smartFactory_simple_Yumin.ipynb

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      "name": "python3",
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      "name": "python"
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    "accelerator": "GPU",
    "gpuClass": "standard"
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  "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": [
        "# Data Description\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"
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    {
      "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": "markdown",
      "source": [
        "# MODULE IMPORT"
      ],
      "metadata": {
        "id": "7c9x-uVynqst"
      }
    },
    {
      "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\n",
        "\n",
        "from sklearn.model_selection import train_test_split \n",
        "from sklearn.metrics import accuracy_score, classification_report, f1_score\n",
        "\n",
        "import tensorflow as tf \n",
        "\n",
        "# warning message delete \n",
        "import warnings\n",
        "warnings.simplefilter(action=\"ignore\", category=FutureWarning)\n",
        "\n",
        "# google drive mount\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')\n",
        "\n",
        "def seed_everything(seed):\n",
        "    random.seed(seed)\n",
        "    os.environ['PYTHONHASHSEED'] = str(seed)\n",
        "    np.random.seed(seed)\n",
        "seed_everything(37)"
      ],
      "metadata": {
        "id": "N524BAv7ohSr",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fc98df45-53ed-46d5-e58b-5338208c2395"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 0.Data Load"
      ],
      "metadata": {
        "id": "RYf_ZaTJ9vgg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train_df = pd.read_csv('/content/drive/MyDrive/train.csv')\n",
        "train_df = train_df.drop(columns=['PRODUCT_ID', 'TIMESTAMP'])"
      ],
      "metadata": {
        "id": "kLBoulM6u2Fb"
      },
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test_df = pd.read_csv('/content/drive/MyDrive/test.csv')\n",
        "test_df = test_df.drop(columns=['PRODUCT_ID','TIMESTAMP'])"
      ],
      "metadata": {
        "id": "vOExaTnZCvnk"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 1.Preprocessing"
      ],
      "metadata": {
        "id": "tZ1zuCbwEzul"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 1.2 Missing Values "
      ],
      "metadata": {
        "id": "uzodBFNdwtQX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train_df.fillna(0, inplace=True)\n",
        "test_df.fillna(0, inplace=True)"
      ],
      "metadata": {
        "id": "DfBC_b8LE1wD"
      },
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_df.loc[train_df['Y_Class']==0]  # 88 rows \n",
        "train_df.loc[train_df['Y_Class']==1]  # 407 rows \n",
        "train_df.loc[train_df['Y_Class']==2]  # 103 rows "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "id": "OgpZHxOeFaug",
        "outputId": "37f1738d-6a61-4723-e678-1a46f37f32ef"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Y_Class  Y_Quality     LINE PRODUCT_CODE  X_1   X_2  X_3   X_4   X_5  \\\n",
              "1          2   0.541819  T050307         A_31  0.0   0.0  0.0   0.0   0.0   \n",
              "3          2   0.537325  T050307         A_31  0.0   0.0  0.0   0.0   0.0   \n",
              "5          2   0.537833  T050307         A_31  0.0   0.0  0.0   0.0   0.0   \n",
              "7          2   0.540003  T050307         A_31  0.0   0.0  0.0   0.0   0.0   \n",
              "9          2   0.538049  T050307         A_31  0.0   0.0  0.0   0.0   0.0   \n",
              "..       ...        ...      ...          ...  ...   ...  ...   ...   ...   \n",
              "563        2   0.537465  T050304         A_31  0.0   0.0  0.0   0.0   0.0   \n",
              "564        2   0.540106  T050304         A_31  0.0   0.0  0.0   0.0   0.0   \n",
              "570        2   0.534951  T100306         O_31  6.0  90.0  0.0  45.0  10.0   \n",
              "572        2   0.535205  T100306         O_31  6.0  89.0  0.0  45.0  10.0   \n",
              "578        2   0.535905  T100306         T_31  2.0  91.0  0.0  45.0  10.0   \n",
              "\n",
              "     X_6  ...  X_2866  X_2867  X_2868  X_2869  X_2870  X_2871  X_2872  X_2873  \\\n",
              "1    0.0  ...   38.89   42.82   43.92   35.34   72.55     0.0     0.0     0.0   \n",
              "3    0.0  ...   37.74   39.17   52.17   30.58   71.78     0.0     0.0     0.0   \n",
              "5    0.0  ...   38.16   51.25   56.01   37.41   71.37     0.0     0.0     0.0   \n",
              "7    0.0  ...   39.00   35.95   59.51   30.49   72.77     0.0     0.0     0.0   \n",
              "9    0.0  ...   34.40   35.88   50.36   38.45   71.42     0.0     0.0     0.0   \n",
