2-1.for_PCA.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "J6wB_RC4AZ3d",
        "outputId": "4ca6bf28-4069-4878-bb55-85fc8c433622"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "MneaVVGtAZvS"
      },
      "outputs": [],
      "source": [
        "# 파일 경로를 각자 컴퓨터 환경에 맞게 수정하세요!\n",
        "file_path = '/content/drive/MyDrive/부리부리데마왕/dataset/'"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gzbXOr8aAtj-"
      },
      "source": [
        "## 1. 라이브러리"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "QwT9PCVpATg3"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "import math\n",
        "from sklearn.decomposition import PCA\n",
        "\n",
        "from sklearn.cluster import DBSCAN\n",
        "from sklearn.cluster import KMeans\n",
        "from sklearn.cluster import MeanShift\n",
        "from sklearn.mixture import GaussianMixture\n",
        "\n",
        "from sklearn.cluster import estimate_bandwidth\n",
        "from sklearn.metrics import silhouette_score, silhouette_samples"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "5e9nKOzXAcg8"
      },
      "outputs": [],
      "source": [
        "# 파일 경로를 각자 컴퓨터 환경에 맞게 수정하세요!\n",
        "file_path = '/content/drive/MyDrive/부리부리데마왕/dataset/'"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6nTuMPVWAwL-"
      },
      "source": [
        "## 2. PCA에 앞서 클러스터링을 진행하기 위한 데이터 호출"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "qv_EFKYDAnxu"
      },
      "outputs": [],
      "source": [
        "Train = pd.read_csv(file_path +'creditcard_data.csv')\n",
        "drop_data = ['Amount', 'Time', 'Class']\n",
        "\n",
        "train = Train.drop(columns = drop_data,axis=1)\n",
        "target = Train['Class'].values"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "d4Hnz5PuA6uO"
      },
      "source": [
        "## 3. 모델"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HZ6XR2r8A8Fo"
      },
      "source": [
        "### K-MEANS"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 947
        },
        "id": "yUN-i3ihAsOZ",
        "outputId": "46e7b55c-8111-49bf-a3bc-1190c22d5aec"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Removing the 'Class' column for clustering\n",
        "clustering_data = train.copy()\n",
        "\n",
        "# Finding the optimal number of clusters using the Elbow Method\n",
        "wcss = []  # Within-Cluster Sum of Square\n",
        "for i in range(1, 11):  # Testing for 1 to 10 clusters\n",
        "    kmeans = KMeans(n_clusters=i, init='k-means++', random_state=42)\n",
        "    kmeans.fit(clustering_data)\n",
        "    wcss.append(kmeans.inertia_)\n",
        "\n",
        "# Plotting the Elbow Method graph\n",
        "plt.figure(figsize=(10, 6))\n",
        "plt.plot(range(1, 11), wcss, marker='o')\n",
        "plt.title('Elbow Method')\n",
        "plt.xlabel('Number of clusters')\n",
        "plt.ylabel('WCSS')\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uyAuREE7DSg4",
        "outputId": "54825fbe-cd72-4d09-a321-8b1317063141"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n"
          ]
        }
      ],
      "source": [
        "kmeans_creditcard = KMeans(n_clusters=5, random_state=0)\n",
        "kmeans_creditcard.fit(clustering_data)\n",
        "\n",
        "# 클러스터링 결과 가져오기\n",
        "y_kmeans_creditcard = kmeans_creditcard.predict(clustering_data)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 443
        },
        "id": "LOIl1tj5LBJj",
        "outputId": "80dc1b93-35c3-4e18-dcd8-0de4a03ee4a3"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-a6fa3422-905b-4cf1-b17b-84e6bde8dee8\" class=\"colab-df-container\">\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>V1</th>\n",
              "      <th>V2</th>\n",
              "      <th>V3</th>\n",
              "      <th>V4</th>\n",
              "      <th>V5</th>\n",
              "      <th>V6</th>\n",
              "      <th>V7</th>\n",
              "      <th>V8</th>\n",
              "      <th>V9</th>\n",
              "      <th>V10</th>\n",
              "      <th>...</th>\n",
              "      <th>V19</th>\n",
              "      <th>V20</th>\n",
              "      <th>V21</th>\n",
              "      <th>V22</th>\n",
