smartFactory_preprocessing_Yumin (3).ipynb

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      "name": "python"
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    "accelerator": "GPU",
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  "cells": [
    {
      "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"
      }
    },
    {
      "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",
        "import tensorflow as tf\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",
        "# 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",
        "\n",
        "seed_everything(37)"
      ],
      "metadata": {
        "id": "N524BAv7ohSr",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "89dd6468-27c2-4d01-a15b-de3f836e73b3"
      },
      "execution_count": 425,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\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": 522,
      "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": 523,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 1.Pre-processing\n",
        "1. Missing Values:\n",
        "    - All NaN column: `fillna(0)`\n",
        "    - fill grouped mean \n",
        "    - fill feature(column)'s mean "
      ],
      "metadata": {
        "id": "f5Kb__mmD7CN"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 1.1 EDA\n",
        "1. `Y_Quality` and `X features`: \n",
        "    - NOT high correlation, yet  "
      ],
      "metadata": {
        "id": "iw0f-7TDF9wW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# random 20 features list \n",
        "Xs = train_df.iloc[:,4:].columns\n",
        "null_count = train_df.isnull().sum().to_dict()\n",
        "cols = pd.DataFrame({i for i in null_count if null_count[i]<250 and i in Xs})\n",
        "cols_name = list(cols[0])  # not_all_NAN features name in list \n",
        "\n",
        "import random\n",
        "a = ['Y_Quality']\n",
        "for i in range(20):\n",
        "    num = random.randint(1,2875)\n",
        "    a.append(cols_name[i])\n",
        "\n",
        "# heatmap: `random X features` and `Y_Quality` correlation \n",
        "# corr = train_df[a].corr()  # correlation matrix\n",
        "# fig, ax = plt.subplots(figsize=(15,10))\n",
        "# sns.heatmap(corr, annot=True, fmt='.2f', cmap='Blues', linewidths=2);"
      ],
      "metadata": {
        "id": "E1vfXTKcK9Dt"
      },
      "execution_count": 524,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 1.2 Merge & Encoding\n",
        "1. Merge `LINE` & `PRODUCT_CODE` & `Y_Class`columns by JOIN function\n",
        "    - set the joined column name as `line_pcode_class`\n",
        "2. Label encoding \n",
        "    - encode qualitative into quantitative"
      ],
      "metadata": {
        "id": "E9UrfEIwMJ7z"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# train_df['Y_Class'] = train_df['Y_Class'].astype(str)\n",
        "\n",
        "col = ['LINE', 'PRODUCT_CODE']\n",
        "train_df['line_pcode'] = train_df[col].apply(lambda row: ':'.join(row.values), axis=1)\n",
        "train_df = train_df.drop(columns=['LINE', 'PRODUCT_CODE'])  \n",
        "\n",
        "print(train_df['line_pcode'].value_counts())  "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GqgXqn8c12dV",
        "outputId": "b929d7c2-a2fa-459d-dd49-79495ad7e05a"
      },
      "execution_count": 525,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "T100304:T_31    172\n",
            "T100306:T_31    171\n",
            "T050304:A_31     78\n",
            "T010306:A_31     70\n",
            "T010305:A_31     59\n",
            "T050307:A_31     42\n",
            "T100304:O_31      3\n",
            "T100306:O_31      3\n",
            "Name: line_pcode, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Label Encoding \n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "qual_col = ['line_pcode']\n",
        "\n",
        "for i in qual_col:\n",
        "    le = LabelEncoder()\n",
        "    le = le.fit(train_df[i])\n",
        "    train_df[i] = le.transform(train_df[i])\n",
        "    \n",
        "print('Done.')\n"
      ],
      "metadata": {
        "id": "wWsCTcAI7gsq",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fc18edb9-aeef-408f-e226-67723c3a2fe6"
      },
      "execution_count": 526,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Done.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_df.line_pcode.unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Z7Mcr_nz3NPa",
        "outputId": "49dda129-4975-47d0-efaa-68e58bdb2a25"
      },
      "execution_count": 527,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([2, 3, 5, 7, 1, 0, 4, 6])"
            ]
          },
          "metadata": {},
          "execution_count": 527
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# line_pcode & Y_Class  \n",
        "# sns.countplot(train_df, col='Y_Class', hue='line_pcode');\n",
        "\n",
        "# f = sns.FacetGrid(train_x, col=\"Y_Class\")  \n",
        "# f.map_dataframe(sns.histplot, x=\"line_pcode\", binwidth=0.9);    "
      ],
      "metadata": {
        "id": "BpxzjBdCvxdd"
      },
      "execution_count": 528,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 1.3 Missing Values\n",
        "- [fill out NaN with Column's && Group's mean](https://rfriend.tistory.com/402) "
      ],
      "metadata": {
        "id": "qfIq2KqeFZdt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# ONLY 0 \n",
        "\n",
        "# train_df.fillna(0, inplace=True)\n",
        "# test_df.fillna(0, inplace=True)"
      ],
      "metadata": {
        "id": "DfBC_b8LE1wD"
      },
      "execution_count": 529,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 1) features that have only 'NaN' \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",
        "print(len(all_NaN_name))  "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8e62abc2-3b05-45e7-8a25-4d09ee0ad82a",
        "id": "tmQ4DYKrFZdu"
      },
      "execution_count": 530,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "82\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "all_NaN2 = pd.DataFrame(np.zeros((82,82)), columns=list(all_NaN[0]))\n",
        "\n",
        "for i in all_NaN_name: \n",
        "    train_df[i] = train_df[i].apply(lambda x: 0)\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/"
        },
        "outputId": "a84d0d09-d8ba-4a48-b63b-072cd49b2687",
        "id": "aE5B1BfPFZdu"
      },
      "execution_count": 531,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 531
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_df.isnull().sum().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Seh70wI5oyz3",
        "outputId": "f095be86-fe07-450f-8bcb-64d2ff049b28"
      },
      "execution_count": 532,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1123798"
            ]
          },
          "metadata": {},
          "execution_count": 532
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 2) fill NaN with the feature's `line_pcde` grouped mean \n",
        "fill_group_mean = lambda g: g.fillna(g.mean())\n",
        "train_df.groupby('line_pcode').apply(fill_group_mean)\n",