              "..   ...  ...     ...     ...     ...     ...     ...     ...     ...     ...   \n",
              "563  0.0  ...   49.92   54.66   50.56   48.63   62.17     1.0     0.0     0.0   \n",
              "564  0.0  ...   57.94   55.84   53.24   56.72   64.17     1.0     0.0     0.0   \n",
              "570  0.0  ...    0.00    0.00    0.00    0.00    0.00     0.0     0.0     0.0   \n",
              "572  0.0  ...    0.00    0.00    0.00    0.00    0.00     0.0     0.0     0.0   \n",
              "578  0.0  ...    0.00    0.00    0.00    0.00    0.00     0.0     0.0     0.0   \n",
              "\n",
              "     X_2874  X_2875  \n",
              "1       0.0     0.0  \n",
              "3       0.0     0.0  \n",
              "5       0.0     0.0  \n",
              "7       0.0     0.0  \n",
              "9       0.0     0.0  \n",
              "..      ...     ...  \n",
              "563     0.0     0.0  \n",
              "564     0.0     0.0  \n",
              "570     0.0     0.0  \n",
              "572     0.0     0.0  \n",
              "578     0.0     0.0  \n",
              "\n",
              "[103 rows x 2879 columns]"
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Y_Class</th>\n",
              "      <th>Y_Quality</th>\n",
              "      <th>LINE</th>\n",
              "      <th>PRODUCT_CODE</th>\n",
              "      <th>X_1</th>\n",
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              "      <th>X_2873</th>\n",
              "      <th>X_2874</th>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>0.541819</td>\n",
              "      <td>T050307</td>\n",
              "      <td>A_31</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>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>0.0</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2</td>\n",
              "      <td>0.537325</td>\n",
              "      <td>T050307</td>\n",
              "      <td>A_31</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <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>0.0</td>\n",
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              "      <th>5</th>\n",
              "      <td>2</td>\n",
              "      <td>0.537833</td>\n",
              "      <td>T050307</td>\n",
              "      <td>A_31</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",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>38.16</td>\n",
              "      <td>51.25</td>\n",
              "      <td>56.01</td>\n",
              "      <td>37.41</td>\n",
              "      <td>71.37</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>2</td>\n",
              "      <td>0.540003</td>\n",
              "      <td>T050307</td>\n",
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              "      <td>39.00</td>\n",
              "      <td>35.95</td>\n",
              "      <td>59.51</td>\n",
              "      <td>30.49</td>\n",
              "      <td>72.77</td>\n",
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              "      <th>9</th>\n",
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              "      <td>49.92</td>\n",
              "      <td>54.66</td>\n",
              "      <td>50.56</td>\n",
              "      <td>48.63</td>\n",
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              "      <td>1.0</td>\n",
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              "    <tr>\n",
              "      <th>564</th>\n",
              "      <td>2</td>\n",
              "      <td>0.540106</td>\n",
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              "    <tr>\n",
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              "  </tbody>\n",
              "</table>\n",
              "<p>103 rows × 2879 columns</p>\n",
              "</div>\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for i in range(2): \n",
        "    train_df = train_df.append(train_df.loc[train_df['Y_Class']==0])\n",
        "train_df.loc[train_df['Y_Class']==0]  # 352 rows"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "id": "t7Anq7AQvuCK",
        "outputId": "7f48bb08-3241-4f84-f45f-8ba20afb272f"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "     Y_Class  Y_Quality     LINE PRODUCT_CODE  X_1    X_2  X_3   X_4   X_5  \\\n",
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              "<p>352 rows × 2879 columns</p>\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for i in range(2): \n",
        "    train_df = train_df.append(train_df.loc[train_df['Y_Class']==2])\n",
        "train_df.loc[train_df['Y_Class']==2]  # 412 rows"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "id": "FvSHkJc7vKiZ",
