차원_축소(0302).ipynb

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      "cell_type": "markdown",
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        "<a href=\"https://colab.research.google.com/github/cnddlf/2022_1_Study_AI/blob/main/%EC%B0%A8%EC%9B%90_%EC%B6%95%EC%86%8C(0302).ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
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    {
      "cell_type": "markdown",
      "source": [
        "#1. iris 데이터 읽어오기"
      ],
      "metadata": {
        "id": "TSwHp6dbGmBa"
      }
    },
    {
      "cell_type": "code",
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        "outputId": "81caaa0f-8bb6-4e5d-96e6-fae395c5886e"
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              "array([[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]])"
            ]
          },
          "metadata": {},
          "execution_count": 1
        }
      ],
      "source": [
        "from sklearn.datasets import load_iris\n",
        "\n",
        "iris = load_iris()\n",
        "iris.data[:5]"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "columns = ['sepal_length', 'sepal_width', 'petal_length', 'petal_width']\n",
        "irisDF = pd.DataFrame(iris.data, columns=columns)\n",
        "irisDF"
      ],
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          "height": 423
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        "id": "5FVeU8DCGrw-",
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              "      <th></th>\n",
              "      <th>sepal_length</th>\n",
              "      <th>sepal_width</th>\n",
              "      <th>petal_length</th>\n",
              "      <th>petal_width</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5.1</td>\n",
              "      <td>3.5</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4.9</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>4.7</td>\n",
              "      <td>3.2</td>\n",
              "      <td>1.3</td>\n",
              "      <td>0.2</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4.6</td>\n",
              "      <td>3.1</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.2</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5.0</td>\n",
              "      <td>3.6</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
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              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>145</th>\n",
              "      <td>6.7</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.2</td>\n",
              "      <td>2.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>146</th>\n",
              "      <td>6.3</td>\n",
              "      <td>2.5</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1.9</td>\n",
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              "    <tr>\n",
              "      <th>147</th>\n",
              "      <td>6.5</td>\n",
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              "      <td>5.2</td>\n",
              "      <td>2.0</td>\n",
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              "    <tr>\n",
              "      <th>148</th>\n",
              "      <td>6.2</td>\n",
              "      <td>3.4</td>\n",
              "      <td>5.4</td>\n",
              "      <td>2.3</td>\n",
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              "    <tr>\n",
              "      <th>149</th>\n",
              "      <td>5.9</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.1</td>\n",
              "      <td>1.8</td>\n",
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              "  </tbody>\n",
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              "<p>150 rows × 4 columns</p>\n",
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              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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              "  "
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            "text/plain": [
              "     sepal_length  sepal_width  petal_length  petal_width\n",
              "0             5.1          3.5           1.4          0.2\n",
              "1             4.9          3.0           1.4          0.2\n",
              "2             4.7          3.2           1.3          0.2\n",
              "3             4.6          3.1           1.5          0.2\n",
              "4             5.0          3.6           1.4          0.2\n",
              "..            ...          ...           ...          ...\n",
              "145           6.7          3.0           5.2          2.3\n",
              "146           6.3          2.5           5.0          1.9\n",
              "147           6.5          3.0           5.2          2.0\n",
              "148           6.2          3.4           5.4          2.3\n",
              "149           5.9          3.0           5.1          1.8\n",
              "\n",
              "[150 rows x 4 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "irisDF['target']=iris.target\n",
        "irisDF.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "R5thLxQlITnm",
        "outputId": "b2cf9263-04a8-4a20-91e2-9a06728188c4"
