2_1_sklearn.ipynb

{
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/minyoung347/python101/blob/main/2_1_sklearn.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 사이킷런을 이용하여 Iris 데이터 예측"
      ],
      "metadata": {
        "id": "DMC0SO06GU3n"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd"
      ],
      "metadata": {
        "id": "hPkuIeULuTXg"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "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": "oTKmwjVIGLRh"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 붓꽃 데이터 세트를 로딩합니다.\n",
        "iris = load_iris()\n",
        "\n",
        "# iris.data는 Iris 데이터 세트에서 피처(feature)만으로 된 데이터를 numpy로 가지고 있습니다.\n",
        "iris_data = iris.data"
      ],
      "metadata": {
        "id": "oZD-jUcvuUq5"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "iris.data.shape"
      ],
      "metadata": {
        "id": "M9XFrAJguWcp",
        "outputId": "5d7bba44-7442-499c-fe79-a74ff8ba1c96",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(150, 4)"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "iris_label = iris.target\n",
        "print('iris target값:', iris_label)\n",
        "print('iris target명:', iris.target_names)"
      ],
      "metadata": {
        "id": "qPmSz6OCund_",
        "outputId": "e8c70852-da32-420d-ff29-8a5a88288074",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "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"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "iris_label.shape"
      ],
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        }
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      "execution_count": null,
      "outputs": [
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          "data": {
            "text/plain": [
              "(150,)"
            ]
          },
          "metadata": {},
          "execution_count": 10
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      "metadata": {
        "id": "LhYabqUmu5EX"
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    },
    {
      "cell_type": "code",
      "source": [
        "iris_df = pd.DataFrame(data=iris_data, columns=iris.feature_names)"
      ],
      "metadata": {
        "id": "mOKNw341u3Mn"
      },
      "execution_count": null,
      "outputs": []
    },
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        "iris_df"
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              "     sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)\n",
              "0                  5.1               3.5                1.4               0.2\n",
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              "4                  5.0               3.6                1.4               0.2\n",
              "..                 ...               ...                ...               ...\n",
              "145                6.7               3.0                5.2               2.3\n",
              "146                6.3               2.5                5.0               1.9\n",
              "147                6.5               3.0                5.2               2.0\n",
              "148                6.2               3.4                5.4               2.3\n",
              "149                5.9               3.0                5.1               1.8\n",
              "\n",
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        "iris_df['label'] = iris.target"
      ],
      "metadata": {
        "id": "RZGGohqSvCAe"
      },
      "execution_count": null,
      "outputs": []
    },
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          "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",
              "3                  4.6               3.1                1.5               0.2   \n",
              "4                  5.0               3.6                1.4               0.2   \n",
              "..                 ...               ...                ...               ...   \n",
              "145                6.7               3.0                5.2               2.3   \n",
              "146                6.3               2.5                5.0               1.9   \n",
              "147                6.5               3.0                5.2               2.0   \n",
              "148                6.2               3.4                5.4               2.3   \n",
              "149                5.9               3.0                5.1               1.8   \n",
              "\n",
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              "0        0  \n",
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              "..     ...  \n",
              "145      2  \n",
              "146      2  \n",
              "147      2  \n",
              "148      2  \n",
              "149      2  \n",
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              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
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              "\n",
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              "      background-color: #3B4455;\n",
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              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-e8fbb1b3-e9cf-42a6-aa79-117820dd679d button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-e8fbb1b3-e9cf-42a6-aa79-117820dd679d');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "iris_df.head(3)"
      ],
      "metadata": {
        "id": "s5qZoS--vKAv",
        "outputId": "4b5c66d5-2aa3-4ad7-e609-e39c3f4b555c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "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  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-3440f016-fce2-490a-b111-397500d97ac5\">\n",
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              "      <div>\n",
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              "    }\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>sepal length (cm)</th>\n",
              "      <th>sepal width (cm)</th>\n",
              "      <th>petal length (cm)</th>\n",
              "      <th>petal width (cm)</th>\n",
              "      <th>label</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5.1</td>\n",
              "      <td>3.5</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4.9</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>4.7</td>\n",
              "      <td>3.2</td>\n",
              "      <td>1.3</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3440f016-fce2-490a-b111-397500d97ac5')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
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              "    .colab-df-container {\n",
              "      display:flex;\n",
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              "    }\n",
              "\n",
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              "\n",