        "train_df.isnull().sum().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5C_CETzulQ2E",
        "outputId": "9fece46a-6184-47e9-e4d9-7dc69c9800d1"
      },
      "execution_count": 533,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1123798"
            ]
          },
          "metadata": {},
          "execution_count": 533
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 3) fillout remaining NaN with the feature's MEAN \n",
        "cols = train_df.iloc[:,2:].columns\n",
        "for i in cols: \n",
        "    train_df[i].fillna(train_df[i].mean(), inplace=True)\n",
        "train_df.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "HzrSWDpYFzU8",
        "outputId": "ef7aa98f-30ed-4276-f7fb-d6228091ba9e"
      },
      "execution_count": 534,
      "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    2.409742   95.123209    0.0   45.0   \n",
              "std      0.565069    0.007401    4.500960    3.136136    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   95.123209    0.0   45.0   \n",
              "75%      1.000000    0.533433    2.409742   95.123209    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_2867      X_2868  \\\n",
              "count  598.00000  598.0  598.000000  598.000000  ...  598.00000  598.000000   \n",
              "mean    10.39255    0.0   48.802292   10.048711  ...   53.60770   49.606200   \n",
              "std      0.37336    0.0    3.339364    0.164586  ...    3.31437    2.915263   \n",
              "min     10.00000    0.0   45.000000   10.000000  ...   31.70000   32.560000   \n",
              "25%     10.00000    0.0   45.000000   10.000000  ...   53.60770   49.606200   \n",
              "50%     10.39255    0.0   48.802292   10.000000  ...   53.60770   49.606200   \n",
              "75%     10.39255    0.0   50.000000   10.048711  ...   53.60770   49.606200   \n",
              "max     11.00000    0.0   62.000000   11.000000  ...   68.66000   60.410000   \n",
              "\n",
              "          X_2869     X_2870  X_2871  X_2872  X_2873  X_2874  X_2875  \\\n",
              "count  598.00000  598.00000   598.0   598.0   598.0   598.0   598.0   \n",
              "mean    51.65980   66.64970     1.0     0.0     0.0     0.0     0.0   \n",
              "std      3.62959    1.84382     0.0     0.0     0.0     0.0     0.0   \n",
              "min     30.49000   61.67000     1.0     0.0     0.0     0.0     0.0   \n",
              "25%     51.65980   66.64970     1.0     0.0     0.0     0.0     0.0   \n",
              "50%     51.65980   66.64970     1.0     0.0     0.0     0.0     0.0   \n",
              "75%     51.65980   66.64970     1.0     0.0     0.0     0.0     0.0   \n",
              "max     59.93000   79.75000     1.0     0.0     0.0     0.0     0.0   \n",
              "\n",
              "       line_pcode  \n",
              "count  598.000000  \n",
              "mean     4.078595  \n",
              "std      2.471718  \n",
              "min      0.000000  \n",
              "25%      2.000000  \n",
              "50%      5.000000  \n",
              "75%      7.000000  \n",
              "max      7.000000  \n",
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              "[8 rows x 2878 columns]"
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              "      <th></th>\n",
              "      <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_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",
              "      <th>line_pcode</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
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              "      <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",
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              "      <td>598.0</td>\n",
              "      <td>598.000000</td>\n",
              "      <td>598.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>598.00000</td>\n",
              "      <td>598.000000</td>\n",
              "      <td>598.00000</td>\n",
              "      <td>598.00000</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",
              "      <td>598.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>1.025084</td>\n",
              "      <td>0.530896</td>\n",
              "      <td>2.409742</td>\n",
              "      <td>95.123209</td>\n",
              "      <td>0.0</td>\n",
              "      <td>45.0</td>\n",
              "      <td>10.39255</td>\n",
              "      <td>0.0</td>\n",
              "      <td>48.802292</td>\n",
              "      <td>10.048711</td>\n",
              "      <td>...</td>\n",
              "      <td>53.60770</td>\n",
              "      <td>49.606200</td>\n",
              "      <td>51.65980</td>\n",
              "      <td>66.64970</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",
              "      <td>4.078595</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.565069</td>\n",
              "      <td>0.007401</td>\n",
              "      <td>4.500960</td>\n",
              "      <td>3.136136</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.37336</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.339364</td>\n",
              "      <td>0.164586</td>\n",
              "      <td>...</td>\n",
              "      <td>3.31437</td>\n",
              "      <td>2.915263</td>\n",
              "      <td>3.62959</td>\n",
              "      <td>1.84382</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>2.471718</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>10.00000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>45.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>31.70000</td>\n",
              "      <td>32.560000</td>\n",
              "      <td>30.49000</td>\n",
              "      <td>61.67000</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",
              "      <td>0.000000</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>10.00000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>45.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>53.60770</td>\n",
              "      <td>49.606200</td>\n",
              "      <td>51.65980</td>\n",
              "      <td>66.64970</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",
              "      <td>2.000000</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>95.123209</td>\n",
              "      <td>0.0</td>\n",
              "      <td>45.0</td>\n",
              "      <td>10.39255</td>\n",
              "      <td>0.0</td>\n",
              "      <td>48.802292</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>53.60770</td>\n",
              "      <td>49.606200</td>\n",
              "      <td>51.65980</td>\n",
              "      <td>66.64970</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",
              "      <td>5.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.533433</td>\n",
              "      <td>2.409742</td>\n",
              "      <td>95.123209</td>\n",
              "      <td>0.0</td>\n",
              "      <td>45.0</td>\n",
              "      <td>10.39255</td>\n",
              "      <td>0.0</td>\n",
