데이터분석_4일차_웨이퍼_불량_유형_분류.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/younghoonNa/pythonDT/blob/main/%EB%8D%B0%EC%9D%B4%ED%84%B0%EB%B6%84%EC%84%9D_4%EC%9D%BC%EC%B0%A8_%EC%9B%A8%EC%9D%B4%ED%8D%BC_%EB%B6%88%EB%9F%89_%EC%9C%A0%ED%98%95_%EB%B6%84%EB%A5%98.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "H-TEfjuHwe0M"
      },
      "source": [
        "# Wafer Map Defect Classification\n",
        "\n",
        "## 프로젝트 개요\n",
        "반도체 공정에서 생성되는 웨이퍼 맵 데이터는 엔지니어가 불량의 원인을 식별하는 데 중요한 세부 정보를 제공합니다. 이러한 불량 패턴을 자동으로 식별하여 반도체 공정에서 웨이퍼 생산 공정을 개선할 수 있습니다.\n",
        "\n",
        "본 프로젝트에서는 1만6천장의 이미지 데이터를 기반으로 웨이퍼 맵의 불량을 자동으로 분류하는 검사기를 만들고자 합니다.\n",
        "\n",
        "아래의 이미지는 학습 데이터에서 시각화 한 예시로 이미지의 제목은 불량 유형을 의미합니다.\n",
        "\n",
        "이미지에서 노란색 Pixel은 정상 다이(Die, 1) 검은색 Pixel은 비정상 다이(Die, 2)를 의미합니다.\n",
        "\n",
        "![img](https://i.imgur.com/3nBXUU7_d.webp?maxwidth=760&fidelity=grand)\n",
        "\n",
        "\n",
        "## 데이터 설명\n",
        "#### 파일 구조\n",
        "- train.csv\n",
        "    - 학습을 위하여 활용되는 데이터 셋입니다.\n",
        "- test.csv\n",
        "    - 테스트를 위하여 제공되는 데이터 셋입니다.\n",
        "- submission.csv\n",
        "    - 예측한 불량 유형을 submission 파일에 작성한 후 제출합니다.\n",
        "\n",
        "#### 데이터 구조 설명\n",
        "- lotName : Lot 이름\n",
        "- waferId : 해당 Lot에서의 Wafer 순서\n",
        "- dieSize : 다이(Die)의 개수\n",
        "- pixel_1 ~ pixcel 676 : 다이(Die)에 대한 정보\n",
        "  - 0 : 26 x 26 이미지 형태를 유지를 위한 정보\n",
        "  - 1 : 정상 Die \n",
        "  - 2 : 비정상 Die\n",
        "- label : 불량 유형\n",
        "    - 0: 'Center'\n",
        "    - 1: 'Donut'\n",
        "    - 2: 'Edge-Loc'\n",
        "    - 3: 'Edge-Ring'\n",
        "    - 4: 'Loc'\n",
        "    - 5: 'Near-full'\n",
        "    - 6: 'Random'\n",
        "    - 7: 'Scratch'\n",
        "    - 8: 'none'\n",
        "\n",
        "\n",
        "## 프로젝트 구성\n",
        "* 데이터 로드 (load data, train.csv, test.csv)\n",
        "* 데이터 전처리 및 특성 공학 (train.csv, test.csv)\n",
        "* 모델 생성 및 검증 (train.csv)\n",
        "* 평가 데이터 예측 (test.csv)\n",
        "* 결과 제출 및 점수 확인\n",
        "\n",
        "## 제출 이력 및 순위 조회\n",
        "[평가시스템](http://manage.jaen.kr)에 접속하여 확인 가능\n",
        "> 로그인 -> 프로젝트 -> 웨이퍼 맵 불량 유형 분류 -> 리더보드\n",
        "\n",
        "### 접속 정보\n",
        "- ID : 본인이메일\n",
        "- PWD : hl1227"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6LzDP7Qawe0O"
      },
      "source": [
        "## 환경 설정"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!wget http://49.247.133.7/ws1.zip"
      ],
      "metadata": {
        "id": "4Fnk73TL3-Nk",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "89ce29f6-96a6-4985-a195-394ec8ef3fdf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2021-12-30 08:20:03--  http://49.247.133.7/ws1.zip\n",
            "Connecting to 49.247.133.7:80... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 3194445 (3.0M) [application/zip]\n",
            "Saving to: ‘ws1.zip.1’\n",
            "\n",
            "ws1.zip.1           100%[===================>]   3.05M  3.28MB/s    in 0.9s    \n",
            "\n",
            "2021-12-30 08:20:04 (3.28 MB/s) - ‘ws1.zip.1’ saved [3194445/3194445]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!unzip ws1.zip -y"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8hiB9YJsJwIa",
        "outputId": "898e7fe0-8cac-4190-fc23-473b901acc5e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Archive:  ws1.zip\n",
            "replace submission.csv? [y]es, [n]o, [A]ll, [N]one, [r]ename: y\n",
            "  inflating: submission.csv          \n",
            "replace test.csv? [y]es, [n]o, [A]ll, [N]one, [r]ename: y\n",
            "  inflating: test.csv                \n",
            "replace train.csv? [y]es, [n]o, [A]ll, [N]one, [r]ename: y\n",
            "  inflating: train.csv               \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install jaen"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rF7hFl0BxIjE",
        "outputId": "efea88f6-d139-4e5f-a2fc-b0aa4c4bd6fa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: jaen in /usr/local/lib/python3.7/dist-packages (0.0.1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "nRxhr8rcwe0O"
      },
      "outputs": [],
      "source": [
        "from JAEN.project import Project\n",
        "pjt = Project('웨이퍼 맵 불량 유형 분류', \n",
        "              '한림대-데이터분석과정_전공자',        \n",
        "              '1차수 A반',       \n",
        "              'yh07280@naver.com')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XvkOy_WFwe0Q"
      },
      "source": [
        "## 모듈 import \n",
        "필요한 모듈을 import 합니다. 필요에 따라 추가 패키지를 load 할 수 있습니다."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "diceZv3Jwe0Q"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "import datetime\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "%matplotlib inline\n",
        "\n",
        "# Data 경로 설정\n",
        "DATA_DIR = 'data'\n",
        "\n",
        "# train 데이터셋 로드 (train.csv)\n",
        "train = pd.read_csv('train.csv')\n",
        "                                 \n",
        "# test 데이터셋 로드 (test.csv)\n",
        "test = pd.read_csv('test.csv')\n",
        "\n",
        "# submission 파일 로드 (submission.csv)\n",
        "submission = pd.read_csv('submission.csv')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vt1Fjq0uwe0Q"
      },
      "source": [
        "2개의 데이터 셋트를 나누어 제공합니다.\n",
        "1. train: 학습용 데이터 세트. 정답 값이라고 할 수 있는 `label` 값이 같이 제공됩니다.\n",
        "2. test: 예측용 데이터 세트. 정답 값이 빠진 `feature` 들만 제공됩니다. 우리는 이 `feature`를 활용하여 예측을 진행합니다.\n",
        "3. submission: 정답 제출용 데이터 세트. 주어진 형식에 맞춰 정답 값을 기입하고 제출합니다."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "TlY06iq8we0Q",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "outputId": "3d819951-a454-49b5-db36-5b3bfa8c9b74"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <td>2</td>\n",
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              "      <td>1</td>\n",
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              "      <td>1</td>\n",
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              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>lot7</td>\n",
              "      <td>4</td>\n",
              "      <td>533.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\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",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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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>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\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>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>lot7</td>\n",
              "      <td>5</td>\n",
              "      <td>533.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 680 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f379fb6a-075e-4ec8-b4c2-3900a928011a')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-f379fb6a-075e-4ec8-b4c2-3900a928011a 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-f379fb6a-075e-4ec8-b4c2-3900a928011a');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "  lotName  waferId  dieSize  pixel_1  ...  pixel_674  pixel_675  pixel_676  label\n",
              "0    lot7        1    533.0        0  ...          0          0          0      8\n",
              "1    lot7        2    533.0        0  ...          0          0          0      8\n",
              "2    lot7        3    533.0        0  ...          0          0          0      8\n",
              "3    lot7        4    533.0        0  ...          0          0          0      8\n",
              "4    lot7        5    533.0        0  ...          0          0          0      8\n",
              "\n",
              "[5 rows x 680 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ],
      "source": [
        "# 코드를 입력해 주세요\n",
        "# 학습 데이터 조회\n",
        "train.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PAgn6pJwwe0R"
      },
      "source": [
        "<p><strong>[출력 결과]</strong></p><div>\n",
        "<style scoped>\n",
        "    .dataframe tbody tr th:only-of-type {\n",
        "        vertical-align: middle;\n",
        "    }\n",
        "\n",
        "    .dataframe tbody tr th {\n",
        "        vertical-align: top;\n",
        "    }\n",
        "\n",
        "    .dataframe thead th {\n",
        "        text-align: right;\n",
        "    }\n",
        "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>lotName</th>\n",
        "      <th>waferId</th>\n",
        "      <th>dieSize</th>\n",
        "      <th>pixel_1</th>\n",
        "      <th>pixel_2</th>\n",
        "      <th>pixel_3</th>\n",
        "      <th>pixel_4</th>\n",
        "      <th>pixel_5</th>\n",
        "      <th>pixel_6</th>\n",
        "      <th>pixel_7</th>\n",
        "      <th>...</th>\n",
        "      <th>pixel_668</th>\n",
        "      <th>pixel_669</th>\n",
        "      <th>pixel_670</th>\n",
        "      <th>pixel_671</th>\n",
        "      <th>pixel_672</th>\n",
        "      <th>pixel_673</th>\n",
        "      <th>pixel_674</th>\n",
        "      <th>pixel_675</th>\n",
