PCA.ipynb

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    "colab": {
      "name": "PCA.ipynb",
      "provenance": [],
      "mount_file_id": "1gTWmoosYz7uMNLm5SZSY7kh6MZU6j2RR",
      "authorship_tag": "ABX9TyPqqC03nJ0WWyZfPIzIutKx"
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gV3_a74ybJ5e"
      },
      "source": [
        "- 출처 : https://www.kaggle.com/yairhadad1/detect-outliers-with-pca\n",
        "- PCA를 통해 MNIST 데이터셋 중 MSE가 안좋은 값들을 도출하여 이상치를 탐지하는 예시"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nztgwObEa4dv"
      },
      "source": [
        "import numpy as np \n",
        "import pandas as pd \n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "# PCA Package\n",
        "from sklearn.decomposition import PCA\n",
        "\n",
        "#ignore warning messages \n",
        "import warnings\n",
        "warnings.filterwarnings('ignore') \n",
        "\n",
        "sns.set()"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ldco6XkKbUNG",
        "outputId": "c4619e4b-68d2-4986-c138-257770420be2"
      },
      "source": [
        "# Data set 다운로드 (개인 kaggle 인증 json 필요)\n",
        "! cp /content/drive/MyDrive/kaggle/kaggle.json /root/.kaggle/\n",
        "! kaggle competitions download -c digit-recognizer"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Warning: Looks like you're using an outdated API Version, please consider updating (server 1.5.12 / client 1.5.4)\n",
            "Downloading train.csv.zip to /content\n",
            " 55% 5.00M/9.16M [00:00<00:00, 36.0MB/s]\n",
            "100% 9.16M/9.16M [00:00<00:00, 44.7MB/s]\n",
            "Downloading sample_submission.csv to /content\n",
            "  0% 0.00/235k [00:00<?, ?B/s]\n",
            "100% 235k/235k [00:00<00:00, 74.9MB/s]\n",
            "Downloading test.csv.zip to /content\n",
            " 82% 5.00M/6.09M [00:00<00:00, 25.3MB/s]\n",
            "100% 6.09M/6.09M [00:00<00:00, 29.8MB/s]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
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      "metadata": {
        "colab": {
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          "height": 428
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        "id": "1iyomnqTa8rG",
        "outputId": "18e91729-32ef-481c-a92f-5f212a3fe575"
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      "source": [
        "# Get the train data \n",
        "train = pd.read_csv(\"/content/train.csv.zip\")\n",
        "\n",
        "display(train)"
      ],
      "execution_count": 13,
      "outputs": [
        {
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</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",
              "      <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",
              "      <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",
              "      <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>41995</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41996</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41997</th>\n",
