2_Scikit-learn.ipynb

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    {
      "cell_type": "markdown",
      "source": [
        "## 사이킷런"
      ],
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        "id": "rLAbZ2Q_bb-1"
      }
    },
    {
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      "execution_count": null,
      "metadata": {
        "id": "pkeunq1f_BdR"
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      "source": [
        "pip install scikit-learn==1.0.2"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import sklearn\n",
        "\n",
        "print(sklearn.__version__)"
      ],
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        },
        "id": "ZqCyrC5I_fmV",
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      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1.2.2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 사이킷런을 이용하여 붓꽃(Iris) 데이터 품종 예측하기"
      ],
      "metadata": {
        "id": "4Yb4h4eYbkS9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.datasets import load_iris\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.model_selection import train_test_split"
      ],
      "metadata": {
        "id": "NESOhEA5_mGf"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "#붓꽃 데이터 세트 로딩\n",
        "iris = load_iris()\n",
        "\n",
        "#iris.data는 Iris 데이터 세트에서 피처만으로 된 데이터를 numpy로 가지고 있다.\n",
        "iris_data = iris.data\n",
        "\n",
        "iris_label = iris.target\n",
        "print('iris target값:',iris_label)\n",
        "print('iris_target명:',iris.target_names)\n",
        "\n",
        "\n",
        "iris_df = pd.DataFrame(data=iris_data, columns=iris.feature_names)\n",
        "iris_df['label'] = iris.target\n",
        "iris_df.head(3)"
      ],
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        "id": "ttRkx4vMAizl",
        "outputId": "04de436a-ebef-43c1-f329-75be098c0141"
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      "execution_count": 4,
      "outputs": [
        {
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          "name": "stdout",
          "text": [
            "iris target값: [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
            " 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
            " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2\n",
            " 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2\n",
            " 2 2]\n",
            "iris_target명: ['setosa' 'versicolor' 'virginica']\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "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",
              "\n",
              "   label  \n",
              "0      0  \n",
              "1      0  \n",
              "2      0  "
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              "  <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",
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              "  </thead>\n",
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              "    <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",
              "      <td>0</td>\n",
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              "    <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",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>4.7</td>\n",
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              "summary": "{\n  \"name\": \"iris_df\",\n  \"rows\": 150,\n  \"fields\": [\n    {\n      \"column\": \"sepal length (cm)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.828066127977863,\n        \"min\": 4.3,\n        \"max\": 7.9,\n        \"num_unique_values\": 35,\n        \"samples\": [\n          6.2,\n          4.5,\n          5.6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sepal width (cm)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.4358662849366982,\n        \"min\": 2.0,\n        \"max\": 4.4,\n        \"num_unique_values\": 23,\n        \"samples\": [\n          2.3,\n          4.0,\n          3.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"petal length (cm)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.7652982332594662,\n        \"min\": 1.0,\n        \"max\": 6.9,\n        \"num_unique_values\": 43,\n        \"samples\": [\n          6.7,\n          3.8,\n          3.7\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"petal width (cm)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.7622376689603465,\n        \"min\": 0.1,\n        \"max\": 2.5,\n        \"num_unique_values\": 22,\n        \"samples\": [\n          0.2,\n          1.2,\n          1.3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"label\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 2,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0,\n          1,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
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          "metadata": {},
          "execution_count": 4
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      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(iris_data, iris_label, test_size=0.2, random_state=11)"
      ],
      "metadata": {
        "id": "fsHTPJiCBt5i"
      },
      "execution_count": 5,
      "outputs": []
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        "dt_clf = DecisionTreeClassifier(random_state=11)"
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        "id": "zxIuavJ1r59J"
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      "execution_count": 6,
      "outputs": []
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              "DecisionTreeClassifier(random_state=11)"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier(random_state=11)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier(random_state=11)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pred = dt_clf.predict(X_test)"
      ],
      "metadata": {
        "id": "hhhwvE9AtDrP"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import accuracy_score"
      ],
      "metadata": {
        "id": "QTMgmhmVtPel"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print('예측 정확도: {0:.4f}'.format(accuracy_score(y_test,pred)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1uB6mxOftXTM",
        "outputId": "1834b31d-e440-4b0e-df40-4a50e454268d"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "예측 정확도: 0.9333\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 사이킷런의 기반 프레임워크 익히기"
      ],
      "metadata": {
        "id": "qS_BNgCYbtr-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.datasets import load_iris\n",
        "\n",
        "iris_data = load_iris()\n",
        "print(type(iris_data))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0o7dJVGUuDgR",
        "outputId": "f27c7b42-81ba-4aaa-99cd-092a6a95cf77"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'sklearn.utils._bunch.Bunch'>\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "keys = iris_data.keys()\n",
        "print('붓꽃 데이터 세트의 키들:', keys)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UgwkByvew1f1",
        "outputId": "b8334590-c495-4a4b-a9a4-b7d3513e70c2"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "붓꽃 데이터 세트의 키들: dict_keys(['data', 'target', 'frame', 'target_names', 'DESCR', 'feature_names', 'filename', 'data_module'])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print('\\n feature_names 의 type:',type(iris_data.feature_names))\n",
        "print(' feature_names 의 shape:',len(iris_data.feature_names))\n",
        "print(iris_data.feature_names)\n",
        "\n",
        "print('\\n target_names 의 type:',type(iris_data.target_names))\n",
        "print(' feature_names 의 shape:',len(iris_data.target_names))\n",
        "print(iris_data.target_names)\n",
        "\n",
        "print('\\n data 의 type:',type(iris_data.data))\n",
        "print(' data 의 shape:',iris_data.data.shape)\n",
        "print(iris_data['data'])\n",
        "\n",
        "print('\\n target 의 type:',type(iris_data.target))\n",