        "outputId": "74d8bc03-afef-4259-80e5-62a83fe0eef8"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "     Y_Class  Y_Quality     LINE PRODUCT_CODE  X_1   X_2  X_3   X_4   X_5  \\\n",
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          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_df.loc[train_df['Y_Class']==0]  # 352 rows \n",
        "train_df.loc[train_df['Y_Class']==1]  # 407 rows \n",
        "train_df.loc[train_df['Y_Class']==2]  # 412 rows "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "id": "xRXa0EM6wO3W",
        "outputId": "7bca8080-72f6-4866-c26e-73b53219a1bb"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</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>594</th>\n",
              "      <td>0</td>\n",
              "      <td>0.524022</td>\n",
              "      <td>T050304</td>\n",
              "      <td>A_31</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",
              "      <td>0.0</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>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>595</th>\n",
              "      <td>0</td>\n",
              "      <td>0.521289</td>\n",
              "      <td>T050304</td>\n",
              "      <td>A_31</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",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</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>352 rows × 2879 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-02d32ea5-2cf3-437a-b557-751a1e23d381')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-02d32ea5-2cf3-437a-b557-751a1e23d381 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-02d32ea5-2cf3-437a-b557-751a1e23d381');\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": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.countplot(x='Y_Class', data=train_df);"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 280
        },
        "id": "2lqJXokkwex_",
        "outputId": "e54a0171-a264-4942-bea2-f350f3dde055"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 1.3 Encoding\n"
      ],
      "metadata": {
        "id": "qaRnhsuCwyj3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# # Label Encoding \n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "encoder = LabelEncoder()\n",
        "col = ['LINE', 'PRODUCT_CODE']\n",
        "\n",
        "for i in col:\n",
        "    train_df[i] = encoder.fit_transform(train_df[i])\n",
        "    test_df[i] = encoder.fit_transform(test_df[i])\n",
        "\n",
        "# # correlation\n",
        "# # print(train_x[['Y_Quality', 'line_pcode']].corr()) "
      ],
      "metadata": {
        "id": "wWsCTcAI7gsq"
      },
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "8eF-67lGE16e"
      },
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "wJDyjUcxE1-2"
      },
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 2.Modeling_ML"
      ],
      "metadata": {
        "id": "7GodRp6O-5TM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X_train = train_df.drop(columns=['Y_Class','Y_Quality'])\n",
        "y_train = train_df['Y_Quality']\n",
        "\n",
        "test_x = test_df"
      ],
      "metadata": {
        "id": "mtzzkA9n_flg"
      },
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Normalization\n",
        "# from sklearn.preprocessing import MinMaxScaler   \n",
        "# scaler = MinMaxScaler()\n",
        "# X_train = pd.DataFrame(scaler.fit_transform(X_train))\n",
        "# test_x = pd.DataFrame(scaler.fit_transform(test_x))\n",
        "# X_train.describe()"
      ],
      "metadata": {
        "id": "DXfFYWAT2C7L"
      },
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test_x.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "70a8a308-0de7-4c2b-d15f-6d55f0c88d13",
        "id": "TTEMi25a_fli"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LINE            0\n",
              "PRODUCT_CODE    0\n",
              "X_1             0\n",
              "X_2             0\n",
              "X_3             0\n",
              "               ..\n",
              "X_2871          0\n",
              "X_2872          0\n",
              "X_2873          0\n",
              "X_2874          0\n",
              "X_2875          0\n",
              "Length: 2877, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Y_Quality 예측"
      ],
      "metadata": {
        "id": "Aveyf4DbuLHD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import GradientBoostingRegressor\n",
        "\n",
        "GBR_model = GradientBoostingRegressor(random_state=37).fit(X_train, y_train) \n",
        "print('Done.') "
      ],
      "metadata": {
        "id": "U7pCY5HPsv4Q",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f8bee1a4-e507-4f84-f8ef-4cf85668171f"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Done.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "preds=GBR_model.predict(test_x)  \n",
        "print('Done.')"