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              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-3440f016-fce2-490a-b111-397500d97ac5 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-3440f016-fce2-490a-b111-397500d97ac5');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "\n",
        "# 붓꽃 데이터 세트를 로딩합니다.\n",
        "iris = load_iris()\n",
        "\n",
        "# iris.data는 Iris 데이터 세트에서 피처(feature)만으로 된 데이터를 numpy로 가지고 있습니다.\n",
        "iris_data = iris.data\n",
        "\n",
        "# iris.target은 붓꽃 데이터 세트에서 레이블(결정 값) 데이터를 numpy로 가지고 있습니다.\n",
        "iris_label = iris.target\n",
        "print('iris target값:', iris_label)\n",
        "print('iris target명:', iris.target_names)\n",
        "\n",
        "# 붓꽃 데이터 세트를 자세히 보기 위해 DataFrame으로 변환합니다.\n",
        "iris_df = pd.DataFrame(data=iris_data, columns=iris.feature_names)\n",
        "iris_df['label'] = iris.target\n",
        "iris_df.head(3)"
      ],
      "metadata": {
        "id": "QOP1DZHGGLOP",
        "outputId": "d6fb7fa2-6c12-434f-d414-bbdd4d7ea57c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 254
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "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  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-8327813f-238e-442e-8d6f-da257dd098d2\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>sepal length (cm)</th>\n",
              "      <th>sepal width (cm)</th>\n",
              "      <th>petal length (cm)</th>\n",
              "      <th>petal width (cm)</th>\n",
              "      <th>label</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5.1</td>\n",
              "      <td>3.5</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4.9</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>4.7</td>\n",
              "      <td>3.2</td>\n",
              "      <td>1.3</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8327813f-238e-442e-8d6f-da257dd098d2')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-8327813f-238e-442e-8d6f-da257dd098d2 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-8327813f-238e-442e-8d6f-da257dd098d2');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split"
      ],
      "metadata": {
        "id": "4IcseJ6BvYBG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(iris_data, iris_label,\n",
        "                                                    test_size=0.2, random_state=11)"
      ],
      "metadata": {
        "id": "mfhwH4lPGLLv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(iris_data.shape)\n",
        "print(iris_label.shape)"
      ],
      "metadata": {
        "id": "pYjg_k2ovvUl",
        "outputId": "19c2f443-7d71-4d70-e039-e82608db2bb4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(150, 4)\n",
            "(150,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(X_train.shape)\n",
        "print(X_test.shape)\n",
        "print('----------------')\n",
        "print(y_train.shape)\n",
        "print(y_test.shape)"
      ],
      "metadata": {
        "id": "AHkCZzpivznF",
        "outputId": "b4f22c31-9985-4d5d-86b1-9bb5a8b685eb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(120, 4)\n",
            "(30, 4)\n",
            "----------------\n",
            "(120,)\n",
            "(30,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.tree import DecisionTreeClassifier"
      ],
      "metadata": {
        "id": "aDvfopPfwqZV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# DecisionTreeClassifier 객체 생성\n",
        "dt_clf = DecisionTreeClassifier(random_state=11)"
      ],
      "metadata": {
        "id": "HyzrqTk4wNRP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 학습 수행\n",
        "dt_clf.fit(X_train, y_train)"
      ],
      "metadata": {
        "id": "qoKhLnavwkQd",
        "outputId": "c471ee9c-a213-4a2c-e01b-e2012fc35519",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeClassifier(random_state=11)"
            ],
            "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(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-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(random_state=11)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 학습이 완료된 DecisionTreeClassifier 객체에서 테스트 데이터 세트로 예측 수행.\n",
        "pred = dt_clf.predict(X_test)"
      ],
      "metadata": {
        "id": "FidUv3HBw4Td"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import accuracy_score"
      ],
      "metadata": {
        "id": "nhdO3BGIxTSd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "accuracy_score(y_test,pred)"
      ],
      "metadata": {
        "id": "pctJNaQMxR9k",
        "outputId": "3267bf4c-2d8e-4a9e-cd07-c5ee25e5afe2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9333333333333333"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import accuracy_score\n",
        "print('예측 정확도: {0:.4f}'.format(accuracy_score(y_test,pred)))"
      ],
      "metadata": {
        "id": "Y5S0safNxNtN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pred"
      ],
      "metadata": {
        "id": "EqgUxzU1xEwt",
        "outputId": "424ce858-c3c7-49ac-b913-f3222b6da83f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([2, 2, 1, 1, 2, 0, 1, 0, 0, 1, 1, 1, 1, 2, 2, 0, 2, 1, 2, 2, 1, 0,\n",
              "       0, 1, 0, 0, 2, 1, 0, 1])"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# DecisionTreeClassifier 객체 생성\n",
        "dt_clf = DecisionTreeClassifier(random_state=11)\n",
        "\n",
        "# 학습 수행\n",
        "dt_clf.fit(X_train, y_train)"
      ],
      "metadata": {
        "id": "iUec7ZiqGPI3",
        "outputId": "52cfd964-497c-492f-b98e-9ba065d4c5ec",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "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": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 학습이 완료된 DecisionTreeClassifier 객체에서 테스트 데이터 세트로 예측 수행.\n",
        "pred = dt_clf.predict(X_test)"
      ],
      "metadata": {
        "id": "HLgNPjTHGPF3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import accuracy_score\n",
        "print('예측 정확도: {0:.4f}'.format(accuracy_score(y_test,pred)))"
      ],
      "metadata": {
        "id": "LfPOfg5jGPDu",
        "outputId": "9c38a1fa-7c23-44d5-ecaf-4ecd527da297",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "예측 정확도: 0.9333\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "bLQzuHVAGPBH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Iris 데이터 살펴보기"
      ],
      "metadata": {
        "id": "_ktnN_EWGoKm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dict_a = {'a': [1, 2, 3], 'b': [5, 6]}"
      ],
      "metadata": {
        "id": "hNEWTQ9wzMdr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dict_a.keys()"
      ],
      "metadata": {
        "id": "ePSioGAQzTu7",
        "outputId": "27bb33c9-89c3-48a0-8862-34d17112f533",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dict_keys(['a', 'b'])"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dict_a['a']"
      ],
      "metadata": {
        "id": "bGLuffktzevK",
        "outputId": "dfe17e48-447a-4c7a-b654-0e740ff4c378",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[1, 2, 3]"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.datasets import load_iris\n",