        "      <th>pixel_676</th>\n",
        "      <th>label</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>lot7</td>\n",
        "      <td>1</td>\n",
        "      <td>533.0</td>\n",
        "      <td>0</td>\n",
        "      <td>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>8</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>lot7</td>\n",
        "      <td>2</td>\n",
        "      <td>533.0</td>\n",
        "      <td>0</td>\n",
        "      <td>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>8</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>lot7</td>\n",
        "      <td>3</td>\n",
        "      <td>533.0</td>\n",
        "      <td>0</td>\n",
        "      <td>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>8</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>lot7</td>\n",
        "      <td>4</td>\n",
        "      <td>533.0</td>\n",
        "      <td>0</td>\n",
        "      <td>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>8</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>lot7</td>\n",
        "      <td>5</td>\n",
        "      <td>533.0</td>\n",
        "      <td>0</td>\n",
        "      <td>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>8</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>5 rows × 680 columns</p>\n",
        "</div>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "KIIKOm1Wwe0S",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "outputId": "ee0e67cf-0d00-42bd-e7ad-b0e3a1beaad0"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-9c05ad2b-429c-425b-9268-ed404c014ea0\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>lotName</th>\n",
              "      <th>waferId</th>\n",
              "      <th>dieSize</th>\n",
              "      <th>pixel_1</th>\n",
              "      <th>pixel_2</th>\n",
              "      <th>pixel_3</th>\n",
              "      <th>pixel_4</th>\n",
              "      <th>pixel_5</th>\n",
              "      <th>pixel_6</th>\n",
              "      <th>pixel_7</th>\n",
              "      <th>pixel_8</th>\n",
              "      <th>pixel_9</th>\n",
              "      <th>pixel_10</th>\n",
              "      <th>pixel_11</th>\n",
              "      <th>pixel_12</th>\n",
              "      <th>pixel_13</th>\n",
              "      <th>pixel_14</th>\n",
              "      <th>pixel_15</th>\n",
              "      <th>pixel_16</th>\n",
              "      <th>pixel_17</th>\n",
              "      <th>pixel_18</th>\n",
              "      <th>pixel_19</th>\n",
              "      <th>pixel_20</th>\n",
              "      <th>pixel_21</th>\n",
              "      <th>pixel_22</th>\n",
              "      <th>pixel_23</th>\n",
              "      <th>pixel_24</th>\n",
              "      <th>pixel_25</th>\n",
              "      <th>pixel_26</th>\n",
              "      <th>pixel_27</th>\n",
              "      <th>pixel_28</th>\n",
              "      <th>pixel_29</th>\n",
              "      <th>pixel_30</th>\n",
              "      <th>pixel_31</th>\n",
              "      <th>pixel_32</th>\n",
              "      <th>pixel_33</th>\n",
              "      <th>pixel_34</th>\n",
              "      <th>pixel_35</th>\n",
              "      <th>pixel_36</th>\n",
              "      <th>pixel_37</th>\n",
              "      <th>...</th>\n",
              "      <th>pixel_637</th>\n",
              "      <th>pixel_638</th>\n",
              "      <th>pixel_639</th>\n",
              "      <th>pixel_640</th>\n",
              "      <th>pixel_641</th>\n",
              "      <th>pixel_642</th>\n",
              "      <th>pixel_643</th>\n",
              "      <th>pixel_644</th>\n",
              "      <th>pixel_645</th>\n",
              "      <th>pixel_646</th>\n",
              "      <th>pixel_647</th>\n",
              "      <th>pixel_648</th>\n",
              "      <th>pixel_649</th>\n",
              "      <th>pixel_650</th>\n",
              "      <th>pixel_651</th>\n",
              "      <th>pixel_652</th>\n",
              "      <th>pixel_653</th>\n",
              "      <th>pixel_654</th>\n",
              "      <th>pixel_655</th>\n",
              "      <th>pixel_656</th>\n",
              "      <th>pixel_657</th>\n",
              "      <th>pixel_658</th>\n",
              "      <th>pixel_659</th>\n",
              "      <th>pixel_660</th>\n",
              "      <th>pixel_661</th>\n",
              "      <th>pixel_662</th>\n",
              "      <th>pixel_663</th>\n",
              "      <th>pixel_664</th>\n",
              "      <th>pixel_665</th>\n",
              "      <th>pixel_666</th>\n",
              "      <th>pixel_667</th>\n",
              "      <th>pixel_668</th>\n",
              "      <th>pixel_669</th>\n",
              "      <th>pixel_670</th>\n",
              "      <th>pixel_671</th>\n",
              "      <th>pixel_672</th>\n",
              "      <th>pixel_673</th>\n",
              "      <th>pixel_674</th>\n",
              "      <th>pixel_675</th>\n",
              "      <th>pixel_676</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>lot7</td>\n",
              "      <td>9</td>\n",
              "      <td>533.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>1</td>\n",
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              "      <td>2</td>\n",
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              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>lot7</td>\n",
              "      <td>10</td>\n",
              "      <td>533.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>1</td>\n",
              "      <td>1</td>\n",
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              "      <td>0</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>lot7</td>\n",
              "      <td>14</td>\n",
              "      <td>533.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
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              "      <td>2</td>\n",
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              "      <td>0</td>\n",
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              "      <td>2</td>\n",
              "      <td>1</td>\n",
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              "      <td>...</td>\n",
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              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>1</td>\n",
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              "      <td>1</td>\n",
              "      <td>1</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>lot7</td>\n",
              "      <td>16</td>\n",
              "      <td>533.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>1</td>\n",
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              "      <td>1</td>\n",
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              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 679 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9c05ad2b-429c-425b-9268-ed404c014ea0')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
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              "    .colab-df-convert:hover {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-9c05ad2b-429c-425b-9268-ed404c014ea0 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-9c05ad2b-429c-425b-9268-ed404c014ea0');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "  lotName  waferId  dieSize  ...  pixel_674  pixel_675  pixel_676\n",
              "0    lot7        9    533.0  ...          0          0          0\n",
              "1    lot7       10    533.0  ...          0          0          0\n",
              "2    lot7       12    533.0  ...          0          0          0\n",
              "3    lot7       14    533.0  ...          0          0          0\n",
              "4    lot7       16    533.0  ...          0          0          0\n",
              "\n",
              "[5 rows x 679 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ],
      "source": [
        "# 코드를 입력해 주세요\n",
        "# 테스트 데이터 조회\n",
        "test.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MLXILSU5we0S"
      },
      "source": [
        "<p><strong>[출력 결과]</strong></p><div>\n",
        "<style scoped>\n",
        "    .dataframe tbody tr th:only-of-type {\n",
        "        vertical-align: middle;\n",
        "    }\n",
        "\n",
        "    .dataframe tbody tr th {\n",
        "        vertical-align: top;\n",
        "    }\n",
        "\n",
        "    .dataframe thead th {\n",
        "        text-align: right;\n",
        "    }\n",
        "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>lotName</th>\n",
        "      <th>waferId</th>\n",
        "      <th>dieSize</th>\n",
        "      <th>pixel_1</th>\n",
        "      <th>pixel_2</th>\n",
        "      <th>pixel_3</th>\n",
        "      <th>pixel_4</th>\n",
        "      <th>pixel_5</th>\n",
        "      <th>pixel_6</th>\n",
        "      <th>pixel_7</th>\n",
        "      <th>...</th>\n",
        "      <th>pixel_667</th>\n",
        "      <th>pixel_668</th>\n",