              "      <th>V23</th>\n",
              "      <th>V24</th>\n",
              "      <th>V25</th>\n",
              "      <th>V26</th>\n",
              "      <th>V27</th>\n",
              "      <th>V28</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2.286478</td>\n",
              "      <td>-1.530436</td>\n",
              "      <td>-2.852483</td>\n",
              "      <td>-1.480494</td>\n",
              "      <td>-1.754829</td>\n",
              "      <td>1.902396</td>\n",
              "      <td>1.889078</td>\n",
              "      <td>-1.886128</td>\n",
              "      <td>-1.829936</td>\n",
              "      <td>1.855851</td>\n",
              "      <td>...</td>\n",
              "      <td>-1.899419</td>\n",
              "      <td>-1.889407</td>\n",
              "      <td>-1.885275</td>\n",
              "      <td>-1.895055</td>\n",
              "      <td>-1.886321</td>\n",
              "      <td>-1.885390</td>\n",
              "      <td>-1.874215</td>\n",
              "      <td>-1.886408</td>\n",
              "      <td>-1.886501</td>\n",
              "      <td>-1.885623</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-2.323395</td>\n",
              "      <td>1.613871</td>\n",
              "      <td>1.791369</td>\n",
              "      <td>2.159168</td>\n",
              "      <td>2.042038</td>\n",
              "      <td>-2.115913</td>\n",
              "      <td>-2.073394</td>\n",
              "      <td>2.113892</td>\n",
              "      <td>2.069696</td>\n",
              "      <td>-2.071992</td>\n",
              "      <td>...</td>\n",
              "      <td>2.118270</td>\n",
              "      <td>2.115134</td>\n",
              "      <td>2.117149</td>\n",
              "      <td>2.137510</td>\n",
              "      <td>2.114880</td>\n",
              "      <td>2.112849</td>\n",
              "      <td>2.120588</td>\n",
              "      <td>2.114780</td>\n",
              "      <td>2.114324</td>\n",
              "      <td>2.114259</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2.285429</td>\n",
              "      <td>-0.829156</td>\n",
              "      <td>-2.495814</td>\n",
              "      <td>-1.730505</td>\n",
              "      <td>-1.716385</td>\n",
              "      <td>1.954952</td>\n",
              "      <td>2.019049</td>\n",
              "      <td>-1.886818</td>\n",
              "      <td>-2.050484</td>\n",
              "      <td>1.839969</td>\n",
              "      <td>...</td>\n",
              "      <td>-1.800206</td>\n",
              "      <td>-1.893704</td>\n",
              "      <td>-1.888849</td>\n",
              "      <td>-1.914227</td>\n",
              "      <td>-1.880186</td>\n",
              "      <td>-1.888597</td>\n",
              "      <td>-1.902272</td>\n",
              "      <td>-1.886214</td>\n",
              "      <td>-1.885301</td>\n",
              "      <td>-1.885514</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2.002345</td>\n",
              "      <td>-1.468217</td>\n",
              "      <td>-2.505061</td>\n",
              "      <td>-2.041792</td>\n",
              "      <td>-1.831311</td>\n",
              "      <td>1.933221</td>\n",
              "      <td>1.888610</td>\n",
              "      <td>-1.887420</td>\n",
              "      <td>-2.035499</td>\n",
              "      <td>1.875660</td>\n",
              "      <td>...</td>\n",
              "      <td>-1.838510</td>\n",
              "      <td>-1.882190</td>\n",
              "      <td>-1.884068</td>\n",
              "      <td>-1.884474</td>\n",
              "      <td>-1.886801</td>\n",
              "      <td>-1.890660</td>\n",
              "      <td>-1.842306</td>\n",
              "      <td>-1.886535</td>\n",
              "      <td>-1.886051</td>\n",
              "      <td>-1.885857</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2.140941</td>\n",
              "      <td>-2.056387</td>\n",
              "      <td>-2.390893</td>\n",
              "      <td>-1.724682</td>\n",
              "      <td>-1.738770</td>\n",
              "      <td>1.888003</td>\n",
              "      <td>1.972295</td>\n",
