smartFactory_LG_AI_hackathon_Yumin (2).ipynb

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    "colab": {
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      "toc_visible": true,
      "collapsed_sections": [
        "ex7768-Ktt2u"
      ]
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    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU",
    "gpuClass": "standard"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "LG AI hackathon Smart Factory\n",
        "[대회 링크](https://dacon.io/competitions/official/236055/overview/description)"
      ],
      "metadata": {
        "id": "7Nq-Y7oIvUIC"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Import & Data Load\n",
        "\n",
        "**1. train.csv [파일]**\n",
        "- PRODUCT_ID : 제품의 고유 ID\n",
        "- Y_Class : 제품 품질 상태(Target) \n",
        "    - 0 : 적정 기준 미달 (부적합)\n",
        "    - 1 : 적합\n",
        "    - 2 : 적정 기준 초과 (부적합)\n",
        "- Y_Quality : 제품 품질 관련 정량적 수치\n",
        "- TIMESTAMP : 제품이 공정에 들어간 시각\n",
        "- LINE : 제품이 들어간 공정 LINE 종류 ('T050304', 'T050307', 'T100304', 'T100306', 'T010306', 'T010305' 존재)\n",
        "- PRODUCT_CODE : 제품의 CODE 번호 ('A_31', 'T_31', 'O_31' 존재)\n",
        "- X_1 ~ X_2875 : 공정 과정에서 추출되어 비식별화된 변수\n",
        "- 06.13.2022~09.08.2022 DATA\n",
        "\n",
        "\n",
        "\n",
        "**2. test.csv [파일]**\n",
        "- PRODUCT_ID : 제품의 고유 ID\n",
        "- TIMESTAMP : 제품이 공정에 들어간 시각\n",
        "- LINE : 제품이 들어간 공정 LINE 종류 ('T050304', 'T050307', 'T100304', 'T100306', 'T010306', 'T010305' 존재)\n",
        "- PRODUCT_CODE : 제품의 CODE 번호 ('A_31', 'T_31', 'O_31' 존재)\n",
        "- X_1 ~ X_2875 : 공정 과정에서 추출되어 비식별화된 변수\n",
        "- 09.09.2022~11.05.2022 DATA \n",
        "\n",
        "**3. sample_submission.csv [파일] - 제출 양식**\n",
        "- PRODUCT_ID : 제품의 고유 ID\n",
        "- Y_Class : 예측한 제품 품질 상태\n",
        "    - 0 : 적정 기준 미달 (부적합)\n",
        "    - 1 : 적합\n",
        "    - 2 : 적정 기준 초과 (부적합)\n",
        "\n",
        "- 실제 공정 과정에서의 데이터로, 보안상의 이유로 일부 변수가 비식별화 처리 되었습니다. (X변수)\n",
        "- 'LINE', 'PRODUCT_CODE'는 Train / Test 모두 동일한 종류가 존재합니다."
      ],
      "metadata": {
        "id": "AQlbReiWmbvJ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## DATA LEAKAGE - RULE \n",
        "1. 대표적인 Data Leakage에 해당하는 사례\n",
        "    - Label Encoding 시 Test 데이터로부터 Encoder를 fit 시키는 경우\n",
        "    - Scaler 사용 시 Test 데이터로부터 Scaler를 fit 시키는 경우\n",
        "    Test 데이터에 대한 결측치 보간 시 Test 데이터의 통계 정보(평균, 최빈값 등)를 사용하는 경우\n",
        "    - 파생변수를 생성할 시 2개 이상의 Test 데이터들의 정보를 활용할 경우\n",
        "    - Test 데이터들의 이동 평균을 활용하는 경우\n",
        "    - Test 데이터들을 모델 학습에 활용하는 경우 (이번 해커톤에서는 Pseudo-Labeling도 허용하지 않습니다)\n",
        "    - Test 데이터에 pandas의 get_dummies() 함수를 적용하는 경우\n",
        "        - (=) Test DATA로 Encoder를 fitting 한 것과 같으므로\n",
        "        - ==> Encoder를 Train data로 fitting 한 후, Test data에 Transform (GOOD)  \n",
        "    - Test 데이터의 index 정보 혹은 row 개수 정보를 활용하는 경우\n",
        "    - Test 데이터로부터 EDA를 진행한 결과(인사이트)를 전처리, 모델 학습에 적용하는 경우\n",
        "    - 이 밖의 Test 데이터들의 정보를 모델 학습에 활용하는 모든 경우\n",
        "\n",
        "\n",
        "2. 대표적인 Data Leakage에 해당하지 않는 사례\n",
        "    - 독립적인 샘플(행(row)) 내에서의 모든 연산 혹은 파생 변수 생성\n",
        "        - 예를 들어, Test['C'] = Test['A'] + Test['B']와 같이 독립적인 샘플 내에서 A Column과 B Column을 더한 새로운 파생 변수 C Column을 생성하는 경우\n",
        "    - Train 데이터의 통계 정보를 Test 데이터에 적용하여 전처리하는 경우 혹은 파생 변수를 생성하는 경우\n",
        "    - Test 데이터의 결측치를 Train 데이터의 통계 정보를 바탕으로 보간하는 경우 \n"
      ],
      "metadata": {
        "id": "WcRYcvVrk-xf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import random\n",
        "import os\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt \n",
        "import seaborn as sns\n",
        "\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "from sklearn.ensemble import RandomForestClassifier"
      ],
      "metadata": {
        "id": "N524BAv7ohSr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eUhKXAUQrscr",
        "outputId": "cefa3140-d2d6-4d6f-d63b-2963106413a1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def seed_everything(seed):\n",
        "    random.seed(seed)\n",
        "    os.environ['PYTHONHASHSEED'] = str(seed)\n",
        "    np.random.seed(seed)\n",
        "\n",
        "seed_everything(37)"
      ],
      "metadata": {
        "id": "i7gVwKrJohX6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_df = pd.read_csv('/content/drive/MyDrive/train.csv')\n",
        "test_df = pd.read_csv('/content/drive/MyDrive/test.csv')"
      ],
      "metadata": {
        "id": "mo2eqzuoohcs"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 1.Data Load"
      ],
      "metadata": {
        "id": "RYf_ZaTJ9vgg"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 1.1 Trainig Set"
      ],
      "metadata": {
        "id": "7IyBO2IY9yLb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 661
        },
        "id": "kLBoulM6u2Fb",
        "outputId": "5482f1d5-b6c3-41c1-9521-4fa030da1c3e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    PRODUCT_ID  Y_Class  Y_Quality         TIMESTAMP     LINE PRODUCT_CODE  \\\n",
              "0    TRAIN_000        1   0.533433   2022-06-13 5:14  T050304         A_31   \n",
              "1    TRAIN_001        2   0.541819   2022-06-13 5:22  T050307         A_31   \n",
              "2    TRAIN_002        1   0.531267   2022-06-13 5:30  T050304         A_31   \n",
              "3    TRAIN_003        2   0.537325   2022-06-13 5:39  T050307         A_31   \n",
              "4    TRAIN_004        1   0.531590   2022-06-13 5:47  T050304         A_31   \n",
              "..         ...      ...        ...               ...      ...          ...   \n",
              "593  TRAIN_593        1   0.526546  2022-09-08 14:30  T100306         T_31   \n",
              "594  TRAIN_594        0   0.524022  2022-09-08 22:38  T050304         A_31   \n",
              "595  TRAIN_595        0   0.521289  2022-09-08 22:47  T050304         A_31   \n",
              "596  TRAIN_596        1   0.531375  2022-09-08 14:38  T100304         O_31   \n",
              "597  TRAIN_597        1   0.533702  2022-09-08 14:46  T100306         O_31   \n",
              "\n",
              "      X_1   X_2  X_3   X_4  ...  X_2866  X_2867  X_2868  X_2869  X_2870  \\\n",
              "0     NaN   NaN  NaN   NaN  ...   39.34   40.89   32.56   34.09   77.77   \n",
              "1     NaN   NaN  NaN   NaN  ...   38.89   42.82   43.92   35.34   72.55   \n",
              "2     NaN   NaN  NaN   NaN  ...   39.19   36.65   42.47   36.53   78.35   \n",
              "3     NaN   NaN  NaN   NaN  ...   37.74   39.17   52.17   30.58   71.78   \n",
              "4     NaN   NaN  NaN   NaN  ...   38.70   41.89   46.93   33.09   76.97   \n",
              "..    ...   ...  ...   ...  ...     ...     ...     ...     ...     ...   \n",
              "593   2.0  95.0  0.0  45.0  ...     NaN     NaN     NaN     NaN     NaN   \n",
              "594   NaN   NaN  NaN   NaN  ...   49.47   53.07   50.89   55.10   66.49   \n",
              "595   NaN   NaN  NaN   NaN  ...     NaN     NaN     NaN     NaN     NaN   \n",
              "596  40.0  94.0  0.0  45.0  ...     NaN     NaN     NaN     NaN     NaN   \n",
              "597  21.0  87.0  0.0  45.0  ...     NaN     NaN     NaN     NaN     NaN   \n",
              "\n",
              "     X_2871  X_2872  X_2873  X_2874  X_2875  \n",
              "0       NaN     NaN     NaN     NaN     NaN  \n",
              "1       NaN     NaN     NaN     NaN     NaN  \n",
              "2       NaN     NaN     NaN     NaN     NaN  \n",
              "3       NaN     NaN     NaN     NaN     NaN  \n",
              "4       NaN     NaN     NaN     NaN     NaN  \n",
              "..      ...     ...     ...     ...     ...  \n",
              "593     NaN     NaN     NaN     NaN     NaN  \n",
              "594     1.0     NaN     NaN     NaN     NaN  \n",
              "595     1.0     NaN     NaN     NaN     NaN  \n",
              "596     NaN     NaN     NaN     NaN     NaN  \n",
              "597     NaN     NaN     NaN     NaN     NaN  \n",
              "\n",
              "[598 rows x 2881 columns]"
            ],
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PRODUCT_ID</th>\n",
              "      <th>Y_Class</th>\n",
              "      <th>Y_Quality</th>\n",
              "      <th>TIMESTAMP</th>\n",
              "      <th>LINE</th>\n",
              "      <th>PRODUCT_CODE</th>\n",
              "      <th>X_1</th>\n",
              "      <th>X_2</th>\n",
              "      <th>X_3</th>\n",
              "      <th>X_4</th>\n",
              "      <th>...</th>\n",
              "      <th>X_2866</th>\n",
              "      <th>X_2867</th>\n",
              "      <th>X_2868</th>\n",
              "      <th>X_2869</th>\n",
              "      <th>X_2870</th>\n",
              "      <th>X_2871</th>\n",
              "      <th>X_2872</th>\n",
              "      <th>X_2873</th>\n",
              "      <th>X_2874</th>\n",
              "      <th>X_2875</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>TRAIN_000</td>\n",
              "      <td>1</td>\n",
              "      <td>0.533433</td>\n",
              "      <td>2022-06-13 5:14</td>\n",
              "      <td>T050304</td>\n",
              "      <td>A_31</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>39.34</td>\n",
              "      <td>40.89</td>\n",
              "      <td>32.56</td>\n",
              "      <td>34.09</td>\n",
              "      <td>77.77</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>TRAIN_001</td>\n",
              "      <td>2</td>\n",
              "      <td>0.541819</td>\n",
              "      <td>2022-06-13 5:22</td>\n",
              "      <td>T050307</td>\n",