        "\n",
        "iris_data = load_iris()\n",
        "print(type(iris_data))"
      ],
      "metadata": {
        "id": "nYvImM6YGSlG",
        "outputId": "14eeb143-3abd-48fe-920f-b71ed0cbb3cd",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "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": {
        "id": "aGae1HTpGSiG",
        "outputId": "df7d3f8e-98a2-4a2f-af8e-aaff9576f2e4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "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": [
        "keys"
      ],
      "metadata": {
        "id": "H76j1mtLzH20",
        "outputId": "88d870e1-50cd-461d-9f46-60be89c5aa9e",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dict_keys(['data', 'target', 'frame', 'target_names', 'DESCR', 'feature_names', 'filename', 'data_module'])"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "iris_data.feature_names"
      ],
      "metadata": {
        "id": "9xPb_iNWziqL",
        "outputId": "fc732a50-270a-4ce7-9c5a-d17fa5364cdd",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['sepal length (cm)',\n",
              " 'sepal width (cm)',\n",
              " 'petal length (cm)',\n",
              " 'petal width (cm)']"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "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)"
      ],
      "metadata": {
        "id": "JT86okuPzyWi",
        "outputId": "f939ee5d-b29d-4253-c3f1-5817234169dc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            " feature_names 의 type: <class 'list'>\n",
            " feature_names 의 shape: 4\n",
            "['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np"
      ],
      "metadata": {
        "id": "WV49BYC00Kk6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "np.shape(iris_data.feature_names)"
      ],
      "metadata": {
        "id": "67rqGJBZ0IWq",
        "outputId": "ad65e88f-14a9-434e-c9e6-3cc50413af9c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(4,)"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(len([[1, 2], [3, 4]]))\n",
        "print(np.shape(([[1, 2], [3, 4]])))"
      ],
      "metadata": {
        "id": "_jEBUSqk0Rfj",
        "outputId": "f4f3f8c1-5002-4f25-b66a-d10ef30fbeb7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2\n",
            "(2, 2)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "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)"
      ],
      "metadata": {
        "id": "sGbhiO2-0biK",
        "outputId": "e41fa0de-2d37-4058-c011-2e8a5e0d92c2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            " target_names 의 type: <class 'numpy.ndarray'>\n",
            " feature_names 의 shape: 3\n",
            "['setosa' 'versicolor' 'virginica']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print('\\n data 의 type:',type(iris_data.data))\n",
        "print(' data 의 shape:',iris_data.data.shape)\n",
        "# print(iris_data['data'])"
      ],
      "metadata": {
        "id": "Wc2w-wLU0lTC",
        "outputId": "ce97d0a1-c655-4fce-86a7-0e0a8ff13d15",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            " data 의 type: <class 'numpy.ndarray'>\n",
            " data 의 shape: (150, 4)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "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": "oX2bWQHS0qaJ",
        "outputId": "484de17f-b7c5-40fa-b120-4ccf926f40c0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            " target 의 type: <class 'numpy.ndarray'>\n",
            " target 의 shape: (150,)\n",
            "[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"
          ]
        }
      ]
    },
    {
      "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": "DTZ84dxdGO-e",
        "outputId": "82a5dc7b-5446-4208-e589-332fd41a1c11",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            " feature_names 의 type: <class 'list'>\n",
            " feature_names 의 shape: 4\n",
            "['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)']\n",
            "\n",
            " target_names 의 type: <class 'numpy.ndarray'>\n",
            " feature_names 의 shape: 3\n",
            "['setosa' 'versicolor' 'virginica']\n",
            "\n",
            " data 의 type: <class 'numpy.ndarray'>\n",
            " data 의 shape: (150, 4)\n",
            "[[5.1 3.5 1.4 0.2]\n",
            " [4.9 3.  1.4 0.2]\n",
            " [4.7 3.2 1.3 0.2]\n",
            " [4.6 3.1 1.5 0.2]\n",
            " [5.  3.6 1.4 0.2]\n",
            " [5.4 3.9 1.7 0.4]\n",
            " [4.6 3.4 1.4 0.3]\n",
            " [5.  3.4 1.5 0.2]\n",
            " [4.4 2.9 1.4 0.2]\n",
            " [4.9 3.1 1.5 0.1]\n",
            " [5.4 3.7 1.5 0.2]\n",
            " [4.8 3.4 1.6 0.2]\n",
            " [4.8 3.  1.4 0.1]\n",
            " [4.3 3.  1.1 0.1]\n",
            " [5.8 4.  1.2 0.2]\n",
            " [5.7 4.4 1.5 0.4]\n",
            " [5.4 3.9 1.3 0.4]\n",
            " [5.1 3.5 1.4 0.3]\n",
            " [5.7 3.8 1.7 0.3]\n",
            " [5.1 3.8 1.5 0.3]\n",
            " [5.4 3.4 1.7 0.2]\n",
            " [5.1 3.7 1.5 0.4]\n",
            " [4.6 3.6 1.  0.2]\n",
            " [5.1 3.3 1.7 0.5]\n",
            " [4.8 3.4 1.9 0.2]\n",
            " [5.  3.  1.6 0.2]\n",
            " [5.  3.4 1.6 0.4]\n",
            " [5.2 3.5 1.5 0.2]\n",
            " [5.2 3.4 1.4 0.2]\n",
            " [4.7 3.2 1.6 0.2]\n",
            " [4.8 3.1 1.6 0.2]\n",
            " [5.4 3.4 1.5 0.4]\n",
            " [5.2 4.1 1.5 0.1]\n",
            " [5.5 4.2 1.4 0.2]\n",
            " [4.9 3.1 1.5 0.2]\n",
            " [5.  3.2 1.2 0.2]\n",
            " [5.5 3.5 1.3 0.2]\n",
            " [4.9 3.6 1.4 0.1]\n",
            " [4.4 3.  1.3 0.2]\n",
            " [5.1 3.4 1.5 0.2]\n",
            " [5.  3.5 1.3 0.3]\n",
            " [4.5 2.3 1.3 0.3]\n",
            " [4.4 3.2 1.3 0.2]\n",
            " [5.  3.5 1.6 0.6]\n",
            " [5.1 3.8 1.9 0.4]\n",
            " [4.8 3.  1.4 0.3]\n",
            " [5.1 3.8 1.6 0.2]\n",
            " [4.6 3.2 1.4 0.2]\n",
            " [5.3 3.7 1.5 0.2]\n",
            " [5.  3.3 1.4 0.2]\n",
            " [7.  3.2 4.7 1.4]\n",
            " [6.4 3.2 4.5 1.5]\n",
            " [6.9 3.1 4.9 1.5]\n",
            " [5.5 2.3 4.  1.3]\n",
            " [6.5 2.8 4.6 1.5]\n",
            " [5.7 2.8 4.5 1.3]\n",
            " [6.3 3.3 4.7 1.6]\n",
            " [4.9 2.4 3.3 1. ]\n",
            " [6.6 2.9 4.6 1.3]\n",
            " [5.2 2.7 3.9 1.4]\n",
            " [5.  2.  3.5 1. ]\n",
            " [5.9 3.  4.2 1.5]\n",
            " [6.  2.2 4.  1. ]\n",
            " [6.1 2.9 4.7 1.4]\n",
            " [5.6 2.9 3.6 1.3]\n",
            " [6.7 3.1 4.4 1.4]\n",
            " [5.6 3.  4.5 1.5]\n",
            " [5.8 2.7 4.1 1. ]\n",
            " [6.2 2.2 4.5 1.5]\n",
            " [5.6 2.5 3.9 1.1]\n",
            " [5.9 3.2 4.8 1.8]\n",
            " [6.1 2.8 4.  1.3]\n",
            " [6.3 2.5 4.9 1.5]\n",
            " [6.1 2.8 4.7 1.2]\n",
            " [6.4 2.9 4.3 1.3]\n",
            " [6.6 3.  4.4 1.4]\n",