        "      <th>pixel_669</th>\n",
        "      <th>pixel_670</th>\n",
        "      <th>pixel_671</th>\n",
        "      <th>pixel_672</th>\n",
        "      <th>pixel_673</th>\n",
        "      <th>pixel_674</th>\n",
        "      <th>pixel_675</th>\n",
        "      <th>pixel_676</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>lot7</td>\n",
        "      <td>9</td>\n",
        "      <td>533.0</td>\n",
        "      <td>0</td>\n",
        "      <td>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",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>lot7</td>\n",
        "      <td>10</td>\n",
        "      <td>533.0</td>\n",
        "      <td>0</td>\n",
        "      <td>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",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>lot7</td>\n",
        "      <td>12</td>\n",
        "      <td>533.0</td>\n",
        "      <td>0</td>\n",
        "      <td>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",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>lot7</td>\n",
        "      <td>14</td>\n",
        "      <td>533.0</td>\n",
        "      <td>0</td>\n",
        "      <td>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",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>lot7</td>\n",
        "      <td>16</td>\n",
        "      <td>533.0</td>\n",
        "      <td>0</td>\n",
        "      <td>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",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>5 rows × 679 columns</p>\n",
        "</div>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "d4V8fAawwe0T",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "7711e0a3-c916-4061-f4f8-362019d60999"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-4f8f6e22-3403-471e-b71f-3975ec5dcc47\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>lotName</th>\n",
              "      <th>label</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>lot7</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>lot7</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>lot7</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>lot7</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>lot7</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4f8f6e22-3403-471e-b71f-3975ec5dcc47')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-4f8f6e22-3403-471e-b71f-3975ec5dcc47 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-4f8f6e22-3403-471e-b71f-3975ec5dcc47');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "  lotName  label\n",
              "0    lot7      0\n",
              "1    lot7      0\n",
              "2    lot7      0\n",
              "3    lot7      0\n",
              "4    lot7      0"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "# 코드를 입력해 주세요\n",
        "# 정답 제출용 데이터 조회\n",
        "submission.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QVyNkHOawe0T"
      },
      "source": [
        "<p><strong>[출력 결과]</strong></p><div>\n",
        "<style scoped>\n",
        "    .dataframe tbody tr th:only-of-type {\n",
        "        vertical-align: middle;\n",
        "    }\n",
        "\n",
        "    .dataframe tbody tr th {\n",
        "        vertical-align: top;\n",
        "    }\n",
        "\n",
        "    .dataframe thead th {\n",
        "        text-align: right;\n",
        "    }\n",
        "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>lotName</th>\n",
        "      <th>label</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>lot7</td>\n",
        "      <td>0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>lot7</td>\n",
        "      <td>0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>lot7</td>\n",
        "      <td>0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>lot7</td>\n",
        "      <td>0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>lot7</td>\n",
        "      <td>0</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0JHgYdznwe0U",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e4c71762-c715-438a-e953-69cce73d8d73"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "lot40483    25\n",
              "lot42164    25\n",
              "lot40490    25\n",
              "lot42160    25\n",
              "lot40455    25\n",
              "            ..\n",
              "lot8344      1\n",
              "lot10992     1\n",
              "lot22216     1\n",
              "lot13087     1\n",
              "lot16433     1\n",
              "Name: lotName, Length: 743, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "# 코드를 입력해 주세요\n",
        "# train 데이터 프레임의 lotName별 데이터의 개수를 확인하세요.\n",
        "train['lotName'].value_counts()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uzp2UvDMwe0U"
      },
      "source": [
        "<p><strong>[출력 결과]</strong></p><pre>lot40341    25\n",
        "lot42163    25\n",
        "lot42177    25\n",
        "lot40461    25\n",
        "lot42127    25\n",
        "            ..\n",
        "lot10638     1\n",
        "lot10992     1\n",
        "lot10971     1\n",
        "lot10587     1\n",
        "lot7654      1\n",
        "Name: lotName, Length: 743, dtype: int64</pre>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "GZ_gbDiWwe0U",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "aea8344b-b922-4458-89d8-42427adac03e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "25    150\n",
              "24     95\n",
              "23     83\n",
              "22     71\n",
              "21     65\n",
              "1      59\n",
              "20     50\n",
              "19     28\n",
              "18     23\n",
              "17     19\n",
              "14     18\n",
              "15     14\n",
              "13     14\n",
              "16     12\n",
              "12     10\n",
              "11     10\n",
              "3       6\n",
              "6       5\n",
              "2       4\n",
              "10      3\n",
              "4       2\n",
              "7       1\n",
              "5       1\n",
              "Name: lotName, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "# 코드를 입력해 주세요\n",
        "# lotName의 개수 결과를 대상으로 한번 더 데이터의 개수를 확인하세요. \n",
        "train['lotName'].value_counts().value_counts()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UIVcvZIXwe0U"
      },
      "source": [
        "<p><strong>[출력 결과]</strong></p><pre>25    150\n",
        "24     95\n",
        "23     83\n",
        "22     71\n",
        "21     65\n",
        "1      59\n",
        "20     50\n",
        "19     28\n",
        "18     23\n",
        "17     19\n",
        "14     18\n",
        "15     14\n",
        "13     14\n",
        "16     12\n",
        "12     10\n",
        "11     10\n",
        "3       6\n",
        "6       5\n",
        "2       4\n",
        "10      3\n",
        "4       2\n",
        "7       1\n",
        "5       1\n",
        "Name: lotName, dtype: int64</pre>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "tLsRnxlNwe0U",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 771
        },
        "outputId": "4c66ca57-7bf7-4963-9a2f-1ec171554c82"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-8acaf1ee-493b-4f3e-ad01-80a8e67312fc\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>index</th>\n",
              "      <th>lotName</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>1</td>\n",
              "      <td>59</td>\n",
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              "      <th>18</th>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
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              "      <td>3</td>\n",
              "      <td>6</td>\n",
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              "      <td>1</td>\n",
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              "      <th>11</th>\n",
              "      <td>15</td>\n",
              "      <td>14</td>\n",
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              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>16</td>\n",
              "      <td>12</td>\n",
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              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>17</td>\n",
              "      <td>19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>18</td>\n",
              "      <td>23</td>\n",
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              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>19</td>\n",
              "      <td>28</td>\n",
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              "      <th>6</th>\n",
              "      <td>20</td>\n",
              "      <td>50</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>21</td>\n",
              "      <td>65</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>22</td>\n",
              "      <td>71</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>23</td>\n",
              "      <td>83</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>24</td>\n",
              "      <td>95</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>25</td>\n",