              "      <td>50.000000</td>\n",
              "      <td>10.048711</td>\n",
              "      <td>...</td>\n",
              "      <td>53.60770</td>\n",
              "      <td>49.606200</td>\n",
              "      <td>51.65980</td>\n",
              "      <td>66.64970</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",
              "      <td>7.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.578841</td>\n",
              "      <td>103.000000</td>\n",
              "      <td>102.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>45.0</td>\n",
              "      <td>11.00000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>62.000000</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>68.66000</td>\n",
              "      <td>60.410000</td>\n",
              "      <td>59.93000</td>\n",
              "      <td>79.75000</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",
              "      <td>7.000000</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "<p>8 rows × 2878 columns</p>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 534
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 4) check the remaining NULL \n",
        "miss_dic = train_df.isnull().sum().to_dict()\n",
        "check = {i for i in miss_dic if miss_dic[i]>0}\n",
        "check  # all_NaN features name in list   "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YthctslpG5cd",
        "outputId": "355e0ce8-5c17-4aa7-9555-ce4aa0674956"
      },
      "execution_count": 535,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "set()"
            ]
          },
          "metadata": {},
          "execution_count": 535
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "9PAmM2lyzU0f"
      },
      "execution_count": 535,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 1.5 Outlier \n",
        "\n",
        "[outlier Dection: IsolationForest](https://scikit-learn.org/stable/auto_examples/miscellaneous/plot_anomaly_comparison.html)\n"
      ],
      "metadata": {
        "id": "4038bXsY3ngy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# outlier (replace 1Q, 3Q with MEDIAN)\n",
        "# xs = list(train_df.iloc[:, 6:].columns)\n",
        "# def outlier(df):\n",
        "#     for col in xs: \n",
        "#         q1 = train_df[col].quantile(0.25)\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]<q1, median, train_df[col])\n",
        "#         train_df[col] = np.where(train_df[col]>q3, median, train_df[col])\n",
        "\n",
        "# train_df "
      ],
      "metadata": {
        "id": "nJuv_OqajZze"
      },
      "execution_count": 536,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 1-1. Test_df Preprocessing"
      ],
      "metadata": {
        "id": "AQf-saI4BQyu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Merge \n",
        "col = ['LINE', 'PRODUCT_CODE']\n",
        "test_df['line_pcode'] = test_df[col].apply(lambda row: ':'.join(row.values), axis=1)\n",
        "test_df = test_df.drop(columns=['LINE', 'PRODUCT_CODE'])\n",
        "\n",
        "# Label Encoding \n",
        "for i in qual_col:\n",
        "    for label in np.unique(test_df[i]): \n",
        "        if label not in le.classes_: \n",
        "            le.classes_ = np.append(le.classes_, label)\n",
        "    test_df[i] = le.transform(test_df[i])  # CAUTION: DO NOT fit test_df (only fit train_df) \n",
        "print('Done.')"
      ],
      "metadata": {
        "id": "nT3nx4o7B085",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "754dbf48-93fd-4c2c-9da0-19b2b10691bb"
      },
      "execution_count": 537,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Done.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Missing Values\n",
        "# 1) features that have only 'NaN' without any recorded values \n",
        "## Total 506 features only have 'NaN' \n",
        "miss_dic = test_df.isnull().sum().to_dict()\n",
        "all_nan = pd.DataFrame({i for i in miss_dic if miss_dic[i]==310})\n",
        "all_nan_name = list(all_nan[0])  # all_NaN features name in list \n",
        "\n",
        "for i in all_nan_name: \n",
        "    test_df[i].fillna(0, inplace=True)\n",
        "test_df.X_2277.isnull().sum()  # e.g. check whether or not it's perfectly fill with ZERO "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a6fa78c7-398b-414f-ac53-12e0b52b9a95",
        "id": "9vLY-L_IBQy5"
      },
      "execution_count": 538,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 538
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(len(test_df['line_pcode'].unique()))\n",
        "print(test_df.isnull().sum().sum())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_7VrI1t3C5tK",
        "outputId": "a93d7976-4ce3-4ff9-8b0e-2dcf55d1a296"
      },
      "execution_count": 539,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "8\n",
            "491347\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 2) fill NaN with the feature's `line_pcde` grouped mean \n",
        "fill_group_mean = lambda g: g.fillna(g.mean())\n",
        "test_df.groupby('line_pcode').apply(fill_group_mean)\n",
        "test_df.isnull().sum().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6808cdb3-7a0b-448e-c281-1580ce21e086",
        "id": "se7WLEdQrokm"
      },
      "execution_count": 540,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "491347"
            ]
          },
          "metadata": {},
          "execution_count": 540
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 3) fillout remaining NaN with the feature's MEAN \n",
        "cols = test_df.iloc[:,:].columns\n",
        "for i in cols: \n",
        "    test_df[i].fillna(test_df[i].mean(), inplace=True)\n",
        "test_df.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "5512b2c0-5123-425a-d079-c52c3446f4ae",
        "id": "pNDoPbW5rokn"
      },
      "execution_count": 541,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              X_1         X_2    X_3    X_4         X_5    X_6         X_7  \\\n",
              "count  310.000000  310.000000  310.0  310.0  310.000000  310.0  310.000000   \n",
              "mean     4.337449   94.448560    0.0   45.0   10.271605    0.0   48.485597   \n",
              "std     14.711941    3.595263    0.0    0.0    0.394436    0.0    3.303768   \n",
              "min      1.000000   87.000000    0.0   45.0   10.000000    0.0   45.000000   \n",
              "25%      2.000000   93.000000    0.0   45.0   10.000000    0.0   45.000000   \n",
              "50%      2.000000   94.448560    0.0   45.0   10.000000    0.0   48.485597   \n",
              "75%      2.000000   96.000000    0.0   45.0   10.271605    0.0   50.000000   \n",
              "max    154.000000  102.000000    0.0   45.0   11.000000    0.0   67.000000   \n",
              "\n",
              "              X_8         X_9   X_10  ...      X_2867      X_2868      X_2869  \\\n",
              "count  310.000000  310.000000  310.0  ...  310.000000  310.000000  310.000000   \n",
              "mean    10.012346   42.407407    2.0  ...   53.014706   49.994412   54.202353   \n",