            " [6.8 2.8 4.8 1.4]\n",
            " [6.7 3.  5.  1.7]\n",
            " [6.  2.9 4.5 1.5]\n",
            " [5.7 2.6 3.5 1. ]\n",
            " [5.5 2.4 3.8 1.1]\n",
            " [5.5 2.4 3.7 1. ]\n",
            " [5.8 2.7 3.9 1.2]\n",
            " [6.  2.7 5.1 1.6]\n",
            " [5.4 3.  4.5 1.5]\n",
            " [6.  3.4 4.5 1.6]\n",
            " [6.7 3.1 4.7 1.5]\n",
            " [6.3 2.3 4.4 1.3]\n",
            " [5.6 3.  4.1 1.3]\n",
            " [5.5 2.5 4.  1.3]\n",
            " [5.5 2.6 4.4 1.2]\n",
            " [6.1 3.  4.6 1.4]\n",
            " [5.8 2.6 4.  1.2]\n",
            " [5.  2.3 3.3 1. ]\n",
            " [5.6 2.7 4.2 1.3]\n",
            " [5.7 3.  4.2 1.2]\n",
            " [5.7 2.9 4.2 1.3]\n",
            " [6.2 2.9 4.3 1.3]\n",
            " [5.1 2.5 3.  1.1]\n",
            " [5.7 2.8 4.1 1.3]\n",
            " [6.3 3.3 6.  2.5]\n",
            " [5.8 2.7 5.1 1.9]\n",
            " [7.1 3.  5.9 2.1]\n",
            " [6.3 2.9 5.6 1.8]\n",
            " [6.5 3.  5.8 2.2]\n",
            " [7.6 3.  6.6 2.1]\n",
            " [4.9 2.5 4.5 1.7]\n",
            " [7.3 2.9 6.3 1.8]\n",
            " [6.7 2.5 5.8 1.8]\n",
            " [7.2 3.6 6.1 2.5]\n",
            " [6.5 3.2 5.1 2. ]\n",
            " [6.4 2.7 5.3 1.9]\n",
            " [6.8 3.  5.5 2.1]\n",
            " [5.7 2.5 5.  2. ]\n",
            " [5.8 2.8 5.1 2.4]\n",
            " [6.4 3.2 5.3 2.3]\n",
            " [6.5 3.  5.5 1.8]\n",
            " [7.7 3.8 6.7 2.2]\n",
            " [7.7 2.6 6.9 2.3]\n",
            " [6.  2.2 5.  1.5]\n",
            " [6.9 3.2 5.7 2.3]\n",
            " [5.6 2.8 4.9 2. ]\n",
            " [7.7 2.8 6.7 2. ]\n",
            " [6.3 2.7 4.9 1.8]\n",
            " [6.7 3.3 5.7 2.1]\n",
            " [7.2 3.2 6.  1.8]\n",
            " [6.2 2.8 4.8 1.8]\n",
            " [6.1 3.  4.9 1.8]\n",
            " [6.4 2.8 5.6 2.1]\n",
            " [7.2 3.  5.8 1.6]\n",
            " [7.4 2.8 6.1 1.9]\n",
            " [7.9 3.8 6.4 2. ]\n",
            " [6.4 2.8 5.6 2.2]\n",
            " [6.3 2.8 5.1 1.5]\n",
            " [6.1 2.6 5.6 1.4]\n",
            " [7.7 3.  6.1 2.3]\n",
            " [6.3 3.4 5.6 2.4]\n",
            " [6.4 3.1 5.5 1.8]\n",
            " [6.  3.  4.8 1.8]\n",
            " [6.9 3.1 5.4 2.1]\n",
            " [6.7 3.1 5.6 2.4]\n",
            " [6.9 3.1 5.1 2.3]\n",
            " [5.8 2.7 5.1 1.9]\n",
            " [6.8 3.2 5.9 2.3]\n",
            " [6.7 3.3 5.7 2.5]\n",
            " [6.7 3.  5.2 2.3]\n",
            " [6.3 2.5 5.  1.9]\n",
            " [6.5 3.  5.2 2. ]\n",
            " [6.2 3.4 5.4 2.3]\n",
            " [5.9 3.  5.1 1.8]]\n",
            "\n",
            " target 의 type: <class 'numpy.ndarray'>\n",
            " target 의 shape: (150,)\n",
            "[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"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "1UqC1rDxGmku"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Model Selection"
      ],
      "metadata": {
        "id": "o2c2wM-FG2Hm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.datasets import load_iris\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.metrics import accuracy_score\n",
        "\n",
        "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)\n",
        "\n",
        "# 학습 데이터 셋으로 예측 수행\n",
        "pred = dt_clf.predict(train_data)\n",
        "print('예측 정확도:',accuracy_score(train_label,pred))"
      ],
      "metadata": {
        "id": "Uv9TGLRHGmh-",
        "outputId": "b243292d-5395-4f34-bde9-11ec17ca5935",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "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,\n",
        "                                                    test_size=0.3, random_state=121)\n",
        "\n",
        "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": {
        "id": "kHz4Xs67GmfO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def abc(x, y):\n",
        "    return x, x+y, x*y"
      ],
      "metadata": {
        "id": "QmOfcZ4B14o4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "a, b, c = abc(2, 3)\n",
        "d = abc(2, 3)"
      ],
      "metadata": {
        "id": "PIaj2WnX2GdA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(a, b, c)"
      ],
      "metadata": {
        "id": "zv4D5I4j2MgQ",
        "outputId": "6e9cbc11-6a76-40df-bfee-9b9f3352e12a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2 5 6\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(d[0], d[1], d[2])"
      ],
      "metadata": {
        "id": "gAeieZ1N2Rgg",
        "outputId": "198ead90-c812-4306-d202-56ccc686724f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2 5 6\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 교차검증"
      ],
      "metadata": {
        "id": "X8P_IRYiG9i9"
      }
    },
    {
      "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",
        "# 5개의 폴드 세트로 분리하는 KFold 객체와 폴드 세트별 정확도를 담을 리스트 객체 생성.\n",
        "kfold = KFold(n_splits=5)\n",
        "cv_accuracy = []\n",
        "print('붓꽃 데이터 세트 크기:',features.shape[0])"
      ],
      "metadata": {
        "id": "VycyDbXqG68O",
        "outputId": "4fb54f7b-14e2-42a0-97b6-b5786d518999",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "붓꽃 데이터 세트 크기: 150\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "n_iter = 0\n",
        "\n",
        "# KFold객체의 split( ) 호출하면 폴드 별 학습용, 검증용 테스트의 로우 인덱스를 array로 반환\n",
        "for train_index, test_index  in kfold.split(features):\n",
        "    # kfold.split( )으로 반환된 인덱스를 이용하여 학습용, 검증용 테스트 데이터 추출\n",
        "    X_train, X_test = features[train_index], features[test_index]\n",
        "    y_train, y_test = label[train_index], label[test_index]\n",
        "    #학습 및 예측\n",
        "    dt_clf.fit(X_train , y_train)\n",
        "    pred = dt_clf.predict(X_test)\n",
        "    n_iter += 1\n",
        "    # 반복 시 마다 정확도 측정\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}'\n",
        "          .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",
        "# 개별 iteration별 정확도를 합하여 평균 정확도 계산\n",
        "print('\\n## 평균 검증 정확도:', np.mean(cv_accuracy))"
      ],
      "metadata": {
        "id": "DhH61E3HG65V",
        "outputId": "ad7029b2-d9a7-4c64-a9e7-82d7cb0093eb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\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 정확도: 0.9\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "iris = load_iris()\n",
        "\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": {
        "id": "L4jMbrEyGmcO",
        "outputId": "f19f74d6-819c-468a-e0ce-7c7b23afca85",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    50\n",
              "1    50\n",
              "2    50\n",
              "Name: label, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "kfold = KFold(n_splits=3)\n",
        "# kfold.split(X)는 폴드 세트를 3번 반복할 때마다 달라지는 학습/테스트 용 데이터 로우 인덱스 번호 반환.\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('## 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())\n"
      ],
      "metadata": {