              "      <td>A_31</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>38.89</td>\n",
              "      <td>42.82</td>\n",
              "      <td>43.92</td>\n",
              "      <td>35.34</td>\n",
              "      <td>72.55</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>TRAIN_002</td>\n",
              "      <td>1</td>\n",
              "      <td>0.531267</td>\n",
              "      <td>2022-06-13 5:30</td>\n",
              "      <td>T050304</td>\n",
              "      <td>A_31</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>39.19</td>\n",
              "      <td>36.65</td>\n",
              "      <td>42.47</td>\n",
              "      <td>36.53</td>\n",
              "      <td>78.35</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>TRAIN_003</td>\n",
              "      <td>2</td>\n",
              "      <td>0.537325</td>\n",
              "      <td>2022-06-13 5:39</td>\n",
              "      <td>T050307</td>\n",
              "      <td>A_31</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>37.74</td>\n",
              "      <td>39.17</td>\n",
              "      <td>52.17</td>\n",
              "      <td>30.58</td>\n",
              "      <td>71.78</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>TRAIN_004</td>\n",
              "      <td>1</td>\n",
              "      <td>0.531590</td>\n",
              "      <td>2022-06-13 5:47</td>\n",
              "      <td>T050304</td>\n",
              "      <td>A_31</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>38.70</td>\n",
              "      <td>41.89</td>\n",
              "      <td>46.93</td>\n",
              "      <td>33.09</td>\n",
              "      <td>76.97</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>593</th>\n",
              "      <td>TRAIN_593</td>\n",
              "      <td>1</td>\n",
              "      <td>0.526546</td>\n",
              "      <td>2022-09-08 14:30</td>\n",
              "      <td>T100306</td>\n",
              "      <td>T_31</td>\n",
              "      <td>2.0</td>\n",
              "      <td>95.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>45.0</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>594</th>\n",
              "      <td>TRAIN_594</td>\n",
              "      <td>0</td>\n",
              "      <td>0.524022</td>\n",
              "      <td>2022-09-08 22:38</td>\n",
              "      <td>T050304</td>\n",
              "      <td>A_31</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>49.47</td>\n",
              "      <td>53.07</td>\n",
              "      <td>50.89</td>\n",
              "      <td>55.10</td>\n",
              "      <td>66.49</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>595</th>\n",
              "      <td>TRAIN_595</td>\n",
              "      <td>0</td>\n",
              "      <td>0.521289</td>\n",
              "      <td>2022-09-08 22:47</td>\n",
              "      <td>T050304</td>\n",
              "      <td>A_31</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>596</th>\n",
              "      <td>TRAIN_596</td>\n",
              "      <td>1</td>\n",
              "      <td>0.531375</td>\n",
              "      <td>2022-09-08 14:38</td>\n",
              "      <td>T100304</td>\n",
              "      <td>O_31</td>\n",
              "      <td>40.0</td>\n",
              "      <td>94.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>45.0</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>597</th>\n",
              "      <td>TRAIN_597</td>\n",
              "      <td>1</td>\n",
              "      <td>0.533702</td>\n",
              "      <td>2022-09-08 14:46</td>\n",
              "      <td>T100306</td>\n",
              "      <td>O_31</td>\n",
              "      <td>21.0</td>\n",
              "      <td>87.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>45.0</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>598 rows × 2881 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-213354c1-2a1a-40e0-9913-8f39a8c6321a')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "\n",
              "      <script>\n",
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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-213354c1-2a1a-40e0-9913-8f39a8c6321a');\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",
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              "          dataTable['output_type'] = 'display_data';\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_df.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        },
        "id": "rpnKC9Mr96u_",
        "outputId": "ca4b79f5-bfca-4cd9-9818-6e30087e94f7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          Y_Class   Y_Quality         X_1         X_2    X_3    X_4  \\\n",
              "count  598.000000  598.000000  349.000000  349.000000  349.0  349.0   \n",
              "mean     1.025084    0.530896    2.409742   95.123209    0.0   45.0   \n",
              "std      0.565069    0.007401    5.895256    4.107640    0.0    0.0   \n",
              "min      0.000000    0.500856    1.000000   87.000000    0.0   45.0   \n",
              "25%      1.000000    0.527535    2.000000   93.000000    0.0   45.0   \n",
              "50%      1.000000    0.530436    2.000000   95.000000    0.0   45.0   \n",
              "75%      1.000000    0.533433    2.000000   98.000000    0.0   45.0   \n",
              "max      2.000000    0.578841  103.000000  102.000000    0.0   45.0   \n",
              "\n",
              "              X_5    X_6         X_7         X_8  ...      X_2866     X_2867  \\\n",
              "count  349.000000  349.0  349.000000  349.000000  ...  100.000000  100.00000   \n",
              "mean    10.392550    0.0   48.802292   10.048711  ...   50.807300   53.60770   \n",
              "std      0.489019    0.0    4.373824    0.215571  ...    7.011828    8.13899   \n",
              "min     10.000000    0.0   45.000000   10.000000  ...   32.120000   31.70000   \n",
              "25%     10.000000    0.0   45.000000   10.000000  ...   49.485000   52.20000   \n",
              "50%     10.000000    0.0   45.000000   10.000000  ...   53.425000   55.92500   \n",
              "75%     11.000000    0.0   51.000000   10.000000  ...   55.287500   58.97500   \n",
              "max     11.000000    0.0   62.000000   11.000000  ...   60.240000   68.66000   \n",
              "\n",
              "           X_2868      X_2869     X_2870  X_2871  X_2872  X_2873  X_2874  \\\n",
              "count  100.000000  100.000000  100.00000    99.0     0.0     0.0     0.0   \n",
              "mean    49.606200   51.659800   66.64970     1.0     NaN     NaN     NaN   \n",
              "std      7.158917    8.913065    4.52781     0.0     NaN     NaN     NaN   \n",
              "min     32.560000   30.490000   61.67000     1.0     NaN     NaN     NaN   \n",
              "25%     42.160000   49.915000   63.64500     1.0     NaN     NaN     NaN   \n",
              "50%     51.460000   56.175000   65.14000     1.0     NaN     NaN     NaN   \n",
              "75%     55.030000   57.175000   67.11500     1.0     NaN     NaN     NaN   \n",
              "max     60.410000   59.930000   79.75000     1.0     NaN     NaN     NaN   \n",
              "\n",
              "       X_2875  \n",
              "count     0.0  \n",
              "mean      NaN  \n",
              "std       NaN  \n",
              "min       NaN  \n",
              "25%       NaN  \n",
              "50%       NaN  \n",
              "75%       NaN  \n",
              "max       NaN  \n",
              "\n",
              "[8 rows x 2877 columns]"
            ],
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              "      <th></th>\n",
              "      <th>Y_Class</th>\n",
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              "      <td>0.0</td>\n",
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              "      <td>53.60770</td>\n",
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              "      <td>0.007401</td>\n",
              "      <td>5.895256</td>\n",
              "      <td>4.107640</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.489019</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.373824</td>\n",
              "      <td>0.215571</td>\n",
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              "      <td>7.011828</td>\n",
              "      <td>8.13899</td>\n",
              "      <td>7.158917</td>\n",
              "      <td>8.913065</td>\n",
              "      <td>4.52781</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
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              "      <td>10.000000</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>45.000000</td>\n",
              "      <td>10.000000</td>\n",
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              "      <td>52.20000</td>\n",
              "      <td>42.160000</td>\n",
              "      <td>49.915000</td>\n",
              "      <td>63.64500</td>\n",
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              "      <td>0.0</td>\n",
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              "      <td>55.92500</td>\n",
              "      <td>51.460000</td>\n",
              "      <td>56.175000</td>\n",
              "      <td>65.14000</td>\n",
              "      <td>1.0</td>\n",
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              "      <td>NaN</td>\n",
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              "      <td>45.0</td>\n",
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              "      <td>0.0</td>\n",
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              "      <td>10.000000</td>\n",