      ],
      "metadata": {
        "id": "N08krIFjsv-M",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2f645e41-34d1-4c00-e760-9d6dc6d8b6a5"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Done.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_x['Y_Quality'] = preds\n",
        "test_x  "
      ],
      "metadata": {
        "id": "ykSx7MEFswDT",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "outputId": "e681ee5f-a0db-4dab-9798-33fa8a08c99f"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "\n",
              "     Y_Quality  \n",
              "0     0.528390  \n",
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              "4     0.532744  \n",
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              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-f858a2c1-0d17-4051-9178-06a350833f03 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-f858a2c1-0d17-4051-9178-06a350833f03');\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": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.countplot(x=preds);"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "bP9e3szwGvDU",
        "outputId": "a6923e16-bd07-4719-f137-ac18e8d7b190"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 다시 Y_Class 예측"
      ],
      "metadata": {
        "id": "UgNEMkg7uGNe"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# X_train = train_df\n",
        "X_train = train_df.drop(columns=['Y_Class'])\n",
        "X_train = pd.DataFrame(train_df['Y_Quality'])\n",
        "y_train = train_df['Y_Class']"
      ],
      "metadata": {
        "id": "XyiKQJUeuHOu"
      },
      "execution_count": 50,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WV4E6K-JyShA",
        "outputId": "16011fd5-4ea5-41c4-f149-79ab42fcd38c"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Y_Quality    0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import GradientBoostingClassifier\n",
        "\n",
        "GBC_model =  GradientBoostingClassifier(random_state=37).fit(X_train, y_train)\n",
        "print('Done.')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ov8uVwu2uHfF",
        "outputId": "7c3f1fdf-5608-4b22-ffc4-d3ef59ff09a7"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Done.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_x\n",
        "# test_x = test_x.drop(columns=['LINE//','PRODUCT_CODE'])\n",
        "# test_x = test_x['Y_Quality']  \n",
        "test_x = pd.DataFrame(test_x, columns=['Y_Quality'])\n",
        "# test_x = test_x['Y_Quality']"
      ],
      "metadata": {
        "id": "b4_T3w6sv3SA"
      },
      "execution_count": 53,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test_x.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UNOiP_f83OEB",
        "outputId": "5b313281-6409-451f-965d-40d1234631d2"
      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Y_Quality    0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "preds = GBC_model.predict(test_x)  \n",
        "print('Done.')   "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mncvR7m1wE-3",
        "outputId": "b2628fca-a363-45f7-99e3-d322ff8857b8"
      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Done.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "preds"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GVailz2p8_VT",