        "id": "a2fd1RgdHDEO",
        "outputId": "ca81491f-6f21-4a98-8369-a1e24e7eccb3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "## 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",
            "## 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",
            "## 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('## 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": {
        "id": "z85gIewVHDBO",
        "outputId": "a91335cd-177d-406f-c8ca-0da31dc20272",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "## 교차 검증: 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",
            "## 교차 검증: 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",
            "## 교차 검증: 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",
        "# StratifiedKFold의 split( ) 호출시 반드시 레이블 데이터 셋도 추가 입력 필요\n",
        "for train_index, test_index  in skfold.split(features, label):\n",
        "    # split( )으로 반환된 인덱스를 이용하여 학습용, 검증용 테스트 데이터 추출\n",
        "    X_train, X_test = features[train_index], features[test_index]\n",
        "    y_train, y_test = label[train_index], label[test_index]\n",
        "    #학습 및 예측\n",
        "    dt_clf.fit(X_train , y_train)\n",
        "    pred = dt_clf.predict(X_test)\n",
        "\n",
        "    # 반복 시 마다 정확도 측정\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}'\n",
        "          .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",
        "# 교차 검증별 정확도 및 평균 정확도 계산\n",
        "print('\\n## 교차 검증별 정확도:', np.round(cv_accuracy, 4))\n",
        "print('## 평균 검증 정확도:', np.round(np.mean(cv_accuracy), 4))"
      ],
      "metadata": {
        "id": "KIO-V_S2HC-l",
        "outputId": "57b122ac-3731-4d64-c585-c767852176cf",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "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",
        "\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",
        "# 성능 지표는 정확도(accuracy) , 교차 검증 세트는 3개\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": {
        "id": "kVwQhJq_HJyu",
        "outputId": "9595986a-5f19-4c60-926c-862b70470c26",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "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": [
        "from sklearn.datasets import load_iris\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "\n",
        "# 데이터를 로딩하고 학습데이타와 테스트 데이터 분리\n",
        "iris = load_iris()\n",
        "X_train, X_test, y_train, y_test = train_test_split(iris_data.data, iris_data.target,\n",
        "                                                    test_size=0.2, random_state=121)\n",
        "dtree = DecisionTreeClassifier()\n",
        "\n",
        "### parameter 들을 dictionary 형태로 설정\n",
        "parameters = {'max_depth':[1,2,3], 'min_samples_split':[2,3]}"
      ],
      "metadata": {
        "id": "ZXRLemuFHJv2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "# param_grid의 하이퍼 파라미터들을 3개의 train, test set fold 로 나누어서 테스트 수행 설정.\n",
        "### refit=True 가 default 임. True이면 가장 좋은 파라미터 설정으로 재 학습 시킴.\n",
        "grid_dtree = GridSearchCV(dtree, param_grid=parameters, cv=3, refit=True)\n",
        "\n",
        "# 붓꽃 Train 데이터로 param_grid의 하이퍼 파라미터들을 순차적으로 학습/평가 .\n",
        "grid_dtree.fit(X_train, y_train)\n",
        "\n",
        "# GridSearchCV 결과 추출하여 DataFrame으로 변환\n",
        "scores_df = pd.DataFrame(grid_dtree.cv_results_)\n",
        "scores_df[['params', 'mean_test_score', 'rank_test_score', \\\n",
        "           'split0_test_score', 'split1_test_score', 'split2_test_score']]"
      ],
      "metadata": {
        "id": "PLrzieCLHJtO",
        "outputId": "4fb17ba1-c3c3-4283-868c-439d638ecb2f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        }
      },
      "execution_count": null,
      "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": [
              "\n",
              "  <div id=\"df-6ebaae41-d51b-467d-9ac4-375e02a099ec\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>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",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>{'max_depth': 1, 'min_samples_split': 2}</td>\n",
              "      <td>0.700000</td>\n",
              "      <td>5</td>\n",
              "      <td>0.700</td>\n",
              "      <td>0.7</td>\n",
              "      <td>0.70</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>{'max_depth': 1, 'min_samples_split': 3}</td>\n",
              "      <td>0.700000</td>\n",
              "      <td>5</td>\n",
              "      <td>0.700</td>\n",
              "      <td>0.7</td>\n",
              "      <td>0.70</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>{'max_depth': 2, 'min_samples_split': 2}</td>\n",
              "      <td>0.958333</td>\n",
              "      <td>3</td>\n",
              "      <td>0.925</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.95</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>{'max_depth': 2, 'min_samples_split': 3}</td>\n",
              "      <td>0.958333</td>\n",
              "      <td>3</td>\n",
              "      <td>0.925</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.95</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>{'max_depth': 3, 'min_samples_split': 2}</td>\n",
              "      <td>0.975000</td>\n",
              "      <td>1</td>\n",
              "      <td>0.975</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.95</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>{'max_depth': 3, 'min_samples_split': 3}</td>\n",
              "      <td>0.975000</td>\n",
              "      <td>1</td>\n",
              "      <td>0.975</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.95</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6ebaae41-d51b-467d-9ac4-375e02a099ec')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-6ebaae41-d51b-467d-9ac4-375e02a099ec button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-6ebaae41-d51b-467d-9ac4-375e02a099ec');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "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": {
        "id": "d5l9XitpHJqm",
        "outputId": "71090b5b-7bc6-41f2-f575-5e4eea04c762",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "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": [
        "# GridSearchCV의 refit으로 이미 학습이 된 estimator 반환\n",
        "estimator = grid_dtree.best_estimator_\n",
        "\n",
        "# GridSearchCV의 best_estimator_는 이미 최적 하이퍼 파라미터로 학습이 됨\n",
        "pred = estimator.predict(X_test)\n",
        "print('테스트 데이터 세트 정확도: {0:.4f}'.format(accuracy_score(y_test,pred)))"
      ],
      "metadata": {
        "id": "tjRJOKTvHO52",
        "outputId": "d71c4eeb-e4e1-48d0-d191-059f078dadc8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "테스트 데이터 세트 정확도: 0.9667\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 데이터 전처리"
      ],
      "metadata": {
        "id": "YBC5lhHBHhFV"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 라벨 인코딩(label encoding)"
      ],
      "metadata": {
        "id": "IjIazqh7HlMu"
      }
    },
    {