              "      <td>150</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8acaf1ee-493b-4f3e-ad01-80a8e67312fc')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-8acaf1ee-493b-4f3e-ad01-80a8e67312fc 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-8acaf1ee-493b-4f3e-ad01-80a8e67312fc');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "    index  lotName\n",
              "5       1       59\n",
              "18      2        4\n",
              "16      3        6\n",
              "20      4        2\n",
              "22      5        1\n",
              "17      6        5\n",
              "21      7        1\n",
              "19     10        3\n",
              "15     11       10\n",
              "14     12       10\n",
              "12     13       14\n",
              "10     14       18\n",
              "11     15       14\n",
              "13     16       12\n",
              "9      17       19\n",
              "8      18       23\n",
              "7      19       28\n",
              "6      20       50\n",
              "4      21       65\n",
              "3      22       71\n",
              "2      23       83\n",
              "1      24       95\n",
              "0      25      150"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "# 코드를 입력해 주세요\n",
        "# 위의 결과를 시각화 하세요. (먼저 인덱스를 기준으로 데이터를 정렬하세요)\n",
        "lotName_train = train['lotName'].value_counts().value_counts()\n",
        "lotName_train = lotName_train.reset_index().sort_values('index')\n",
        "lotName_train"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "lotName_train.plot(kind='bar', x='index', y='lotName')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        },
        "id": "oyB5wZwiLCAn",
        "outputId": "57599e3c-5cff-446f-fb10-e8dff714f50a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f130b4df8d0>"
            ]
          },
          "metadata": {},
          "execution_count": 9
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mfTugLNTwe0V"
      },
      "source": [
        "<img src=\"data:image/png;base64,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\"/>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "au2EKbYzwe0V",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a9e78292-f301-406d-c423-78e2ac5835f4"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "lot12126    25\n",
              "lot8347     25\n",
              "lot12083    25\n",
              "lot7593     25\n",
              "lot12116    25\n",
              "            ..\n",
              "lot1622      1\n",
              "lot40365     1\n",
              "lot85        1\n",
              "lot117       1\n",
              "lot40500     1\n",
              "Name: lotName, Length: 1151, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ],
      "source": [
        "# 코드를 입력해 주세요\n",
        "# test 데이터 프레임의 lotName별 데이터의 개수를 확인하세요.\n",
        "test['lotName'].value_counts()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SoLsyPQDwe0V"
      },
      "source": [
        "<p><strong>[출력 결과]</strong></p><pre>lot7578     25\n",
        "lot12065    25\n",
        "lot12018    25\n",
        "lot10619    25\n",
        "lot12143    25\n",
        "            ..\n",
        "lot249       1\n",
        "lot221       1\n",
        "lot275       1\n",
        "lot157       1\n",
        "lot149       1\n",
        "Name: lotName, Length: 1151, dtype: int64</pre>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "c3uxuAwmwe0V",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "75bbd72c-26fc-40ff-a8a3-4ed9a8754a6c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "25    349\n",
              "1     127\n",
              "2     100\n",
              "24     93\n",
              "3      71\n",
              "4      71\n",
              "5      53\n",
              "6      34\n",
              "12     33\n",
              "13     27\n",
              "7      26\n",
              "23     25\n",
              "8      24\n",
              "11     20\n",
              "10     16\n",
              "22     15\n",
              "9      13\n",
              "14     11\n",
              "20     11\n",
              "15      8\n",
              "18      8\n",
              "19      7\n",
              "17      4\n",
              "21      4\n",
              "16      1\n",
              "Name: lotName, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ],
      "source": [
        "# 코드를 입력해 주세요\n",
        "# lotName의 개수 결과를 대상으로 한번 더 데이터의 개수를 확인하세요. \n",
        "test['lotName'].value_counts().value_counts()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Q9I6RSO2we0V"
      },
      "source": [
        "<p><strong>[출력 결과]</strong></p><pre>25    349\n",
        "1     127\n",
        "2     100\n",
        "24     93\n",
        "3      71\n",
        "4      71\n",
        "5      53\n",
        "6      34\n",
        "12     33\n",
        "13     27\n",
        "7      26\n",
        "23     25\n",
        "8      24\n",
        "11     20\n",
        "10     16\n",
        "22     15\n",
        "9      13\n",
        "14     11\n",
        "20     11\n",
        "15      8\n",
        "18      8\n",
        "19      7\n",
        "17      4\n",
        "21      4\n",
        "16      1\n",
        "Name: lotName, dtype: int64</pre>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_jsBq5nnwe0W",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 833
        },
        "outputId": "b3d58041-f6ed-454a-c6b6-808946e4db38"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-b87bb233-8adc-461d-9d4c-58d31708c863\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>index</th>\n",
              "      <th>lotName</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>127</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3</td>\n",
              "      <td>71</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>4</td>\n",
              "      <td>71</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>5</td>\n",
              "      <td>53</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>6</td>\n",
              "      <td>34</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>7</td>\n",
              "      <td>26</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>8</td>\n",
              "      <td>24</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>9</td>\n",
              "      <td>13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>10</td>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>11</td>\n",
              "      <td>20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>12</td>\n",
              "      <td>33</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>13</td>\n",
              "      <td>27</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>14</td>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>15</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>16</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>17</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>18</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>19</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>20</td>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>21</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>22</td>\n",
              "      <td>15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>23</td>\n",
              "      <td>25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>24</td>\n",
              "      <td>93</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>25</td>\n",
              "      <td>349</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b87bb233-8adc-461d-9d4c-58d31708c863')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-b87bb233-8adc-461d-9d4c-58d31708c863 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-b87bb233-8adc-461d-9d4c-58d31708c863');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "    index  lotName\n",
              "1       1      127\n",
              "2       2      100\n",
              "4       3       71\n",
              "5       4       71\n",
              "6       5       53\n",
              "7       6       34\n",
              "10      7       26\n",
              "12      8       24\n",
              "16      9       13\n",
              "14     10       16\n",
              "13     11       20\n",
              "8      12       33\n",
              "9      13       27\n",
              "17     14       11\n",
              "19     15        8\n",
              "24     16        1\n",
              "22     17        4\n",
              "20     18        8\n",
              "21     19        7\n",
              "18     20       11\n",
              "23     21        4\n",
              "15     22       15\n",
              "11     23       25\n",
              "3      24       93\n",
              "0      25      349"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ],
      "source": [
        "# 코드를 입력해 주세요\n",
        "# 위의 결과를 시각화 하세요. (먼저 인덱스를 기준으로 데이터를 정렬하세요)\n",
        "lotName_test = test['lotName'].value_counts().value_counts()\n",
        "lotName_test = lotName_test.reset_index().sort_values('index')\n",