              "      <td>-1.884417</td>\n",
              "      <td>-1.776637</td>\n",
              "      <td>1.765833</td>\n",
              "      <td>...</td>\n",
              "      <td>-1.914287</td>\n",
              "      <td>-1.891875</td>\n",
              "      <td>-1.885394</td>\n",
              "      <td>-1.915260</td>\n",
              "      <td>-1.886483</td>\n",
              "      <td>-1.885074</td>\n",
              "      <td>-1.894791</td>\n",
              "      <td>-1.883734</td>\n",
              "      <td>-1.887046</td>\n",
              "      <td>-1.886292</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>284801</th>\n",
              "      <td>1.217827</td>\n",
              "      <td>-2.085862</td>\n",
              "      <td>-1.411876</td>\n",
              "      <td>-2.012194</td>\n",
              "      <td>-2.097267</td>\n",
              "      <td>1.874967</td>\n",
              "      <td>2.024056</td>\n",
              "      <td>-1.886204</td>\n",
              "      <td>-1.896607</td>\n",
              "      <td>1.957548</td>\n",
              "      <td>...</td>\n",
              "      <td>-1.889182</td>\n",
              "      <td>-1.885469</td>\n",
              "      <td>-1.881305</td>\n",
              "      <td>-1.852881</td>\n",
              "      <td>-1.885354</td>\n",
              "      <td>-1.885238</td>\n",
              "      <td>-1.908928</td>\n",
              "      <td>-1.885197</td>\n",
              "      <td>-1.887037</td>\n",
              "      <td>-1.885878</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>284802</th>\n",
              "      <td>9.190981</td>\n",
              "      <td>-6.310768</td>\n",
              "      <td>3.813495</td>\n",
              "      <td>-1.374998</td>\n",
              "      <td>0.389544</td>\n",
              "      <td>0.782620</td>\n",
              "      <td>-0.297523</td>\n",
              "      <td>-0.919538</td>\n",
              "      <td>-0.654791</td>\n",
              "      <td>0.285380</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.859661</td>\n",
              "      <td>-0.908762</td>\n",
              "      <td>-0.888475</td>\n",
              "      <td>-0.889366</td>\n",
              "      <td>-0.879572</td>\n",
              "      <td>-0.887843</td>\n",
              "      <td>-0.795647</td>\n",
              "      <td>-0.884717</td>\n",
              "      <td>-0.891667</td>\n",
              "      <td>-0.888018</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>284803</th>\n",
              "      <td>1.833770</td>\n",
              "      <td>-1.540231</td>\n",
              "      <td>-2.618182</td>\n",
              "      <td>-2.010565</td>\n",
              "      <td>-2.036157</td>\n",
              "      <td>1.925806</td>\n",
              "      <td>1.838380</td>\n",
              "      <td>-1.887037</td>\n",
              "      <td>-1.803986</td>\n",
              "      <td>2.000840</td>\n",
              "      <td>...</td>\n",
              "      <td>-1.826864</td>\n",
              "      <td>-1.886394</td>\n",
              "      <td>-1.888395</td>\n",
              "      <td>-1.920155</td>\n",
              "      <td>-1.885582</td>\n",
              "      <td>-1.889984</td>\n",
              "      <td>-1.919430</td>\n",
              "      <td>-1.887205</td>\n",
              "      <td>-1.886087</td>\n",
              "      <td>-1.885532</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>284804</th>\n",
              "      <td>-1.465016</td>\n",
              "      <td>0.261909</td>\n",
              "      <td>1.619845</td>\n",
              "      <td>-0.029023</td>\n",
              "      <td>-0.502193</td>\n",
              "      <td>0.004835</td>\n",
              "      <td>-0.180999</td>\n",
              "      <td>0.111025</td>\n",
              "      <td>0.164294</td>\n",
              "      <td>-0.047356</td>\n",
              "      <td>...</td>\n",
              "      <td>0.135714</td>\n",
              "      <td>0.114273</td>\n",
              "      <td>0.111185</td>\n",
              "      <td>0.091775</td>\n",
              "      <td>0.114082</td>\n",
              "      <td>0.117024</td>\n",