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        {
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              "      <th>petal_length</th>\n",
              "      <th>petal_width</th>\n",
              "      <th>target</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5.1</td>\n",
              "      <td>3.5</td>\n",
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              "      <td>0</td>\n",
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              "      <th>1</th>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4.6</td>\n",
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              "      <th>4</th>\n",
              "      <td>5.0</td>\n",
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              "      <td>1.4</td>\n",
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              "                                                     [key], {});\n",
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              "\n",
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              "            '<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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            "text/plain": [
              "   sepal_length  sepal_width  petal_length  petal_width  target\n",
              "0           5.1          3.5           1.4          0.2       0\n",
              "1           4.9          3.0           1.4          0.2       0\n",
              "2           4.7          3.2           1.3          0.2       0\n",
              "3           4.6          3.1           1.5          0.2       0\n",
              "4           5.0          3.6           1.4          0.2       0"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "irisDF[irisDF['target']==0][:5]"
      ],
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              "      <th></th>\n",
              "      <th>sepal_length</th>\n",
              "      <th>sepal_width</th>\n",
              "      <th>petal_length</th>\n",
              "      <th>petal_width</th>\n",
              "      <th>target</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>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",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4.6</td>\n",
              "      <td>3.1</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5.0</td>\n",
              "      <td>3.6</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-02c7d337-1c21-4414-8598-2a6d6773dc12')\"\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-02c7d337-1c21-4414-8598-2a6d6773dc12 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-02c7d337-1c21-4414-8598-2a6d6773dc12');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "   sepal_length  sepal_width  petal_length  petal_width  target\n",
              "0           5.1          3.5           1.4          0.2       0\n",
              "1           4.9          3.0           1.4          0.2       0\n",
              "2           4.7          3.2           1.3          0.2       0\n",
              "3           4.6          3.1           1.5          0.2       0\n",
              "4           5.0          3.6           1.4          0.2       0"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "irisDF[irisDF['target']==0]['sepal_length'][:5]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3QEt1KAeI5BJ",
        "outputId": "ade7548b-af30-4386-c9b1-fff2530094e9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    5.1\n",
              "1    4.9\n",
              "2    4.7\n",
              "3    4.6\n",
              "4    5.0\n",
              "Name: sepal_length, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "#setosa는 세모, versicolor는 네모, virginica는 동그라미로 표현\n",
        "markers=['^', 's', 'o']\n",
        "\n",
        "#setosa의 target 값은 0, versicolor는 1, virginica는 2. 각 target 별로 다른 shape으로 scatter plot \n",
        "for i, marker in enumerate(markers):\n",
        "    print(i, ' : ', marker)\n",
        "    x_axis_data = irisDF[irisDF['target']==i]['sepal_length']\n",
        "    y_axis_data = irisDF[irisDF['target']==i]['sepal_width']\n",
        "    plt.scatter(x_axis_data, y_axis_data, marker=marker,label=iris.target_names[i])\n",
        "\n",
        "plt.legend()\n",
        "plt.xlabel('sepal length')\n",
        "plt.ylabel('sepal width')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 337
        },
        "id": "xv83vTIVJcCg",
        "outputId": "145ffab9-37f7-4dc8-a403-0090d3a249d4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0  :  ^\n",
            "1  :  s\n",
            "2  :  o\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "> 데이터 표준화"
      ],
      "metadata": {
        "id": "Wh3NQfvOJi0k"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "scaler = StandardScaler()\n",
        "scaler.fit(iris.data)\n",
        "iris_scaled = scaler.transform(iris.data)\n",
        "iris_scaled"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0z1tecIrJkkq",
        "outputId": "b014f634-63a7-40d9-c58f-d5b55733f127"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[-9.00681170e-01,  1.01900435e+00, -1.34022653e+00,\n",
              "        -1.31544430e+00],\n",