        "lotName_test"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "lotName_train.plot(kind='bar', x='index', y='lotName')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        },
        "id": "RCQRCL0fLxbd",
        "outputId": "e7c3e629-4fef-4240-df7e-cf5c89dc232d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f130aeead50>"
            ]
          },
          "metadata": {},
          "execution_count": 13
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a8lH2FZ3we0W"
      },
      "source": [
        "<img src=\"data:image/png;base64,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\"/>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "scrolled": true,
        "id": "LXcmMmHbwe0W",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5df1ebbe-413b-415e-ea7d-ba86480de9b2"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2     589\n",
              "3     589\n",
              "10    582\n",
              "18    582\n",
              "4     581\n",
              "8     579\n",
              "1     579\n",
              "16    578\n",
              "14    577\n",
              "25    577\n",
              "23    575\n",
              "22    575\n",
              "19    575\n",
              "15    574\n",
              "13    574\n",
              "17    574\n",
              "24    572\n",
              "11    570\n",
              "5     570\n",
              "6     570\n",
              "12    569\n",
              "7     569\n",
              "9     564\n",
              "20    562\n",
              "21    560\n",
              "Name: waferId, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ],
      "source": [
        "# 코드를 입력해 주세요\n",
        "# train 데이터 프레임의 waferId별 데이터의 개수를 확인하세요.\n",
        "train['waferId'].value_counts()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kj8LmKWSwe0W"
      },
      "source": [
        "<p><strong>[출력 결과]</strong></p><pre>2     589\n",
        "3     589\n",
        "10    582\n",
        "18    582\n",
        "4     581\n",
        "8     579\n",
        "1     579\n",
        "16    578\n",
        "14    577\n",
        "25    577\n",
        "23    575\n",
        "22    575\n",
        "19    575\n",
        "15    574\n",
        "13    574\n",
        "17    574\n",
        "24    572\n",
        "11    570\n",
        "5     570\n",
        "6     570\n",
        "12    569\n",
        "7     569\n",
        "9     564\n",
        "20    562\n",
        "21    560\n",
        "Name: waferId, dtype: int64</pre>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "d1sx14kUwe0X",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        },
        "outputId": "a5867263-c77c-426e-a4c2-e6ce16372cef"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f130ae3de50>"
            ]
          },
          "metadata": {},
          "execution_count": 15
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# 코드를 입력해 주세요\n",
        "# 위의 결과를 시각화 하세요. (먼저 인덱스를 기준으로 데이터를 정렬하세요)\n",
        "waferId_train = train['waferId'].value_counts().reset_index().sort_values('index')\n",
        "waferId_train.plot(kind='bar', x='index', y='waferId')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "m0AJwVeSwe0X"
      },
      "source": [
        "<img src=\"data:image/png;base64,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\"/>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "n7CM-8dPwe0X",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ecfb1373-5228-4c6b-e9e9-0343031d5d3c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1     730\n",
              "2     682\n",
              "5     678\n",
              "6     673\n",
              "4     671\n",
              "7     668\n",
              "9     665\n",
              "3     665\n",
              "12    658\n",
              "11    658\n",
              "8     655\n",
              "10    647\n",
              "13    635\n",
              "15    614\n",
              "14    611\n",
              "17    607\n",
              "16    607\n",
              "20    606\n",
              "21    600\n",
              "19    595\n",
              "18    595\n",
              "22    580\n",
              "23    571\n",
              "24    553\n",
              "25    488\n",
              "Name: waferId, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ],
      "source": [
        "# 코드를 입력해 주세요\n",
        "# test 데이터 프레임의 waferId별 데이터의 개수를 확인하세요.\n",
        "test['waferId'].value_counts()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "g_F1fHTRwe0X"
      },
      "source": [
        "<p><strong>[출력 결과]</strong></p><pre>1     730\n",
        "2     682\n",
        "5     678\n",
        "6     673\n",
        "4     671\n",
        "7     668\n",
        "9     665\n",
        "3     665\n",
        "12    658\n",
        "11    658\n",
        "8     655\n",
        "10    647\n",
        "13    635\n",
        "15    614\n",
        "14    611\n",
        "17    607\n",
        "16    607\n",
        "20    606\n",
        "21    600\n",
        "19    595\n",
        "18    595\n",
        "22    580\n",
        "23    571\n",
        "24    553\n",
        "25    488\n",
        "Name: waferId, dtype: int64</pre>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "YhkrTFmMwe0X",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        },
        "outputId": "bcc17308-d37d-4de5-fb0c-82752c92246c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f130ad48e90>"
            ]
          },
          "metadata": {},
          "execution_count": 17
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# 코드를 입력해 주세요\n",
        "# 위의 결과를 시각화 하세요. (먼저 인덱스를 기준으로 데이터를 정렬하세요)\n",
        "waferId_test = test['waferId'].value_counts().reset_index().sort_values('index')\n",
        "waferId_test.plot(kind='bar', x='index', y='waferId')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xoMpQhq5we0X"
      },
      "source": [
        "<img src=\"data:image/png;base64,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\"/>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "fukEHE_qwe0X",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3d448f67-7477-4d6e-c980-9dfbfa3f7b0e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "8    13489\n",
              "4      297\n",
              "2      296\n",
              "0       90\n",
              "6       74\n",
              "7       72\n",
              "3       31\n",
              "5       16\n",
              "1        1\n",
              "Name: label, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ],
      "source": [
        "# 코드를 입력해 주세요\n",
        "# train 데이터의 label 컬럼의 개수를 계산하세요\n",
        "train['label'].value_counts()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ORhQbI8Ywe0Y"
      },
      "source": [
        "<p><strong>[출력 결과]</strong></p><pre>8    13489\n",
        "4      297\n",
        "2      296\n",
        "0       90\n",
        "6       74\n",
        "7       72\n",
        "3       31\n",
        "5       16\n",
        "1        1\n",
        "Name: label, dtype: int64</pre>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "fRhKpsv-we0Y",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "44a79ee0-4722-47ca-feb1-e6b0cac62a88"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0        none\n",
              "1        none\n",
              "2        none\n",
              "3        none\n",
              "4        none\n",
              "         ... \n",
              "14361    none\n",
              "14362    none\n",
              "14363    none\n",
              "14364    none\n",
              "14365    none\n",
              "Name: label_name, Length: 14366, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ],
      "source": [
        "# 코드를 입력해주세요\n",
        "# 아래의 정보를 참고하여 label_name 컬럼을 생성하세요.\n",
        "# (0 -> Center), (1 -> Donut), (2 -> Edge-Loc), (3 -> Edge-Ring), (4 -> Loc), (5 -> Near-full),(6 -> Random), (7 -> Scratch), (8 -> none)\n",
        "def rename(x):\n",
        "    data = {\n",
        "        0: 'Center', 1: 'Donut', 2: 'Edge-Loc',\n",
        "        3: 'Edge-Ring', 4: 'Loc', 5: 'Near-full',\n",
        "        6: 'Random', 7: 'Scratch', 8: 'none'\n",
        "    } \n",
        "    return data[x]\n",
        "train['label_name'] = train['label'].apply(rename)\n",
        "train['label_name']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Yk_UNeUkwe0Y",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "76168f5d-19f7-4434-e58e-659c6493199f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "none         13489\n",
              "Loc            297\n",
              "Edge-Loc       296\n",
              "Center          90\n",
              "Random          74\n",
              "Scratch         72\n",
              "Edge-Ring       31\n",
              "Near-full       16\n",
              "Donut            1\n",
              "Name: label_name, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ],
      "source": [
        "# 코드를 입력해 주세요\n",
        "# train 데이터의 label_name 컬럼의 개수를 계산하세요\n",
        "train['label_name'].value_counts()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5cQYD4V_we0Y"
      },
      "source": [