              "      <td>0.130437</td>\n",
              "      <td>0.113971</td>\n",
              "      <td>0.114279</td>\n",
              "      <td>0.114384</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>284805</th>\n",
              "      <td>1.478294</td>\n",
              "      <td>-1.864240</td>\n",
              "      <td>-1.995400</td>\n",
              "      <td>-1.652871</td>\n",
              "      <td>-1.745586</td>\n",
              "      <td>1.908732</td>\n",
              "      <td>1.671046</td>\n",
              "      <td>-1.888818</td>\n",
              "      <td>-1.826613</td>\n",
              "      <td>1.922441</td>\n",
              "      <td>...</td>\n",
              "      <td>-1.992232</td>\n",
              "      <td>-1.887460</td>\n",
              "      <td>-1.889080</td>\n",
              "      <td>-1.915328</td>\n",
              "      <td>-1.886639</td>\n",
              "      <td>-1.885151</td>\n",
              "      <td>-1.917126</td>\n",
              "      <td>-1.883562</td>\n",
              "      <td>-1.886344</td>\n",
              "      <td>-1.885979</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>284806 rows × 28 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a6fa3422-905b-4cf1-b17b-84e6bde8dee8')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "\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",
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              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-a6fa3422-905b-4cf1-b17b-84e6bde8dee8 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-a6fa3422-905b-4cf1-b17b-84e6bde8dee8');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-5cbabb20-e0fa-4c8e-8d16-8b42c6643cba\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-5cbabb20-e0fa-4c8e-8d16-8b42c6643cba')\"\n",
              "            title=\"Suggest charts\"\n",
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              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
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              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
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              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-5cbabb20-e0fa-4c8e-8d16-8b42c6643cba button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_a6649cbc-2752-48e7-bf88-ad7a913de394\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('merged_data_df')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_a6649cbc-2752-48e7-bf88-ad7a913de394 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('merged_data_df');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "              V1        V2        V3        V4        V5        V6        V7  \\\n",
              "0       2.286478 -1.530436 -2.852483 -1.480494 -1.754829  1.902396  1.889078   \n",
              "1      -2.323395  1.613871  1.791369  2.159168  2.042038 -2.115913 -2.073394   \n",
              "2       2.285429 -0.829156 -2.495814 -1.730505 -1.716385  1.954952  2.019049   \n",
              "3       2.002345 -1.468217 -2.505061 -2.041792 -1.831311  1.933221  1.888610   \n",
              "4       2.140941 -2.056387 -2.390893 -1.724682 -1.738770  1.888003  1.972295   \n",
              "...          ...       ...       ...       ...       ...       ...       ...   \n",
              "284801  1.217827 -2.085862 -1.411876 -2.012194 -2.097267  1.874967  2.024056   \n",
              "284802  9.190981 -6.310768  3.813495 -1.374998  0.389544  0.782620 -0.297523   \n",
              "284803  1.833770 -1.540231 -2.618182 -2.010565 -2.036157  1.925806  1.838380   \n",
              "284804 -1.465016  0.261909  1.619845 -0.029023 -0.502193  0.004835 -0.180999   \n",
              "284805  1.478294 -1.864240 -1.995400 -1.652871 -1.745586  1.908732  1.671046   \n",
              "\n",
              "              V8        V9       V10  ...       V19       V20       V21  \\\n",
              "0      -1.886128 -1.829936  1.855851  ... -1.899419 -1.889407 -1.885275   \n",
              "1       2.113892  2.069696 -2.071992  ...  2.118270  2.115134  2.117149   \n",
              "2      -1.886818 -2.050484  1.839969  ... -1.800206 -1.893704 -1.888849   \n",
