        "# 예측\n",
        "y_knn_pred = knn.predict(X_test)                    \n",
        "\n",
        "# 평가\n",
        "from sklearn.metrics import accuracy_score\n",
        "knn_acc = accuracy_score(y_test, y_knn_pred)\n",
        "knn_acc\n",
        "# 일부 피처값만 대상으로 한 결과\n",
        "# k = 7,  0.9333333333333333\n",
        "# k = 20, 0.9666666666666667\n",
        "# k =100, 0.5666666666666667\n",
        "\n",
        "# 모든 피처를 대상으로 한 결과\n",
        "# k = 7,  1.0\n",
        "# k = 20, 1.0\n",
        "# k =100, 0.5666666666666667"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "C7_eP1H2C2To",
        "outputId": "c99ab096-074b-4081-e4bd-6ef3307a433a"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.5666666666666667"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# SVM Support Vector Machine\n",
        "\n",
        "# 각 피처 들이  고유의 축을 갖는 공간을 이룬다고 가정한다. \n",
        "# 모든 데이터를 공간 내의 좌표에 점으로 표시하고 각 데이터가 속하는 군집을 이룬다고 가정한다. \n",
        "# 이때 각 군집까지의 거리를 최대 한 멀리 유지하는 경계면을 찾는다. 이렇게 하여 각 군집을\n",
        "# 서로 확연하게 구분할 수 있다. \n",
        "\n",
        "from sklearn.svm import SVC\n",
        "svc = SVC(kernel='rbf')  # Linear, RBF(Radial Basis Function), Polynomial Kernel\n",
        "\n",
        "# 학습\n",
        "svc.fit(X_train, y_train)\n",
        "\n",
        "# 예측\n",
        "y_svc_pred = svc.predict(X_test)\n",
        "\n",
        "# 평가\n",
        "svc_acc = accuracy_score(y_test, y_svc_pred)\n",
        "svc_acc\n",
        "# 일부 피처값만 대상으로 한 결과\n",
        "\n",
        "# kernel='rbf'    0.9333333333333333\n",
        "# kernel='poly'   0.9666666666666667\n",
        "# kernel='linear' 0.9666666666666667\n",
        "\n",
        "\n",
        "# 모든 피처를 대상으로 한 결과\n",
        "# kernel='rbf'    1.0\n",
        "# kernel='poly'   0.9666666666666667\n",
        "# kernel='linear' 0.9666666666666667"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5pwcsjTHC2Q9",
        "outputId": "72dc18ad-1028-4b5c-891a-9dba9d6a4a91"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.0"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Decision Tree 의사결정나무\n",
        "\n",
        "# 트리의 각 분기node 에는 데이터 셋의 피처를 위치시킨다. \n",
        "# 각 피처에 대한 임의의 조건식을 계속 2개 이상의 줄기로 가지를 나누면서 데이터를 구분한다. \n",
        "# 이때 각 분기점에서 분류가 가장 잘 되는 최적의 기준을 찾는 과정이 중요하다. \n",
        "\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "\n",
        "dtc = DecisionTreeClassifier(max_depth = 1, random_state=20)\n",
        "\n",
        "# 학습\n",
        "dtc.fit(X_train, y_train)\n",
        "\n",
        "# 예측\n",
        "y_dtc_pred = dtc.predict(X_test)\n",
        "\n",
        "# 평가\n",
        "dtc_acc = accuracy_score(y_test, y_dtc_pred)\n",
        "dtc_acc\n",
        "# 일부\n",
        "# max_depth = 5    0.9333333333333333\n",
        "# max_depth = 3    0.9333333333333333\n",
        "# max_depth = 2    0.9\n",
        "# max_depth = 1    0.6333333333333333\n",
        "\n",
        "#모든 데이터\n",
        "# max_depth = 5    0.9333333333333333\n",
        "# max_depth = 3    0.9333333333333333\n",
        "# max_depth = 2    0.9\n",
        "# max_depth = 1    0.6333333333333333"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PKlwwv96C2Md",