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              "      <td>55.287500</td>\n",
              "      <td>58.97500</td>\n",
              "      <td>55.030000</td>\n",
              "      <td>57.175000</td>\n",
              "      <td>67.11500</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>102.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>45.0</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>62.000000</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>60.240000</td>\n",
              "      <td>68.66000</td>\n",
              "      <td>60.410000</td>\n",
              "      <td>59.930000</td>\n",
              "      <td>79.75000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "<p>8 rows × 2877 columns</p>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2628c6dd-20d1-40f9-bfb3-9b465e66369a')\"\n",
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              "\n",
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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
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              "          const element = document.querySelector('#df-2628c6dd-20d1-40f9-bfb3-9b465e66369a');\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",
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              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 1.2 Test Set"
      ],
      "metadata": {
        "id": "gkVr3UvcDtBs"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "test_df"
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          "base_uri": "https://localhost:8080/",
          "height": 661
        },
        "outputId": "7cbfb874-50e1-4395-bcd9-e191ec28bdd0",
        "id": "J_kNKtVZDtQt"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "    PRODUCT_ID         TIMESTAMP     LINE PRODUCT_CODE  X_1   X_2  X_3   X_4  \\\n",
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              "1     TEST_001   2022-09-09 2:09  T100304         T_31  2.0  93.0  0.0  45.0   \n",
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              "4     TEST_004  2022-09-09 11:04  T010306         A_31  NaN   NaN  NaN   NaN   \n",
              "..         ...               ...      ...          ...  ...   ...  ...   ...   \n",
              "305   TEST_305  2022-11-05 11:18  T100306         T_31  2.0  91.0  0.0  45.0   \n",
              "306   TEST_306  2022-11-05 16:39  T100304         T_31  2.0  96.0  0.0  45.0   \n",
              "307   TEST_307  2022-11-05 16:47  T100306         T_31  2.0  91.0  0.0  45.0   \n",
              "308   TEST_308  2022-11-05 20:53  T100306         T_31  2.0  95.0  0.0  45.0   \n",
              "309   TEST_309  2022-11-05 21:01  T100306         T_31  2.0  87.0  0.0  45.0   \n",
              "\n",
              "      X_5  X_6  ...  X_2866  X_2867  X_2868  X_2869  X_2870  X_2871  X_2872  \\\n",
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              "      <th>3</th>\n",
              "      <td>TEST_003</td>\n",
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              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-6be8fd48-72ed-48e1-a576-71a6640c20c3 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-6be8fd48-72ed-48e1-a576-71a6640c20c3');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_x = train_df.drop(columns=['PRODUCT_ID', 'TIMESTAMP','Y_Class','Y_Quality'])\n",
        "train_y = train_df['Y_Class']\n",
        "\n",
        "test_x = test_df.drop(columns=['PRODUCT_ID','TIMESTAMP'])"
      ],
      "metadata": {
        "id": "OUkEVKJ0DtQu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test_x"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "outputId": "6e8ce969-d6c0-488e-a8db-d38165f30ec7",
        "id": "jlq5ilE5DtQu"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        LINE PRODUCT_CODE  X_1   X_2  X_3   X_4   X_5  X_6   X_7   X_8  ...  \\\n",
              "0    T100306         T_31  2.0  94.0  0.0  45.0  10.0  0.0  51.0  10.0  ...   \n",
              "1    T100304         T_31  2.0  93.0  0.0  45.0  11.0  0.0  45.0  10.0  ...   \n",
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              "4    T010306         A_31  NaN   NaN  NaN   NaN   NaN  NaN   NaN   NaN  ...   \n",
              "..       ...          ...  ...   ...  ...   ...   ...  ...   ...   ...  ...   \n",
              "305  T100306         T_31  2.0  91.0  0.0  45.0  10.0  0.0  51.0  10.0  ...   \n",
              "306  T100304         T_31  2.0  96.0  0.0  45.0  11.0  0.0  45.0  10.0  ...   \n",
              "307  T100306         T_31  2.0  91.0  0.0  45.0  10.0  0.0  50.0  10.0  ...   \n",
              "308  T100306         T_31  2.0  95.0  0.0  45.0  10.0  0.0  51.0  10.0  ...   \n",
              "309  T100306         T_31  2.0  87.0  0.0  45.0  10.0  0.0  51.0  10.0  ...   \n",
              "\n",
              "     X_2866  X_2867  X_2868  X_2869  X_2870  X_2871  X_2872  X_2873  X_2874  \\\n",
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              "\n",
              "     X_2875  \n",
              "0       NaN  \n",
              "1       NaN  \n",
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              "3       NaN  \n",
              "4       NaN  \n",
              "..      ...  \n",
              "305     NaN  \n",
              "306     NaN  \n",
              "307     NaN  \n",
              "308     NaN  \n",
              "309     NaN  \n",
              "\n",
              "[310 rows x 2877 columns]"
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              "      <th>3</th>\n",
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              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>308</th>\n",
              "      <td>T100306</td>\n",
              "      <td>T_31</td>\n",
              "      <td>2.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>45.0</td>\n",
              "      <td>10.0</td>\n",
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              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>309</th>\n",
              "      <td>T100306</td>\n",
              "      <td>T_31</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "<p>310 rows × 2877 columns</p>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e5c35c17-a97d-4319-a2b0-b84a819201fd')\"\n",
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              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# train_x = train_x.fillna(0)\n",
        "test_x = test_x.fillna(0)"
      ],
      "metadata": {
        "id": "8J6_u_0-DtQu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# qualitative to quantitative\n",
        "qual_col = ['LINE','PRODUCT_CODE']\n",
        "\n",
        "for i in qual_col:\n",
        "    le = LabelEncoder()\n",
        "    le = le.fit(train_x[i])\n",
        "    train_x[i] = le.transform(train_x[i])\n",
        "\n",
        "    for label in np.unique(test_x[i]):\n",
        "        if label not in le.classes_:\n",
        "            le.classes_ = np.append(le.classes_, label)\n",
        "    test_x[i] = le.transform(test_x[i])\n",
        "print('Done')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pdr3_b5-DR2F",
        "outputId": "73a395c6-2e3a-4405-9d47-75753cbe10a7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Done\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 2.EDA & Pre-processing"
      ],
      "metadata": {
        "id": "f5Kb__mmD7CN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# sorting according to LINE, PRODUCT_CODE \n",
        "print(train_df.LINE.unique())\n",
        "print(train_df.PRODUCT_CODE.unique())\n",
        "\n",
        "train_df.sort_values(['LINE', 'PRODUCT_CODE'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 696
        },
        "id": "AIrKKAETvXqr",
        "outputId": "63479533-9fc3-4d18-e9b1-42b5cc025552"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['T050304' 'T050307' 'T100304' 'T100306' 'T010306' 'T010305']\n",
            "['A_31' 'T_31' 'O_31']\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    PRODUCT_ID  Y_Class  Y_Quality         TIMESTAMP     LINE PRODUCT_CODE  \\\n",
              "28   TRAIN_028        0   0.521249  2022-06-19 20:26  T010305         A_31   \n",
              "33   TRAIN_033        1   0.526656   2022-06-21 7:14  T010305         A_31   \n",
              "39   TRAIN_039        1   0.531583   2022-06-22 4:45  T010305         A_31   \n",
              "40   TRAIN_040        1   0.530829  2022-06-22 12:26  T010305         A_31   \n",
              "41   TRAIN_041        1   0.528270  2022-06-22 12:34  T010305         A_31   \n",
              "..         ...      ...        ...               ...      ...          ...   \n",
              "588  TRAIN_588        1   0.530121  2022-09-06 17:51  T100306         T_31   \n",
              "589  TRAIN_589        1   0.529510  2022-09-06 18:00  T100306         T_31   \n",
              "590  TRAIN_590        1   0.529948   2022-09-07 1:01  T100306         T_31   \n",
              "591  TRAIN_591        1   0.529308   2022-09-07 1:09  T100306         T_31   \n",
              "593  TRAIN_593        1   0.526546  2022-09-08 14:30  T100306         T_31   \n",
              "\n",
              "     X_1   X_2  X_3   X_4  ...  X_2866  X_2867  X_2868  X_2869  X_2870  \\\n",
              "28   NaN   NaN  NaN   NaN  ...     NaN     NaN     NaN     NaN     NaN   \n",
              "33   NaN   NaN  NaN   NaN  ...     NaN     NaN     NaN     NaN     NaN   \n",
              "39   NaN   NaN  NaN   NaN  ...     NaN     NaN     NaN     NaN     NaN   \n",
              "40   NaN   NaN  NaN   NaN  ...     NaN     NaN     NaN     NaN     NaN   \n",
              "41   NaN   NaN  NaN   NaN  ...     NaN     NaN     NaN     NaN     NaN   \n",