              "       [-1.14301691e+00, -1.31979479e-01, -1.34022653e+00,\n",
              "        -1.31544430e+00],\n",
              "       [-1.38535265e+00,  3.28414053e-01, -1.39706395e+00,\n",
              "        -1.31544430e+00],\n",
              "       [-1.50652052e+00,  9.82172869e-02, -1.28338910e+00,\n",
              "        -1.31544430e+00],\n",
              "       [-1.02184904e+00,  1.24920112e+00, -1.34022653e+00,\n",
              "        -1.31544430e+00],\n",
              "       [-5.37177559e-01,  1.93979142e+00, -1.16971425e+00,\n",
              "        -1.05217993e+00],\n",
              "       [-1.50652052e+00,  7.88807586e-01, -1.34022653e+00,\n",
              "        -1.18381211e+00],\n",
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              "         1.32509732e-01],\n",
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              "       [-4.16009689e-01, -1.05276654e+00,  3.64896281e-01,\n",
              "         8.77547895e-04],\n",
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              "         8.77547895e-04],\n",
              "       [-1.73673948e-01, -3.62176246e-01,  2.51221427e-01,\n",
              "         1.32509732e-01],\n",
              "       [ 4.32165405e-01, -3.62176246e-01,  3.08058854e-01,\n",
              "         1.32509732e-01],\n",
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              "       [-1.73673948e-01, -5.92373012e-01,  1.94384000e-01,\n",
              "         1.32509732e-01],\n",
              "       [ 5.53333275e-01,  5.58610819e-01,  1.27429511e+00,\n",
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              "       [-5.25060772e-02, -8.22569778e-01,  7.62758269e-01,\n",
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              "       [ 7.95669016e-01, -1.31979479e-01,  1.16062026e+00,\n",
              "         1.31719939e+00],\n",
              "       [ 2.12851559e+00, -1.31979479e-01,  1.61531967e+00,\n",
              "         1.18556721e+00],\n",
              "       [-1.14301691e+00, -1.28296331e+00,  4.21733708e-01,\n",
              "         6.59038469e-01],\n",
              "       [ 1.76501198e+00, -3.62176246e-01,  1.44480739e+00,\n",
              "         7.90670654e-01],\n",
              "       [ 1.03800476e+00, -1.28296331e+00,  1.16062026e+00,\n",
              "         7.90670654e-01],\n",
              "       [ 1.64384411e+00,  1.24920112e+00,  1.33113254e+00,\n",
              "         1.71209594e+00],\n",
              "       [ 7.95669016e-01,  3.28414053e-01,  7.62758269e-01,\n",
              "         1.05393502e+00],\n",
              "       [ 6.74501145e-01, -8.22569778e-01,  8.76433123e-01,\n",
              "         9.22302838e-01],\n",
              "       [ 1.15917263e+00, -1.31979479e-01,  9.90107977e-01,\n",
              "         1.18556721e+00],\n",
              "       [-1.73673948e-01, -1.28296331e+00,  7.05920842e-01,\n",
              "         1.05393502e+00],\n",
              "       [-5.25060772e-02, -5.92373012e-01,  7.62758269e-01,\n",
              "         1.58046376e+00],\n",
              "       [ 6.74501145e-01,  3.28414053e-01,  8.76433123e-01,\n",
              "         1.44883158e+00],\n",
              "       [ 7.95669016e-01, -1.31979479e-01,  9.90107977e-01,\n",
              "         7.90670654e-01],\n",
              "       [ 2.24968346e+00,  1.70959465e+00,  1.67215710e+00,\n",
              "         1.31719939e+00],\n",
              "       [ 2.24968346e+00, -1.05276654e+00,  1.78583195e+00,\n",
              "         1.44883158e+00],\n",
              "       [ 1.89829664e-01, -1.97355361e+00,  7.05920842e-01,\n",
              "         3.95774101e-01],\n",
              "       [ 1.28034050e+00,  3.28414053e-01,  1.10378283e+00,\n",
              "         1.44883158e+00],\n",
              "       [-2.94841818e-01, -5.92373012e-01,  6.49083415e-01,\n",
              "         1.05393502e+00],\n",
              "       [ 2.24968346e+00, -5.92373012e-01,  1.67215710e+00,\n",
              "         1.05393502e+00],\n",
              "       [ 5.53333275e-01, -8.22569778e-01,  6.49083415e-01,\n",
              "         7.90670654e-01],\n",
              "       [ 1.03800476e+00,  5.58610819e-01,  1.10378283e+00,\n",
              "         1.18556721e+00],\n",
              "       [ 1.64384411e+00,  3.28414053e-01,  1.27429511e+00,\n",
              "         7.90670654e-01],\n",
              "       [ 4.32165405e-01, -5.92373012e-01,  5.92245988e-01,\n",
              "         7.90670654e-01],\n",
              "       [ 3.10997534e-01, -1.31979479e-01,  6.49083415e-01,\n",
              "         7.90670654e-01],\n",
              "       [ 6.74501145e-01, -5.92373012e-01,  1.04694540e+00,\n",
              "         1.18556721e+00],\n",
              "       [ 1.64384411e+00, -1.31979479e-01,  1.16062026e+00,\n",
              "         5.27406285e-01],\n",
              "       [ 1.88617985e+00, -5.92373012e-01,  1.33113254e+00,\n",
              "         9.22302838e-01],\n",
              "       [ 2.49201920e+00,  1.70959465e+00,  1.50164482e+00,\n",
              "         1.05393502e+00],\n",