        "print(' target 의 shape:',iris_data.target.shape)\n",
        "print(iris_data.target)"
      ],
      "metadata": {
        "id": "ltNw8sIixCG9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Model Selection 모듈 소개"
      ],
      "metadata": {
        "id": "ukcmErCob3Bb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.datasets import load_iris\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.metrics import accuracy_score"
      ],
      "metadata": {
        "id": "ZMCY5vh5z5yA"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "iris = load_iris()\n",
        "dt_clf = DecisionTreeClassifier()\n",
        "train_data = iris.data\n",
        "train_label = iris.target\n",
        "dt_clf.fit(train_data, train_label)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "Z1tkYgbyDmMy",
        "outputId": "2d370605-dcd9-423a-9fb7-c534ce77ad0f"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeClassifier()"
            ],
            "text/html": [
              "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pred = dt_clf.predict(train_data)\n",
        "print('예측 정확도:', accuracy_score(train_label,pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "er9H8KJjEBuc",
        "outputId": "1ab2b663-88ce-4c87-f939-6978b55c8072"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "예측 정확도: 1.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.datasets import load_iris\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "dt_clf = DecisionTreeClassifier()\n",
        "iris_data = load_iris()\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(iris_data.data, iris_data.target, test_size=0.3, random_state=121)"
      ],
      "metadata": {
        "id": "akn6p21WEfJn"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dt_clf.fit(X_train,y_train)\n",
        "pred = dt_clf.predict(X_test)\n",
        "print('예측 정확도: {0:.4f}'.format(accuracy_score(y_test,pred)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XrFwHrJZFPlU",
        "outputId": "0d3eb734-8c09-4b04-d9c4-1a4f805b8379"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "예측 정확도: 0.9556\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.model_selection import KFold\n",
        "import numpy as np\n",
        "\n",
        "iris = load_iris()\n",
        "features = iris.data\n",
        "label = iris.target\n",
        "dt_clf = DecisionTreeClassifier(random_state=156)\n",
        "\n",
        "kfold = KFold(n_splits=5)\n",
        "cv_accuracy = []\n",
        "print('붓꽃 데이터 세트 크기:',features.shape[0])\n",
        "\n",
        "n_iter = 0\n",
        "\n",
        "for train_index, test_index in kfold.split(features):\n",
        "  X_train, X_test = features[train_index], features[test_index]\n",
        "  y_train, y_test = label[train_index], label[test_index]\n",
        "\n",
        "  dt_clf.fit(X_train,y_train)\n",
        "  pred = dt_clf.predict(X_test)\n",
        "  n_iter += 1\n",
        "\n",
        "  accuracy = np.around(accuracy_score(y_test,pred), 4)\n",
        "  train_size = X_train.shape[0]\n",
        "  test_size = X_test.shape[0]\n",
        "  print('\\n#{0} 교차 검증 정확도 :{1}, 학습 데이터 크기: {2}, 검증 데이터 크기: {3}'.format(n_iter, accuracy, train_size, test_size))\n",
        "  print('#{0} 검증 세트 인덱스:{1}'.format(n_iter, test_index))\n",
        "  cv_accuracy.append(accuracy)\n",
        "\n",
        "print('\\n## 평균 검증 정활도:', np.mean(cv_accuracy))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jnQh93Y3F1Jp",
        "outputId": "c087d413-1213-465e-89c1-86c8c3b30d4d"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "붓꽃 데이터 세트 크기: 150\n",
            "\n",
            "#1 교차 검증 정확도 :1.0, 학습 데이터 크기: 120, 검증 데이터 크기: 30\n",
            "#1 검증 세트 인덱스:[ 0  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23\n",
            " 24 25 26 27 28 29]\n",
            "\n",
            "#2 교차 검증 정확도 :0.9667, 학습 데이터 크기: 120, 검증 데이터 크기: 30\n",
            "#2 검증 세트 인덱스:[30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53\n",
            " 54 55 56 57 58 59]\n",
            "\n",
            "#3 교차 검증 정확도 :0.8667, 학습 데이터 크기: 120, 검증 데이터 크기: 30\n",
            "#3 검증 세트 인덱스:[60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83\n",
            " 84 85 86 87 88 89]\n",
            "\n",
            "#4 교차 검증 정확도 :0.9333, 학습 데이터 크기: 120, 검증 데이터 크기: 30\n",
            "#4 검증 세트 인덱스:[ 90  91  92  93  94  95  96  97  98  99 100 101 102 103 104 105 106 107\n",
            " 108 109 110 111 112 113 114 115 116 117 118 119]\n",
            "\n",
            "#5 교차 검증 정확도 :0.7333, 학습 데이터 크기: 120, 검증 데이터 크기: 30\n",
            "#5 검증 세트 인덱스:[120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137\n",
            " 138 139 140 141 142 143 144 145 146 147 148 149]\n",
            "\n",
            "## 평균 검증 정활도: 0.9\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "GdBLuf5jKad4"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "iris = load_iris()\n",
        "iris_df = pd.DataFrame(data=iris.data, columns=iris.feature_names)\n",
        "iris_df['label']=iris.target\n",
        "iris_df['label'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RIJcocfWUQg6",
        "outputId": "4bd54ff5-b94e-4bb4-9cd3-50f1ca116479"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    50\n",
              "1    50\n",
              "2    50\n",
              "Name: label, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "kfold = KFold(n_splits=3)\n",
        "n_iter =0\n",
        "for train_index, test_index in kfold.split(iris_df):\n",
        "  n_iter += 1\n",
        "  label_train = iris_df['label'].iloc[train_index]\n",
        "  label_test = iris_df['label'].iloc[test_index]\n",
        "  print('\\n## 교차 검증: {0}'.format(n_iter))\n",
        "  print('학습 레이블 데이터 분포:\\n', label_train.value_counts())\n",
        "  print('검증 레이블 데이터 분포:\\n', label_test.value_counts())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cq1CuUSzUsln",
        "outputId": "5e44da2f-c024-4e64-dbfc-4a6028cca161"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "## 교차 검증: 1\n",
            "학습 레이블 데이터 분포:\n",
            " 1    50\n",
            "2    50\n",
            "Name: label, dtype: int64\n",
            "검증 레이블 데이터 분포:\n",
            " 0    50\n",
            "Name: label, dtype: int64\n",
            "\n",
            "## 교차 검증: 2\n",
            "학습 레이블 데이터 분포:\n",
            " 0    50\n",
            "2    50\n",
            "Name: label, dtype: int64\n",
            "검증 레이블 데이터 분포:\n",
            " 1    50\n",
            "Name: label, dtype: int64\n",
            "\n",
            "## 교차 검증: 3\n",
            "학습 레이블 데이터 분포:\n",
            " 0    50\n",
            "1    50\n",
            "Name: label, dtype: int64\n",
            "검증 레이블 데이터 분포:\n",
            " 2    50\n",
            "Name: label, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import StratifiedKFold\n",
        "\n",
        "skf = StratifiedKFold(n_splits=3)\n",
        "n_iter = 0\n",
        "\n",
        "for train_index, test_index in skf.split(iris_df, iris_df['label']):\n",
        "  n_iter += 1\n",
        "  label_train = iris_df['label'].iloc[train_index]\n",
        "  label_test = iris_df['label'].iloc[test_index]\n",
        "  print('## 교차검증: {0}'.format(n_iter))\n",
        "  print('학습 레이블 데이터 분포:\\n', label_train.value_counts())\n",
        "  print('검증 레이블 데이터 분포:\\n', label_test.value_counts())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IiQ2khnJUuRC",
        "outputId": "0e13ce78-0eff-49b2-a03a-f4e5d9711ca4"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "## 교차검증: 1\n",
            "학습 레이블 데이터 분포:\n",
            " 2    34\n",
            "0    33\n",
            "1    33\n",
            "Name: label, dtype: int64\n",
            "검증 레이블 데이터 분포:\n",
            " 0    17\n",
            "1    17\n",
            "2    16\n",
            "Name: label, dtype: int64\n",
            "## 교차검증: 2\n",
            "학습 레이블 데이터 분포:\n",
            " 1    34\n",