        "outputId": "d434d3b8-5738-40eb-9769-792b56be0d9e"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 1, 1, 0, 1, 1, 1, 1, 0, 2, 1, 1, 2, 1, 0, 0, 1, 1, 1, 1, 0, 1,\n",
              "       0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 2, 1, 1, 0, 0, 1,\n",
              "       1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0,\n",
              "       0, 1, 1, 1, 1, 0, 1, 2, 1, 2, 1, 1, 2, 1, 2, 1, 2, 2, 2, 1, 0, 1,\n",
              "       0, 1, 1, 1, 2, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 1, 1, 1, 2, 1, 2, 1,\n",
              "       1, 1, 1, 1, 2, 2, 1, 1, 1, 2, 1, 1, 2, 2, 1, 1, 2, 1, 1, 1, 1, 1,\n",
              "       1, 1, 2, 2, 1, 2, 2, 1, 1, 0, 1, 1, 2, 2, 1, 2, 1, 1, 1, 2, 1, 1,\n",
              "       1, 1, 1, 1, 2, 1, 2, 2, 1, 2, 1, 1, 1, 1, 1, 2, 1, 1, 2, 1, 1, 1,\n",
              "       2, 2, 1, 2, 2, 1, 1, 2, 1, 1, 1, 2, 1, 2, 1, 2, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 2, 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, 0, 0, 0, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 2,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 1, 2, 1, 1,\n",
              "       1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1,\n",
              "       1, 1])"
            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.countplot(x=preds);"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "5ajeXCsP9Awa",
        "outputId": "d9cdf2fc-bf64-4178-d9b8-18a9332f89a5"
      },
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAD4CAYAAAAD6PrjAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAMLElEQVR4nO3df6jd9X3H8efLH93YaqmS2yxNst1SQiH7pd3FyhzDVbaqsMWVThRqs86R/qGlwhi4/bF2BaEw2tG6TcjQmoxOl2KdGcg2CWWy0l83IjYmcw2dzoRobrVUt7KOuPf+uN98PNibePLje77n5jwfcDjf8znfc+5bjvrkfM+vVBWSJAGcN/QAkqTpYRQkSY1RkCQ1RkGS1BgFSVJzwdADnIk1a9bU/Pz80GNI0qqyd+/e71bV3ErXreoozM/Ps7i4OPQYkrSqJHn2RNd5+EiS1BgFSVJjFCRJjVGQJDVGQZLUGAVJUmMUJEmNUZAkNUZBktSs6k80a3b85yd/fugRznk//SffGnoETQGfKUiSGqMgSWqMgiSpMQqSpMYoSJIaoyBJaoyCJKkxCpKkxihIkhqjIElqjIIkqTEKkqSmtygk2Zjky0n2J3kqyce69UuSPJrk2935xd16knwuycEkTyZ5d1+zSZJW1uczhWPAH1TVZuAK4NYkm4E7gD1VtQnY010GuBbY1J22AXf3OJskaQW9RaGqjlTV4932K8ABYD2wBdjR7bYDuL7b3gLsrGVfA96aZF1f80mSftREXlNIMg9cBnwdWFtVR7qrngfWdtvrgedGbnaoW3v9fW1LsphkcWlpqbeZJWkW9R6FJG8GHgRur6qXR6+rqgLqVO6vqrZX1UJVLczNzZ3FSSVJvUYhyYUsB+ELVfWlbvmF44eFuvOj3fphYOPIzTd0a5KkCenz3UcB7gEOVNVnRq7aDWzttrcCD4+sf6h7F9IVwPdHDjNJkiagz99ovhK4GfhWkie6tT8GPgXsSnIL8CxwQ3fdI8B1wEHgB8CHe5xNkrSC3qJQVf8K5ARXX73C/gXc2tc8kqQ35ieaJUmNUZAkNUZBktQYBUlSYxQkSY1RkCQ1RkGS1BgFSVJjFCRJjVGQJDVGQZLUGAVJUmMUJEmNUZAkNUZBktQYBUlSYxQkSY1RkCQ1RkGS1BgFSVJjFCRJjVGQJDVGQZLUGAVJUmMUJEmNUZAkNUZBktQYBUlSYxQkSY1RkCQ1RkGS1BgFSVJjFCRJjVGQJDVGQZLUGAVJUmMUJEmNUZAkNb1FIcm9SY4m2Tey9okkh5M80Z2uG7nuj5IcTPJ0kvf1NZck6cT6fKZwH3DNCut/XlWXdqdHAJJsBm4Efra7zV8lOb/H2SRJK+gtClX1GPDSmLtvAR6oqh9W1X8AB4HL+5pNkrSyIV5TuC3Jk93hpYu7tfXAcyP7HOrWJEkTNOko3A28E7gUOAJ8+lTvIMm2JItJFpeWls72fJI00yYahap6oaperar/A/6a1w4RHQY2juy6oVtb6T62V9VCVS3Mzc31O7AkzZiJRiHJupGLvw0cf2fSbuDGJD+W5B3AJuAbk5xNkgQX9HXHSe4HrgLWJDkEfBy4KsmlQAHPAB8BqKqnkuwC9gPHgFur6tW+ZpMkray3KFTVTSss33OS/e8E7uxrHknSG/MTzZKkxihIkhqjIElqjIIkqTEKkqTGKEiSGqMgSWqMgiSpMQqSpMYoSJIaoyBJaoyCJKkxCpKkxihIkhqjIElqjIIkqTEKkqTGKEiSmrGikGTPOGuSpNXtpL/RnOTHgZ8A1iS5GEh31VuA9T3PJkmasJNGAfgIcDvwdmAvr0XhZeAvepxLkjSAk0ahqj4LfDbJR6vqrgnNJEkayBs9UwCgqu5K8svA/OhtqmpnT3NJkgYwVhSS/A3wTuAJ4NVuuQCjIEnnkLGiACwAm6uq+hxGkjSscT+nsA/4qT4HkSQNb9xnCmuA/Um+Afzw+GJV/VYvU0mSBjFuFD7R5xCSpOkw7ruP/qXvQSRJwxv33UevsPxuI4A3ARcC/11Vb+lrMEnS5I37TOGi49tJAmwBruhrKEnSME75W1Jr2d8D7+thHknSgMY9fPT+kYvnsfy5hf/pZSJJ0mDGfffRb45sHwOeYfkQkiTpHDLuawof7nsQSdLwxv2RnQ1JHkpytDs9mGRD38NJkiZr3BeaPw/sZvl3Fd4O/EO3Jkk6h4wbhbmq+nxVHetO9wFzPc4lSRrAuFF4MckHk5zfnT4IvNjnYJKkyRs3Cr8H3AA8DxwBPgD87slukOTe7vWHfSNrlyR5NMm3u/OLu/Uk+VySg0meTPLu0/qnkSSdkXGj8Elga1XNVdXbWI7En77Bbe4Drnnd2h3AnqraBOzpLgNcC2zqTtuAu8ecS5J0Fo0bhV+oqu8dv1BVLwGXnewGVfUY8NLrlrcAO7rtHcD1I+s7u09Lfw14a5J1Y84mSTpLxo3CeccP9cDyYSDG/+DbqLVVdaTbfh5Y222vB54b2e9Qt/YjkmxLsphkcWlp6TRGkCSdyLj/Y/808NUkX+wu/w5w55n84aqqJKf8855VtR3YDrCwsODPg0rSWTTuJ5p3JlkE3tstvb+q9p/G33shybqqOtIdHjrarR8GNo7st6FbkyRN0NiHgLoInE4IRu0GtgKf6s4fHlm/LckDwHuA748cZpIkTcjpvC4wliT3A1cBa5IcAj7Ocgx2JbkFeJblt7kCPAJcBxwEfgD4XUuSNIDeolBVN53gqqtX2LeAW/uaRZI0nlP+kR1J0rnLKEiSGqMgSWqMgiSpMQqSpMYoSJIaoyBJaoyCJKkxCpKkxihIkhqjIElqjIIkqTEKkqTGKEiSGqMgSWqMgiSpMQqSpMYoSJIaoyBJaoyCJKkxCpKkxihIkhqjIElqjIIkqTEKkqTGKEiSGqMgSWqMgiSpMQqSpMYoSJIaoyBJaoyCJKkxCpKkxihIkhqjIElqjIIkqTEKkqTGKEiSGqMgSWouGOKPJnkGeAV4FThWVQtJLgH+DpgHngFuqKrvDTGfpLPryruuHHqEc95XPvqVs3I/Qz5T+LWqurSqFrrLdwB7qmoTsKe7LEmaoGk6fLQF2NFt7wCuH3AWSZpJQ0WhgH9OsjfJtm5tbVUd6bafB9audMMk25IsJllcWlqaxKySNDMGeU0B+JWqOpzkbcCjSf5t9MqqqiS10g2rajuwHWBhYWHFfSRJp2eQZwpVdbg7Pwo8BFwOvJBkHUB3fnSI2SRplk08Ckl+MslFx7eB3wD2AbuBrd1uW4GHJz2bJM26IQ4frQUeSnL87/9tVf1jkm8Cu5LcAjwL3DDAbJI00yYehar6DvCLK6y/CFw96XkkSa+ZprekSpIGZhQkSY1RkCQ1RkGS1Az14bWJ+6U/3Dn0CDNh7599aOgRJJ0BnylIkhqjIElqjIIkqTEKkqTGKEiSGqMgSWqMgiSpMQqSpMYoSJIaoyBJaoyCJKkxCpKkxihIkhqjIElqjIIkqTEKkqTGKEiSGqMgSWqMgiSpMQqSpMYoSJIaoyBJaoyCJKkxCpKkxihIkhqjIElqjIIkqTEKkqTGKEiSGqMgSWqMgiSpMQqSpMYoSJIaoyBJaqYuCkmuSfJ0koNJ7hh6HkmaJVMVhSTnA38JXAtsBm5KsnnYqSRpdkxVFIDLgYNV9Z2q+l/gAWDLwDNJ0sxIVQ09Q5PkA8A1VfX73eWbgfdU1W0j+2wDtnUX3wU8PfFBJ2cN8N2hh9Bp8/Fbvc71x+5nqmpupSsumPQkZ6qqtgPbh55jEpIsVtXC0HPo9Pj4rV6z/NhN2+Gjw8DGkcsbujVJ0gRMWxS+CWxK8o4kbwJuBHYPPJMkzYypOnxUVceS3Ab8E3A+cG9VPTXwWEOaicNk5zAfv9VrZh+7qXqhWZI0rGk7fCRJGpBRkCQ1RmEK+VUfq1uSe5McTbJv6Fl0apJsTPLlJPuTPJXkY0PPNGm+pjBluq/6+Hfg14FDLL8j66aq2j/oYBpbkl8F/gvYWVU/N/Q8Gl+SdcC6qno8yUXAXuD6Wfrvz2cK08ev+ljlquox4KWh59Cpq6ojVfV4t/0KcABYP+xUk2UUps964LmRy4eYsX8ppWmQZB64DPj6sJNMllGQpNdJ8mbgQeD2qnp56HkmyShMH7/qQxpQkgtZDsIXqupLQ88zaUZh+vhVH9JAkgS4BzhQVZ8Zep4hGIUpU1XHgONf9XEA2DXjX/Wx6iS5H/gq8K4kh5LcMvRMGtuVwM3Ae5M80Z2uG3qoSfItqZKkxmcKkqTGKEiSGqMgSWqMgiSpMQqSpMYoSJIaoyBJav4fiQXFgPeB824AAAAASUVORK5CYII=\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "submit = pd.read_csv('/content/drive/MyDrive/sample_submission.csv')  \n",
        "\n",
        "submit['Y_Class'] = preds \n",
        "submit.to_csv('smartFactory_submission.csv', index=False)  "
      ],
      "metadata": {
        "id": "zAzbAiHOj1CW"
      },
      "execution_count": 69,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "tFejWB5ktGP3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "OekK8FUNtGVy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "N9UhOSaxtGbk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Wpo8mC2UtGwp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 2.2 LGBM"
      ],
      "metadata": {
        "id": "tDwqd38J9J_o"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# X_train = train_df\n",
        "X_train = train_df.drop(columns=['Y_Class', 'Y_Quality'])\n",
        "y_train = train_df[ 'Y_Quality']\n",