              "std      0.097923    9.276526    0.0  ...    1.448128    1.998771    1.117486   \n",
              "min     10.000000   31.000000    2.0  ...   41.630000   39.850000   47.820000   \n",
              "25%     10.000000   31.000000    2.0  ...   53.014706   49.994412   54.202353   \n",
              "50%     10.000000   42.407407    2.0  ...   53.014706   49.994412   54.202353   \n",
              "75%     10.000000   52.000000    2.0  ...   53.014706   49.994412   54.202353   \n",
              "max     11.000000   52.000000    2.0  ...   60.490000   60.310000   58.290000   \n",
              "\n",
              "           X_2870      X_2871      X_2872      X_2873      X_2874      X_2875  \\\n",
              "count  310.000000  310.000000  310.000000  310.000000  310.000000  310.000000   \n",
              "mean    63.806176    0.974359  247.988889  251.888889  243.300000  228.844444   \n",
              "std      0.628799    0.056154    4.097781    3.230763    5.715889    3.139910   \n",
              "min     60.720000    0.000000  199.000000  214.600000  169.700000  191.900000   \n",
              "25%     63.806176    0.974359  247.988889  251.888889  243.300000  228.844444   \n",
              "50%     63.806176    0.974359  247.988889  251.888889  243.300000  228.844444   \n",
              "75%     63.806176    0.974359  247.988889  251.888889  243.300000  228.844444   \n",
              "max     67.240000    1.000000  268.000000  278.600000  271.400000  245.600000   \n",
              "\n",
              "       line_pcode  \n",
              "count  310.000000  \n",
              "mean     5.138710  \n",
              "std      2.064522  \n",
              "min      0.000000  \n",
              "25%      5.000000  \n",
              "50%      5.000000  \n",
              "75%      7.000000  \n",
              "max      7.000000  \n",
              "\n",
              "[8 rows x 2876 columns]"
            ],
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              "      <th></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>X_9</th>\n",
              "      <th>X_10</th>\n",
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              "      <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",
              "      <th>line_pcode</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>310.000000</td>\n",
              "      <td>310.000000</td>\n",
              "      <td>310.0</td>\n",
              "      <td>310.0</td>\n",
              "      <td>310.000000</td>\n",
              "      <td>310.0</td>\n",
              "      <td>310.000000</td>\n",
              "      <td>310.000000</td>\n",
              "      <td>310.000000</td>\n",
              "      <td>310.0</td>\n",
              "      <td>...</td>\n",
              "      <td>310.000000</td>\n",
              "      <td>310.000000</td>\n",
              "      <td>310.000000</td>\n",
              "      <td>310.000000</td>\n",
              "      <td>310.000000</td>\n",
              "      <td>310.000000</td>\n",
              "      <td>310.000000</td>\n",
              "      <td>310.000000</td>\n",
              "      <td>310.000000</td>\n",
              "      <td>310.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>4.337449</td>\n",
              "      <td>94.448560</td>\n",
              "      <td>0.0</td>\n",
              "      <td>45.0</td>\n",
              "      <td>10.271605</td>\n",
              "      <td>0.0</td>\n",
              "      <td>48.485597</td>\n",
              "      <td>10.012346</td>\n",
              "      <td>42.407407</td>\n",
              "      <td>2.0</td>\n",
              "      <td>...</td>\n",
              "      <td>53.014706</td>\n",
              "      <td>49.994412</td>\n",
              "      <td>54.202353</td>\n",
              "      <td>63.806176</td>\n",
              "      <td>0.974359</td>\n",
              "      <td>247.988889</td>\n",
              "      <td>251.888889</td>\n",
              "      <td>243.300000</td>\n",
              "      <td>228.844444</td>\n",
              "      <td>5.138710</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>14.711941</td>\n",
              "      <td>3.595263</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.394436</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.303768</td>\n",
              "      <td>0.097923</td>\n",
              "      <td>9.276526</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>1.448128</td>\n",
              "      <td>1.998771</td>\n",
              "      <td>1.117486</td>\n",
              "      <td>0.628799</td>\n",
              "      <td>0.056154</td>\n",
              "      <td>4.097781</td>\n",
              "      <td>3.230763</td>\n",
              "      <td>5.715889</td>\n",
              "      <td>3.139910</td>\n",
              "      <td>2.064522</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\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>10.000000</td>\n",
              "      <td>31.000000</td>\n",
              "      <td>2.0</td>\n",
              "      <td>...</td>\n",
              "      <td>41.630000</td>\n",
              "      <td>39.850000</td>\n",
              "      <td>47.820000</td>\n",
              "      <td>60.720000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>199.000000</td>\n",
              "      <td>214.600000</td>\n",
              "      <td>169.700000</td>\n",
              "      <td>191.900000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>2.000000</td>\n",
              "      <td>93.000000</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>10.000000</td>\n",
              "      <td>31.000000</td>\n",
              "      <td>2.0</td>\n",
              "      <td>...</td>\n",
              "      <td>53.014706</td>\n",
              "      <td>49.994412</td>\n",
              "      <td>54.202353</td>\n",
              "      <td>63.806176</td>\n",
              "      <td>0.974359</td>\n",
              "      <td>247.988889</td>\n",
              "      <td>251.888889</td>\n",
              "      <td>243.300000</td>\n",
              "      <td>228.844444</td>\n",
              "      <td>5.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>2.000000</td>\n",
              "      <td>94.448560</td>\n",
              "      <td>0.0</td>\n",
              "      <td>45.0</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>48.485597</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>42.407407</td>\n",
              "      <td>2.0</td>\n",
              "      <td>...</td>\n",
              "      <td>53.014706</td>\n",
              "      <td>49.994412</td>\n",
              "      <td>54.202353</td>\n",
              "      <td>63.806176</td>\n",
              "      <td>0.974359</td>\n",
              "      <td>247.988889</td>\n",
              "      <td>251.888889</td>\n",
              "      <td>243.300000</td>\n",
              "      <td>228.844444</td>\n",
              "      <td>5.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>2.000000</td>\n",