        "<p><strong>[출력 결과]</strong></p><pre>none         13489\n",
        "Loc            297\n",
        "Edge-Loc       296\n",
        "Center          90\n",
        "Random          74\n",
        "Scratch         72\n",
        "Edge-Ring       31\n",
        "Near-full       16\n",
        "Donut            1\n",
        "Name: label_name, dtype: int64</pre>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "nezGapi5we0Y",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 325
        },
        "outputId": "07795b55-a1dd-4fcd-9c21-a6ac7dec1f27"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f130ac9ec50>"
            ]
          },
          "metadata": {},
          "execution_count": 21
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# 코드를 입력해 주세요\n",
        "# 위의 결과를 시각화 하세요. \n",
        "train['label_name'].value_counts().plot(kind='bar')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "doHL3Frxwe0Y"
      },
      "source": [
        "<img src=\"data:image/png;base64,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\"/>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "o6wvq49mwe0Y",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "outputId": "9420bf5f-3e96-44d9-9153-82dd6afdb541"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-93804172-7549-4ef2-8c40-9f7eb17ad760\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>pixel_1</th>\n",
              "      <th>pixel_2</th>\n",
              "      <th>pixel_3</th>\n",
              "      <th>pixel_4</th>\n",
              "      <th>pixel_5</th>\n",
              "      <th>pixel_6</th>\n",
              "      <th>pixel_7</th>\n",
              "      <th>pixel_8</th>\n",
              "      <th>pixel_9</th>\n",
              "      <th>pixel_10</th>\n",
              "      <th>pixel_11</th>\n",
              "      <th>pixel_12</th>\n",
              "      <th>pixel_13</th>\n",
              "      <th>pixel_14</th>\n",
              "      <th>pixel_15</th>\n",
              "      <th>pixel_16</th>\n",
              "      <th>pixel_17</th>\n",
              "      <th>pixel_18</th>\n",
              "      <th>pixel_19</th>\n",
              "      <th>pixel_20</th>\n",
              "      <th>pixel_21</th>\n",
              "      <th>pixel_22</th>\n",
              "      <th>pixel_23</th>\n",
              "      <th>pixel_24</th>\n",
              "      <th>pixel_25</th>\n",
              "      <th>pixel_26</th>\n",
              "      <th>pixel_27</th>\n",
              "      <th>pixel_28</th>\n",
              "      <th>pixel_29</th>\n",
              "      <th>pixel_30</th>\n",
              "      <th>pixel_31</th>\n",
              "      <th>pixel_32</th>\n",
              "      <th>pixel_33</th>\n",
              "      <th>pixel_34</th>\n",
              "      <th>pixel_35</th>\n",
              "      <th>pixel_36</th>\n",
              "      <th>pixel_37</th>\n",
              "      <th>pixel_38</th>\n",
              "      <th>pixel_39</th>\n",
              "      <th>pixel_40</th>\n",
              "      <th>...</th>\n",
              "      <th>pixel_637</th>\n",
              "      <th>pixel_638</th>\n",
              "      <th>pixel_639</th>\n",
              "      <th>pixel_640</th>\n",
              "      <th>pixel_641</th>\n",
              "      <th>pixel_642</th>\n",
              "      <th>pixel_643</th>\n",
              "      <th>pixel_644</th>\n",
              "      <th>pixel_645</th>\n",
              "      <th>pixel_646</th>\n",
              "      <th>pixel_647</th>\n",
              "      <th>pixel_648</th>\n",
              "      <th>pixel_649</th>\n",
              "      <th>pixel_650</th>\n",
              "      <th>pixel_651</th>\n",
              "      <th>pixel_652</th>\n",
              "      <th>pixel_653</th>\n",
              "      <th>pixel_654</th>\n",
              "      <th>pixel_655</th>\n",
              "      <th>pixel_656</th>\n",
              "      <th>pixel_657</th>\n",
              "      <th>pixel_658</th>\n",
              "      <th>pixel_659</th>\n",
              "      <th>pixel_660</th>\n",
              "      <th>pixel_661</th>\n",
              "      <th>pixel_662</th>\n",
              "      <th>pixel_663</th>\n",
              "      <th>pixel_664</th>\n",
              "      <th>pixel_665</th>\n",
              "      <th>pixel_666</th>\n",
              "      <th>pixel_667</th>\n",
              "      <th>pixel_668</th>\n",
              "      <th>pixel_669</th>\n",
              "      <th>pixel_670</th>\n",
              "      <th>pixel_671</th>\n",
              "      <th>pixel_672</th>\n",
              "      <th>pixel_673</th>\n",
              "      <th>pixel_674</th>\n",
              "      <th>pixel_675</th>\n",
              "      <th>pixel_676</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</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>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
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              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
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              "      <td>2</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\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>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</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>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\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>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <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>3</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>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\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>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</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>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>...</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <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>14361</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>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <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>14362</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>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\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>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14363</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>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>...</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
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              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <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>14364</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\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",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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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>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14365</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\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",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>14366 rows × 676 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-93804172-7549-4ef2-8c40-9f7eb17ad760')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-93804172-7549-4ef2-8c40-9f7eb17ad760 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-93804172-7549-4ef2-8c40-9f7eb17ad760');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "       pixel_1  pixel_2  pixel_3  ...  pixel_674  pixel_675  pixel_676\n",
              "0            0        0        0  ...          0          0          0\n",
              "1            0        0        0  ...          0          0          0\n",
              "2            0        0        0  ...          0          0          0\n",
              "3            0        0        0  ...          0          0          0\n",
              "4            0        0        0  ...          0          0          0\n",
              "...        ...      ...      ...  ...        ...        ...        ...\n",
              "14361        0        0        0  ...          0          0          0\n",
              "14362        0        0        0  ...          0          0          0\n",
              "14363        0        0        0  ...          0          0          0\n",
              "14364        0        0        0  ...          0          0          0\n",
              "14365        0        0        0  ...          0          0          0\n",
              "\n",
              "[14366 rows x 676 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ],
      "source": [
        "# 코드를 입력해 주세요\n",
        "# train 데이터 프레임에 컬럼명에 pixel 이라는 문자열이 있는 컬럼명만 선택하여 X에 저장하세요.\n",
        "list_px = train.columns\n",
        "list_px\n",
        "\n",
        "X = []\n",
        "for i in list_px:\n",
        "  if (i[0:5] == 'pixel'):\n",
        "    X.append(i)\n",
        "\n",
        "new_X = train[X]\n",
        "new_X"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xaySAmkgwe0Z"