              "..   ...   ...  ...   ...  ...     ...     ...     ...     ...     ...   \n",
              "588  1.0  89.0  0.0  45.0  ...     NaN     NaN     NaN     NaN     NaN   \n",
              "589  1.0  94.0  0.0  45.0  ...     NaN     NaN     NaN     NaN     NaN   \n",
              "590  1.0  89.0  0.0  45.0  ...     NaN     NaN     NaN     NaN     NaN   \n",
              "591  1.0  87.0  0.0  45.0  ...     NaN     NaN     NaN     NaN     NaN   \n",
              "593  2.0  95.0  0.0  45.0  ...     NaN     NaN     NaN     NaN     NaN   \n",
              "\n",
              "     X_2871  X_2872  X_2873  X_2874  X_2875  \n",
              "28      NaN     NaN     NaN     NaN     NaN  \n",
              "33      NaN     NaN     NaN     NaN     NaN  \n",
              "39      NaN     NaN     NaN     NaN     NaN  \n",
              "40      NaN     NaN     NaN     NaN     NaN  \n",
              "41      NaN     NaN     NaN     NaN     NaN  \n",
              "..      ...     ...     ...     ...     ...  \n",
              "588     NaN     NaN     NaN     NaN     NaN  \n",
              "589     NaN     NaN     NaN     NaN     NaN  \n",
              "590     NaN     NaN     NaN     NaN     NaN  \n",
              "591     NaN     NaN     NaN     NaN     NaN  \n",
              "593     NaN     NaN     NaN     NaN     NaN  \n",
              "\n",
              "[598 rows x 2881 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-eb2e505d-6ff8-4d44-8144-a9ba07c8293e\">\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",
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              "\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>PRODUCT_ID</th>\n",
              "      <th>Y_Class</th>\n",
              "      <th>Y_Quality</th>\n",
              "      <th>TIMESTAMP</th>\n",
              "      <th>LINE</th>\n",
              "      <th>PRODUCT_CODE</th>\n",
              "      <th>X_1</th>\n",
              "      <th>X_2</th>\n",
              "      <th>X_3</th>\n",
              "      <th>X_4</th>\n",
              "      <th>...</th>\n",
              "      <th>X_2866</th>\n",
              "      <th>X_2867</th>\n",
              "      <th>X_2868</th>\n",
              "      <th>X_2869</th>\n",
              "      <th>X_2870</th>\n",
              "      <th>X_2871</th>\n",
              "      <th>X_2872</th>\n",
              "      <th>X_2873</th>\n",
              "      <th>X_2874</th>\n",
              "      <th>X_2875</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>TRAIN_028</td>\n",
              "      <td>0</td>\n",
              "      <td>0.521249</td>\n",
              "      <td>2022-06-19 20:26</td>\n",
              "      <td>T010305</td>\n",
              "      <td>A_31</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>TRAIN_033</td>\n",
              "      <td>1</td>\n",
              "      <td>0.526656</td>\n",
              "      <td>2022-06-21 7:14</td>\n",
              "      <td>T010305</td>\n",
              "      <td>A_31</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>TRAIN_039</td>\n",
              "      <td>1</td>\n",
              "      <td>0.531583</td>\n",
              "      <td>2022-06-22 4:45</td>\n",
              "      <td>T010305</td>\n",
              "      <td>A_31</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40</th>\n",
              "      <td>TRAIN_040</td>\n",
              "      <td>1</td>\n",
              "      <td>0.530829</td>\n",
              "      <td>2022-06-22 12:26</td>\n",
              "      <td>T010305</td>\n",
              "      <td>A_31</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41</th>\n",
              "      <td>TRAIN_041</td>\n",
              "      <td>1</td>\n",
              "      <td>0.528270</td>\n",
              "      <td>2022-06-22 12:34</td>\n",
              "      <td>T010305</td>\n",
              "      <td>A_31</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</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>588</th>\n",
              "      <td>TRAIN_588</td>\n",
              "      <td>1</td>\n",
              "      <td>0.530121</td>\n",
              "      <td>2022-09-06 17:51</td>\n",
              "      <td>T100306</td>\n",
              "      <td>T_31</td>\n",
              "      <td>1.0</td>\n",
              "      <td>89.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>45.0</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>589</th>\n",
              "      <td>TRAIN_589</td>\n",
              "      <td>1</td>\n",
              "      <td>0.529510</td>\n",
              "      <td>2022-09-06 18:00</td>\n",
              "      <td>T100306</td>\n",
              "      <td>T_31</td>\n",
              "      <td>1.0</td>\n",
              "      <td>94.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>45.0</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>590</th>\n",
              "      <td>TRAIN_590</td>\n",
              "      <td>1</td>\n",
              "      <td>0.529948</td>\n",
              "      <td>2022-09-07 1:01</td>\n",
              "      <td>T100306</td>\n",
              "      <td>T_31</td>\n",
              "      <td>1.0</td>\n",
              "      <td>89.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>45.0</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>591</th>\n",
              "      <td>TRAIN_591</td>\n",
              "      <td>1</td>\n",
              "      <td>0.529308</td>\n",
              "      <td>2022-09-07 1:09</td>\n",
              "      <td>T100306</td>\n",
              "      <td>T_31</td>\n",
              "      <td>1.0</td>\n",
              "      <td>87.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>45.0</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>593</th>\n",
              "      <td>TRAIN_593</td>\n",
              "      <td>1</td>\n",
              "      <td>0.526546</td>\n",
              "      <td>2022-09-08 14:30</td>\n",
              "      <td>T100306</td>\n",
              "      <td>T_31</td>\n",
              "      <td>2.0</td>\n",
              "      <td>95.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>45.0</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>598 rows × 2881 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-eb2e505d-6ff8-4d44-8144-a9ba07c8293e')\"\n",
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              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
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              "      gap: 12px;\n",
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              "\n",
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              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# scatterplot - relationship between\n",
        "fig, ax = plt.subplots(figsize=(5,5))\n",
        "ax.scatter(train_df['PRODUCT_CODE'], train_df['Y_Quality'])\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 321
        },
        "id": "lCZEVc8pMJ3F",
        "outputId": "ce7ea45e-c70c-49bf-d222-1628cefcea95"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "E9UrfEIwMJ7z"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "XVSQ6i2_MKBc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 2.1 Outliers\n",
        "\n",
        "[outlier Dection: IsolationForest](https://scikit-learn.org/stable/auto_examples/miscellaneous/plot_anomaly_comparison.html)"
      ],
      "metadata": {
        "id": "4038bXsY3ngy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "xs = list(train_df.iloc[:, 6:].columns)"
      ],
      "metadata": {
        "id": "Ynke3TMsTBJ_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for col in xs: \n",
        "    q3 = train_df[col].quantile(0.75)\n",
        "    median = train_df[col].quantile(0.50)\n",
        "\n",
        "    train_df[col] = np.where(train_df[col]>q3, median, train_df[col])\n",
        "\n",
        "train_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 661
        },
        "id": "nJuv_OqajZze",
        "outputId": "48ebf4b0-2560-424b-9592-e257fbe281e1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    PRODUCT_ID  Y_Class  Y_Quality         TIMESTAMP     LINE PRODUCT_CODE  \\\n",
              "0    TRAIN_000        1   0.533433   2022-06-13 5:14  T050304         A_31   \n",
              "1    TRAIN_001        2   0.541819   2022-06-13 5:22  T050307         A_31   \n",
              "2    TRAIN_002        1   0.531267   2022-06-13 5:30  T050304         A_31   \n",
              "3    TRAIN_003        2   0.537325   2022-06-13 5:39  T050307         A_31   \n",
              "4    TRAIN_004        1   0.531590   2022-06-13 5:47  T050304         A_31   \n",
              "..         ...      ...        ...               ...      ...          ...   \n",
              "593  TRAIN_593        1   0.526546  2022-09-08 14:30  T100306         T_31   \n",
              "594  TRAIN_594        0   0.524022  2022-09-08 22:38  T050304         A_31   \n",
              "595  TRAIN_595        0   0.521289  2022-09-08 22:47  T050304         A_31   \n",
              "596  TRAIN_596        1   0.531375  2022-09-08 14:38  T100304         O_31   \n",
              "597  TRAIN_597        1   0.533702  2022-09-08 14:46  T100306         O_31   \n",
              "\n",
              "     X_1   X_2  X_3   X_4  ...  X_2866  X_2867  X_2868  X_2869  X_2870  \\\n",
              "0    NaN   NaN  NaN   NaN  ...   39.34   40.89   32.56   34.09   65.14   \n",
              "1    NaN   NaN  NaN   NaN  ...   38.89   42.82   43.92   35.34   72.55   \n",
              "2    NaN   NaN  NaN   NaN  ...   39.19   36.65   42.47   36.53   65.14   \n",
              "3    NaN   NaN  NaN   NaN  ...   37.74   39.17   52.17   30.58   71.78   \n",