              "      <td>96.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>45.0</td>\n",
              "      <td>10.271605</td>\n",
              "      <td>0.0</td>\n",
              "      <td>50.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>52.000000</td>\n",
              "      <td>2.0</td>\n",
              "      <td>...</td>\n",
              "      <td>53.014706</td>\n",
              "      <td>49.994412</td>\n",
              "      <td>54.202353</td>\n",
              "      <td>63.806176</td>\n",
              "      <td>0.974359</td>\n",
              "      <td>247.988889</td>\n",
              "      <td>251.888889</td>\n",
              "      <td>243.300000</td>\n",
              "      <td>228.844444</td>\n",
              "      <td>7.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>154.000000</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>67.000000</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>52.000000</td>\n",
              "      <td>2.0</td>\n",
              "      <td>...</td>\n",
              "      <td>60.490000</td>\n",
              "      <td>60.310000</td>\n",
              "      <td>58.290000</td>\n",
              "      <td>67.240000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>268.000000</td>\n",
              "      <td>278.600000</td>\n",
              "      <td>271.400000</td>\n",
              "      <td>245.600000</td>\n",
              "      <td>7.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>8 rows × 2876 columns</p>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ba6fa947-60d8-4d11-aa08-ffc07011c212')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\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-ba6fa947-60d8-4d11-aa08-ffc07011c212');\n",
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              "            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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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 541
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 4) check the remaining NULL \n",
        "miss_dic = test_df.isnull().sum().to_dict()\n",
        "check = {i for i in miss_dic if miss_dic[i]>0}\n",
        "check  # all_NaN features name in list   "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d0a62154-996c-4c45-f5a3-36cce1ff1513",
        "id": "P-vVEt6EHe7p"
      },
      "execution_count": 542,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "set()"
            ]
          },
          "metadata": {},
          "execution_count": 542
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# outlier (replace 1Q, 3Q with MEDIAN)\n",
        "# xs = list(test_df.iloc[:, 6:].columns)\n",
        "# def outlier(df):\n",
        "#     for col in xs: \n",
        "#         q1 = test_df[col].quantile(0.25)\n",
        "#         q3 = test_df[col].quantile(0.75)\n",
        "#         median = test_df[col].quantile(0.50)\n",
        "\n",
        "#         test_df[col] = np.where(test_df[col]>q3, median, test_df[col])\n",
        "\n",
        "# test_df.describe()"
      ],
      "metadata": {
        "id": "skEOybzqBQy6"
      },
      "execution_count": 543,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 2.Modeling_ML"
      ],
      "metadata": {
        "id": "7GodRp6O-5TM"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 2.0 OverSampling"
      ],
      "metadata": {
        "id": "yKYVY4DwWSts"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X_train = train_df.drop(columns=['Y_Class'])\n",
        "y_train = train_df['Y_Class']\n",
        "test_x = test_df"
      ],
      "metadata": {
        "id": "mtzzkA9n_flg"
      },
      "execution_count": 544,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 545,
      "metadata": {
        "id": "ZgdDmu73y4ij",
        "outputId": "e1511302-601c-407b-e623-19328e190bd7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1    0.680602\n",
            "2    0.172241\n",
            "0    0.147157\n",
            "Name: Y_Class, dtype: float64 \n",
            "\n",
            "----------------------------------------------\n",
            "\n",
            "Over-Sampling 전:\n",
            " 1    407\n",
            "2    103\n",
            "0     88\n",
            "Name: Y_Class, dtype: int64 \n",
            "\n",
            "Over-Sampling 후 Train X: (1221, 2877)\n",
            "Over-Sampling 후 Train Y: (1221,) \n",
            "\n",
            "Over-Sampling 후 '2':407\n",
            "Over-Sampling 후 '1':407\n",
            "Over-Sampling 후 '0':407\n"
          ]
        }
      ],
      "source": [
        "# OverSampling for Y_Class 0, 2 \n",
        "from imblearn.over_sampling import SMOTE\n",
        "\n",
        "# 목표변수 빈도 확인\n",
        "print(y_train.value_counts(1),\"\\n\")\n",
        "\n",
        "# Over-sampling 설정\n",
        "sm = SMOTE(sampling_strategy='auto')\n",
        "\n",
        "# train데이터를 이용한 Over-sampling\n",
        "x_resampled, y_resampled = sm.fit_resample(X_train, y_train)\n",
        "\n",
        "# 결과 확인\n",
        "print('----------------------------------------------\\n')\n",
        "print('Over-Sampling 전:\\n', y_train.value_counts(),\"\\n\")\n",
        "print('Over-Sampling 후 Train X: {}'.format(x_resampled.shape))\n",
        "print('Over-Sampling 후 Train Y: {} \\n'.format(y_resampled.shape))\n",
        "print(\"Over-Sampling 후 '2':{}\".format(sum(y_resampled==2)))\n",
        "print(\"Over-Sampling 후 '1':{}\".format(sum(y_resampled==1)))\n",
        "print(\"Over-Sampling 후 '0':{}\".format(sum(y_resampled==0)))\n",
        "\n",
        "# 데이터 결합\n",
        "train_df = pd.concat([x_resampled,y_resampled], axis=1)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train = train_df.drop(columns=['Y_Class', 'Y_Quality'])\n",
        "y_train = train_df['Y_Quality']\n",
        "test_x = test_df"
      ],
      "metadata": {
        "id": "xPHBVUkH7_IA"
      },
      "execution_count": 546,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 2.1 GradientBoosting [0.63]"
      ],
      "metadata": {
        "id": "yXJSNKPRYsvb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import GradientBoostingRegressor   \n",
        "\n",
        "GB_model = GradientBoostingRegressor(random_state=37).fit(X_train, y_train) \n",
        "print('Done.') "
      ],
      "metadata": {
        "id": "U7pCY5HPsv4Q",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d4b29a22-fbda-43f3-b37b-d3d56d0e4f70"
      },
      "execution_count": 547,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Done.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "preds=GB_model.predict(test_x)  \n",
        "print('Done.')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "359fc337-05f0-45d4-d44b-81903d19d18d",
        "id": "Hpn3cF6CVVgM"
      },
      "execution_count": 548,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Done.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_x['Y_Quality'] = preds\n",
        "test_x  "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "outputId": "779efa31-eb0a-4b82-89a6-82db1155da56",
        "id": "xPXPRUoYVVgM"
      },