              "      <td>7</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41998</th>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41999</th>\n",
              "      <td>9</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>42000 rows × 785 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       label  pixel0  pixel1  pixel2  ...  pixel780  pixel781  pixel782  pixel783\n",
              "0          1       0       0       0  ...         0         0         0         0\n",
              "1          0       0       0       0  ...         0         0         0         0\n",
              "2          1       0       0       0  ...         0         0         0         0\n",
              "3          4       0       0       0  ...         0         0         0         0\n",
              "4          0       0       0       0  ...         0         0         0         0\n",
              "...      ...     ...     ...     ...  ...       ...       ...       ...       ...\n",
              "41995      0       0       0       0  ...         0         0         0         0\n",
              "41996      1       0       0       0  ...         0         0         0         0\n",
              "41997      7       0       0       0  ...         0         0         0         0\n",
              "41998      6       0       0       0  ...         0         0         0         0\n",
              "41999      9       0       0       0  ...         0         0         0         0\n",
              "\n",
              "[42000 rows x 785 columns]"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 656
        },
        "id": "DtxWdT7pcRH2",
        "outputId": "a896d5bb-8f70-4ca7-f73c-6f28ab6a5b54"
      },
      "source": [
        "# Splite data the X, y - target\n",
        "X = train.drop('label',axis = 1)\n",
        "y = train['label'].astype('category')\n",
        "\n",
        "display(X)\n",
        "display(y)"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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              "      <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",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41995</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41996</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41997</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41998</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41999</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>42000 rows × 784 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       pixel0  pixel1  pixel2  pixel3  ...  pixel780  pixel781  pixel782  pixel783\n",
              "0           0       0       0       0  ...         0         0         0         0\n",
              "1           0       0       0       0  ...         0         0         0         0\n",
              "2           0       0       0       0  ...         0         0         0         0\n",