      "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": {
        "id": "YJ6-los0HO2u",
        "outputId": "31185e5d-772e-480b-edc6-37c00171aa58",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "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": {
        "id": "A-UR0ZzXHO0N",
        "outputId": "58bd05e9-b55b-47c4-b75b-5ff4fc613cbd",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "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": {
        "id": "B1czJcjWHspP",
        "outputId": "bf60d4d7-2d3d-4d73-b80a-7a819a82825a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "디코딩 원본 값: ['전자레인지' '컴퓨터' '믹서' 'TV' '냉장고' '냉장고' '선풍기' '선풍기']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## One-Hot encoding"
      ],
      "metadata": {
        "id": "8A256RB0HvIF"
      }
    },
    {
      "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": {
        "id": "NZAxlRxKHsmN",
        "outputId": "81a35bf5-194b-4cdb-a221-117679f35698",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "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": {
        "id": "mcYNhgQ6Hsjm",
        "outputId": "a17e2324-91f1-43ff-d382-99deed690727",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        }
      },
      "execution_count": null,
      "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"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-6ebc7eed-8e19-4563-88b3-08b1d57db113\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>item_TV</th>\n",
              "      <th>item_냉장고</th>\n",
              "      <th>item_믹서</th>\n",
              "      <th>item_선풍기</th>\n",
              "      <th>item_전자레인지</th>\n",
              "      <th>item_컴퓨터</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
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              "      <th>1</th>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
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              "      <td>1</td>\n",
              "      <td>0</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6ebc7eed-8e19-4563-88b3-08b1d57db113')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
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              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-6ebc7eed-8e19-4563-88b3-08b1d57db113 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-6ebc7eed-8e19-4563-88b3-08b1d57db113');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 피처 스케일링과 정규화"
      ],
      "metadata": {
        "id": "dMPpjtHLHsg1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## feature scaling and normalization"
      ],
      "metadata": {
        "id": "Dlx8qE7aH3jt"
      }
    },
    {
      "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": {
        "id": "MY-SmPDMHsd-",
        "outputId": "7164406b-0160-4605-f824-c6b6d63d0d22",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "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": {
        "id": "3piIqhI8H-CF",
        "outputId": "44371ae2-adda-4683-998c-d20a8d7b2802",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "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": {
        "id": "c4iC3F0wH_rM",
        "outputId": "e5afd72e-33f2-4838-8e27-49cdc3cd834f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "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": "-6inW9aLH_od"
      },
      "execution_count": null,
      "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": {
        "id": "Is8DcGGHH_ll",
        "outputId": "b972172c-39ed-498d-fd97-0e7b5690bb3c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "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": {
        "id": "j5rWkQatIEw0",
        "outputId": "06454045-c296-441a-a97a-a306d3a23669",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "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": {
        "id": "5NO5y16EIEt0",
        "outputId": "a6680e98-2fd8-436e-e74e-f2f073d07fd9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "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": [
        "# 타이타닉 데이터 예측"
      ],
      "metadata": {
        "id": "8yEPIxmAHdAl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!rm -r python101/\n",
        "!git clone https://github.com/minyoung347/python101.git"
      ],
      "metadata": {
        "id": "9Iypvj9n5HfT",
        "outputId": "e53ec1e2-299a-4d88-8115-54ace0e66d77",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "rm: cannot remove 'python101/': No such file or directory\n",
            "Cloning into 'python101'...\n",
            "remote: Enumerating objects: 588, done.\u001b[K\n",
            "remote: Counting objects: 100% (125/125), done.\u001b[K\n",
            "remote: Compressing objects: 100% (71/71), done.\u001b[K\n",
            "remote: Total 588 (delta 69), reused 99 (delta 53), pack-reused 463\u001b[K\n",
            "Receiving objects: 100% (588/588), 73.63 MiB | 23.42 MiB/s, done.\n",
            "Resolving deltas: 100% (310/310), done.\n",
            "Updating files: 100% (35/35), done.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3YE2jtzs5DBR",
        "outputId": "f25c568e-6c48-4c70-c035-7efccb82014a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "titanic 변수 type: <class 'pandas.core.frame.DataFrame'>\n",
            "     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",
            "886          887         0       2   \n",
            "887          888         1       1   \n",
            "888          889         0       3   \n",
            "889          890         1       1   \n",
            "890          891         0       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",
            "3         Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
            "4                             Allen, Mr. William Henry    male  35.0      0   \n",
            "..                                                 ...     ...   ...    ...   \n",
            "886                              Montvila, Rev. Juozas    male  27.0      0   \n",
            "887                       Graham, Miss. Margaret Edith  female  19.0      0   \n",
            "888           Johnston, Miss. Catherine Helen \"Carrie\"  female   NaN      1   \n",
            "889                              Behr, Mr. Karl Howell    male  26.0      0   \n",
            "890                                Dooley, Mr. Patrick    male  32.0      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  \n",
            "3        0            113803  53.1000  C123        S  \n",
            "4        0            373450   8.0500   NaN        S  \n",
            "..     ...               ...      ...   ...      ...  \n",
            "886      0            211536  13.0000   NaN        S  \n",
            "887      0            112053  30.0000   B42        S  \n",
            "888      2        W./C. 6607  23.4500   NaN        S  \n",
            "889      0            111369  30.0000  C148        C  \n",
            "890      0            370376   7.7500   NaN        Q  \n",