              "4    NaN   NaN  NaN   NaN  ...   38.70   41.89   46.93   33.09   76.97   \n",
              "..   ...   ...  ...   ...  ...     ...     ...     ...     ...     ...   \n",
              "593  2.0  95.0  0.0  45.0  ...     NaN     NaN     NaN     NaN     NaN   \n",
              "594  NaN   NaN  NaN   NaN  ...   49.47   53.07   50.89   55.10   66.49   \n",
              "595  NaN   NaN  NaN   NaN  ...     NaN     NaN     NaN     NaN     NaN   \n",
              "596  2.0  94.0  0.0  45.0  ...     NaN     NaN     NaN     NaN     NaN   \n",
              "597  2.0  87.0  0.0  45.0  ...     NaN     NaN     NaN     NaN     NaN   \n",
              "\n",
              "     X_2871  X_2872  X_2873  X_2874  X_2875  \n",
              "0       NaN     NaN     NaN     NaN     NaN  \n",
              "1       NaN     NaN     NaN     NaN     NaN  \n",
              "2       NaN     NaN     NaN     NaN     NaN  \n",
              "3       NaN     NaN     NaN     NaN     NaN  \n",
              "4       NaN     NaN     NaN     NaN     NaN  \n",
              "..      ...     ...     ...     ...     ...  \n",
              "593     NaN     NaN     NaN     NaN     NaN  \n",
              "594     1.0     NaN     NaN     NaN     NaN  \n",
              "595     1.0     NaN     NaN     NaN     NaN  \n",
              "596     NaN     NaN     NaN     NaN     NaN  \n",
              "597     NaN     NaN     NaN     NaN     NaN  \n",
              "\n",
              "[598 rows x 2881 columns]"
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              "<style scoped>\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PRODUCT_ID</th>\n",
              "      <th>Y_Class</th>\n",
              "      <th>Y_Quality</th>\n",
              "      <th>TIMESTAMP</th>\n",
              "      <th>LINE</th>\n",
              "      <th>PRODUCT_CODE</th>\n",
              "      <th>X_1</th>\n",
              "      <th>X_2</th>\n",
              "      <th>X_3</th>\n",
              "      <th>X_4</th>\n",
              "      <th>...</th>\n",
              "      <th>X_2866</th>\n",
              "      <th>X_2867</th>\n",
              "      <th>X_2868</th>\n",
              "      <th>X_2869</th>\n",
              "      <th>X_2870</th>\n",
              "      <th>X_2871</th>\n",
              "      <th>X_2872</th>\n",
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              "      <th>X_2874</th>\n",
              "      <th>X_2875</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>TRAIN_000</td>\n",
              "      <td>1</td>\n",
              "      <td>0.533433</td>\n",
              "      <td>2022-06-13 5:14</td>\n",
              "      <td>T050304</td>\n",
              "      <td>A_31</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>39.34</td>\n",
              "      <td>40.89</td>\n",
              "      <td>32.56</td>\n",
              "      <td>34.09</td>\n",
              "      <td>65.14</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>TRAIN_001</td>\n",
              "      <td>2</td>\n",
              "      <td>0.541819</td>\n",
              "      <td>2022-06-13 5:22</td>\n",
              "      <td>T050307</td>\n",
              "      <td>A_31</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>38.89</td>\n",
              "      <td>42.82</td>\n",
              "      <td>43.92</td>\n",
              "      <td>35.34</td>\n",
              "      <td>72.55</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>TRAIN_002</td>\n",
              "      <td>1</td>\n",
              "      <td>0.531267</td>\n",
              "      <td>2022-06-13 5:30</td>\n",
              "      <td>T050304</td>\n",
              "      <td>A_31</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>39.19</td>\n",
              "      <td>36.65</td>\n",
              "      <td>42.47</td>\n",
              "      <td>36.53</td>\n",
              "      <td>65.14</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>TRAIN_003</td>\n",
              "      <td>2</td>\n",
              "      <td>0.537325</td>\n",
              "      <td>2022-06-13 5:39</td>\n",
              "      <td>T050307</td>\n",
              "      <td>A_31</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>37.74</td>\n",
              "      <td>39.17</td>\n",
              "      <td>52.17</td>\n",
              "      <td>30.58</td>\n",
              "      <td>71.78</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>TRAIN_004</td>\n",
              "      <td>1</td>\n",
              "      <td>0.531590</td>\n",
              "      <td>2022-06-13 5:47</td>\n",
              "      <td>T050304</td>\n",
              "      <td>A_31</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>38.70</td>\n",
              "      <td>41.89</td>\n",
              "      <td>46.93</td>\n",
              "      <td>33.09</td>\n",
              "      <td>76.97</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</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",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>593</th>\n",
              "      <td>TRAIN_593</td>\n",
              "      <td>1</td>\n",
              "      <td>0.526546</td>\n",
              "      <td>2022-09-08 14:30</td>\n",
              "      <td>T100306</td>\n",
              "      <td>T_31</td>\n",
              "      <td>2.0</td>\n",
              "      <td>95.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>45.0</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>594</th>\n",
              "      <td>TRAIN_594</td>\n",
              "      <td>0</td>\n",
              "      <td>0.524022</td>\n",
              "      <td>2022-09-08 22:38</td>\n",
              "      <td>T050304</td>\n",
              "      <td>A_31</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>49.47</td>\n",
              "      <td>53.07</td>\n",
              "      <td>50.89</td>\n",
              "      <td>55.10</td>\n",
              "      <td>66.49</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>595</th>\n",
              "      <td>TRAIN_595</td>\n",
              "      <td>0</td>\n",
              "      <td>0.521289</td>\n",
              "      <td>2022-09-08 22:47</td>\n",
              "      <td>T050304</td>\n",
              "      <td>A_31</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>596</th>\n",
              "      <td>TRAIN_596</td>\n",
              "      <td>1</td>\n",
              "      <td>0.531375</td>\n",
              "      <td>2022-09-08 14:38</td>\n",
              "      <td>T100304</td>\n",
              "      <td>O_31</td>\n",
              "      <td>2.0</td>\n",
              "      <td>94.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>45.0</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>597</th>\n",
              "      <td>TRAIN_597</td>\n",
              "      <td>1</td>\n",
              "      <td>0.533702</td>\n",
              "      <td>2022-09-08 14:46</td>\n",
              "      <td>T100306</td>\n",
              "      <td>O_31</td>\n",
              "      <td>2.0</td>\n",
              "      <td>87.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>45.0</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>598 rows × 2881 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3633fcbc-f535-4666-a903-d86b9fcf431f')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\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",
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              "      fill: #1967D2;\n",
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              "      padding: 0 0 0 0;\n",
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              "\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",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-3633fcbc-f535-4666-a903-d86b9fcf431f 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-3633fcbc-f535-4666-a903-d86b9fcf431f');\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",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_df.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        },
        "id": "fAQulBUxkwNq",
        "outputId": "2a2dbdf2-c614-49fa-da99-f071d972cf69"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          Y_Class   Y_Quality         X_1         X_2    X_3    X_4  \\\n",
              "count  598.000000  598.000000  349.000000  349.000000  349.0  349.0   \n",
              "mean     1.025084    0.530896    1.882521   95.123209    0.0   45.0   \n",
              "std      0.565069    0.007401    0.322452    4.107640    0.0    0.0   \n",
              "min      0.000000    0.500856    1.000000   87.000000    0.0   45.0   \n",
              "25%      1.000000    0.527535    2.000000   93.000000    0.0   45.0   \n",
              "50%      1.000000    0.530436    2.000000   95.000000    0.0   45.0   \n",
              "75%      1.000000    0.533433    2.000000   98.000000    0.0   45.0   \n",
              "max      2.000000    0.578841    2.000000  102.000000    0.0   45.0   \n",
              "\n",
              "              X_5    X_6         X_7    X_8  ...      X_2866      X_2867  \\\n",
              "count  349.000000  349.0  349.000000  349.0  ...  100.000000  100.000000   \n",
              "mean    10.392550    0.0   48.212034   10.0  ...   50.535850   53.173650   \n",
              "std      0.489019    0.0    3.807249    0.0  ...    6.792048    7.742964   \n",
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              "\n",
              "           X_2868      X_2869      X_2870  X_2871  X_2872  X_2873  X_2874  \\\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>7.742964</td>\n",
              "      <td>6.775488</td>\n",
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              "      <td>3.586874</td>\n",