              "3           0       0       0       0  ...         0         0         0         0\n",
              "4           0       0       0       0  ...         0         0         0         0\n",
              "...       ...     ...     ...     ...  ...       ...       ...       ...       ...\n",
              "41995       0       0       0       0  ...         0         0         0         0\n",
              "41996       0       0       0       0  ...         0         0         0         0\n",
              "41997       0       0       0       0  ...         0         0         0         0\n",
              "41998       0       0       0       0  ...         0         0         0         0\n",
              "41999       0       0       0       0  ...         0         0         0         0\n",
              "\n",
              "[42000 rows x 784 columns]"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "0        1\n",
              "1        0\n",
              "2        1\n",
              "3        4\n",
              "4        0\n",
              "        ..\n",
              "41995    0\n",
              "41996    1\n",
              "41997    7\n",
              "41998    6\n",
              "41999    9\n",
              "Name: label, Length: 42000, dtype: category\n",
              "Categories (10, int64): [0, 1, 2, 3, ..., 6, 7, 8, 9]"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "qMs_IZy_bBVW",
        "outputId": "d091ac31-cd93-401d-e01a-aa875967fb74"
      },
      "source": [
        "# y= 7인 X만 추출\n",
        "dataset_7 = X[y == 7].reset_index().drop(\"index\",axis = 1)\n",
        "\n",
        "# 9개만 뽑아서 시각화\n",
        "plt.figure(figsize = (10,8))\n",
        "row, colums = 3, 3\n",
        "for i in range(9):  \n",
        "    plt.subplot(colums, row, i+1)\n",
        "    plt.imshow(dataset_7.iloc[i].values.reshape(28,28),interpolation='nearest', cmap='Greys')\n",
        "plt.show()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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t3WSMGSNprqSuanrU+CRr7cakckWLNJ9T2agJxCXN51Q2aiJeSV95mSNptrV2qKTZavpFpdGTkr4p6f2c8TTn3yjpFmutsdYOl/SepFnGmGpJ8yRNyeT9oqRZCeYJV5rPqWzUBOKS5nMqGzURo8SaF2NMP0m1kuZnhuZLqjXG9E0qp72x1i621q7NHkt7/tbardba57OGXpY0WNIxknZYaxdnxudIuiDm9BAh7edUNmoCcUj7OZWNmohXkldeBklaZ62tl6TMv+sz4yEIJv9MF325pIWSDlPWfxlYazdLqjbG9E4oPbQI5pzai2DypyaCEcw5tRfB5B9aTST9tRHicaekbZLuSjoRICWoCcAVVE0k2byslXSoMaaDJGX+HZAZD0EQ+WcWkR0p6dvW2gZJa9R0WbD55wdJarDWbk0oRbQI4pzahyDypyaCEsQ5tQ9B5B9iTSTWvGRWLb8haUJmaIKk1621m5LKqTVCyN8YM1NN312eba1tfqT4q5K6GmO+kYknS3osifzgCuGc2pcQ8qcmwhLCObUvIeQfak1UNTY2JnZwY8xRarqF7EBJH6vpFjKbWEJ7YYy5Q9K5kvpL2ixpi7V2WJrzN8YMk7Rc0gpJX2SGV1lrzzHGjFXTivcuarkF7qNEEoUjzedUNmoCcUnzOZWNmohXos0LAABAa7FgFwAABIXmBQAABIXmBQAABIXmBQAABIXmBQAABIXmBQAABKVjWz+gDY/77ixppKQNkurbmgeC00HSIZKWSdqZZ25QqAkUiZrwURPt1z7roc3Ni1oe9z3PGDNJTZvanFjA+0ZK+kMJjo+wHS9pcd5ZYaEm0BbURAtqApH10Kavjdr4uO8NbTk2KkZFnQfUBEqgos4DagJtFHkOtHXNS1se980lQEiVdx5QE2irSjsPqAm0ReQ5wIJdAAAQlLY2L0E87huIETUBuKgJlFybmpcQHvcNxImaAFzUBMqhFHcbTZZ0vzHmemUe912CzwRCRk0ALmoCJVXV2NiY1LFrJK1K6uBIjSGSViedRErUiJoANZGtRtREexdZDyzYBQAAQaF5AQAAQaF5AQAAQaF5AQAAQaF5AQAAQaF5AQAAQaF5AQAAQaF5AQAAQaF5AQAAQaF5AQAAQaF5AQAAQaF5AQAAQaF5AQAAQaF5AQAAQaF5AQAAQaF5AQAAQaF5AQAAQaF5AQAAQaF5AQAAQaF5AQAAQaF5AQAAQaF5AQAAQaF5AQAAQaF5AQAAQemYdAKlsG3bNm/svvvuc+K5c+d6c9599902Hbe+vl4dOnSI/FlDQ4MTH3DAAd6c1157zRurqalx4upq+ku0b+vXr3fib33rW94ca60TX3311d6cMWPGOPGGDRskSZdffrnuvvtuSdL777/vve+aa65x4r59+xaQNSrZF1984cQ/+9nPvDnPPfecJOnPf/6zvvrVr2rEiBHenC5dujjx2LFjvTmHH354W1Ldo0ePHt7Y0UcfXZLPTgJ/GQEAQFBoXgAAQFBoXgAAQFBoXgAAQFCqGhsbkzp2jaRVpfigxYsXe2Pjxo0rxUfv0+7du9WxY/Sa59zfa1VVVUGf+d3vfteJzzzzTG/O+eefX2CGQRgiaXXSSaREjUpUE2m0efNmJz7jjDO8OV/72te8sXnz5jlx7mLJQvXq1cuJmxfeWmtljJEk1dXVee97++23nfioo44q6vitQE20qFEKa+I3v/mNE1922WXenCuuuEKSdNddd+nKK69U9+7dvTnNi8abffnll96c5oW/+7J169a8c3JvBpGkd955x4k7d+6c93MSEFkPXHkBAABBoXkBAABBoXkBAABBqYhN6kaOHOmN5X6fvnr1am/OlClTvLHcDYGWLVvmzXnkkUf2vP7KV74SmdPJJ5/sxM8884w3J3djLUl6+OGHnfiFF17w5nzyySdOfNFFF3lz9rYWB4jDunXrvLHzzjvPiV955RVvTtRYbi399Kc/LSqnv/qrv3LiBx54YM/r73//+5Kkm266yXtf1OZeQD4333yz8zp3zVVb1NfXO3HU35LvfOc7TnzWWWd5c1K6xqUgXHkBAABBoXkBAABByfvdgjHmNknj1XTL2nBr7fLM+FBJ90vqI2mLpAuttf59hkCFoSYAFzWBuBVy5eVJSd+UlPvEsjmSZltrh0qaLcl/8iFQmagJwEVNIFZ5r7xYaxdL2rOJU+Z1P0m1kk7JDM2XdJcxpq+1dlMZ8tynqEVHuZsIFfsk2JNOOskb+6d/+qc9r994442CPmfWrFne2PTp072x7EWEUvTCx8mTJzvxaaed5s059NBDC8oLrRdCTcRt6dKlkqTRo0dr6dKlkXWzfft2J47aIDNq47rcJ8IPGDCgLanukf2k9+bXUXVKLeVHTcSrQ4cOTnzYYYd5c5YvX+7Et99+e1lziluxa14GSVpnra2XpMy/6zPjQHtETQAuagJlU7H30xZ7paVcom5dvvXWWwsaA9Ju9OjRzutt27YlmE1hLr/88sjXQD6XXHLJPuNcpbxNOkrUowcaGhrKesykFdu8rJV0qDGmg7W23hjTQdKAzHgqbNrkXpVMupnZvXu3N1bI10ZbtmzJ+9nvv5/7NTOXuhOQ+poopxC/Nrr77rslNTUuza+j6nTq1KklOV47VLE1UcizjZqfN9SrVy/95S9/KWsDE/UfCz179nTiRYsWeXNOOOGEsuVUbkU1L9bajcaYNyRNkDQv8+/rafoeM+lmJVehV14OOuggJ54xY0bez/7oo4+8MZqXeIVQE+XU/LDQNWvW6Pzzz/caFUmqra114htuuMGbc/rpp3tjhT7UtLUmTJjgvY56MB6K095rIm2OPPLIpFMoqbxrXowxdxhjPpA0UNL/GGPeyvxosqSpxpgVkqZmYqDiUROAi5pA3Aq52+iHkn4YMf6upNH+O4DKRk0ALmoCcWOHXQAAEBSaFwAAEJSKvVU6VBMnTnTiQhbsRj0J99FHH3Xi/fbbr22JARm5d/JJ0l/+8hfn9amnnurNefLJJ524S5cuJcsp986lP/7xj96c3Fq65557JDXdDdJ8V98RRxxRspxQuUaNGuXEUYvPs59GXu4nk0ctkO/Tp48Tl/t27bhx5QUAAASF5gUAAASF5gUAAASFNS8V4KmnnvLGcndc7N27d1zpoMLt2LHDG8t+WOjkyZN14403enNKucYlV+7mcpMmTfLmvP76606c/WBG1rqgNb72ta/tM86V+yDFUluyZIk3lrvhadQjBELGlRcAABAUmhcAABAUmhcAABAUmhcAABAUFuxWgNyN7SR3MSJQSoMGDfLGbrnllsjXcZk2bZoTH3fccd6cnj17xpUOEKulS5cmnULsuPICAACCQvMCAACCQvMCAACCQvMCAACCwoLdCnD99dd7Y+Xe0RFIym9+8xtv7N5773Xi3AW8klRVVVW2nIAkrVy5MukUYseVFwAAEBSaFwAAEBSaFwAAEBTWvKTMbbfd1ur3lPNpvUCctm/f7o1deumlTjx//nxvzrp165z44IMPLm1iQIrkPtl9wYIF3pybb745rnQSwZUXAAAQFJoXAAAQFJoXAAAQFJoXAAAQFBbspszOnTuduLGxMaFMgPhFne9Llixx4vPPP9+b06NHDydmQzq0J/X19d5YdXVlX5uo7P91AACg4tC8AACAoNC8AACAoLDmJUENDQ3eWO7mQ3x3j0r1+eefe2PHH3+8NzZ9+nQnvuSSS8qWExCiqL8T48aNiz+RGHHlBQAABIXmBQAABIXmBQAABIXmBQAABIUFuwn6+OOPvbEHH3ww7/vGjBnjxL179y5ZTkC5WGud+B/+4R+8OVGb1P393/992XICQvTWW285cffu3b05Q4cOjSudRHDlBQAABIXmBQAABCXv10bGmD6SHpR0hKRdkuokXWat3WSMGSNprqSuklZLmmSt3Vi+dIHkUROAi5pA3ApZ89Io6RZr7fOSZIy5VdIsY8wlkuZJushau9gY81NJsyRdXK5kQxb14Kz777+/qM86/PDDnbhr165FfQ6KRk0U4Y477nDi3AcuStKLL77ojUVt5ojUoSZitHnzZic+8MADvTm9evWKK51E5P3ayFq7tfmEzHhZ0mBJx0jaYa1dnBmfI+mCkmcIpAw1AbioCcStVWtejDHVki6XtFDSYZLeb/6ZtXazpGpjDLe+oN2gJgAXNYE4tPZW6TslbZN0l6RzSp9O5erQoYM3dvXVVxc0hlSjJgo0e/bsfcaoGNREmX3rW99y4vfff38vMytXwc2LMeY2SUdKOtNa22CMWaOmy4LNPz9IUoO1dmvp0wxf1JqX22+/3Ru79tpr837Wd7/7XSd+4IEHik8MRaMmWmfKlClOfPfdd3tzota8HHvssU7cpUuX0iaGkqEm4vHss8868aWXXurNqfSGpqDmxRgzU03fXZ5urd2ZGX5VUldjzDcy32dOlvRYedIM3xdffOGNFdKoRPn+97/f1nTQRtSEq3lRbXV1tRoaGiKvqtxzzz1OfN1113lzvv71r3tjPFk9DNREfKI2OG1vCrlVepik6ZJWSFpijJGkVdbac4wx35M01xjTRZlb4MqYK5AK1ATgoiYQt7zNi7X2LUmR/+ljrV0iaXipkwLSjJoAXNQE4sYOuwAAICg0LwAAICg8VTrlDjroIG/sr//6rxPIBNi7yZMnS5L+4z/+Q5MnT/YW50rSTTfd5MQzZsyIJTeg0vzud79LOoXEceUFAAAEheYFAAAEheYFAAAEhTUvZZK7o+6NN95Y1Occd9xx3lj//v2L+iygFP73f//XG6urq3Nejx492pszaRLbewAoDa68AACAoNC8AACAoNC8AACAoNC8AACAoLBgt0wefvhhJ/63f/u3oj6ntra2FOkARduxY4cTT5gwwZvz+eef73n9pz/9SUuWLPHmDB48uPTJARVu9+7d3tjjjz/uxH379o0rndTgygsAAAgKzQsAAAgKzQsAAAgKa17K5OOPPy7qfddcc40TT58+vRTpAAXZvHmzN/bVr37ViTdu3OjNefTRR/e8vu+++zR8+PDSJwe0Q42Njd5Y7jq0E044Ia50UoMrLwAAICg0LwAAICg0LwAAICg0LwAAICgs2C2TyZMnO/Fnn33mzVm5cqU39qMf/ciJO3bk/0SIT48ePbyx/fbbz4mnTZvmzTn33HMjXwNom+pq/xrDwIEDnbhTp05xpZMaXHkBAABBoXkBAABBoXkBAABBqYraACcmNZJWJXVwpMYQSauTTiIlakRNgJrIViNqor2LrAeuvAAAgKDQvAAAgKDQvAAAgKAk2bx0SPDYSA/Ogxb8LiBxHmTjd4HIcyDJ5uWQBI+N9OA8aMHvAhLnQTZ+F4g8B5K826izpJGSNkiqTyoJJKaDmk7KZZJ2JpxLWlAT7Rs14aMm2q991kOSzQsAAECrsWAXAAAEheYFAAAEheYFAAAEheYFAAAEheYFAAAEheYFAAAEheYFAAAEpWOSBzfGDJV0v6Q+krZIutBaW5dkTlGMMbdJGq+mx7MPt9Yuz4ynNn9jTB9JD0o6QtIuSXWSLrPWbjLGjJE0V1JXNT1qfJK1dmNSuaJFms+pbNQE4pLmcyobNRGvpK+8zJE021o7VNJsNf2i0uhJSd+U9H7OeJrzb5R0i7XWWGuHS3pP0ixjTLWkeZKmZPJ+UdKsBPOEK83nVDZqAnFJ8zmVjZqIUWLNizGmn6RaSfMzQ/Ml1Rpj+iaV095Yaxdba9dmj6U9f2vtVmvt81lDL0saLOkYSTustYsz43MkXRBzeoiQ9nMqGzWBOKT9nMpGTcQrySsvgySts9bWS1Lm3/WZ8RAEk3+mi75c0kJJhynrvwystZslVRtjeieUHloEc07tRTD5UxPBCOac2otg8g+tJpL+2gjxuFPSNkl3JZ0IkBLUBOAKqiaSbF7WSjrUGNNBkjL/DsiMhyCI/DOLyI6U9G1rbYOkNWq6LNj884MkNVhrtyaUIloEcU7tQxD5UxNBCeKc2ocg8g+xJhJrXjKrlt+QNCEzNEHS69baTUnl1Boh5G+Mmamm7y7PttY2P1L8VUldjTHfyMSTJT2WRH5whXBO7UsI+VMTYQnhnNqXEPIPtSaqGlsXHCwAABJaSURBVBsbEzu4MeYoNd1CdqCkj9V0C5lNLKG9MMbcIelcSf0lbZa0xVo7LM35G2OGSVouaYWkLzLDq6y15xhjxqppxXsXtdwC91EiicKR5nMqGzWBuKT5nMpGTcQr0eYFAACgtViwCwAAgkLzAgAAgkLzAgAAgkLzAgAAgkLzAgAAgtLmp0q34YmZnSWNlLRBUn1b80BwOkg6RNIySTvzzA0KNYEiURM+aqL92mc9tLl5UcsTM+cZYyap6b7wEwt430hJfyjB8RG24yUtzjsrLNQE2oKaaEFNILIe2vS1URufmLmhLcdGxaio84CaQAlU1HlATaCNIs+Btq55acsTM7kECKnyzgNqAm1VaecBNYG2iDwHWLALAACC0tbmJYgnZgIxoiYAFzWBkmtT8xLCEzOBOFETgIuaQDmU4m6jyZLuN8Zcr8wTM0vwmUDIqAnARU2gpJJ8qnSNpFVJHRypMURNj1sHNYEm1ESLGlET7V1kPbBgFwAABIXmBQAABIXmBQAABIXmBQAABIXmBQAABIXmBQAABIXmBQAABIXmBQAABIXmBQAABIXmBQAABIXmBQAABIXmBQAABIXmBQAABIXmBQAABIXmBQAABIXmBQAABIXmBQAABIXmBQAABIXmBQAABKVj0gmk3eeff+6NjRs3TpK0bNkyjRw5UsuXL/fm/OpXv3LiSZMmeXN69uxZmiQBAGhHuPICAACCQvMCAACCQvMCAACCQvMCAACCUtXY2JjUsWskrUrq4IV66aWXvLHjjz9ektTQ0KDq6sL6v759+3pjP/7xj72xyy67rJUZSh07+uuu999//1Z/TkKGSFqddBIpUaMAaqJUli5d6o09+OCD3thjjz3mxBs3bvTm3HLLLU580kkneXNya/Dggw+WJO23337atWvXntcpQE20qFHCNbF7925v7O2333bip556aq/vnzFjhn7xi1/ovvvu8362evVqJ25oaPDmFPI35rTTTvPGzjvvPCeOumkk6m9HCkXWA1deAABAUGheAABAUGheAABAUGheAABAUFiwm8e9997rjV1yySWSWhbsdu7c2ZuTu/D217/+tTcnaiFYMaIWA+cuBOvatWtJjlUGLE5sUaMAaqIQmzZt8sa+973vOfFzzz3nzSnn/z/KXZzYXLfbtm1T9+7dJUlTp0713vcv//IvZctpL6iJFjVKuCbuuOMOb+zqq68u+P27d+8ueGHs+PHjvbGqqipv7Nlnn3Xizz77LO9nL1682BsbM2ZMQXkljAW7AAAgfDQvAAAgKDQvAAAgKEHsUJOk3//+93nnHHDAAd7Yv//7vzvxv/7rv3pzPvzwQ29swYIFTnzrrbd6c9asWePEUesL6uvro5MFWmnz5s1OPHbsWG/OQw89JEkaOXKkli1bphNOOMGbk/uE9sMOO8ybc84553hjI0eOzJvjokWLnDhq07Dc/x3Za86ac8vd7E6SLr30UiceMmRI3nxQOVauXJl3zqOPPuqNff3rX9/z+oMPPijoWP379y9o3tatW5046u/LrFmznHj+/PnenEDWvETiygsAAAgKzQsAAAgKzQsAAAhK3jUvxpjbJI1X0/32w621yzPjQyXdL6mPpC2SLrTW1pUvVSAdqAnARU0gboUs2H1S0u2S/pAzPkfSbGvtPGPMJElzJZ1Y4vwKsm3bNm+sU6dOThy1kVyU7du3O/HChQuLTyxL1JNBBwwY4I1NmTLFiaP+t02fPr0kOaFoqa+JYkUt9L7pppucuK7O/9szatQoSU2bzI0aNUrdunXz5uSe27/4xS+8OVGL3wsxceJEJ46qm7Vr1zrx6NGj97zu0aOHpOjNvh5//HEnnjZtWlE5VriKrYlTTz3VG3vnnXec+O/+7u+8Odl/gwpdiFuo3r17O3G/fv1K+vkhyPu1kbV2sbXWqXpjTD9JtZKaly/Pl1RrjPG3egUqDDUBuKgJxK3gxwMYY1ZLOsNau9wYc4ykB6y1w7J+/rakSdba1wo8do0qZCt0tEmwW6FTEygTaqJFjaiJ9i6yHipin5dyfm108MEHe3Oa94RofrZR1CW7qD1civHLX/7SGyvka6NPP/3UiZuf3QLsS9TXRrnPcYl61kuzxsZGVVVVRX5tdNFFFzlxKb82ytWar40+/fRT9ezZU1L010a5e7/wtVH78vTTT3tjt99+uxP/7ne/8+bk/g0qp0Kev5T7ta3k/+8ISbHNy1pJhxpjOlhr640xHSQNyIzHrpR/mHObjtyNteL25JNP5p0zcOBAb6zQB4GhZFJVE8VatmyZN7avZmVvbrzxRm/smmuuKSqnYkT9/4Tc2s5uVJpfR73vggsuKHF27UZF1MQZZ5zhjWWvl5LibVTQpKhbpa21GyW9IWlCZmiCpNettf5Wr0A7QE0ALmoC5ZS3eTHG3GGM+UDSQEn/Y4x5K/OjyZKmGmNWSJqaiYGKR00ALmoCccv73YK19oeSfhgx/q6k0f47gMpGTQAuagJxY4ddAAAQFFZ15nj++ecTPf6dd97pxEuXLs37nvHjx3tjXbp0KVlOqAwNDQ1O/Oabb3pzTj755KI++7rrrnNe597pELdPPvnEG7vyyivzvu/EE/390wYPHlySnFA5+vZlq5qkceUFAAAEheYFAAAEheYFAAAEhTUvJZC7c2ihdu/e7Y099NBDed+Xu5EWO36iED/5yU+cOGr35kJkr29pNmvWrMjXcfm///s/Jz7ttNO8OStWrHDi7N18m1//6Ec/8t6Xu+tw7i7cUsuDHYEk7Nq1K++csWPHxpBJfLjyAgAAgkLzAgAAgkLzAgAAgkLzAgAAgsKC3RzDhg1z4gEDBnhz1q9f78SjRo0q6livvvqqN/bKK6/kfV/uBmBROaJ9e+aZZ7yxQhboduvWzRvLfUJ00hvQffrpp97YKaec4sS5C3gl6cADD3Ti7Ce2L1y4UJL0zW9+03vfzp07nXjlypXenKOPPnofGQOlVVdX58Q/+9nPvDn777+/E0ed2yHjygsAAAgKzQsAAAgKzQsAAAgKzQsAAAgKC3ZzjB492olXrVrlzcl+Ou8XX3yhTp06FXWs3B1PC/U3f/M3Rb0P7cd//dd/5Z2Tu1OzJM2bN88bO+uss0qSU7Fyd7idOHGiNyd3gW7u4lzJf0L7kUceued1pS1mRGX7+c9/7sRffvmlN+eEE05w4kMOOaSsOcWNKy8AACAoNC8AACAoNC8AACAorHnJI2o9S/aal06dOhX0RM9nn33WG3vvvfeKyumqq65y4hEjRnhz7rnnHifeb7/9ijoWwhS1nuqLL75w4qOOOsqbk/T6ligzZ8504qefftqb06tXLydesGCBNyd7jQsQsqiNEtsbrrwAAICg0LwAAICg0LwAAICg0LwAAICgVDU2NiZ17BpJ/g5wAXjxxRclNW1s9eKLL2rcuHHJJhQh98m7URuSpcQQSauTTiIlahRoTZTKo48+6o1dfPHFTrx9+3ZvzgsvvODEpdx0Lvep0m+//bY3p8RPlaYmWtSonddElDFjxjjxa6+95s15+eWXnbi2trasOZVRZD1w5QUAAASF5gUAAASF5gUAAASFTeqKkP3dYW1trU4//XRvTu734lEPeCyVnj17emPV1fSlSLd169Z5Y7nrWyR/c71f/vKX3hwerIj2pKqqyon79+/vzQl4jUtB+AsHAACCQvMCAACCQvMCAACCQvMCAACCwoLdImRv+Na9e3c99dRT3pwdO3Y4cd++fb05n3/+ed5jXXTRRd7YP//zPztxt27dvDn7779/3s8G4rRt2zYnjlpQGLUBXe4C3Wuvvba0ibVS7mJJoJw+++wzb+yjjz5KIJN04coLAAAICs0LAAAICs0LAAAISt41L8aYPpIelHSEpF2S6iRdZq3dZIwZI2mupK5qenDSJGvtxvKlG44uXbo4cceOxS0vmj17tjfWtWvXoj4LpUFNFOeUU05x4o0b/V/Lbbfd5o1dc801ZcupEJ07d3biESNGJJRJelET5fPBBx94Y2vWrHHiI488Mq50UqOQKy+Nkm6x1hpr7XBJ70maZYypljRP0hRr7VBJL0qaVb5UgdSgJgAXNYFY5W1erLVbrbXPZw29LGmwpGMk7bDWLs6Mz5F0QckzBFKGmgBc1ATiVtXY2Fjw5EwX/ZykhZLWSbrYWnt61s+3Sxpord1awMfVSCrfA38QiiFqupQcJGoCZUBNtKgRNdHeRdZDaxdi3Clpm6S7JJ3T9pzajwMPPNAb++STT/K+L2ovGNa8pAo1UaDjjjvOiV9++WVvThrXvKDVqIkSeuedd7yx4cOHO3HUmpeo91WSgpsXY8xtko6UdKa1tsEYs0ZNlwWbf36QpIYCu+mK9+GHHzpx7qZ1e3PZZZc5ce7CX6QHNbF3uRspSn6zMnjwYG/OlClTypYTyo+aKL2nn34675xp06bFkEm6FHSrtDFmppq+uzzbWrszM/yqpK7GmG9k4smSHit9ikD6UBOAi5pAnAq5VXqYpOmSVkhaYoyRpFXW2nOMMd+TNNcY00WZW+DKmCuQCtQE4KImELe8zYu19i1JkQ/zsNYukTQ86mdApaImABc1gbixwy4AAAgKT5Uuk9WrVzvxzp07oyfmyH2KNE+wRQiadwEdOHCgPvjgA91000153/PEE094YyxQR3sW9QTpqDvwwJUXAAAQGJoXAAAQFJoXAAAQFNa8lMmcOXOSTgEoi927d3tjo0ePliStW7dOo0ePVtRjR0aNGuXEw4YNK0+CQKCi1kZu2bIl7/u+853vlCOdVOPKCwAACArNCwAACArNCwAACArNCwAACAoLdksg6onRzzzzjBNHLWBM4wZ0S5YsceKxY8cmlAnS6j//8z+9sfXr1zuv+/Tp48159tlnnZgN6QDX3Llzi3rfhg0bvLEjjjiiremkGldeAABAUGheAABAUGheAABAUFjzUgJR393nfr/fvIlXtosvvtgbGz482SfHb9++PdHjI31y13Rdd911ed+zatUqb6xHjx4lywmoRCtXrixoXu56lsGDB5cjnVTjygsAAAgKzQsAAAgKzQsAAAgKzQsAAAgKC3bLZMSIEU4c9bTQNDr55JOTTgEpU19f78Tbtm3z5rz22mvO6+7du5c9L6DSnH766d5YdbV/jWHWrFlO3LFj+/tTzpUXAAAQFJoXAAAQFJoXAAAQlKqoBwbGpEaSv5MV2pshklYnnURK1IiaADWRrUbURHsXWQ9ceQEAAEGheQEAAEGheQEAAEFJsnnpkOCxkR6cBy34XUDiPMjG7wKR50CSzcshCR4b6cF50ILfBSTOg2z8LhB5DiR5t1FnSSMlbZBUn2cuKk8HNZ2UyySFsf1w+VET7Rs14aMm2q991kOSzQsAAECrsWAXAAAEheYFAAAEheYFAAAEheYFAAAEheYFAAAEheYFAAAEheYFAAAEpWOSBzfGDJV0v6Q+krZIutBaW5dkTlGMMbdJGq+mx7MPt9Yuz4ynNn9jTB9JD0o6QtIuSXWSLrPWbjLGjJE0V1JXNT1qfJK1dmNSuaJFms+pbNQE4pLmcyobNRGvpK+8zJE021o7VNJsNf2i0uhJSd+U9H7OeJrzb5R0i7XWWGuHS3pP0ixjTLWkeZKmZPJ+UdKsBPOEK83nVDZqAnFJ8zmVjZqIUWLNizGmn6RaSfMzQ/Ml1Rpj+iaV095Yaxdba9dmj6U9f2vtVmvt81lDL0saLOkYSTustYsz43MkXRBzeoiQ9nMqGzWBOKT9nMpGTcQrySsvgySts9bWS1Lm3/WZ8RAEk3+mi75c0kJJhynrvwystZslVRtjeieUHloEc07tRTD5UxPBCOac2otg8g+tJpL+2gjxuFPSNkl3JZ0IkBLUBOAKqiaSbF7WSjrUGNNBkjL/DsiMhyCI/DOLyI6U9G1rbYOkNWq6LNj884MkNVhrtyaUIloEcU7tQxD5UxNBCeKc2ocg8g+xJhJrXjKrlt+QNCEzNEHS69baTUnl1Boh5G+Mmamm7y7PttY2P1L8VUldjTHfyMSTJT2WRH5whXBO7UsI+VMTYQnhnNqXEPIPtSaqGhsbEzu4MeYoNd1CdqCkj9V0C5lNLKG9MMbcIelcSf0lbZa0xVo7LM35G2OGSVouaYWkLzLDq6y15xhjxqppxXsXtdwC91EiicKR5nMqGzWBuKT5nMpGTcQr0eYFAACgtViwCwAAgkLzAgAAgkLzAgAAgkLzAgAAgkLzAgAAgkLzAgAAgkLzAgAAgkLzAgAAgvL/WZF8hTJ20z0AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 720x576 with 9 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MOwx6n0NbDYQ",
        "outputId": "11bcdc7f-645f-4f82-a5d4-68d38085ffa7"
      },
      "source": [
        "# 주 성분 갯수 설정\n",
        "n_components = 5\n",
        "\n",
        "# PCA 생성\n",
        "pca = PCA(n_components = n_components)\n",
        "\n",
        "# 7만 추출한 X에 PCA 실행\n",
        "pca_dataset_7 = pca.fit_transform(dataset_7)\n",
        "\n",
        "# PCA 실행 데이터 → 일반 세트로 역변환\n",
        "inverse_transform_dataset_7 = pca.inverse_transform(pca_dataset_7)\n",
        "\n",
        "print(\"dataset_7 shape\",dataset_7.shape)\n",