      },
      "source": [
        "<p><strong>[출력 결과]</strong></p><div>\n",
        "<style scoped>\n",
        "    .dataframe tbody tr th:only-of-type {\n",
        "        vertical-align: middle;\n",
        "    }\n",
        "\n",
        "    .dataframe tbody tr th {\n",
        "        vertical-align: top;\n",
        "    }\n",
        "\n",
        "    .dataframe thead th {\n",
        "        text-align: right;\n",
        "    }\n",
        "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>pixel_1</th>\n",
        "      <th>pixel_2</th>\n",
        "      <th>pixel_3</th>\n",
        "      <th>pixel_4</th>\n",
        "      <th>pixel_5</th>\n",
        "      <th>pixel_6</th>\n",
        "      <th>pixel_7</th>\n",
        "      <th>pixel_8</th>\n",
        "      <th>pixel_9</th>\n",
        "      <th>pixel_10</th>\n",
        "      <th>...</th>\n",
        "      <th>pixel_667</th>\n",
        "      <th>pixel_668</th>\n",
        "      <th>pixel_669</th>\n",
        "      <th>pixel_670</th>\n",
        "      <th>pixel_671</th>\n",
        "      <th>pixel_672</th>\n",
        "      <th>pixel_673</th>\n",
        "      <th>pixel_674</th>\n",
        "      <th>pixel_675</th>\n",
        "      <th>pixel_676</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>0</td>\n",
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        "      <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",
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        "      <td>0</td>\n",
        "      <td>0</td>\n",
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        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
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        "      <td>0</td>\n",
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        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "      <td>...</td>\n",
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        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
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        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
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        "      <td>...</td>\n",
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        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
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        "      <td>0</td>\n",
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        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>0</td>\n",
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        "    </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>14361</th>\n",
        "      <td>0</td>\n",
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        "    </tr>\n",
        "    <tr>\n",
        "      <th>14362</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>...</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "      <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>14363</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>...</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "      <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>14364</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>...</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "      <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>14365</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>...</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "      <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>14366 rows × 676 columns</p>\n",
        "</div>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "WuUpW_rmwe0Z",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "88fc2bd1-afd8-4414-d893-fde9634d5c06"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(14366, 26, 26)"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ],
      "source": [
        "# 코드를 입력해 주세요.\n",
        "# X 데이터를 Numpy Array로 변경하여 (14366, 26, 26) 으로 shape을 변경하세요. (images변수에 저장하세요.)\n",
        "new_X = np.array(new_X)\n",
        "images = new_X.reshape(14366, 26, 26)\n",
        "images.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Qnlgtsrqwe0Z"
      },
      "source": [
        "<p><strong>[출력 결과]</strong></p><pre>(14366, 26, 26)</pre>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1ALtBqYCwe0Z",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "4eb26cea-cfbe-4c76-b7c1-ce98011d2935"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f130ab79ed0>"
            ]
          },
          "metadata": {},
          "execution_count": 24
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# 코드를 입력해 주세요.\n",
        "# images 변수의 첫번째 데이터를 선택하여 시각화 하세요 (plt.imshow 함수 이용)\n",
        "plt.imshow(images[0])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RIAkN_Ubwe0a"
      },
      "source": [
        "<img src=\"data:image/png;base64,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\"/>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "pnwunJGgwe0a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c1b4db2a-d4a5-4241-b79c-c2dd1ba2d61a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[['none', 0],\n",
              " ['Edge-Ring', 27],\n",
              " ['Edge-Loc', 99],\n",
              " ['Scratch', 392],\n",
              " ['Random', 395],\n",
              " ['Center', 397],\n",
              " ['Loc', 432],\n",
              " ['Near-full', 668],\n",
              " ['Donut', 10686]]"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ],
      "source": [
        "# 각 레이블별 데이터 1개씩 추출\n",
        "info = [[label, train[train['label_name'] == label].index[0]] for label in train['label_name'].unique()]\n",
        "info"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Qy_Wxw28we0a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 536
        },
        "outputId": "16aa8cd5-ca9b-42f8-93e9-f6461fdb2423"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 648x648 with 9 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# 코드를 입력해 주세요.\n",
        "# 위의 info 변수를 이용하여 각 레이블별 웨이퍼맵을 시각화 하세요.\n",
        "plt.figure(figsize=(9, 9))\n",
        "for cnt, (name, index) in enumerate(info):\n",
        "    plt.subplot(3, 3, cnt+1)\n",
        "    plt.imshow(images[index])\n",
        "    plt.title(name)\n",
        "    plt.axis('off')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rPD2bALpwe0a"
      },
      "source": [
        "<img 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\"/>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "v8QphKVowe0a"
      },
      "outputs": [],
      "source": [
        "# 코드를 입력해 주세요\n",
        "# label 컬럼을 Y에 저장하세요.\n",
        "y = train['label']"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LRdLLWiKwe0a"
      },
      "source": [
        "<p><strong>[출력 결과]</strong></p><pre>0        8\n",
        "1        8\n",
        "2        8\n",
        "3        8\n",
        "4        8\n",
        "        ..\n",
        "14361    8\n",
        "14362    8\n",
        "14363    8\n",
        "14364    8\n",
        "14365    8\n",
        "Name: label, Length: 14366, dtype: int64</pre>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "scaler = StandardScaler()\n",
        "scaler.fit(new_X)\n",
        "X_scaled = scaler.transform(new_X)"
      ],
      "metadata": {
        "id": "jbcaZbhCdc1H"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "oaHjS4sQwe0a"
      },
      "outputs": [],
      "source": [
        "# 코드를 입력해 주세요\n",
        "# 분리된 X와 Y를 Train과 Test로 분할하세요.\n",
        "from sklearn.model_selection import train_test_split\n",
        "x_train, x_test, y_train, y_test = train_test_split(X_scaled, y, random_state = 23, test_size = 0.05)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "dHilBw5Lwe0b"
      },
      "outputs": [],
      "source": [
        "# 코드를 입력해 주세요\n",
        "# KNN 알고리즘을 이용하여 모델링하고 성능을 출력하세요. (K는 9로 지정하세요.)\n",
        "\n",
        "# from sklearn.neighbors import KNeighborsClassifier\n",
        "# model = KNeighborsClassifier(n_neighbors=1)\n",
        "# model.fit(X_scaled, y_train)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "\n",
        "# parameters = {\n",
        "#     #kernal = \"rbf\" 고정\n",
        "#     \"kernel\": [\"rbf\"],\n",
        "#     #C 값은 다양하게 소프트 마진부터 라지마진까지\n",
        "#     \"C\": [10, 100],\n",
        "#     #복잡도도 낮지 않게 설정\n",
        "#     \"gamma\" : [8],\n",
        "#     #coef0 rbf에 대한 kernal 계수\n",
        "#     \"coef0\" : [0.01]\n",
        "#     }\n",
        "\n",
        "# #SVR 모델 Grid Search , 파라메타들은 위에서 설정한 파라메타\n",
        "# #verbose = 2 -> Grid Search 반복시 하이퍼 파라메타별 메시지를 화면에 출력 \n",
        "# #cv=5 5번의 교차검증.\n",
        "# grid = GridSearchCV(SVR(), parameters, verbose=2, cv=5)\n",
        "# grid.fit(new_X, y)"
      ],
      "metadata": {
        "id": "nRjn6UJsZC9b"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# grid.best_params_"
      ],
      "metadata": {
        "id": "X44TC8WpagmZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.svm import SVC\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "svm_rbf_clf =  SVC(kernel=\"rbf\")\n",
        "svm_rbf_clf.fit(X_scaled, y)"