              "       [ 6.74501145e-01, -5.92373012e-01,  1.04694540e+00,\n",
              "         1.31719939e+00],\n",
              "       [ 5.53333275e-01, -5.92373012e-01,  7.62758269e-01,\n",
              "         3.95774101e-01],\n",
              "       [ 3.10997534e-01, -1.05276654e+00,  1.04694540e+00,\n",
              "         2.64141916e-01],\n",
              "       [ 2.24968346e+00, -1.31979479e-01,  1.33113254e+00,\n",
              "         1.44883158e+00],\n",
              "       [ 5.53333275e-01,  7.88807586e-01,  1.04694540e+00,\n",
              "         1.58046376e+00],\n",
              "       [ 6.74501145e-01,  9.82172869e-02,  9.90107977e-01,\n",
              "         7.90670654e-01],\n",
              "       [ 1.89829664e-01, -1.31979479e-01,  5.92245988e-01,\n",
              "         7.90670654e-01],\n",
              "       [ 1.28034050e+00,  9.82172869e-02,  9.33270550e-01,\n",
              "         1.18556721e+00],\n",
              "       [ 1.03800476e+00,  9.82172869e-02,  1.04694540e+00,\n",
              "         1.58046376e+00],\n",
              "       [ 1.28034050e+00,  9.82172869e-02,  7.62758269e-01,\n",
              "         1.44883158e+00],\n",
              "       [-5.25060772e-02, -8.22569778e-01,  7.62758269e-01,\n",
              "         9.22302838e-01],\n",
              "       [ 1.15917263e+00,  3.28414053e-01,  1.21745768e+00,\n",
              "         1.44883158e+00],\n",
              "       [ 1.03800476e+00,  5.58610819e-01,  1.10378283e+00,\n",
              "         1.71209594e+00],\n",
              "       [ 1.03800476e+00, -1.31979479e-01,  8.19595696e-01,\n",
              "         1.44883158e+00],\n",
              "       [ 5.53333275e-01, -1.28296331e+00,  7.05920842e-01,\n",
              "         9.22302838e-01],\n",
              "       [ 7.95669016e-01, -1.31979479e-01,  8.19595696e-01,\n",
              "         1.05393502e+00],\n",
              "       [ 4.32165405e-01,  7.88807586e-01,  9.33270550e-01,\n",
              "         1.44883158e+00],\n",
              "       [ 6.86617933e-02, -1.31979479e-01,  7.62758269e-01,\n",
              "         7.90670654e-01]])"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "iris_scaled = StandardScaler().fit_transform(iris.data)\n",
        "iris_scaled"
      ],
      "metadata": {
        "id": "7AEEwCg-KqOU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "iris_data = irisDF.iloc[:, :-1]\n",
        "iris_scaled = StandardScaler().fit_transform(iris_data)"
      ],
      "metadata": {
        "id": "yUwFJh7sK7g5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.decomposition import PCA\n",
        "\n",
        "pca = PCA()\n",
        "pca.fit(iris_data)\n",
        "pca.explained_variance_\n",
        "pca.explained_variance_ratio_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jPk6P63uOOH8",
        "outputId": "5425da4b-c5e5-40e7-b65c-7511a3cf6f8f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.92461872, 0.05306648, 0.01710261, 0.00521218])"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "\n",
        "x = np.arange(4)\n",
        "x"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tT9z9TqQO4t8",
        "outputId": "3174162d-0739-4967-9d73-c0c0df913096"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 1, 2, 3])"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.bar(x, pca.explained_variance_ratio_)\n",
        "plt.xticks(x, x)\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "bOoqkpCLOuWX",
        "outputId": "d9810bd1-7d17-4ae0-c568-85c1f7045e5f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.decomposition import PCA\n",
        "\n",
        "pca = PCA(n_components=2)\n",
        "\n",
        "pca.fit(iris_scaled)\n",
        "iris_pca = pca.transform(iris_scaled)\n",
        "iris_pca.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WG_PJtLwPSfZ",
        "outputId": "8ebef0d6-7e7d-4fef-ae2d-9547d9995854"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(150, 2)"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pca_col = ['pca_component1', 'pca_component2']\n",
        "irisDF_pca = pd.DataFrame(iris_pca, columns=pca_col)\n",
        "irisDF_pca.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "bRfGF7BHRHP2",
        "outputId": "51b0848e-f2f2-4e2c-9773-d19413910321"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-795e252e-55dc-4214-88c9-8b8e93b9d4ea\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
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              "        vertical-align: top;\n",
              "    }\n",
              "\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>pca_component1</th>\n",
              "      <th>pca_component2</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>-2.264703</td>\n",
              "      <td>0.480027</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-2.080961</td>\n",
              "      <td>-0.674134</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-2.364229</td>\n",
              "      <td>-0.341908</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-2.299384</td>\n",
              "      <td>-0.597395</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-2.389842</td>\n",