            "0    33\n",
            "2    33\n",
            "Name: label, dtype: int64\n",
            "검증 레이블 데이터 분포:\n",
            " 0    17\n",
            "2    17\n",
            "1    16\n",
            "Name: label, dtype: int64\n",
            "## 교차검증: 3\n",
            "학습 레이블 데이터 분포:\n",
            " 0    34\n",
            "1    33\n",
            "2    33\n",
            "Name: label, dtype: int64\n",
            "검증 레이블 데이터 분포:\n",
            " 1    17\n",
            "2    17\n",
            "0    16\n",
            "Name: label, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dt_clf = DecisionTreeClassifier(random_state=156)\n",
        "\n",
        "skfold = StratifiedKFold(n_splits=3)\n",
        "n_iter = 0\n",
        "cv_accuracy=[]\n",
        "\n",
        "for train_index, test_index in skfold.split(features, label):\n",
        "  X_train, X_test = features[train_index], features[test_index]\n",
        "  y_train, y_test = label[train_index], label[test_index]\n",
        "  dt_clf.fit(X_train, y_train)\n",
        "  pred = dt_clf.predict(X_test)\n",
        "\n",
        "  n_iter += 1\n",
        "  accuracy = np.round(accuracy_score(y_test, pred),4)\n",
        "  train_size = X_train.shape[0]\n",
        "  test_size = X_test.shape[0]\n",
        "  print('\\n#{0} 교차 검증 정확도 :{1}, 학습 데이터 크기: {2}, 검증 데이터 크기: {3}'.format(n_iter, accuracy, train_size, test_size))\n",
        "  print('#{0} 검증 세트 인덱스:{1}'.format(n_iter, test_index))\n",
        "  cv_accuracy.append(accuracy)\n",
        "\n",
        "print('\\n## 교차 검증별 정확도:', np.round(cv_accuracy,4))\n",
        "print('## 평균 검증 정확도:', np.round(np.mean(cv_accuracy),4))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9CNnkAo0VheD",
        "outputId": "859086f1-132e-4624-bc38-6771f8b5da9c"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "#1 교차 검증 정확도 :0.98, 학습 데이터 크기: 100, 검증 데이터 크기: 50\n",
            "#1 검증 세트 인덱스:[  0   1   2   3   4   5   6   7   8   9  10  11  12  13  14  15  16  50\n",
            "  51  52  53  54  55  56  57  58  59  60  61  62  63  64  65  66 100 101\n",
            " 102 103 104 105 106 107 108 109 110 111 112 113 114 115]\n",
            "\n",
            "#2 교차 검증 정확도 :0.94, 학습 데이터 크기: 100, 검증 데이터 크기: 50\n",
            "#2 검증 세트 인덱스:[ 17  18  19  20  21  22  23  24  25  26  27  28  29  30  31  32  33  67\n",
            "  68  69  70  71  72  73  74  75  76  77  78  79  80  81  82 116 117 118\n",
            " 119 120 121 122 123 124 125 126 127 128 129 130 131 132]\n",
            "\n",
            "#3 교차 검증 정확도 :0.98, 학습 데이터 크기: 100, 검증 데이터 크기: 50\n",
            "#3 검증 세트 인덱스:[ 34  35  36  37  38  39  40  41  42  43  44  45  46  47  48  49  83  84\n",
            "  85  86  87  88  89  90  91  92  93  94  95  96  97  98  99 133 134 135\n",
            " 136 137 138 139 140 141 142 143 144 145 146 147 148 149]\n",
            "\n",
            "## 교차 검증별 정확도: [0.98 0.94 0.98]\n",
            "## 평균 검증 정확도: 0.9667\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.model_selection import cross_val_score, cross_validate\n",
        "from sklearn.datasets import load_iris\n",
        "import numpy as np\n",
        "\n",
        "\n",
        "iris_data = load_iris()\n",
        "dt_clf = DecisionTreeClassifier(random_state=156)\n",
        "\n",
        "data = iris_data.data\n",
        "label = iris_data.target\n",
        "\n",
        "scores = cross_val_score(dt_clf, data, label, scoring='accuracy', cv=3)\n",
        "print('교차 검증별 정확도:', np.round(scores,4))\n",
        "print('평균 검증 정확도:', np.round(np.mean(scores),4))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3JbiqzZNYiiY",
        "outputId": "6b6125c0-e39b-47b7-a235-a7c043757f33"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "교차 검증별 정확도: [0.98 0.94 0.98]\n",
            "평균 검증 정확도: 0.9667\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "grid_parameters = {'max_depth': [1,2,3], 'min_samples_split':[2,3]}"
      ],
      "metadata": {
        "id": "V-A3ydF6zJw0"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.datasets import load_iris\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "iris_data = load_iris()\n",
        "X_train, X_test, y_train, y_test = train_test_split(iris_data.data, iris_data.target, test_size = 0.2, random_state=121)\n",
        "\n",
        "dtree = DecisionTreeClassifier()\n",
        "\n",
        "parameters = {'max_depth':[1,2,3], 'min_samples_split':[2,3]}"
      ],
      "metadata": {
        "id": "ceRD9g_K1ESh"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "grid_dtree = GridSearchCV(dtree, param_grid=parameters, cv=3, refit=True)\n",
        "\n",
        "grid_dtree.fit(X_train, y_train)\n",
        "\n",
        "scores_df = pd.DataFrame(grid_dtree.cv_results_)\n",
        "scores_df[['params','mean_test_score','rank_test_score','split0_test_score','split1_test_score','split2_test_score']]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "id": "fvCoQOAD3ovU",
        "outputId": "0616f846-f805-4188-ddc8-48081a9685f3"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                     params  mean_test_score  rank_test_score  \\\n",
              "0  {'max_depth': 1, 'min_samples_split': 2}         0.700000                5   \n",
              "1  {'max_depth': 1, 'min_samples_split': 3}         0.700000                5   \n",
              "2  {'max_depth': 2, 'min_samples_split': 2}         0.958333                3   \n",
              "3  {'max_depth': 2, 'min_samples_split': 3}         0.958333                3   \n",
              "4  {'max_depth': 3, 'min_samples_split': 2}         0.975000                1   \n",
              "5  {'max_depth': 3, 'min_samples_split': 3}         0.975000                1   \n",
              "\n",
              "   split0_test_score  split1_test_score  split2_test_score  \n",
              "0              0.700                0.7               0.70  \n",
              "1              0.700                0.7               0.70  \n",
              "2              0.925                1.0               0.95  \n",
              "3              0.925                1.0               0.95  \n",
              "4              0.975                1.0               0.95  \n",
              "5              0.975                1.0               0.95  "
            ],
            "text/html": [
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>params</th>\n",
              "      <th>mean_test_score</th>\n",
              "      <th>rank_test_score</th>\n",
              "      <th>split0_test_score</th>\n",
              "      <th>split1_test_score</th>\n",
              "      <th>split2_test_score</th>\n",
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              "      <td>{'max_depth': 1, 'min_samples_split': 2}</td>\n",
              "      <td>0.700000</td>\n",
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              "      <td>0.70</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>{'max_depth': 1, 'min_samples_split': 3}</td>\n",
              "      <td>0.700000</td>\n",
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              "      <td>0.958333</td>\n",
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              "      <th>5</th>\n",
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              "      <td>0.95</td>\n",
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              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "      border-radius: 50%;\n",
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              "        document.querySelector('#df-ebe74dc4-2cf0-4f7c-9cc5-2716dd8bfb9b button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
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              "        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",
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              "\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",