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          "metadata": {},
          "execution_count": 11
        }
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    },
    {
      "cell_type": "markdown",
      "source": [
        "## 2.2 Missing Values"
      ],
      "metadata": {
        "id": "dq-Aw05f97R7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 1) features that have only 'NaN' without any recorded values \n",
        "## Total 81 features only have 'NaN' \n",
        "miss_dic = train_df.isnull().sum().to_dict()\n",
        "all_NaN = pd.DataFrame({i for i in miss_dic if miss_dic[i]==598})\n",
        "all_NaN_name = list(all_NaN[0])  # all_NaN features name in list \n",
        "len(all_NaN_name)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ukw7LLAJ97Lh",
        "outputId": "9d8458da-6782-44fd-a953-353c215a4f80"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "82"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "all_NaN2 = pd.DataFrame(np.zeros((82,82)), columns=list(all_NaN[0]))\n",
        "all_NaN2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "id": "6xGYW6oc4rAt",
        "outputId": "e5fa7f7b-1ab8-49bd-c3ef-95f1989b514f"
      },
      "execution_count": null,
      "outputs": [
        {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>X_2662</th>\n",
              "      <th>X_2678</th>\n",
              "      <th>X_2638</th>\n",
              "      <th>X_2684</th>\n",
              "      <th>X_2683</th>\n",
              "      <th>X_2838</th>\n",
              "      <th>X_2691</th>\n",
              "      <th>X_2648</th>\n",
              "      <th>X_2873</th>\n",
              "      <th>X_2694</th>\n",
              "      <th>...</th>\n",
              "      <th>X_2653</th>\n",
              "      <th>X_2659</th>\n",
              "      <th>X_2629</th>\n",
              "      <th>X_2673</th>\n",
              "      <th>X_2687</th>\n",
              "      <th>X_2635</th>\n",
              "      <th>X_2652</th>\n",
              "      <th>X_2668</th>\n",
              "      <th>X_2647</th>\n",
              "      <th>X_2657</th>\n",
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              "  </thead>\n",
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              "      <td>0.0</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.0</td>\n",
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              "    <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",
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              "    <tr>\n",
              "      <th>77</th>\n",
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              "    <tr>\n",
              "      <th>78</th>\n",
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              "    <tr>\n",
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              "    <tr>\n",
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              "    <tr>\n",
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              "  </tbody>\n",
              "</table>\n",
              "<p>82 rows × 82 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e25edde0-01cf-4f2d-b28c-9176b5b3e034')\"\n",
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            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for i in all_NaN_name: \n",
        "    train_df[i] = train_df[i].apply(lambda x: 0)\n",
        "\n",
        "train_df.X_2686.isnull().sum()  # e.g. check whether or not it's perfectly fill with ZERO "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ARZoEUSBAg48",
        "outputId": "4f1c32e6-ef4f-4c74-852e-52ae91c4d83c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 2) relace missing values with MEDIAN\n",
        "train_df.fillna(train_df.median(), inplace=True)\n",
        "train_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 696
        },
        "id": "3nuVzmdT3sTy",
        "outputId": "6f47d3f2-63c4-46aa-9953-f9e0fe5734e9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-15-af1f9814f8ca>:2: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError.  Select only valid columns before calling the reduction.\n",
            "  train_df.fillna(train_df.median(), inplace=True)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    PRODUCT_ID  Y_Class  Y_Quality         TIMESTAMP     LINE PRODUCT_CODE  \\\n",
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              "1    TRAIN_001        2   0.541819   2022-06-13 5:22  T050307         A_31   \n",
              "2    TRAIN_002        1   0.531267   2022-06-13 5:30  T050304         A_31   \n",
              "3    TRAIN_003        2   0.537325   2022-06-13 5:39  T050307         A_31   \n",
              "4    TRAIN_004        1   0.531590   2022-06-13 5:47  T050304         A_31   \n",
              "..         ...      ...        ...               ...      ...          ...   \n",
              "593  TRAIN_593        1   0.526546  2022-09-08 14:30  T100306         T_31   \n",
              "594  TRAIN_594        0   0.524022  2022-09-08 22:38  T050304         A_31   \n",
              "595  TRAIN_595        0   0.521289  2022-09-08 22:47  T050304         A_31   \n",
              "596  TRAIN_596        1   0.531375  2022-09-08 14:38  T100304         O_31   \n",
              "597  TRAIN_597        1   0.533702  2022-09-08 14:46  T100306         O_31   \n",
              "\n",
              "     X_1   X_2  X_3   X_4  ...   X_2866   X_2867  X_2868   X_2869  X_2870  \\\n",
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              "1    2.0  95.0  0.0  45.0  ...  38.8900  42.8200  43.920  35.3400   72.55   \n",
              "2    2.0  95.0  0.0  45.0  ...  39.1900  36.6500  42.470  36.5300   65.14   \n",
              "3    2.0  95.0  0.0  45.0  ...  37.7400  39.1700  52.170  30.5800   71.78   \n",
              "4    2.0  95.0  0.0  45.0  ...  38.7000  41.8900  46.930  33.0900   76.97   \n",
              "..   ...   ...  ...   ...  ...      ...      ...     ...      ...     ...   \n",
              "593  2.0  95.0  0.0  45.0  ...  53.4125  55.8825  51.445  56.1575   65.14   \n",
              "594  2.0  95.0  0.0  45.0  ...  49.4700  53.0700  50.890  55.1000   66.49   \n",
              "595  2.0  95.0  0.0  45.0  ...  53.4125  55.8825  51.445  56.1575   65.14   \n",
              "596  2.0  94.0  0.0  45.0  ...  53.4125  55.8825  51.445  56.1575   65.14   \n",
              "597  2.0  87.0  0.0  45.0  ...  53.4125  55.8825  51.445  56.1575   65.14   \n",
              "\n",
              "     X_2871  X_2872  X_2873  X_2874  X_2875  \n",
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              "3       1.0       0       0       0       0  \n",
              "4       1.0       0       0       0       0  \n",
              "..      ...     ...     ...     ...     ...  \n",
              "593     1.0       0       0       0       0  \n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PRODUCT_ID</th>\n",
              "      <th>Y_Class</th>\n",
              "      <th>Y_Quality</th>\n",
              "      <th>TIMESTAMP</th>\n",
              "      <th>LINE</th>\n",
              "      <th>PRODUCT_CODE</th>\n",
              "      <th>X_1</th>\n",
              "      <th>X_2</th>\n",
              "      <th>X_3</th>\n",
              "      <th>X_4</th>\n",
              "      <th>...</th>\n",
              "      <th>X_2866</th>\n",
              "      <th>X_2867</th>\n",
              "      <th>X_2868</th>\n",
              "      <th>X_2869</th>\n",
              "      <th>X_2870</th>\n",
              "      <th>X_2871</th>\n",
              "      <th>X_2872</th>\n",
              "      <th>X_2873</th>\n",
              "      <th>X_2874</th>\n",
              "      <th>X_2875</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>TRAIN_000</td>\n",
              "      <td>1</td>\n",
              "      <td>0.533433</td>\n",
              "      <td>2022-06-13 5:14</td>\n",
              "      <td>T050304</td>\n",
              "      <td>A_31</td>\n",
              "      <td>2.0</td>\n",
              "      <td>95.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>45.0</td>\n",
              "      <td>...</td>\n",
              "      <td>39.3400</td>\n",
              "      <td>40.8900</td>\n",
              "      <td>32.560</td>\n",
              "      <td>34.0900</td>\n",
              "      <td>65.14</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>TRAIN_001</td>\n",
              "      <td>2</td>\n",
              "      <td>0.541819</td>\n",
              "      <td>2022-06-13 5:22</td>\n",
              "      <td>T050307</td>\n",
              "      <td>A_31</td>\n",
              "      <td>2.0</td>\n",
              "      <td>95.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>45.0</td>\n",
              "      <td>...</td>\n",
              "      <td>38.8900</td>\n",
              "      <td>42.8200</td>\n",
              "      <td>43.920</td>\n",
              "      <td>35.3400</td>\n",
              "      <td>72.55</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>TRAIN_002</td>\n",
              "      <td>1</td>\n",
              "      <td>0.531267</td>\n",
              "      <td>2022-06-13 5:30</td>\n",
              "      <td>T050304</td>\n",
              "      <td>A_31</td>\n",
              "      <td>2.0</td>\n",
              "      <td>95.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>45.0</td>\n",
              "      <td>...</td>\n",
              "      <td>39.1900</td>\n",
              "      <td>36.6500</td>\n",
              "      <td>42.470</td>\n",
              "      <td>36.5300</td>\n",
              "      <td>65.14</td>\n",