              "      <td>0.646835</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-795e252e-55dc-4214-88c9-8b8e93b9d4ea')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "      \n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "\n",
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              "        const buttonEl =\n",
              "          document.querySelector('#df-795e252e-55dc-4214-88c9-8b8e93b9d4ea 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-795e252e-55dc-4214-88c9-8b8e93b9d4ea');\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",
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              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "   pca_component1  pca_component2\n",
              "0       -2.264703        0.480027\n",
              "1       -2.080961       -0.674134\n",
              "2       -2.364229       -0.341908\n",
              "3       -2.299384       -0.597395\n",
              "4       -2.389842        0.646835"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "irisDF_pca['target']=iris.target\n",
        "irisDF_pca.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "TCvTrtrKRmDR",
        "outputId": "bdb6d8a4-e703-4d0f-f7fd-c871c7f11c88"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "\n",
              "  <div id=\"df-494b611f-837f-49bb-81ed-b54d90d6af01\">\n",
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              "\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>pca_component1</th>\n",
              "      <th>pca_component2</th>\n",
              "      <th>target</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>-2.264703</td>\n",
              "      <td>0.480027</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-2.080961</td>\n",
              "      <td>-0.674134</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-2.364229</td>\n",
              "      <td>-0.341908</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-2.299384</td>\n",
              "      <td>-0.597395</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-2.389842</td>\n",
              "      <td>0.646835</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-494b611f-837f-49bb-81ed-b54d90d6af01')\"\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-494b611f-837f-49bb-81ed-b54d90d6af01 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-494b611f-837f-49bb-81ed-b54d90d6af01');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "   pca_component1  pca_component2  target\n",
              "0       -2.264703        0.480027       0\n",
              "1       -2.080961       -0.674134       0\n",
              "2       -2.364229       -0.341908       0\n",
              "3       -2.299384       -0.597395       0\n",
              "4       -2.389842        0.646835       0"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "markers=['^', 's', 'o']\n",
        "\n",
        "#setosa의 target 값은 0, versicolor는 1, virginica는 2. 각 target 별로 다른 shape으로 scatter plot \n",
        "for i, marker in enumerate(markers):\n",
        "    print(i, ' : ', marker)\n",
        "    x_axis_data = irisDF_pca[irisDF_pca['target']==i]['pca_component1']\n",
        "    y_axis_data = irisDF_pca[irisDF_pca['target']==i]['pca_component2']\n",
        "    plt.scatter(x_axis_data, y_axis_data, marker=marker,label=iris.target_names[i])\n",
        "\n",
        "plt.legend()\n",
        "plt.xlabel('pca_component1')\n",
        "plt.ylabel('pca_component2')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 334
        },
        "id": "HX_GTLUTTNGN",
        "outputId": "0472bd37-56d8-4f5a-ab60-0ed00f28e65a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0  :  ^\n",
            "1  :  s\n",
            "2  :  o\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.tree import DecisionTreeClassifier\n",
        "# 모델 로드\n",
        "dt_clf = DecisionTreeClassifier()"
      ],
      "metadata": {
        "id": "voQJIbf9WtT8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import cross_val_score\n",
        "\n",
        "scores = cross_val_score(dt_clf, iris.data, iris.target, scoring='accuracy', cv=3)\n",
        "np.mean(scores)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5JZDAPVkVZVg",
        "outputId": "d5ecfb45-f962-44dc-a377-8ec2432321f8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9666666666666667"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import cross_val_score\n",
        "\n",
        "# target을 제외한 데이터 분리\n",
        "pca_X = irisDF_pca[['pca_component1', 'pca_component2']]\n",
        "pca_y = irisDF_pca[['target']]\n",
        "\n",
        "\n",
        "# scores = cross_val_score(dt_clf, pca_X, iris.target, scoring='accuracy', cv=3)\n",
        "scores = cross_val_score(dt_clf, pca_X, pca_y, scoring='accuracy', cv=3)\n",
        "print('원본 데이터 교차 검증 개별 정확도:', scores)\n",
        "print('원본 데이터 평균 정확도:', np.mean(scores))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "D5B__XIHU42T",
        "outputId": "61ba66c5-8ff8-4210-a095-5591ff01b3c7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "원본 데이터 교차 검증 개별 정확도: [0.9  0.9  0.88]\n",
            "원본 데이터 평균 정확도: 0.8933333333333334\n"
          ]
        }
      ]
    }
  ]
}