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              "      border-top-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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              "    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-21a98fec-a816-4f03-8402-fc74ed5e7bbe button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
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              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"scores_df[['params','mean_test_score','rank_test_score','split0_test_score','split1_test_score','split2_test_score']]\",\n  \"rows\": 6,\n  \"fields\": [\n    {\n      \"column\": \"params\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mean_test_score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.13790764488786886,\n        \"min\": 0.6999999999999998,\n        \"max\": 0.975,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.6999999999999998,\n          0.9583333333333334,\n          0.975\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"rank_test_score\",\n      \"properties\": {\n        \"dtype\": \"int32\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          5,\n          3,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"split0_test_score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.131021626713557,\n        \"min\": 0.7,\n        \"max\": 0.975,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.7,\n          0.925,\n          0.975\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"split1_test_score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.1549193338482967,\n        \"min\": 0.7,\n        \"max\": 1.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1.0,\n          0.7\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"split2_test_score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.12909944487358058,\n        \"min\": 0.7,\n        \"max\": 0.95,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.95,\n          0.7\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"GridSearchCV 최적 파라미터:\",grid_dtree.best_params_)\n",
        "print(\"GridSearchCV 최고 정확도:{0:.4f}\".format(grid_dtree.best_score_))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QqJo8mdw5YL1",
        "outputId": "80867df7-3e50-4245-fad4-3ef89665549c"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "GridSearchCV 최적 파라미터: {'max_depth': 3, 'min_samples_split': 2}\n",
            "GridSearchCV 최고 정확도:0.9750\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import accuracy_score\n",
        "\n",
        "estimator = grid_dtree.best_estimator_\n",
        "pred = estimator.predict(X_test)\n",
        "print('테스트 데이터 세트 정확도: {0:.4f}'.format(accuracy_score(y_test,pred)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uqpt-Xec7New",
        "outputId": "5fec5cc5-6c93-4190-c8c8-bffd71402eed"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "테스트 데이터 세트 정확도: 0.9667\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 데이터_전처리"
      ],
      "metadata": {
        "id": "0iCmHqwccMZt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "\n",
        "items=['TV','냉장고','전자레인지','컴퓨터','선풍기','선풍기','믹서','믹서']\n",
        "\n",
        "# LabelEncoder를 객체로 생성한 후 , fit( ) 과 transform( ) 으로 label 인코딩 수행.\n",
        "encoder = LabelEncoder()\n",
        "encoder.fit(items)\n",
        "labels = encoder.transform(items)\n",
        "print('인코딩 변환값:',labels)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DIjnGv8DcQMC",
        "outputId": "b024f3c4-84aa-4d7e-c935-f09f82b9eeb7"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "인코딩 변환값: [0 1 4 5 3 3 2 2]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print('인코딩 클래스:',encoder.classes_)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CNpcz-eecUa4",
        "outputId": "392aa31b-ad22-4b8c-a056-d7ec68ccb89a"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "인코딩 클래스: ['TV' '냉장고' '믹서' '선풍기' '전자레인지' '컴퓨터']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print('디코딩 원본 값:',encoder.inverse_transform([4, 5, 2, 0, 1, 1, 3, 3]))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4qFT3-OVcWkJ",
        "outputId": "885e59d3-ee74-400e-a736-855c904b9a36"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "디코딩 원본 값: ['전자레인지' '컴퓨터' '믹서' 'TV' '냉장고' '냉장고' '선풍기' '선풍기']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import OneHotEncoder\n",
        "import numpy as np\n",
        "\n",
        "items=['TV','냉장고','전자레인지','컴퓨터','선풍기','선풍기','믹서','믹서']\n",
        "\n",
        "# 2차원 ndarray로 변환합니다.\n",
        "items = np.array(items).reshape(-1, 1)\n",
        "\n",
        "# 원-핫 인코딩을 적용합니다.\n",
        "oh_encoder = OneHotEncoder()\n",
        "oh_encoder.fit(items)\n",
        "oh_labels = oh_encoder.transform(items)\n",
        "\n",
        "# OneHotEncoder로 변환한 결과는 희소행렬이므로 toarray()를 이용해 밀집 행렬로 변환.\n",
        "print('원-핫 인코딩 데이터')\n",
        "print(oh_labels.toarray())\n",
        "print('원-핫 인코딩 데이터 차원')\n",
        "print(oh_labels.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cQhbLPK1cYLP",
        "outputId": "04803831-3c60-4019-b5f1-60b75da7dabb"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "원-핫 인코딩 데이터\n",
            "[[1. 0. 0. 0. 0. 0.]\n",
            " [0. 1. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 1. 0.]\n",
            " [0. 0. 0. 0. 0. 1.]\n",
            " [0. 0. 0. 1. 0. 0.]\n",
            " [0. 0. 0. 1. 0. 0.]\n",
            " [0. 0. 1. 0. 0. 0.]\n",
            " [0. 0. 1. 0. 0. 0.]]\n",
            "원-핫 인코딩 데이터 차원\n",
            "(8, 6)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "df = pd.DataFrame({'item':['TV','냉장고','전자레인지','컴퓨터','선풍기','선풍기','믹서','믹서'] })\n",
        "pd.get_dummies(df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "OgdkgtVxcaAd",
        "outputId": "ebde7dbc-a5bc-4a8b-c0d7-28b58cd0abff"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   item_TV  item_냉장고  item_믹서  item_선풍기  item_전자레인지  item_컴퓨터\n",
              "0        1         0        0         0           0         0\n",
              "1        0         1        0         0           0         0\n",
              "2        0         0        0         0           1         0\n",
              "3        0         0        0         0           0         1\n",
              "4        0         0        0         1           0         0\n",
              "5        0         0        0         1           0         0\n",
              "6        0         0        1         0           0         0\n",
              "7        0         0        1         0           0         0"
            ],
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              "\n",
              "  <div id=\"df-d855b8cc-350c-498a-9636-daac95e54c98\" 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",
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              "\n",
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              "\n",
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              "    [theme=dark] .colab-df-convert {\n",
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              "    [theme=dark] .colab-df-convert:hover {\n",
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              "\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",
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              "            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-06469c4b-2169-4d31-ace3-f3affb03a84c button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"pd\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"item_TV\",\n      \"properties\": {\n        \"dtype\": \"uint8\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"item_\\ub0c9\\uc7a5\\uace0\",\n      \"properties\": {\n        \"dtype\": \"uint8\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"item_\\ubbf9\\uc11c\",\n      \"properties\": {\n        \"dtype\": \"uint8\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"item_\\uc120\\ud48d\\uae30\",\n      \"properties\": {\n        \"dtype\": \"uint8\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"item_\\uc804\\uc790\\ub808\\uc778\\uc9c0\",\n      \"properties\": {\n        \"dtype\": \"uint8\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"item_\\ucef4\\ud4e8\\ud130\",\n      \"properties\": {\n        \"dtype\": \"uint8\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.datasets import load_iris\n",