      ],
      "metadata": {
        "id": "s_F95o6Kaj12"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# x_train, x_test, y_train, y_test  = train_test_split(new_X , y,random_state = 23)\n",
        "\n",
        "# k_range = range(3,20)\n",
        "\n",
        "# train_acc = []\n",
        "# test_acc = []\n",
        "\n",
        "# for k in k_range:\n",
        "#   model = KNeighborsClassifier(n_neighbors=k).fit(x_train, y_train)\n",
        "#   train_acc.append(model.score(x_train, y_train))\n",
        "#   test_acc.append(model.score(x_test, y_test))"
      ],
      "metadata": {
        "id": "ILFojWWnXgBn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# # 실험 결과 시각화\n",
        "# plt.figure(figsize=(30,15))\n",
        "# plt.plot(k_range[:20], train_acc[:20], label='Train Accuracy')\n",
        "# plt.plot(k_range[:20], test_acc[:20], label='Test Accuracy')\n",
        "# plt.legend()\n",
        "# plt.grid()\n",
        "# plt.xticks(k_range[:20])\n",
        "# plt.show()"
      ],
      "metadata": {
        "id": "e0NIivGfXzMV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#test_size 0.3 일 때 정확도 0.9454, 0.9464\n",
        "#test_size 0.2 일 때 정확도 0.945, 0.947\n",
        "#test_size 0.1 일 때 정확도 0.94562, 0.9540\n",
        "#test_size 0.05 일 때 정확도 0.9464, 0.9527\n",
        "\n",
        "\n",
        "model.score(x_train, y_train), model.score(x_test, y_test)"
      ],
      "metadata": {
        "id": "aPyrScSzTvZf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qK_aNDjQwe0b"
      },
      "source": [
        "<p><strong>[출력 결과]</strong></p><pre>(0.946723593837015, 0.9457126948775055)</pre>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "RdXqiuRDwe0b"
      },
      "outputs": [],
      "source": [
        "# 코드를 입력해 주세요\n",
        "# test 데이터 프레임에 컬럼명에 pixel 이라는 문자열이 있는 컬럼명만 선택하여 X2에 저장하세요.\n",
        "t_list_px = test.columns\n",
        "t_list_px\n",
        "\n",
        "X = []\n",
        "for i in t_list_px:\n",
        "  if (i[0:5] == 'pixel'):\n",
        "    X.append(i)\n",
        "\n",
        "test_X = test[X]\n",
        "test_X"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ObHhWzcywe0b"
      },
      "source": [
        "<p><strong>[출력 결과]</strong></p><div>\n",
        "<style scoped>\n",
        "    .dataframe tbody tr th:only-of-type {\n",
        "        vertical-align: middle;\n",
        "    }\n",
        "\n",
        "    .dataframe tbody tr th {\n",
        "        vertical-align: top;\n",
        "    }\n",
        "\n",
        "    .dataframe thead th {\n",
        "        text-align: right;\n",
        "    }\n",
        "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>pixel_1</th>\n",
        "      <th>pixel_2</th>\n",
        "      <th>pixel_3</th>\n",
        "      <th>pixel_4</th>\n",
        "      <th>pixel_5</th>\n",
        "      <th>pixel_6</th>\n",
        "      <th>pixel_7</th>\n",
        "      <th>pixel_8</th>\n",
        "      <th>pixel_9</th>\n",
        "      <th>pixel_10</th>\n",
        "      <th>...</th>\n",
        "      <th>pixel_667</th>\n",
        "      <th>pixel_668</th>\n",
        "      <th>pixel_669</th>\n",
        "      <th>pixel_670</th>\n",
        "      <th>pixel_671</th>\n",
        "      <th>pixel_672</th>\n",
        "      <th>pixel_673</th>\n",
        "      <th>pixel_674</th>\n",
        "      <th>pixel_675</th>\n",
        "      <th>pixel_676</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</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>...</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "      <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>1</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>...</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "      <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>2</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>...</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "      <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>3</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>...</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "      <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>4</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>...</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "      <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",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>15707</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>...</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "      <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>15708</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>...</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "      <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>15709</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>...</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "      <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>15710</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>...</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "      <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>15711</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>...</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "      <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>15712 rows × 676 columns</p>\n",
        "</div>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "j9HKFi2-we0c"
      },
      "outputs": [],
      "source": [
        "# 코드를 입력해 주세요\n",
        "# X2 데이터 프레임을 예측하여 submission 데이터 프레임의 label 컬럼에 추가하세요.\n",
        "\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "# scaler = StandardScaler()\n",
        "# scaler.fit(new_X)\n",
        "# X_scaled = scaler.transform(new_X)\n",
        "\n",
        "scaler.fit(test_X)\n",
        "X_scaled_test = scaler.transform(test_X)\n",
        "\n",
        "pred_x2 = model.predict(X_scaled_test)\n",
        "pred_x2"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "submission['label'] = pred_x2\n",
        "submission"
      ],
      "metadata": {
        "id": "nPDUaL-MV80J"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0GUJAKbywe0c"
      },
      "source": [
        "<p><strong>[출력 결과]</strong></p><div>\n",
        "<style scoped>\n",
        "    .dataframe tbody tr th:only-of-type {\n",
        "        vertical-align: middle;\n",
        "    }\n",
        "\n",
        "    .dataframe tbody tr th {\n",
        "        vertical-align: top;\n",
        "    }\n",
        "\n",
        "    .dataframe thead th {\n",
        "        text-align: right;\n",
        "    }\n",
        "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>lotName</th>\n",
        "      <th>label</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>lot7</td>\n",
        "      <td>8</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>lot7</td>\n",
        "      <td>8</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>lot7</td>\n",
        "      <td>8</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>lot7</td>\n",
        "      <td>8</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>lot7</td>\n",
        "      <td>8</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>15707</th>\n",
        "      <td>lot42180</td>\n",
        "      <td>8</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>15708</th>\n",
        "      <td>lot42180</td>\n",
        "      <td>8</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>15709</th>\n",
        "      <td>lot42180</td>\n",
        "      <td>8</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>15710</th>\n",
        "      <td>lot42180</td>\n",
        "      <td>8</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>15711</th>\n",
        "      <td>lot42180</td>\n",
        "      <td>8</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>15712 rows × 2 columns</p>\n",
        "</div>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WM3UO2NNwe0c"
      },
      "source": [
        "# 순서대로 평가가 진행됩니다. 실행은 1회만 해주세요.\n",
        "\n",
        "- 느리다고 중지 후 다시 평가 코드를 실행하는 경우 제출 과정에서 패널티가 발생할 수 있습니다. (제출 횟수 이슈 발생 가능)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "LsliBwC9we0c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "86d1f4d2-8721-4a86-b162-4f8d28873f4d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "파일을 저장하였습니다. 파일명: submission-08-41-39.csv\n",
            "제출 여부 :success\n",
            "오늘 제출 횟수 : 10\n",
            "제출 결과:0.9246684730246849\n"
          ]
        }
      ],
      "source": [
        "pjt.submit(submission)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#1차 0.8896 -> test size 0.1\n",
        "#3차 0.8899 -> test size 0.005"
      ],
      "metadata": {
        "id": "VkfIprDHWW-h"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YIiXPqFXwe0c"
      },
      "source": [
        "# 축하드립니다. 모델의 성능을 개선하여 더 좋은 점수를 획득해보세요!"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "gyLD4hk_we0c"
      },
      "outputs": [],
      "source": [
        ""
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.8.5"
    },
    "colab": {
      "name": "데이터분석_4일차_웨이퍼 불량 유형 분류.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}