      "execution_count": 549,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          X_1       X_2  X_3   X_4        X_5  X_6        X_7        X_8  \\\n",
              "0    2.000000  94.00000  0.0  45.0  10.000000  0.0  51.000000  10.000000   \n",
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              "3    4.337449  94.44856  0.0  45.0  10.271605  0.0  48.485597  10.012346   \n",
              "4    4.337449  94.44856  0.0  45.0  10.271605  0.0  48.485597  10.012346   \n",
              "..        ...       ...  ...   ...        ...  ...        ...        ...   \n",
              "305  2.000000  91.00000  0.0  45.0  10.000000  0.0  51.000000  10.000000   \n",
              "306  2.000000  96.00000  0.0  45.0  11.000000  0.0  45.000000  10.000000   \n",
              "307  2.000000  91.00000  0.0  45.0  10.000000  0.0  50.000000  10.000000   \n",
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              "309  2.000000  87.00000  0.0  45.0  10.000000  0.0  51.000000  10.000000   \n",
              "\n",
              "           X_9  X_10  ...     X_2868     X_2869     X_2870    X_2871  \\\n",
              "0    52.000000   2.0  ...  49.994412  54.202353  63.806176  0.974359   \n",
              "1    31.000000   2.0  ...  49.994412  54.202353  63.806176  0.974359   \n",
              "2    31.000000   2.0  ...  49.994412  54.202353  63.806176  0.974359   \n",
              "3    42.407407   2.0  ...  49.994412  54.202353  63.806176  0.974359   \n",
              "4    42.407407   2.0  ...  49.994412  54.202353  63.806176  0.974359   \n",
              "..         ...   ...  ...        ...        ...        ...       ...   \n",
              "305  52.000000   2.0  ...  49.994412  54.202353  63.806176  0.974359   \n",
              "306  31.000000   2.0  ...  49.994412  54.202353  63.806176  0.974359   \n",
              "307  52.000000   2.0  ...  49.994412  54.202353  63.806176  0.974359   \n",
              "308  52.000000   2.0  ...  49.994412  54.202353  63.806176  0.974359   \n",
              "309  52.000000   2.0  ...  49.994412  54.202353  63.806176  0.974359   \n",
              "\n",
              "         X_2872      X_2873  X_2874      X_2875  line_pcode  Y_Quality  \n",
              "0    247.988889  251.888889   243.3  228.844444           7   0.532668  \n",
              "1    247.988889  251.888889   243.3  228.844444           5   0.534932  \n",
              "2    247.988889  251.888889   243.3  228.844444           5   0.534789  \n",
              "3    247.988889  251.888889   243.3  228.844444           0   0.533128  \n",
              "4    247.988889  251.888889   243.3  228.844444           1   0.537065  \n",
              "..          ...         ...     ...         ...         ...        ...  \n",
              "305  247.988889  251.888889   243.3  228.844444           7   0.531996  \n",
              "306  247.988889  251.888889   243.3  228.844444           5   0.535282  \n",
              "307  247.988889  251.888889   243.3  228.844444           7   0.532321  \n",
              "308  247.988889  251.888889   243.3  228.844444           7   0.532803  \n",
              "309  247.988889  251.888889   243.3  228.844444           7   0.531677  \n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></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",
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              "      <th>X_8</th>\n",
              "      <th>X_9</th>\n",
              "      <th>X_10</th>\n",
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              "      <th>X_2869</th>\n",
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              "      <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",
              "      <th>line_pcode</th>\n",
              "      <th>Y_Quality</th>\n",
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              "  </thead>\n",
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              "      <th>0</th>\n",
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              "      <td>45.0</td>\n",
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              "      <td>54.202353</td>\n",
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              "      <td>247.988889</td>\n",
              "      <td>251.888889</td>\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-0d8709cc-cf56-420f-8aea-5a8af3a8d2be 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-0d8709cc-cf56-420f-8aea-5a8af3a8d2be');\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": 549
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.countplot(x=preds);"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "bP9e3szwGvDU",
        "outputId": "6eadc6e8-809c-4f20-bbf0-97c5b8521764"
      },
      "execution_count": 550,
      "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": "code",
      "source": [],
      "metadata": {
        "id": "sjXjfwJCWCYF"
      },
      "execution_count": 550,
      "outputs": []
    },
    {
      "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": 551,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "52c88101-fe85-4f38-8b13-5ee25a48b322",
        "id": "Plefo87K5yqD"
      },
      "execution_count": 552,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Y_Quality    0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 552
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "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": "DoAIburW6xpJ"
      },
      "execution_count": 553,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import GradientBoostingClassifier\n",
        "\n",
        "GB_model =  GradientBoostingClassifier(random_state=37).fit(X_train, y_train)\n",
        "print('Done.')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ov8uVwu2uHfF",
        "outputId": "9cf2b26f-7bac-42fe-c910-5af94a5baa30"
      },
      "execution_count": 554,
      "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": "T0ovkbQa57OV"
      },
      "execution_count": 555,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test_x = pd.DataFrame(test_x)\n",
        "\n",
        "preds = GB_model.predict(test_x)   \n",
        "print('Done.')   "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mncvR7m1wE-3",
        "outputId": "4b72844d-f2a6-46d1-fde0-a1edfb3daf02"
      },
      "execution_count": 556,
      "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": "d92258bf-7b56-4277-942b-cf9d96d1ff8c"
      },
      "execution_count": 557,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 2, 1, 1, 2, 2, 2, 1, 1, 2, 2, 2, 2, 1, 1, 1, 2, 1, 1, 2, 1, 2,\n",
              "       1, 2, 1, 2, 2, 1, 1, 1, 1, 2, 1, 1, 2, 2, 2, 1, 2, 1, 2, 2, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 2,\n",
              "       1, 1, 2, 1, 1, 1, 1, 1, 2, 2, 1, 1, 2, 2, 1, 2, 2, 2, 2, 2, 1, 1,\n",
              "       1, 1, 2, 2, 2, 2, 1, 2, 2, 2, 1, 2, 1, 2, 1, 2, 2, 2, 2, 1, 2, 1,\n",
              "       2, 1, 1, 2, 2, 2, 2, 1, 1, 2, 1, 1, 2, 2, 1, 1, 2, 2, 1, 1, 2, 1,\n",
              "       1, 1, 2, 2, 2, 2, 2, 1, 2, 1, 1, 2, 2, 1, 2, 2, 1, 1, 1, 2, 1, 2,\n",