        "import pandas as pd\n",
        "# 붓꽃 데이터 셋을 로딩하고 DataFrame으로 변환합니다.\n",
        "iris = load_iris()\n",
        "iris_data = iris.data\n",
        "iris_df = pd.DataFrame(data=iris_data, columns=iris.feature_names)\n",
        "\n",
        "print('feature 들의 평균 값')\n",
        "print(iris_df.mean())\n",
        "print('\\nfeature 들의 분산 값')\n",
        "print(iris_df.var())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Vjfqs2XwccDc",
        "outputId": "f98c8067-14ad-49e6-89ea-9c0e100d7779"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "feature 들의 평균 값\n",
            "sepal length (cm)    5.843333\n",
            "sepal width (cm)     3.057333\n",
            "petal length (cm)    3.758000\n",
            "petal width (cm)     1.199333\n",
            "dtype: float64\n",
            "\n",
            "feature 들의 분산 값\n",
            "sepal length (cm)    0.685694\n",
            "sepal width (cm)     0.189979\n",
            "petal length (cm)    3.116278\n",
            "petal width (cm)     0.581006\n",
            "dtype: float64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "# StandardScaler객체 생성\n",
        "scaler = StandardScaler()\n",
        "# StandardScaler 로 데이터 셋 변환. fit( ) 과 transform( ) 호출.\n",
        "scaler.fit(iris_df)\n",
        "iris_scaled = scaler.transform(iris_df)\n",
        "\n",
        "#transform( )시 scale 변환된 데이터 셋이 numpy ndarry로 반환되어 이를 DataFrame으로 변환\n",
        "iris_df_scaled = pd.DataFrame(data=iris_scaled, columns=iris.feature_names)\n",
        "print('feature 들의 평균 값')\n",
        "print(iris_df_scaled.mean())\n",
        "print('\\nfeature 들의 분산 값')\n",
        "print(iris_df_scaled.var())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yo3pSnM3cd42",
        "outputId": "8a0418d0-d122-48b1-f924-5c0b704d43fb"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "feature 들의 평균 값\n",
            "sepal length (cm)   -1.690315e-15\n",
            "sepal width (cm)    -1.842970e-15\n",
            "petal length (cm)   -1.698641e-15\n",
            "petal width (cm)    -1.409243e-15\n",
            "dtype: float64\n",
            "\n",
            "feature 들의 분산 값\n",
            "sepal length (cm)    1.006711\n",
            "sepal width (cm)     1.006711\n",
            "petal length (cm)    1.006711\n",
            "petal width (cm)     1.006711\n",
            "dtype: float64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import MinMaxScaler\n",
        "\n",
        "# MinMaxScaler객체 생성\n",
        "scaler = MinMaxScaler()\n",
        "# MinMaxScaler 로 데이터 셋 변환. fit() 과 transform() 호출.\n",
        "scaler.fit(iris_df)\n",
        "iris_scaled = scaler.transform(iris_df)\n",
        "\n",
        "# transform()시 scale 변환된 데이터 셋이 numpy ndarry로 반환되어 이를 DataFrame으로 변환\n",
        "iris_df_scaled = pd.DataFrame(data=iris_scaled, columns=iris.feature_names)\n",
        "print('feature들의 최솟값')\n",
        "print(iris_df_scaled.min())\n",
        "print('\\nfeature들의 최댓값')\n",
        "print(iris_df_scaled.max())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZAB0ghZLcfsK",
        "outputId": "6b927377-bcb6-4086-9890-d40b150d1682"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "feature들의 최솟값\n",
            "sepal length (cm)    0.0\n",
            "sepal width (cm)     0.0\n",
            "petal length (cm)    0.0\n",
            "petal width (cm)     0.0\n",
            "dtype: float64\n",
            "\n",
            "feature들의 최댓값\n",
            "sepal length (cm)    1.0\n",
            "sepal width (cm)     1.0\n",
            "petal length (cm)    1.0\n",
            "petal width (cm)     1.0\n",
            "dtype: float64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import MinMaxScaler\n",
        "import numpy as np\n",
        "\n",
        "# 학습 데이터는 0 부터 10까지, 테스트 데이터는 0 부터 5까지 값을 가지는 데이터 세트로 생성\n",
        "# Scaler클래스의 fit(), transform()은 2차원 이상 데이터만 가능하므로 reshape(-1, 1)로 차원 변경\n",
        "train_array = np.arange(0, 11).reshape(-1, 1)\n",
        "test_array =  np.arange(0, 6).reshape(-1, 1)"
      ],
      "metadata": {
        "id": "_p6HvYgZchi9"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# MinMaxScaler 객체에 별도의 feature_range 파라미터 값을 지정하지 않으면 0~1 값으로 변환\n",
        "scaler = MinMaxScaler()\n",
        "\n",
        "# fit()하게 되면 train_array 데이터의 최솟값이 0, 최댓값이 10으로 설정.\n",
        "scaler.fit(train_array)\n",
        "\n",
        "# 1/10 scale로 train_array 데이터 변환함. 원본 10-> 1로 변환됨.\n",
        "train_scaled = scaler.transform(train_array)\n",
        "\n",
        "print('원본 train_array 데이터:', np.round(train_array.reshape(-1), 2))\n",
        "print('Scale된 train_array 데이터:', np.round(train_scaled.reshape(-1), 2))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mgodvo9OcjJf",
        "outputId": "5d0a7bb1-5beb-4e24-f857-d1a048daa95e"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "원본 train_array 데이터: [ 0  1  2  3  4  5  6  7  8  9 10]\n",
            "Scale된 train_array 데이터: [0.  0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1. ]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# MinMaxScaler에 test_array를 fit()하게 되면 원본 데이터의 최솟값이 0, 최댓값이 5로 설정됨\n",
        "scaler.fit(test_array)\n",
        "\n",
        "# 1/5 scale로 test_array 데이터 변환함. 원본 5->1로 변환.\n",
        "test_scaled = scaler.transform(test_array)\n",
        "\n",
        "# test_array의 scale 변환 출력.\n",
        "print('원본 test_array 데이터:', np.round(test_array.reshape(-1), 2))\n",
        "print('Scale된 test_array 데이터:', np.round(test_scaled.reshape(-1), 2))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "V8wD-4SGck-e",
        "outputId": "ecdec1aa-9593-4f02-f7bc-fa878f017022"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "원본 test_array 데이터: [0 1 2 3 4 5]\n",
            "Scale된 test_array 데이터: [0.  0.2 0.4 0.6 0.8 1. ]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "scaler = MinMaxScaler()\n",
        "scaler.fit(train_array)\n",
        "train_scaled = scaler.transform(train_array)\n",
        "print('원본 train_array 데이터:', np.round(train_array.reshape(-1), 2))\n",
        "print('Scale된 train_array 데이터:', np.round(train_scaled.reshape(-1), 2))\n",
        "\n",
        "# test_array에 Scale 변환을 할 때는 반드시 fit()을 호출하지 않고 transform() 만으로 변환해야 함.\n",
        "test_scaled = scaler.transform(test_array)\n",
        "print('\\n원본 test_array 데이터:', np.round(test_array.reshape(-1), 2))\n",
        "print('Scale된 test_array 데이터:', np.round(test_scaled.reshape(-1), 2))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e0c8FjNwcmpN",
        "outputId": "27c8d3b7-fb97-49c5-8b09-e2bd8879b0e4"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "원본 train_array 데이터: [ 0  1  2  3  4  5  6  7  8  9 10]\n",
            "Scale된 train_array 데이터: [0.  0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1. ]\n",
            "\n",
            "원본 test_array 데이터: [0 1 2 3 4 5]\n",
            "Scale된 test_array 데이터: [0.  0.1 0.2 0.3 0.4 0.5]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 사이킷런으로 수행하는 타이타닉 생존자 예측"
      ],
      "metadata": {
        "id": "Jrs9sA46cv9q"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "%matplotlib inline\n",
        "\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')\n",
        "\n",
        "titanic_df = pd.read_csv(r'/content/drive/MyDrive/CUAI/titanic.csv')\n",
        "titanic_df.head(3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 161
        },
        "id": "hEMo3B_PczGp",
        "outputId": "6d941842-7e1f-47a8-8b56-231beefc8412"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   PassengerId  Survived  Pclass  \\\n",