              "      <td>1.0</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>TRAIN_003</td>\n",
              "      <td>2</td>\n",
              "      <td>0.537325</td>\n",
              "      <td>2022-06-13 5:39</td>\n",
              "      <td>T050307</td>\n",
              "      <td>A_31</td>\n",
              "      <td>2.0</td>\n",
              "      <td>95.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>45.0</td>\n",
              "      <td>...</td>\n",
              "      <td>37.7400</td>\n",
              "      <td>39.1700</td>\n",
              "      <td>52.170</td>\n",
              "      <td>30.5800</td>\n",
              "      <td>71.78</td>\n",
              "      <td>1.0</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>TRAIN_004</td>\n",
              "      <td>1</td>\n",
              "      <td>0.531590</td>\n",
              "      <td>2022-06-13 5:47</td>\n",
              "      <td>T050304</td>\n",
              "      <td>A_31</td>\n",
              "      <td>2.0</td>\n",
              "      <td>95.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>45.0</td>\n",
              "      <td>...</td>\n",
              "      <td>38.7000</td>\n",
              "      <td>41.8900</td>\n",
              "      <td>46.930</td>\n",
              "      <td>33.0900</td>\n",
              "      <td>76.97</td>\n",
              "      <td>1.0</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>593</th>\n",
              "      <td>TRAIN_593</td>\n",
              "      <td>1</td>\n",
              "      <td>0.526546</td>\n",
              "      <td>2022-09-08 14:30</td>\n",
              "      <td>T100306</td>\n",
              "      <td>T_31</td>\n",
              "      <td>2.0</td>\n",
              "      <td>95.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>45.0</td>\n",
              "      <td>...</td>\n",
              "      <td>53.4125</td>\n",
              "      <td>55.8825</td>\n",
              "      <td>51.445</td>\n",
              "      <td>56.1575</td>\n",
              "      <td>65.14</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>594</th>\n",
              "      <td>TRAIN_594</td>\n",
              "      <td>0</td>\n",
              "      <td>0.524022</td>\n",
              "      <td>2022-09-08 22:38</td>\n",
              "      <td>T050304</td>\n",
              "      <td>A_31</td>\n",
              "      <td>2.0</td>\n",
              "      <td>95.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>45.0</td>\n",
              "      <td>...</td>\n",
              "      <td>49.4700</td>\n",
              "      <td>53.0700</td>\n",
              "      <td>50.890</td>\n",
              "      <td>55.1000</td>\n",
              "      <td>66.49</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>595</th>\n",
              "      <td>TRAIN_595</td>\n",
              "      <td>0</td>\n",
              "      <td>0.521289</td>\n",
              "      <td>2022-09-08 22:47</td>\n",
              "      <td>T050304</td>\n",
              "      <td>A_31</td>\n",
              "      <td>2.0</td>\n",
              "      <td>95.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>45.0</td>\n",
              "      <td>...</td>\n",
              "      <td>53.4125</td>\n",
              "      <td>55.8825</td>\n",
              "      <td>51.445</td>\n",
              "      <td>56.1575</td>\n",
              "      <td>65.14</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>596</th>\n",
              "      <td>TRAIN_596</td>\n",
              "      <td>1</td>\n",
              "      <td>0.531375</td>\n",
              "      <td>2022-09-08 14:38</td>\n",
              "      <td>T100304</td>\n",
              "      <td>O_31</td>\n",
              "      <td>2.0</td>\n",
              "      <td>94.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>45.0</td>\n",
              "      <td>...</td>\n",
              "      <td>53.4125</td>\n",
              "      <td>55.8825</td>\n",
              "      <td>51.445</td>\n",
              "      <td>56.1575</td>\n",
              "      <td>65.14</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>597</th>\n",
              "      <td>TRAIN_597</td>\n",
              "      <td>1</td>\n",
              "      <td>0.533702</td>\n",
              "      <td>2022-09-08 14:46</td>\n",
              "      <td>T100306</td>\n",
              "      <td>O_31</td>\n",
              "      <td>2.0</td>\n",
              "      <td>87.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>45.0</td>\n",
              "      <td>...</td>\n",
              "      <td>53.4125</td>\n",
              "      <td>55.8825</td>\n",
              "      <td>51.445</td>\n",
              "      <td>56.1575</td>\n",
              "      <td>65.14</td>\n",
              "      <td>1.0</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>598 rows × 2881 columns</p>\n",
              "</div>\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
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              "          if (!dataTable) return;\n",
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              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "            + ' to learn more about interactive tables.';\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 3) check is there still any missing value in train_df\n",
        "miss = train_df.isnull().sum().to_dict()\n",
        "print({i for i in miss if miss[i] > 0})"
      ],
      "metadata": {
        "id": "TpfRqPNmvX46",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5b55b203-e303-48f9-ae4a-c12c19532905"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "set()\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_df.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        },
        "id": "v8eaNAPmvYRM",
        "outputId": "f58a8edf-ae80-4d35-c177-1fb83f4f791d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          Y_Class   Y_Quality         X_1         X_2    X_3    X_4  \\\n",
              "count  598.000000  598.000000  598.000000  598.000000  598.0  598.0   \n",
              "mean     1.025084    0.530896    1.931438   95.071906    0.0   45.0   \n",
              "std      0.565069    0.007401    0.252919    3.136725    0.0    0.0   \n",
              "min      0.000000    0.500856    1.000000   87.000000    0.0   45.0   \n",
              "25%      1.000000    0.527535    2.000000   94.000000    0.0   45.0   \n",
              "50%      1.000000    0.530436    2.000000   95.000000    0.0   45.0   \n",
              "75%      1.000000    0.533433    2.000000   95.000000    0.0   45.0   \n",
              "max      2.000000    0.578841    2.000000  102.000000    0.0   45.0   \n",
              "\n",
              "              X_5    X_6         X_7    X_8  ...      X_2866      X_2867  \\\n",
              "count  598.000000  598.0  598.000000  598.0  ...  598.000000  598.000000   \n",
              "mean    10.229097    0.0   46.874582   10.0  ...   52.931455   55.429515   \n",
              "std      0.420604    0.0    3.310709    0.0  ...    2.967211    3.311438   \n",
              "min     10.000000    0.0   45.000000   10.0  ...   32.120000   31.700000   \n",
              "25%     10.000000    0.0   45.000000   10.0  ...   53.412500   55.882500   \n",
              "50%     10.000000    0.0   45.000000   10.0  ...   53.412500   55.882500   \n",
              "75%     10.000000    0.0   50.000000   10.0  ...   53.412500   55.882500   \n",
              "max     11.000000    0.0   57.000000   10.0  ...   57.940000   61.840000   \n",
              "\n",
              "           X_2868      X_2869      X_2870  X_2871  X_2872  X_2873  X_2874  \\\n",
              "count  598.000000  598.000000  598.000000   598.0   598.0   598.0   598.0   \n",
              "mean    51.067090   55.379849   65.279532     1.0     0.0     0.0     0.0   \n",
              "std      2.885337    3.984516    1.493526     0.0     0.0     0.0     0.0   \n",
              "min     32.560000   30.490000   61.670000     1.0     0.0     0.0     0.0   \n",
              "25%     51.445000   56.157500   65.140000     1.0     0.0     0.0     0.0   \n",
              "50%     51.445000   56.157500   65.140000     1.0     0.0     0.0     0.0   \n",
              "75%     51.445000   56.157500   65.140000     1.0     0.0     0.0     0.0   \n",
              "max     59.610000   58.790000   76.970000     1.0     0.0     0.0     0.0   \n",
              "\n",
              "       X_2875  \n",
              "count   598.0  \n",
              "mean      0.0  \n",
              "std       0.0  \n",
              "min       0.0  \n",
              "25%       0.0  \n",
              "50%       0.0  \n",
              "75%       0.0  \n",
              "max       0.0  \n",
              "\n",
              "[8 rows x 2877 columns]"
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              "      <th>Y_Class</th>\n",
              "      <th>Y_Quality</th>\n",
              "      <th>X_1</th>\n",
              "      <th>X_2</th>\n",
              "      <th>X_3</th>\n",
              "      <th>X_4</th>\n",
              "      <th>X_5</th>\n",
              "      <th>X_6</th>\n",
              "      <th>X_7</th>\n",
              "      <th>X_8</th>\n",
              "      <th>...</th>\n",
              "      <th>X_2866</th>\n",
              "      <th>X_2867</th>\n",
              "      <th>X_2868</th>\n",
              "      <th>X_2869</th>\n",
              "      <th>X_2870</th>\n",
              "      <th>X_2871</th>\n",
              "      <th>X_2872</th>\n",
              "      <th>X_2873</th>\n",
              "      <th>X_2874</th>\n",
              "      <th>X_2875</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>598.000000</td>\n",