        "outputId": "65bc701e-1149-4fa2-9b08-35040f5b38c6"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.6333333333333333"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "####################################################################\n",
        "# Machine Learning 2\n",
        "####################################################################\n",
        "\n",
        "# 앙상블 모델 ensemble model - voting\n",
        "# 여러 모델을 결합하여 성능 높이는 방법이다. \n",
        "# voting 은 여러개의 모델이 예측한 값 중에서 다수결로 최종 최종 결과를 \n",
        "# 정한다. 단, 앙상블 학습을 하게되면 개별 모델에 비해 학습 시간이\n",
        "# 오래 걸리는 단점이 있다.\n",
        "\n",
        "from sklearn.ensemble import VotingClassifier\n",
        "from sklearn.neighbors import KNeighborsClassifier  # KNN 불러오기\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "\n",
        "hvc = VotingClassifier(estimators = [('KNN',knn),('SVM',svc),('DT',dtc)], voting='hard')\n",
        "\n",
        "# 학습\n",
        "hvc.fit(X_train, y_train)\n",
        "\n",
        "# 예측\n",
        "y_hvc_pred = hvc.predict(X_test)\n",
        "\n",
        "# 평가\n",
        "hvc_acc = accuracy_score(y_test, y_hvc_pred)\n",
        "hvc_acc"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xL7fDzvAIpOF",
        "outputId": "e87a6e43-9e9f-4c68-c586-aca9543b5730"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.6333333333333333"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 앙상블 모델 ensemble model - bagging\n",
        "\n",
        "# Decision Tree 는 한개의 트리를 사용한다. 여러개의 트리를 사용하여 각 모델의 개별\n",
        "# 예측 값을 보팅을 통해 결정한다. 이처럼 같은 알고리즘 모델을 여러번 돌려 예측하는 방법을\n",
        "# bagging이라고 한다.  \n",
        "\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "\n",
        "rfc = RandomForestClassifier(n_estimators= 100, max_depth=10, random_state=20)\n",
        "\n",
        "#학습\n",
        "rfc.fit(X_train, y_train)\n",
        "\n",
        "# 예측\n",
        "y_rfc_pred = rfc.predict(X_test)\n",
        "\n",
        "# 평가\n",
        "rfc_acc = accuracy_score(y_test, y_rfc_pred)\n",
        "rfc_acc\n",
        "\n",
        "# n_estimators= 50   0.9"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ikvaNbVXPa3z",
        "outputId": "5f4f85e4-202a-45b3-9d36-4b8a02b6f58d"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 앙상블 모델 ensemble model - boosting\n",
        "# 부스팅은 여러개의 약한 학습기(가벼운 모델)를 순차적으로 학습한다. \n",
        "# 잘못 예측한 데이터에 대한 예측 오차를 줄이는 방향으로 모델을 계속 업데이트 한다. \n",
        "# 여러 모델을 동시에 학습하지 않고 순서대로 학습하는 점에서 배깅과는 다르다. \n",
        "\n",
        "# 캐글, 데이콘 등 경진대회에서 가장 많이 사용되는 알고리즘이다. \n",
        "# 모델 학습 속도 빠르고 예측력이 상당히 좋은 편으로 알려져 있다. \n",
        "# !pip install xgboost\n",
        "from xgboost import XGBClassifier\n",
        "\n",
        "xgbc = XGBClassifier(n_estimators=50, max_depth=3, random_state=20)\n",
        "\n",
        "#학습\n",
        "xgbc.fit(X_train, y_train) \n",
        "\n",
        "#예측\n",
        "y_xgbc_pred = xgbc.predict(X_test)\n",
        "\n",
        "#평가\n",
        "xgbc_acc = accuracy_score(y_test, y_xgbc_pred)\n",
        "xgbc_acc\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "441AVVlQRhfs",
        "outputId": "cb5fa289-e19e-4122-ac9e-04e2e20b547e"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9333333333333333"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 과대 적합 overfitting & 과소 적합 underfitting\n",