              "0            1         0       3   \n",
              "1            2         1       1   \n",
              "2            3         1       3   \n",
              "\n",
              "                                                Name     Sex   Age  SibSp  \\\n",
              "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
              "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
              "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
              "\n",
              "   Parch            Ticket     Fare Cabin Embarked  \n",
              "0      0         A/5 21171   7.2500   NaN        S  \n",
              "1      0          PC 17599  71.2833   C85        C  \n",
              "2      0  STON/O2. 3101282   7.9250   NaN        S  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-0744f629-1a72-4ae8-a36c-73115bb3a32c\" 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>PassengerId</th>\n",
              "      <th>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Name</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Ticket</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Cabin</th>\n",
              "      <th>Embarked</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Braund, Mr. Owen Harris</td>\n",
              "      <td>male</td>\n",
              "      <td>22.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>A/5 21171</td>\n",
              "      <td>7.2500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
              "      <td>female</td>\n",
              "      <td>38.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>PC 17599</td>\n",
              "      <td>71.2833</td>\n",
              "      <td>C85</td>\n",
              "      <td>C</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>Heikkinen, Miss. Laina</td>\n",
              "      <td>female</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>STON/O2. 3101282</td>\n",
              "      <td>7.9250</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "          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",
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              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
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              "  }\n",
              "\n",
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              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "    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",
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              "  }\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",
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              "      border-color: transparent;\n",
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              "      border-bottom-color: var(--fill-color);\n",
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              "      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",
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              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "titanic_df",
              "summary": "{\n  \"name\": \"titanic_df\",\n  \"rows\": 891,\n  \"fields\": [\n    {\n      \"column\": \"PassengerId\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 257,\n        \"min\": 1,\n        \"max\": 891,\n        \"num_unique_values\": 891,\n        \"samples\": [\n          710,\n          440,\n          841\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Survived\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Pclass\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 3,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          3,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 891,\n        \"samples\": [\n          \"Moubarek, Master. Halim Gonios (\\\"William George\\\")\",\n          \"Kvillner, Mr. Johan Henrik Johannesson\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sex\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"female\",\n          \"male\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14.526497332334042,\n        \"min\": 0.42,\n        \"max\": 80.0,\n        \"num_unique_values\": 88,\n        \"samples\": [\n          0.75,\n          22.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SibSp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 8,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Parch\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 6,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Ticket\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 681,\n        \"samples\": [\n          \"11774\",\n          \"248740\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Fare\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 49.6934285971809,\n        \"min\": 0.0,\n        \"max\": 512.3292,\n        \"num_unique_values\": 248,\n        \"samples\": [\n          11.2417,\n          51.8625\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Cabin\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 147,\n        \"samples\": [\n          \"D45\",\n          \"B49\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Embarked\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"S\",\n          \"C\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print('\\n ### train 데이터 정보 ###  \\n')\n",
        "print(titanic_df.info())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nGqXG4MEc0r7",
        "outputId": "e0bae313-958e-4500-eb8b-1fb9e1bdf5b3"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            " ### train 데이터 정보 ###  \n",
            "\n",
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 891 entries, 0 to 890\n",
            "Data columns (total 12 columns):\n",
            " #   Column       Non-Null Count  Dtype  \n",
            "---  ------       --------------  -----  \n",
            " 0   PassengerId  891 non-null    int64  \n",
            " 1   Survived     891 non-null    int64  \n",
            " 2   Pclass       891 non-null    int64  \n",
            " 3   Name         891 non-null    object \n",
            " 4   Sex          891 non-null    object \n",
            " 5   Age          714 non-null    float64\n",
            " 6   SibSp        891 non-null    int64  \n",
            " 7   Parch        891 non-null    int64  \n",
            " 8   Ticket       891 non-null    object \n",
            " 9   Fare         891 non-null    float64\n",
            " 10  Cabin        204 non-null    object \n",
            " 11  Embarked     889 non-null    object \n",
            "dtypes: float64(2), int64(5), object(5)\n",
            "memory usage: 83.7+ KB\n",
            "None\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "titanic_df['Age'].fillna(titanic_df['Age'].mean(),inplace=True)\n",
        "titanic_df['Cabin'].fillna('N',inplace=True)\n",
        "titanic_df['Embarked'].fillna('N',inplace=True)\n",
        "print('데이터 세트 Null 값 갯수 ',titanic_df.isnull().sum().sum())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xXbOldpTdY2T",
        "outputId": "4d0fdb77-02c7-455e-efd8-4330c0bb7368"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "데이터 세트 Null 값 갯수  0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(' Sex 값 분포 :\\n',titanic_df['Sex'].value_counts())\n",
        "print('\\n Cabin 값 분포 :\\n',titanic_df['Cabin'].value_counts())\n",
        "print('\\n Embarked 값 분포 :\\n',titanic_df['Embarked'].value_counts())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EXn2Q15ldaYh",
        "outputId": "4f597adc-52e2-4336-d492-4d87bebb3ec4"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            " Sex 값 분포 :\n",
            " male      577\n",
            "female    314\n",
            "Name: Sex, dtype: int64\n",
            "\n",
            " Cabin 값 분포 :\n",
            " N              687\n",
            "C23 C25 C27      4\n",
            "G6               4\n",