              "       1, 1, 1, 1, 2, 1, 2, 2, 1, 2, 1, 2, 1, 1, 1, 2, 2, 1, 2, 1, 1, 1,\n",
              "       2, 2, 1, 2, 2, 1, 1, 2, 1, 1, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 1,\n",
              "       2, 1, 2, 1, 2, 1, 2, 2, 2, 1, 1, 2, 1, 1, 1, 2, 2, 1, 1, 2, 1, 2,\n",
              "       1, 2, 2, 1, 1, 1, 1, 1, 2, 1, 1, 1, 2, 2, 2, 1, 1, 1, 2, 1, 1, 2,\n",
              "       1, 2, 2, 2, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 2, 1, 2, 1, 1, 2,\n",
              "       2, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2,\n",
              "       1, 2, 1, 1, 1, 1, 2, 2, 1, 1, 1, 2, 2, 2, 2, 1, 1, 1, 1, 1, 2, 1,\n",
              "       1, 1])"
            ]
          },
          "metadata": {},
          "execution_count": 557
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.countplot(x=preds);"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "5ajeXCsP9Awa",
        "outputId": "15105ab3-69d0-40d9-8677-7255ba7216c8"
      },
      "execution_count": 558,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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BNwAvS7Kzm84ZOtRq5i2pkqTGIwVJUmMpSJIaS0GS1FgKkqTGUpAkNZaCJKmxFCRJzf8DZkiRYhRSyCIAAAAASUVORK5CYII=\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "q5miG4FKZgP8"
      },
      "execution_count": 144,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "l9KnrCyaZgWD"
      },
      "execution_count": 144,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 2.2 LGBM [0.5]"
      ],
      "metadata": {
        "id": "E4reyRMdiyQA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from lightgbm import LGBMRegressor\n",
        "\n",
        "LGBM_model = LGBMRegressor(random_state=37).fit(X_train, y_train) \n",
        "print('Done.') "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b383ec96-e0c5-4034-f56d-31225c565ad2",
        "id": "JHtmoDYXiyQB"
      },
      "execution_count": 224,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Done.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "preds=LGBM_model.predict(test_x)  \n",
        "print('Done.')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e1e463a2-501b-472f-90ad-be4628631993",
        "id": "IkvFzh12iyQB"
      },
      "execution_count": 225,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Done.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_x['Y_Quality'] = preds\n",
        "test_x  "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "04689c6d-48ce-4d25-9677-02ef6b852fbd",
        "id": "cFIbunxpiyQB"
      },
      "execution_count": 226,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     X_1   X_2  X_3   X_4   X_5  X_6   X_7   X_8   X_9  X_10  ...  X_2868  \\\n",
              "0    2.0  94.0  0.0  45.0  10.0  0.0  51.0  10.0  52.0   2.0  ...     0.0   \n",
              "1    2.0  93.0  0.0  45.0  11.0  0.0  45.0  10.0  31.0   2.0  ...     0.0   \n",
              "2    2.0  95.0  0.0  45.0  11.0  0.0  45.0  10.0  31.0   2.0  ...     0.0   \n",
              "3    0.0   0.0  0.0   0.0   0.0  0.0   0.0   0.0   0.0   0.0  ...     0.0   \n",
              "4    0.0   0.0  0.0   0.0   0.0  0.0   0.0   0.0   0.0   0.0  ...     0.0   \n",
              "..   ...   ...  ...   ...   ...  ...   ...   ...   ...   ...  ...     ...   \n",
              "305  2.0  91.0  0.0  45.0  10.0  0.0  51.0  10.0  52.0   2.0  ...     0.0   \n",
              "306  2.0  96.0  0.0  45.0  11.0  0.0  45.0  10.0  31.0   2.0  ...     0.0   \n",
              "307  2.0  91.0  0.0  45.0  10.0  0.0  50.0  10.0  52.0   2.0  ...     0.0   \n",
              "308  2.0  95.0  0.0  45.0  10.0  0.0  51.0  10.0  52.0   2.0  ...     0.0   \n",
              "309  2.0  87.0  0.0  45.0  10.0  0.0  51.0  10.0  52.0   2.0  ...     0.0   \n",
              "\n",
              "     X_2869  X_2870  X_2871  X_2872  X_2873  X_2874  X_2875  line_pcode  \\\n",
              "0       0.0     0.0     0.0     0.0     0.0     0.0     0.0           7   \n",
              "1       0.0     0.0     0.0     0.0     0.0     0.0     0.0           5   \n",
              "2       0.0     0.0     0.0     0.0     0.0     0.0     0.0           5   \n",
              "3       0.0     0.0     0.0     0.0     0.0     0.0     0.0           0   \n",
              "4       0.0     0.0     0.0     0.0     0.0     0.0     0.0           1   \n",
              "..      ...     ...     ...     ...     ...     ...     ...         ...   \n",
              "305     0.0     0.0     0.0     0.0     0.0     0.0     0.0           7   \n",
              "306     0.0     0.0     0.0     0.0     0.0     0.0     0.0           5   \n",
              "307     0.0     0.0     0.0     0.0     0.0     0.0     0.0           7   \n",
              "308     0.0     0.0     0.0     0.0     0.0     0.0     0.0           7   \n",
              "309     0.0     0.0     0.0     0.0     0.0     0.0     0.0           7   \n",
              "\n",
              "     Y_Quality  \n",
              "0     0.531415  \n",
              "1     0.530844  \n",
              "2     0.531456  \n",
              "3     0.524097  \n",
              "4     0.528226  \n",
              "..         ...  \n",
              "305   0.527747  \n",
              "306   0.528078  \n",
              "307   0.527309  \n",
              "308   0.527817  \n",
              "309   0.527303  \n",
              "\n",
              "[310 rows x 2877 columns]"
            ],
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              "\n",
              "  <div id=\"df-e4edc22a-2a8b-4efc-8ee5-d20270484753\">\n",
              "    <div class=\"colab-df-container\">\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></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>X_9</th>\n",
              "      <th>X_10</th>\n",
              "      <th>...</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",
              "      <th>line_pcode</th>\n",
              "      <th>Y_Quality</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2.0</td>\n",
              "      <td>94.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>45.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>51.0</td>\n",
              "      <td>10.0</td>\n",
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              "    <tr>\n",
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              "      <td>2.0</td>\n",
              "      <td>93.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>45.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>10.0</td>\n",
              "      <td>31.0</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2.0</td>\n",
              "      <td>95.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>45.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>45.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>31.0</td>\n",
              "      <td>2.0</td>\n",
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              "    <tr>\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",
              "      <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>0.0</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>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.528226</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>305</th>\n",