        "\n",
        "# 과소 적합 underfitting - 머신 러닝을 진행할때 학습 시킬 데이터가 \n",
        "#                          현저히 적을때 예측력이 떨어지는 증상\n",
        "\n",
        "\n",
        "# 과대 적합 overfitting - 학습 시킨 데이터와 비슷한 것은 예측력이 우수하게 판단하지만\n",
        "#                         새로운데이터에는 예측력이 떨어지는 증상\n",
        "\n",
        "\n",
        "# solution : 데이터의 엄청나게 양을 늘린다. "
      ],
      "metadata": {
        "id": "jsEAeGBHVmrl"
      },
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# K-fold 교차검증\n",
        "\n",
        "# 데이터 셋을 5개의 Fold로 분할\n",
        "from sklearn.model_selection import KFold\n",
        "kfold = KFold(n_splits=5, shuffle=True, random_state=20)\n",
        "\n",
        "# 훈련용 데이터와 검증용 데이터의 행 인덱스를 각각의  Fold별로 구분하여 확인해보자\n",
        "\n",
        "num_fold = 1\n",
        "\n",
        "for tr_idx, val_idx in kfold.split(X_train):\n",
        "  print('%s Fold ---------------------------------------' % num_fold)\n",
        "  print('훈련 :', len(tr_idx), tr_idx[:])\n",
        "  print('검증 :', len(val_idx), val_idx[:])\n",
        "  num_fold = num_fold + 1\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IZa6r7SuWUQ0",
        "outputId": "44bee665-d57c-4770-b0d5-3f5bb3edf3d5"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1 Fold ---------------------------------------\n",
            "훈련 : 96 [  0   3   4   6   7   8   9  10  11  12  13  14  15  16  17  18  19  20\n",
            "  21  22  23  24  25  26  27  28  29  30  31  32  33  34  36  39  40  41\n",
            "  42  43  45  46  47  48  52  56  57  58  59  60  61  62  63  64  65  66\n",
            "  68  69  71  74  75  77  78  79  80  82  83  85  86  87  88  89  90  91\n",
            "  93  94  95  96  97  99 101 102 103 104 105 106 107 108 109 110 111 112\n",
            " 113 114 115 117 118 119]\n",
            "검증 : 24 [  1   2   5  35  37  38  44  49  50  51  53  54  55  67  70  72  73  76\n",
            "  81  84  92  98 100 116]\n",
            "2 Fold ---------------------------------------\n",
            "훈련 : 96 [  1   2   3   4   5   6   7   8   9  10  11  13  14  15  16  17  18  20\n",
            "  22  24  25  26  27  28  29  30  32  33  34  35  37  38  40  41  42  43\n",
            "  44  45  49  50  51  52  53  54  55  57  58  59  60  61  62  63  66  67\n",
            "  69  70  71  72  73  74  75  76  77  79  80  81  82  83  84  85  87  88\n",
            "  89  90  91  92  94  95  96  97  98  99 100 101 102 103 104 107 109 110\n",
            " 111 114 116 117 118 119]\n",
            "검증 : 24 [  0  12  19  21  23  31  36  39  46  47  48  56  64  65  68  78  86  93\n",
            " 105 106 108 112 113 115]\n",
            "3 Fold ---------------------------------------\n",
            "훈련 : 96 [  0   1   2   3   5   6   7   9  10  12  13  15  16  17  18  19  20  21\n",
            "  22  23  25  26  28  31  32  34  35  36  37  38  39  40  43  44  46  47\n",
            "  48  49  50  51  53  54  55  56  57  58  59  61  62  64  65  67  68  70\n",
            "  71  72  73  74  75  76  77  78  80  81  83  84  85  86  87  89  90  91\n",
            "  92  93  94  95  96  97  98  99 100 101 102 103 105 106 108 109 111 112\n",