              "3      -1.887420 -2.035499  1.875660  ... -1.838510 -1.882190 -1.884068   \n",
              "4      -1.884417 -1.776637  1.765833  ... -1.914287 -1.891875 -1.885394   \n",
              "...          ...       ...       ...  ...       ...       ...       ...   \n",
              "284801 -1.886204 -1.896607  1.957548  ... -1.889182 -1.885469 -1.881305   \n",
              "284802 -0.919538 -0.654791  0.285380  ... -0.859661 -0.908762 -0.888475   \n",
              "284803 -1.887037 -1.803986  2.000840  ... -1.826864 -1.886394 -1.888395   \n",
              "284804  0.111025  0.164294 -0.047356  ...  0.135714  0.114273  0.111185   \n",
              "284805 -1.888818 -1.826613  1.922441  ... -1.992232 -1.887460 -1.889080   \n",
              "\n",
              "             V22       V23       V24       V25       V26       V27       V28  \n",
              "0      -1.895055 -1.886321 -1.885390 -1.874215 -1.886408 -1.886501 -1.885623  \n",
              "1       2.137510  2.114880  2.112849  2.120588  2.114780  2.114324  2.114259  \n",
              "2      -1.914227 -1.880186 -1.888597 -1.902272 -1.886214 -1.885301 -1.885514  \n",
              "3      -1.884474 -1.886801 -1.890660 -1.842306 -1.886535 -1.886051 -1.885857  \n",
              "4      -1.915260 -1.886483 -1.885074 -1.894791 -1.883734 -1.887046 -1.886292  \n",
              "...          ...       ...       ...       ...       ...       ...       ...  \n",
              "284801 -1.852881 -1.885354 -1.885238 -1.908928 -1.885197 -1.887037 -1.885878  \n",
              "284802 -0.889366 -0.879572 -0.887843 -0.795647 -0.884717 -0.891667 -0.888018  \n",
              "284803 -1.920155 -1.885582 -1.889984 -1.919430 -1.887205 -1.886087 -1.885532  \n",
              "284804  0.091775  0.114082  0.117024  0.130437  0.113971  0.114279  0.114384  \n",
              "284805 -1.915328 -1.886639 -1.885151 -1.917126 -1.883562 -1.886344 -1.885979  \n",
              "\n",
              "[284806 rows x 28 columns]"
            ]
          },
          "execution_count": 36,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from sklearn.decomposition import PCA\n",
        "import pandas as pd\n",
        "\n",
        "feature_groups = [['V1', 'Cluster'], ['V2', 'Cluster'], ['V3', 'Cluster'],\n",
        "           ['V4', 'Cluster'], ['V5', 'Cluster'], ['V6', 'Cluster'], ['V7', 'Cluster'],\n",
        "           ['V8', 'Cluster'], ['V9', 'Cluster'], ['V10', 'Cluster'], ['V11', 'Cluster'],\n",
        "           ['V12', 'Cluster'], ['V13', 'Cluster'], ['V14', 'Cluster'], ['V15', 'Cluster'],\n",
        "           ['V16', 'Cluster'], ['V17', 'Cluster'], ['V18', 'Cluster'], ['V19', 'Cluster'],\n",
        "           ['V20', 'Cluster'], ['V21', 'Cluster'], ['V22', 'Cluster'], ['V23', 'Cluster'],\n",
        "           ['V24', 'Cluster'], ['V25', 'Cluster'], ['V26', 'Cluster'], ['V27', 'Cluster'],\n",
        "           ['V28', 'Cluster']]\n",
        "\n",
        "pca_data1 = []\n",
        "pca_columns = ['V1', 'V2', 'V3', 'V4', 'V5', 'V6', 'V7', 'V8', 'V9', 'V10', 'V11',\n",
        "       'V12', 'V13', 'V14', 'V15', 'V16', 'V17', 'V18', 'V19', 'V20', 'V21',\n",
        "       'V22', 'V23', 'V24', 'V25', 'V26', 'V27', 'V28']\n",
        "\n",
        "for i,group in enumerate(feature_groups):\n",
        "    # 데이터 선택\n",
        "    selected_data_X = clustering_data[group]\n",
        "\n",
        "    # PCA\n",
        "    pca = PCA(n_components=1)\n",
        "    selected_data_X_pca = pca.fit_transform(selected_data_X)\n",
        "\n",
        "    # PCA 적용 후 데이터를 데이터프레임에 추가\n",
        "    pca_data1.append(pd.DataFrame(selected_data_X_pca, columns=[pca_columns[i]]))\n",
        "\n",
        "\n",
        "# 각 그룹의 PCA 결과 데이터를 수평으로 연결하여 하나의 변수로 합침\n",
        "merged_data_df = pd.concat(pca_data1, axis=1)\n",
        "\n",
        "# Result 확인\n",
        "merged_data_df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "metadata": {
        "id": "CwKYw6gpBsNz"
      },
      "outputs": [],
      "source": [
        "merged_data_df.to_csv(file_path + 'pca_train.csv', index=False, encoding='utf-8')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "5nMoNI_0M9zs"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}