        "print(\"pca_dataset_7 shape\",pca_dataset_7.shape)\n",
        "print(\"inverse_transform_dataset_7 shape\",inverse_transform_dataset_7.shape)"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "dataset_7 shape (4401, 784)\n",
            "pca_dataset_7 shape (4401, 5)\n",
            "inverse_transform_dataset_7 shape (4401, 784)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rWEhyKJBbE3B",
        "outputId": "fc4b33f4-b25e-4a63-dd1c-2f49fc046320"
      },
      "source": [
        "# X와 inverse_transform _X 사이의 차이를 확인\n",
        "# (X-inverse_transform _X) ** 2) = 모든 픽셀에 대해 MSE, 그리고 이미지 MSE를 얻기 위해 sum()을 생성\n",
        "\n",
        "MSE_score = ((dataset_7-inverse_transform_dataset_7)**2).sum(axis=1)\n",
        "\n",
        "MSE_score.head()"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    1.093067e+06\n",
              "1    7.279877e+05\n",
              "2    1.139929e+06\n",
              "3    9.172267e+05\n",
              "4    1.185464e+06\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "3wREH6UwbGFW",
        "outputId": "1ef2b3e9-421e-4745-820f-9f4cd7308aba"
      },
      "source": [
        "# MSE가 제일 좋지 않은 9개를 뽑는다.\n",
        "MSE_max_scores = MSE_score.nlargest(9).index\n",
        "\n",
        "plt.figure(figsize = (10,8))\n",
        "row, colums = 3, 3\n",
        "for i in range(9):  \n",
        "    plt.subplot(colums, row, i+1)\n",
        "    plt.imshow(dataset_7.iloc[MSE_max_scores[i]].values.reshape(28,28),interpolation='nearest', cmap='Greys')\n",
        "plt.show()"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x576 with 9 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "id": "p0PvQCaGbHjW",
        "outputId": "cea76950-de18-4a31-863f-c61199bcc720"
      },
      "source": [
        "plt.figure(figsize = (10,8))\n",
        "row, colums = 5, 10\n",
        "    \n",
        "for number in range(10):\n",
        "    # Get the current number\n",
        "    dataset = pd.DataFrame(X[(y == number)].reset_index().drop(\"index\",axis = 1))\n",
        "\n",
        "    # Create PCA with components number\n",
        "    pca = PCA(n_components = n_components)\n",
        "\n",
        "    # fit transform with PCA on dataset\n",
        "    pca_dataset = pca.fit_transform(dataset)\n",
        "    \n",
        "    # inverse transform back to regular dataset \n",
        "    inverse_transform_dataset = pca.inverse_transform(pca_dataset)\n",
        "    MSE_score = ((dataset-inverse_transform_dataset)**2).sum(axis=1)\n",
        "    MSE_worst = MSE_score.nlargest(5).index # get max\n",
        "    for number2 in range(0,5):\n",
        "        plt.subplot(colums, row, (number2+(number*5))+ 1)\n",
        "        plt.imshow(dataset.iloc[MSE_worst[number2]].values.reshape(28,28),interpolation='nearest', cmap='Greys')\n",
        "plt.show()"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x576 with 50 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}