            "\n",
            "[891 rows x 12 columns]\n"
          ]
        }
      ],
      "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",
        "titanic_df = pd.read_csv('/content/python101/input/titanic_train.csv')\n",
        "print('titanic 변수 type:',type(titanic_df))\n",
        "print(titanic_df)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "w2ngjTpD5DBU",
        "outputId": "9d88b354-b418-4a31-f8a7-a3dcb64f756f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "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"
          ]
        }
      ],
      "source": [
        "print('\\n ### train 데이터 정보 ###  \\n')\n",
        "print(titanic_df.info())"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "titanic_df['Age'].mean()"
      ],
      "metadata": {
        "id": "lk2o5QrLJDMv",
        "outputId": "7687a9c8-505f-49c0-986b-77509284050c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "29.69911764705882"
            ]
          },
          "metadata": {},
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "titanic_df['Age'] = titanic_df['Age'].fillna(titanic_df['Age'].mean())\n",
        "titanic_df['Age'].fillna(titanic_df['Age'].mean(),inplace=True)"
      ],
      "metadata": {
        "id": "q63zCYBcJGDH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "i2nakyzQ5DBX",
        "outputId": "cc9b0b47-b8df-4732-c39e-80bdd9a2d907",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "데이터 세트 Null 값 갯수  0\n"
          ]
        }
      ],
      "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())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "EL4EmbK75DBX",
        "outputId": "fdc372ce-7251-4d24-8b11-b62676a9add9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "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"
          ]
        }
      ],
      "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())"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "titanic_df['Cabin']"
      ],
      "metadata": {
        "id": "hk1BwpWbJYF-",
        "outputId": "18edf5ce-efba-4721-f6d8-844036f9fddc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0      N\n",
              "1      C\n",
              "2      N\n",
              "3      C\n",
              "4      N\n",
              "      ..\n",
              "886    N\n",
              "887    B\n",
              "888    N\n",
              "889    C\n",
              "890    N\n",
              "Name: Cabin, Length: 891, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 74
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ZjvnsT2L5DBY",
        "outputId": "e19c4c00-8c27-4c9f-96be-2b2ccd534074",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0    N\n",
            "1    C\n",
            "2    N\n",
            "Name: Cabin, dtype: object\n"
          ]
        }
      ],
      "source": [
        "titanic_df['Cabin'] = titanic_df['Cabin'].str[:1]\n",
        "print(titanic_df['Cabin'].head(3))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ksBpIox75DBY",
        "outputId": "65846364-5d10-4627-d2f8-2313182a7dfa",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "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": 75
        }
      ],
      "source": [
        "titanic_df.groupby(['Sex','Survived'])['Survived'].count()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "5GyGLbOh5DBY",
        "outputId": "9e109c8e-7b6d-4eff-c2b9-162303668bc1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 467
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Sex', ylabel='Survived'>"
            ]
          },
          "metadata": {},
          "execution_count": 76
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "sns.barplot(x='Sex', y = 'Survived', data=titanic_df)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "g7tg-5Eq5DBZ",
        "outputId": "9cbacd17-5ff2-4976-8d08-55b07101cafa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 467
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Pclass', ylabel='Survived'>"
            ]
          },
          "metadata": {},
          "execution_count": 77
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "sns.barplot(x='Pclass', y='Survived', hue='Sex', data=titanic_df)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "F4ZvfNRT5DBZ",
        "outputId": "c85b7faf-8c5e-4485-ad07-42b25ce36572",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ],
      "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)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "titanic_df[['Cabin', 'Sex', 'Embarked']]"
      ],
      "metadata": {
        "id": "SxLJZhEGKHQG",
        "outputId": "6e0f9e19-83fb-49e8-dd29-c99465ba7ad4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    Cabin     Sex Embarked\n",
              "0       N    male        S\n",
              "1       C  female        C\n",
              "2       N  female        S\n",
              "3       C  female        S\n",
              "4       N    male        S\n",
              "..    ...     ...      ...\n",
              "886     N    male        S\n",
              "887     B  female        S\n",
              "888     N  female        S\n",
              "889     C    male        C\n",
              "890     N    male        Q\n",
              "\n",
              "[891 rows x 3 columns]"
            ],
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              "          const element = document.querySelector('#df-c2b5dfdc-52c9-4a3b-8f53-ac52df4c760c');\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",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 79
        }
      ]
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    {
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        "outputId": "b08891ca-270c-4771-a2ff-1a17872c2095",
        "colab": {
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          "height": 206
        }
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        {
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          "data": {
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              "   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",
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              "                                                     [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",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 80
        }
      ],
      "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()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "EpGEDNT85DBa"
      },
      "outputs": [],
      "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"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ndq-1IiV5DBa"
      },
      "outputs": [],
      "source": [
        "# 원본 데이터를 재로딩 하고, feature데이터 셋과 Label 데이터 셋 추출.\n",
        "titanic_df = pd.read_csv('/content/python101/input/titanic_train.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)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "A2Lqsnc95DBa"
      },
      "outputs": [],