            "B96 B98          4\n",
            "C22 C26          3\n",
            "              ... \n",
            "E34              1\n",
            "C7               1\n",
            "C54              1\n",
            "E36              1\n",
            "C148             1\n",
            "Name: Cabin, Length: 148, dtype: int64\n",
            "\n",
            " Embarked 값 분포 :\n",
            " S    644\n",
            "C    168\n",
            "Q     77\n",
            "N      2\n",
            "Name: Embarked, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "titanic_df['Cabin'] = titanic_df['Cabin'].str[:1]\n",
        "print(titanic_df['Cabin'].head(3))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RkIlD2-Idb2W",
        "outputId": "d1546a51-d884-46cd-c0a6-5a12eddd9b92"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0    N\n",
            "1    C\n",
            "2    N\n",
            "Name: Cabin, dtype: object\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "titanic_df.groupby(['Sex','Survived'])['Survived'].count()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "o4r-3kvtddKB",
        "outputId": "5636f9aa-9546-45a1-9558-e23e1c3c0156"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Sex     Survived\n",
              "female  0            81\n",
              "        1           233\n",
              "male    0           468\n",
              "        1           109\n",
              "Name: Survived, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.barplot(x='Sex', y = 'Survived', data=titanic_df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "Ph88cj9qdesv",
        "outputId": "b157fb28-1a06-4ba6-aa7c-6cd9e037dd36"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Sex', ylabel='Survived'>"
            ]
          },
          "metadata": {},
          "execution_count": 22
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.barplot(x='Pclass', y='Survived', hue='Sex', data=titanic_df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "Ps_enDHEdf4Q",
        "outputId": "57b4bc5f-87f5-4952-ca17-ebec94b1a5a7"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Pclass', ylabel='Survived'>"
            ]
          },
          "metadata": {},
          "execution_count": 23
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 입력 age에 따라 구분값을 반환하는 함수 설정. DataFrame의 apply lambda식에 사용.\n",
        "def get_category(age):\n",
        "    cat = ''\n",
        "    if age <= -1: cat = 'Unknown'\n",
        "    elif age <= 5: cat = 'Baby'\n",
        "    elif age <= 12: cat = 'Child'\n",
        "    elif age <= 18: cat = 'Teenager'\n",
        "    elif age <= 25: cat = 'Student'\n",
        "    elif age <= 35: cat = 'Young Adult'\n",
        "    elif age <= 60: cat = 'Adult'\n",
        "    else : cat = 'Elderly'\n",
        "\n",
        "    return cat\n",
        "\n",
        "# 막대그래프의 크기 figure를 더 크게 설정\n",
        "plt.figure(figsize=(10,6))\n",
        "\n",
        "#X축의 값을 순차적으로 표시하기 위한 설정\n",
        "group_names = ['Unknown', 'Baby', 'Child', 'Teenager', 'Student', 'Young Adult', 'Adult', 'Elderly']\n",
        "\n",
        "# lambda 식에 위에서 생성한 get_category( ) 함수를 반환값으로 지정.\n",
        "# get_category(X)는 입력값으로 'Age' 컬럼값을 받아서 해당하는 cat 반환\n",
        "titanic_df['Age_cat'] = titanic_df['Age'].apply(lambda x : get_category(x))\n",
        "sns.barplot(x='Age_cat', y = 'Survived', hue='Sex', data=titanic_df, order=group_names)\n",
        "titanic_df.drop('Age_cat', axis=1, inplace=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "AwNNyWkRdhDU",
        "outputId": "01cd7f51-6a70-4dcd-b34e-fbc02724076f"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn import preprocessing\n",
        "\n",
        "def encode_features(dataDF):\n",
        "    features = ['Cabin', 'Sex', 'Embarked']\n",
        "    for feature in features:\n",
        "        le = preprocessing.LabelEncoder()\n",
        "        le = le.fit(dataDF[feature])\n",
        "        dataDF[feature] = le.transform(dataDF[feature])\n",
        "\n",
        "    return dataDF\n",
        "\n",
        "titanic_df = encode_features(titanic_df)\n",
        "titanic_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "hnz2ihJLdiv1",
        "outputId": "c0fe594f-7d6d-489e-ce0d-40827581d219"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   PassengerId  Survived  Pclass  \\\n",
              "0            1         0       3   \n",
              "1            2         1       1   \n",
              "2            3         1       3   \n",
              "3            4         1       1   \n",
              "4            5         0       3   \n",
              "\n",
              "                                                Name  Sex   Age  SibSp  Parch  \\\n",
              "0                            Braund, Mr. Owen Harris    1  22.0      1      0   \n",
              "1  Cumings, Mrs. John Bradley (Florence Briggs Th...    0  38.0      1      0   \n",
              "2                             Heikkinen, Miss. Laina    0  26.0      0      0   \n",
              "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)    0  35.0      1      0   \n",
              "4                           Allen, Mr. William Henry    1  35.0      0      0   \n",
              "\n",
              "             Ticket     Fare  Cabin  Embarked  \n",
              "0         A/5 21171   7.2500      7         3  \n",
              "1          PC 17599  71.2833      2         0  \n",
              "2  STON/O2. 3101282   7.9250      7         3  \n",
              "3            113803  53.1000      2         3  \n",
              "4            373450   8.0500      7         3  "
            ],
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              "      <th>PassengerId</th>\n",
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              "      <td>Braund, Mr. Owen Harris</td>\n",
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              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
              "      <td>0</td>\n",
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              "      <td>3</td>\n",
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              "      <td>0</td>\n",
              "      <td>26.0</td>\n",
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              "      <th>3</th>\n",
              "      <td>4</td>\n",
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              "      <td>1</td>\n",
              "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>113803</td>\n",
              "      <td>53.1000</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
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              "      <td>Allen, Mr. William Henry</td>\n",
              "      <td>1</td>\n",
              "      <td>35.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>373450</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>7</td>\n",
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              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-1948bd83-e3ff-44f0-8d2d-5dc1b3532f47');\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-0d5b0708-de84-4386-a4e9-b9637b48c585\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-0d5b0708-de84-4386-a4e9-b9637b48c585')\"\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-0d5b0708-de84-4386-a4e9-b9637b48c585 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "titanic_df",