        "\n",
        "X_train\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X_train, y_train, \n",
        "                                                    test_size=0.2,\n",
        "                                                    random_state=2023)\n"
      ],
      "metadata": {
        "id": "KFN1bugBdlhh"
      },
      "execution_count": 90,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# hyperparameter - sensitive to setting\n",
        "## ==> setting is recommended.\n",
        "from lightgbm import LGBMRegressor\n",
        "\n",
        "LGBM_model = LGBMRegressor()\n",
        "LGBM_model.fit(X_train, y_train) \n",
        "\n",
        "LGBM_predict = tf.cast(LGBM_model.predict(X_test) > 0.5, \n",
        "                       dtype = tf.float32)\n",
        "\n",
        "print('Accuracy = ', accuracy_score(LGBM_predict, y_test))\n",
        "print('Test Report = \\n', classification_report(LGBM_predict, y_test))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "zkR1yc5gb9DT",
        "outputId": "e6dc4d89-eebd-4bc0-e89a-4361598f0bb1"
      },
      "execution_count": 93,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-93-1709eabcc725>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      9\u001b[0m                        dtype = tf.float32)\n\u001b[1;32m     10\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 11\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Accuracy = '\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maccuracy_score\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mLGBM_predict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     12\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Test Report = \\n'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mclassification_report\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mLGBM_predict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/sklearn/metrics/_classification.py\u001b[0m in \u001b[0;36maccuracy_score\u001b[0;34m(y_true, y_pred, normalize, sample_weight)\u001b[0m\n\u001b[1;32m    209\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    210\u001b[0m     \u001b[0;31m# Compute accuracy for each possible representation\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 211\u001b[0;31m     \u001b[0my_type\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_check_targets\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    212\u001b[0m     \u001b[0mcheck_consistent_length\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    213\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0my_type\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstartswith\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"multilabel\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/sklearn/metrics/_classification.py\u001b[0m in \u001b[0;36m_check_targets\u001b[0;34m(y_true, y_pred)\u001b[0m\n\u001b[1;32m     91\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     92\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_type\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 93\u001b[0;31m         raise ValueError(\n\u001b[0m\u001b[1;32m     94\u001b[0m             \"Classification metrics can't handle a mix of {0} and {1} targets\".format(\n\u001b[1;32m     95\u001b[0m                 \u001b[0mtype_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtype_pred\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: Classification metrics can't handle a mix of binary and continuous targets"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_x"