      "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)\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train"
      ],
      "metadata": {
        "id": "Iib76ob6KppO",
        "outputId": "87790573-0764-48a7-e2ac-cdf5e9fc9b52",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Pclass  Sex   Age  SibSp  Parch      Fare  Cabin  Embarked\n",
              "333       3    1  16.0      2      0   18.0000      7         3\n",
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              "149       2    1  42.0      0      0   13.0000      7         3\n",
              "..      ...  ...   ...    ...    ...       ...    ...       ...\n",
              "269       1    0  35.0      0      0  135.6333      2         3\n",
              "337       1    0  41.0      0      0  134.5000      4         0\n",
              "91        3    1  20.0      0      0    7.8542      7         3\n",
              "80        3    1  22.0      0      0    9.0000      7         3\n",
              "703       3    1  25.0      0      0    7.7417      7         2\n",
              "\n",
              "[712 rows x 8 columns]"
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              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>712 rows × 8 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-08183401-6d4a-4f25-9007-d96c27e5e60c')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-08183401-6d4a-4f25-9007-d96c27e5e60c button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-08183401-6d4a-4f25-9007-d96c27e5e60c');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 84
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_train"
      ],
      "metadata": {
        "id": "6jduOml6KtF1",
        "outputId": "874713ee-4124-47af-9cdd-27bef75a44e3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "333    0\n",
              "662    0\n",
              "382    0\n",
              "331    0\n",
              "149    0\n",
              "      ..\n",
              "269    1\n",
              "337    1\n",
              "91     0\n",
              "80     0\n",
              "703    0\n",
              "Name: Survived, Length: 712, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 86
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "cTbakgs45DBa",
        "outputId": "4a8a47ca-22d6-426c-e262-054463f0f2f4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "DecisionTreeClassifier 정확도: 0.7877\n",
            "RandomForestClassifier 정확도:0.8547\n",
            "LogisticRegression 정확도: 0.8659\n"
          ]
        }
      ],
      "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)))\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "dH0gqTaYLbBl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "aoalApy65DBa",
        "outputId": "a8c29c3d-4ac4-455f-cb4e-40ca35750bc6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "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"
          ]
        }
      ],
      "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)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "GLEpTY8-5DBa",
        "outputId": "a387b803-6b4f-47db-fd1c-bcf8cd081351",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "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"
          ]
        }
      ],
      "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))\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Y_dM_wGi5DBb"
      },
      "outputs": [],
      "source": [
        "def bok(x):\n",
        "    return 2*x"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "bok(2)"
      ],
      "metadata": {
        "id": "T7ichmNKP3k_",
        "outputId": "7c7c88ab-b7dc-4f8a-a9c8-fcdb2a21c3fc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4"
            ]
          },
          "metadata": {},
          "execution_count": 89
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "class Rectangle(object):\n",
        "\n",
        "    def __init__(self, h, v):\n",
        "        self.h = h\n",
        "        self.v = v\n",
        "\n",
        "    def area(self):\n",
        "        return self.h * self.v"
      ],
      "metadata": {
        "id": "wYUAwHw4P5Bx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "r1 = Rectangle(10, 20)\n",
        "r2 = Rectangle(5, 20)\n",
        "r3 = Rectangle(10, 10)"
      ],
      "metadata": {
        "id": "p-LXYCIHQDEB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "type(r)"
      ],
      "metadata": {
        "id": "cHic1qN4QIWA",
        "outputId": "521e2f37-2fc5-4275-eeee-3f494529da8f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "__main__.Rectangle"
            ]
          },
          "metadata": {},
          "execution_count": 92
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(r1.area())\n",
        "print(r2.area())\n",
        "print(r3.area())"
      ],
      "metadata": {
        "id": "rP8GuxkCQKFY",
        "outputId": "21992847-a0d5-47da-e0ad-9ff07baba6e8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "200\n",
            "100\n",
            "100\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "class Account(object):\n",
        "\n",
        "    def __init__(self, m):\n",
        "        self.m = m\n",
        "\n",
        "    def total(self):\n",
        "        return self.m\n",
        "\n",
        "    def add(self, a):\n",
        "        self.m = self.m + a"
      ],
      "metadata": {
        "id": "kYrwdZZTP9g5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "a1 = Account(10000)"
      ],
      "metadata": {
        "id": "uWfLQ3UFQmrf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "a1.add(1000)"
      ],
      "metadata": {
        "id": "y8RRqk3sREBn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "a1.total()"
      ],
      "metadata": {
        "id": "5SagBYV5QqdJ",
        "outputId": "ce11f265-60a7-4b6f-8d6f-54078d7baedd",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "11000"
            ]
          },
          "metadata": {},
          "execution_count": 134
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "class FourCal:\n",
        "     def setdata(self, first, second):\n",
        "         self.first = first\n",
        "         self.second = second\n",
        "     def add(self):\n",
        "         result = self.first + self.second\n",
        "         return result\n",
        "     def mul(self):\n",
        "         result = self.first * self.second\n",
        "         return result\n",
        "     def sub(self):\n",
        "         result = self.first - self.second\n",
        "         return result\n",
        "     def div(self):\n",
        "         result = self.first / self.second\n",
        "         return result"
      ],
      "metadata": {
        "id": "LYmwDnn3Qwek"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "e8y3LGUBRsyX"
      },
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.9.7"
    },
    "colab": {
      "provenance": [],
      "include_colab_link": true
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}