              "summary": "{\n  \"name\": \"titanic_df\",\n  \"rows\": 891,\n  \"fields\": [\n    {\n      \"column\": \"PassengerId\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 257,\n        \"min\": 1,\n        \"max\": 891,\n        \"num_unique_values\": 891,\n        \"samples\": [\n          710,\n          440,\n          841\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Survived\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Pclass\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 3,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          3,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 891,\n        \"samples\": [\n          \"Moubarek, Master. Halim Gonios (\\\"William George\\\")\",\n          \"Kvillner, Mr. Johan Henrik Johannesson\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sex\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 13.002015226002882,\n        \"min\": 0.42,\n        \"max\": 80.0,\n        \"num_unique_values\": 89,\n        \"samples\": [\n          59.0,\n          36.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SibSp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 8,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Parch\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 6,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Ticket\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 681,\n        \"samples\": [\n          \"11774\",\n          \"248740\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Fare\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 49.6934285971809,\n        \"min\": 0.0,\n        \"max\": 512.3292,\n        \"num_unique_values\": 248,\n        \"samples\": [\n          11.2417,\n          51.8625\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Cabin\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2,\n        \"min\": 0,\n        \"max\": 8,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          5,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Embarked\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 3,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "\n",
        "# Null 처리 함수\n",
        "def fillna(df):\n",
        "    df['Age'].fillna(df['Age'].mean(), inplace=True)\n",
        "    df['Cabin'].fillna('N', inplace=True)\n",
        "    df['Embarked'].fillna('N', inplace=True)\n",
        "    df['Fare'].fillna(0, inplace=True)\n",
        "    return df\n",
        "\n",
        "# 머신러닝 알고리즘에 불필요한 피처 제거\n",
        "def drop_features(df):\n",
        "    df.drop(['PassengerId', 'Name', 'Ticket'], axis=1, inplace=True)\n",
        "    return df\n",
        "\n",
        "# 레이블 인코딩 수행.\n",
        "def format_features(df):\n",
        "    df['Cabin'] = df['Cabin'].str[:1]\n",
        "    features = ['Cabin', 'Sex', 'Embarked']\n",
        "    for feature in features:\n",
        "        le = LabelEncoder()\n",
        "        le = le.fit(df[feature])\n",
        "        df[feature] = le.transform(df[feature])\n",
        "    return df\n",
        "\n",
        "# 앞에서 설정한 데이터 전처리 함수 호출\n",
        "def transform_features(df):\n",
        "    df = fillna(df)\n",
        "    df = drop_features(df)\n",
        "    df = format_features(df)\n",
        "    return df"
      ],
      "metadata": {
        "id": "9MsoCBR1dkRq"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 원본 데이터를 재로딩 하고, feature데이터 셋과 Label 데이터 셋 추출.\n",
        "titanic_df = pd.read_csv(r'/content/drive/MyDrive/CUAI/titanic.csv')\n",
        "y_titanic_df = titanic_df['Survived']\n",
        "X_titanic_df= titanic_df.drop('Survived',axis=1)\n",
        "\n",
        "X_titanic_df = transform_features(X_titanic_df)"
      ],
      "metadata": {
        "id": "YJY_ihM8dmZU"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test=train_test_split(X_titanic_df, y_titanic_df, \\\n",
        "                                                  test_size=0.2, random_state=11)"
      ],
      "metadata": {
        "id": "VhbKCW1GdoTE"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.metrics import accuracy_score\n",
        "\n",
        "# 결정트리, Random Forest, 로지스틱 회귀를 위한 사이킷런 Classifier 클래스 생성\n",
        "dt_clf = DecisionTreeClassifier(random_state=11)\n",
        "rf_clf = RandomForestClassifier(random_state=11)\n",
        "lr_clf = LogisticRegression(solver='liblinear')\n",
        "\n",
        "# DecisionTreeClassifier 학습/예측/평가\n",
        "dt_clf.fit(X_train , y_train)\n",
        "dt_pred = dt_clf.predict(X_test)\n",
        "print('DecisionTreeClassifier 정확도: {0:.4f}'.format(accuracy_score(y_test, dt_pred)))\n",
        "\n",
        "# RandomForestClassifier 학습/예측/평가\n",
        "rf_clf.fit(X_train , y_train)\n",
        "rf_pred = rf_clf.predict(X_test)\n",
        "print('RandomForestClassifier 정확도:{0:.4f}'.format(accuracy_score(y_test, rf_pred)))\n",
        "\n",
        "# LogisticRegression 학습/예측/평가\n",
        "lr_clf.fit(X_train , y_train)\n",
        "lr_pred = lr_clf.predict(X_test)\n",
        "print('LogisticRegression 정확도: {0:.4f}'.format(accuracy_score(y_test, lr_pred)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "de6Djg5sduGl",
        "outputId": "0710aad9-ca33-41ac-fb00-ace5a43d911d"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "DecisionTreeClassifier 정확도: 0.7877\n",
            "RandomForestClassifier 정확도:0.8547\n",
            "LogisticRegression 정확도: 0.8659\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import KFold\n",
        "\n",
        "def exec_kfold(clf, folds=5):\n",
        "    # 폴드 세트를 5개인 KFold객체를 생성, 폴드 수만큼 예측결과 저장을 위한  리스트 객체 생성.\n",
        "    kfold = KFold(n_splits=folds)\n",
        "    scores = []\n",
        "\n",
        "    # KFold 교차 검증 수행.\n",
        "    for iter_count , (train_index, test_index) in enumerate(kfold.split(X_titanic_df)):\n",
        "        # X_titanic_df 데이터에서 교차 검증별로 학습과 검증 데이터를 가리키는 index 생성\n",
        "        X_train, X_test = X_titanic_df.values[train_index], X_titanic_df.values[test_index]\n",
        "        y_train, y_test = y_titanic_df.values[train_index], y_titanic_df.values[test_index]\n",
        "\n",
        "        # Classifier 학습, 예측, 정확도 계산\n",
        "        clf.fit(X_train, y_train)\n",
        "        predictions = clf.predict(X_test)\n",
        "        accuracy = accuracy_score(y_test, predictions)\n",
        "        scores.append(accuracy)\n",
        "        print(\"교차 검증 {0} 정확도: {1:.4f}\".format(iter_count, accuracy))\n",
        "\n",
        "    # 5개 fold에서의 평균 정확도 계산.\n",
        "    mean_score = np.mean(scores)\n",
        "    print(\"평균 정확도: {0:.4f}\".format(mean_score))\n",
        "# exec_kfold 호출\n",
        "exec_kfold(dt_clf , folds=5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DiKj2ChqdvlI",
        "outputId": "fde79658-fb80-497e-fdbe-b746eaabb123"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "교차 검증 0 정확도: 0.7542\n",
            "교차 검증 1 정확도: 0.7809\n",
            "교차 검증 2 정확도: 0.7865\n",
            "교차 검증 3 정확도: 0.7697\n",
            "교차 검증 4 정확도: 0.8202\n",
            "평균 정확도: 0.7823\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import GridSearchCV\n",
        "\n",
        "parameters = {'max_depth':[2,3,5,10],\n",
        "             'min_samples_split':[2,3,5], 'min_samples_leaf':[1,5,8]}\n",
        "\n",
        "grid_dclf = GridSearchCV(dt_clf , param_grid=parameters , scoring='accuracy' , cv=5)\n",
        "grid_dclf.fit(X_train , y_train)\n",
        "\n",
        "print('GridSearchCV 최적 하이퍼 파라미터 :',grid_dclf.best_params_)\n",
        "print('GridSearchCV 최고 정확도: {0:.4f}'.format(grid_dclf.best_score_))\n",
        "best_dclf = grid_dclf.best_estimator_\n",
        "\n",
        "# GridSearchCV의 최적 하이퍼 파라미터로 학습된 Estimator로 예측 및 평가 수행.\n",
        "dpredictions = best_dclf.predict(X_test)\n",
        "accuracy = accuracy_score(y_test , dpredictions)\n",
        "print('테스트 세트에서의 DecisionTreeClassifier 정확도 : {0:.4f}'.format(accuracy))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JVlWIhKudxUa",
        "outputId": "64c99fd0-f597-4f01-8c12-d1405313dbe7"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "GridSearchCV 최적 하이퍼 파라미터 : {'max_depth': 3, 'min_samples_leaf': 5, 'min_samples_split': 2}\n",
            "GridSearchCV 최고 정확도: 0.7992\n",
            "테스트 세트에서의 DecisionTreeClassifier 정확도 : 0.8715\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "dHv0XJR2dy2x"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}