            " 113 114 115 116 118 119]\n",
            "검증 : 24 [  4   8  11  14  24  27  29  30  33  41  42  45  52  60  63  66  69  79\n",
            "  82  88 104 107 110 117]\n",
            "4 Fold ---------------------------------------\n",
            "훈련 : 96 [  0   1   2   4   5   6   7   8   9  11  12  14  15  16  19  20  21  22\n",
            "  23  24  26  27  28  29  30  31  33  34  35  36  37  38  39  40  41  42\n",
            "  44  45  46  47  48  49  50  51  52  53  54  55  56  60  62  63  64  65\n",
            "  66  67  68  69  70  71  72  73  75  76  78  79  81  82  83  84  85  86\n",
            "  88  90  92  93  95  96  98  99 100 101 103 104 105 106 107 108 110 112\n",
            " 113 114 115 116 117 118]\n",
            "검증 : 24 [  3  10  13  17  18  25  32  43  57  58  59  61  74  77  80  87  89  91\n",
            "  94  97 102 109 111 119]\n",
            "5 Fold ---------------------------------------\n",
            "훈련 : 96 [  0   1   2   3   4   5   8  10  11  12  13  14  17  18  19  21  23  24\n",
            "  25  27  29  30  31  32  33  35  36  37  38  39  41  42  43  44  45  46\n",
            "  47  48  49  50  51  52  53  54  55  56  57  58  59  60  61  63  64  65\n",
            "  66  67  68  69  70  72  73  74  76  77  78  79  80  81  82  84  86  87\n",
            "  88  89  91  92  93  94  97  98 100 102 104 105 106 107 108 109 110 111\n",
            " 112 113 115 116 117 119]\n",
            "검증 : 24 [  6   7   9  15  16  20  22  26  28  34  40  62  71  75  83  85  90  95\n",
            "  96  99 101 103 114 118]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 훈련용 데이터와 검증용 데이터의 행 인덱스를 각 fold별로 구분하여 생성\n",
        "\n",
        "val_scores = [] # 5번의 예측 정확도를 담을 리스트\n",
        "\n",
        "num_fold = 1\n",
        "\n",
        "for tr_idx, val_idx in kfold.split(X_train, y_train):\n",
        "  # 훈련용 데이터와 검증용 데이터를 행 인덱스 기준으로 추출\n",
        "  X_tr, X_val = X_train.iloc[tr_idx, :], X_train.iloc[val_idx, :]   #  X 문제지\n",
        "  y_tr, y_val = y_train.iloc[tr_idx], y_train.iloc[val_idx]         #  y 답안지\n",
        "\n",
        "  # 학습\n",
        "  rfc = RandomForestClassifier(max_depth= 5, random_state=20)\n",
        "  rfc.fit(X_tr,y_tr)\n",
        "\n",
        "  # 예측\n",
        "  y_val_pred = rfc.predict(X_val)\n",
        "\n",
        "  # 검증\n",
        "  val_acc = accuracy_score(y_val, y_val_pred)\n",
        "\n",
        "  # 결과 출력\n",
        "  print('%d Fold Accuracy : %.4f' % (num_fold, val_acc)) # 각각의 폴드마다의 정확도\n",
        "  val_scores.append(val_acc)\n",
        "  num_fold += 1\n",
        "\n",
        "# 평균 정확도 계산\n",
        "\n",
        "mean_score = np.mean(val_scores)\n",
        "print()\n",
        "print('평균 정확도 : ',np.round(mean_score, 2) * 100, '%')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HR6ZHdzTeLVZ",
        "outputId": "b49dd5ac-bbd2-47b7-d1fb-ea5f16b948b9"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1 Fold Accuracy : 0.8750\n",
            "2 Fold Accuracy : 1.0000\n",
            "3 Fold Accuracy : 0.9583\n",
            "4 Fold Accuracy : 1.0000\n",
            "5 Fold Accuracy : 1.0000\n",
            "\n",
            "평균 정확도 :  97.0 %\n"
          ]
        }
      ]
    }
  ]
}