              "      <td>598.000000</td>\n",
              "      <td>598.000000</td>\n",
              "      <td>598.000000</td>\n",
              "      <td>598.0</td>\n",
              "      <td>598.0</td>\n",
              "      <td>598.000000</td>\n",
              "      <td>598.0</td>\n",
              "      <td>598.000000</td>\n",
              "      <td>598.0</td>\n",
              "      <td>...</td>\n",
              "      <td>598.000000</td>\n",
              "      <td>598.000000</td>\n",
              "      <td>598.000000</td>\n",
              "      <td>598.000000</td>\n",
              "      <td>598.000000</td>\n",
              "      <td>598.0</td>\n",
              "      <td>598.0</td>\n",
              "      <td>598.0</td>\n",
              "      <td>598.0</td>\n",
              "      <td>598.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>1.025084</td>\n",
              "      <td>0.530896</td>\n",
              "      <td>1.931438</td>\n",
              "      <td>95.071906</td>\n",
              "      <td>0.0</td>\n",
              "      <td>45.0</td>\n",
              "      <td>10.229097</td>\n",
              "      <td>0.0</td>\n",
              "      <td>46.874582</td>\n",
              "      <td>10.0</td>\n",
              "      <td>...</td>\n",
              "      <td>52.931455</td>\n",
              "      <td>55.429515</td>\n",
              "      <td>51.067090</td>\n",
              "      <td>55.379849</td>\n",
              "      <td>65.279532</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.565069</td>\n",
              "      <td>0.007401</td>\n",
              "      <td>0.252919</td>\n",
              "      <td>3.136725</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.420604</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.310709</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>2.967211</td>\n",
              "      <td>3.311438</td>\n",
              "      <td>2.885337</td>\n",
              "      <td>3.984516</td>\n",
              "      <td>1.493526</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.500856</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>87.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>45.0</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>45.000000</td>\n",
              "      <td>10.0</td>\n",
              "      <td>...</td>\n",
              "      <td>32.120000</td>\n",
              "      <td>31.700000</td>\n",
              "      <td>32.560000</td>\n",
              "      <td>30.490000</td>\n",
              "      <td>61.670000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.527535</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>94.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>45.0</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>45.000000</td>\n",
              "      <td>10.0</td>\n",
              "      <td>...</td>\n",
              "      <td>53.412500</td>\n",
              "      <td>55.882500</td>\n",
              "      <td>51.445000</td>\n",
              "      <td>56.157500</td>\n",
              "      <td>65.140000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.530436</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>95.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>45.0</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>45.000000</td>\n",
              "      <td>10.0</td>\n",
              "      <td>...</td>\n",
              "      <td>53.412500</td>\n",
              "      <td>55.882500</td>\n",
              "      <td>51.445000</td>\n",
              "      <td>56.157500</td>\n",
              "      <td>65.140000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.533433</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>95.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>45.0</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>50.000000</td>\n",
              "      <td>10.0</td>\n",
              "      <td>...</td>\n",
              "      <td>53.412500</td>\n",
              "      <td>55.882500</td>\n",
              "      <td>51.445000</td>\n",
              "      <td>56.157500</td>\n",
              "      <td>65.140000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.578841</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>102.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>45.0</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>57.000000</td>\n",
              "      <td>10.0</td>\n",
              "      <td>...</td>\n",
              "      <td>57.940000</td>\n",
              "      <td>61.840000</td>\n",
              "      <td>59.610000</td>\n",
              "      <td>58.790000</td>\n",
              "      <td>76.970000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>8 rows × 2877 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0b14caca-7652-4696-b28b-bdd442326816')\"\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",
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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",
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              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-0b14caca-7652-4696-b28b-bdd442326816 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-0b14caca-7652-4696-b28b-bdd442326816');\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",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 3.Modeling"
      ],
      "metadata": {
        "id": "7GodRp6O-5TM"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 3.1 Random Forest"
      ],
      "metadata": {
        "id": "CTdljE99teLX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# RF = RandomForestClassifier(random_state=37).fit(train_x, train_y)\n",
        "# print('Done')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0hMK7g6jtn09",
        "outputId": "5f507cc7-8538-4451-8f36-bbf4a8f4bba3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Done\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "train_features, val_features, train_labels, val_labels = train_test_split(train_x, train_y,\n",
        "                                                                          test_size=0.25,\n",
        "                                                                          random_state=37)"
      ],
      "metadata": {
        "id": "SZaitsWzAy0m"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_labels"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "R2iJP2EYCuLo",
        "outputId": "a070a2f7-79a8-439e-d011-b89f88824d85"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "240    2\n",
              "185    1\n",
              "315    2\n",
              "410    0\n",
              "396    1\n",
              "      ..\n",
              "451    1\n",
              "22     0\n",
              "437    1\n",
              "251    1\n",
              "363    0\n",
              "Name: Y_Class, Length: 448, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model = RandomForestClassifier()"
      ],
      "metadata": {
        "id": "RDzDLlMEEcQK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "fitted = model.fit(train_features, train_labels)"
      ],
      "metadata": {
        "id": "zOitf3hTCWb7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(fitted.score(train_features, train_labels))\n",
        "print(fitted.score(val_features, val_labels))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NusO0ggVBZ1i",
        "outputId": "445479a8-21e1-4a52-ecca-26c59414d75a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1.0\n",
            "0.7733333333333333\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.datasets import make_classification\n",
        "\n",
        "# X, y = make_classification(n_samples=1000, n_features=4,\n",
        "#                            n_informative=2, n_redundant=0,\n",
        "#                            random_state=37, shuffle=False)\n",
        "\n",
        "# clf = RandomForestClassifier(max_depth=2, random_state=37)\n",
        "# clf.fit(X,y)\n",
        "# print(clf.predict([[0,0,0,0]]))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XT-ZYSwF8xJI",
        "outputId": "318a4405-d3f5-4594-b762-8db81b429164"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Inference"
      ],
      "metadata": {
        "id": "ex7768-Ktt2u"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "preds = RF.predict(test_x)\n",
        "print('Done.')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sUcI9qjRtyU2",
        "outputId": "26bf2d2b-7cb5-4687-8349-cb31a619435c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Done.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "preds"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IYsJ59R8DvKQ",
        "outputId": "26464750-6f57-4934-b40c-15bb20cc69c9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0,\n",
              "       0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1,\n",
              "       0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0,\n",
              "       0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0,\n",
              "       1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1])"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Submit"
      ],
      "metadata": {
        "id": "zZCIp_9ct2bQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# submit = pd.read_csv('/content/drive/MyDrive/sample_submission.csv')\n",
        "\n",
        "# submit['Y_Class'] = preds\n",
        "# submit.to_csv('/content/drive/MyDrive/baseline_submission.csv', index=False)"
      ],
      "metadata": {
        "id": "Cyfb21qOt4dn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "hcNFz5zguHMQ"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}