      ],
      "metadata": {
        "id": "NYVZCLOYhc8R"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# test_x = np.array(test_df, dtype=np.float32)\n",
        "selected_model = LGBM_model \n",
        "\n",
        "preds = selected_model.predict(X_test)  \n",
        "preds"
      ],
      "metadata": {
        "id": "KoaYYU4cW-BK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sns.countplot(x=preds);"
      ],
      "metadata": {
        "id": "tnCYxu30eM6R"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "gWwKvwMiizn4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# X_train = train_df\n",
        "X_train = train_df.drop(columns=['Y_Class'])\n",
        "y_train = train_df['Y_Class']"
      ],
      "metadata": {
        "id": "LwGlb3aMi3QC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train = pd.DataFrame(train_df['Y_Quality'])\n",
        "y_train = train_df['Y_Class']   "
      ],
      "metadata": {
        "id": "lB4kcG3ti3QC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# hyperparameter - sensitive to setting\n",
        "## ==> setting is recommended.\n",
        "from lightgbm import LGBMClassifier\n",
        "\n",
        "LGBM_model = LGBMClassifier()\n",
        "LGBM_model.fit(X_train, y_train) \n",
        "\n",
        "LGBM_predict = tf.cast(LGBM_model.predict(X_test) > 0.5, \n",
        "                       dtype = tf.float32)\n",
        "\n",
        "print('Accuracy = ', accuracy_score(LGBM_predict, y_test))\n",
        "print('Test Report = \\n', classification_report(LGBM_predict, y_test))"
      ],
      "metadata": {
        "id": "Wj20sRCfiwi6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "5DJpEAaaity-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 2.3 XGBoost"
      ],
      "metadata": {
        "id": "WOGzYAlUfOsh"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ePm6VW7CfO65"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# hyperparameter setting isn't needed. \n",
        "from xgboost import XGBClassifier\n",
        "\n",
        "Xgboost_model = XGBClassifier()\n",
        "Xgboost_model.fit(X_train, y_train, eval_metric='aucpr')\n",
        "\n",
        "Xg_predict = tf.cast(Xgboost_model.predict(X_test) > 0.5, \n",
        "                     dtype = tf.float32)\n",
        "\n",
        "print('Accuracy = ', accuracy_score(Xg_predict, y_test))\n",
        "print('Test Report = \\n', classification_report(Xg_predict, y_test))"
      ],
      "metadata": {
        "id": "Zr2sMkCVbHVC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "aXeh36DbfO_c"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# test_x = np.array(test_df, dtype=np.float32)\n",
        "selected_model = Xgboost_model \n",
        "\n",
        "preds = test_x['Y_Quality']  \n",
        "preds"
      ],
      "metadata": {
        "id": "dW2Sratzfbc-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sns.countplot(x=preds);"
      ],
      "metadata": {
        "id": "xF15aG3LfbdA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "vX3cInMfguQY"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}