              "      <td>2.0</td>\n",
              "      <td>91.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>45.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>51.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>52.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>...</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>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7</td>\n",
              "      <td>0.527747</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>306</th>\n",
              "      <td>2.0</td>\n",
              "      <td>96.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>45.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>45.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>31.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>...</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>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5</td>\n",
              "      <td>0.528078</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>307</th>\n",
              "      <td>2.0</td>\n",
              "      <td>91.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>45.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>50.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>52.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>...</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>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7</td>\n",
              "      <td>0.527309</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>308</th>\n",
              "      <td>2.0</td>\n",
              "      <td>95.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>45.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>51.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>52.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>...</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>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7</td>\n",
              "      <td>0.527817</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>309</th>\n",
              "      <td>2.0</td>\n",
              "      <td>87.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>45.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>51.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>52.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>...</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>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7</td>\n",
              "      <td>0.527303</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>310 rows × 2877 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e4edc22a-2a8b-4efc-8ee5-d20270484753')\"\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-e4edc22a-2a8b-4efc-8ee5-d20270484753 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-e4edc22a-2a8b-4efc-8ee5-d20270484753');\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": 226
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.countplot(x=preds);"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "ab473a9c-4837-4c41-fb06-4edf6f6a7f6e",
        "id": "2l1zCM2FiyQB"
      },
      "execution_count": 227,
      "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": "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": "Bkd-DzIniyQC"
      },
      "execution_count": 228,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "be39bf31-e27d-4609-9616-04ff88e0d0e9",
        "id": "RZH1smGFiyQC"
      },
      "execution_count": 229,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Y_Quality    0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 229
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from lightgbm import LGBMClassifier \n",
        "\n",
        "LGBM_model =  LGBMClassifier(random_state=37).fit(X_train, y_train)\n",
        "print('Done.')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "208a91f4-262e-44ff-ade2-9346fd12830d",
        "id": "8bo1XHm1iyQC"
      },
      "execution_count": 230,
      "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": "Xfk0Bff6iyQC"
      },
      "execution_count": 231,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test_x = pd.DataFrame(test_x)\n",
        "\n",
        "preds = RF.predict(test_x)   \n",
        "print('Done.')   "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "beaddb3d-fe70-4f57-ff33-6470aa0bc049",
        "id": "mg33TKrciyQD"
      },
      "execution_count": 232,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Done.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "preds"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3b87eaa6-105d-4937-e107-571f44be5a6a",
        "id": "nXoNAT8IiyQD"
      },
      "execution_count": 233,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1., 1., 1., 0., 1., 1., 2., 1., 0., 2., 1., 2., 2., 1., 0., 0., 1.,\n",
              "       1., 1., 1., 0., 1., 0., 0., 0., 0., 1., 0., 0., 1., 0., 1., 0., 1.,\n",
              "       1., 0., 1., 0., 2., 0., 1., 0., 0., 1., 1., 1., 1., 0., 1., 0., 1.,\n",
              "       0., 0., 0., 0., 0., 1., 1., 0., 0., 0., 0., 1., 1., 0., 0., 0., 1.,\n",
              "       1., 1., 1., 0., 1., 1., 1., 1., 2., 1., 2., 1., 1., 1., 1., 1., 1.,\n",
              "       1., 0., 1., 0., 0., 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., 2., 2., 1., 0., 1.,\n",
              "       1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
              "       1., 1., 1., 1., 1., 0., 1., 2., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
              "       1., 1., 1., 1., 1., 1., 1., 2., 2., 1., 2., 1., 1., 1., 1., 1., 2.,\n",
              "       1., 1., 2., 1., 2., 1., 2., 2., 1., 2., 2., 1., 1., 2., 1., 1., 1.,\n",
              "       2., 1., 2., 1., 2., 1., 2., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
              "       2., 2., 1., 1., 1., 2., 1., 1., 1., 1., 1., 1., 1., 2., 1., 2., 1.,\n",
              "       1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 2., 1., 1., 1., 1., 1.,\n",
              "       1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
              "       1., 1., 1., 1., 1., 2., 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.,\n",
              "       1., 1., 1., 1., 1., 0., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
              "       1., 1., 1., 1.])"
            ]
          },
          "metadata": {},
          "execution_count": 233
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.countplot(x=preds);"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "8c89c23f-2d93-45ff-9827-3825e48cba0a",
        "id": "6pqwzfvGiyQD"
      },
      "execution_count": 234,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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a/wcjao1z7egnlQAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "p6NCz8FkmGAX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 2.3 XGBoost [BAD]"
      ],
      "metadata": {
        "id": "37DAV5FriyQD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from xgboost import XGBRegressor\n",
        "\n",
        "XGB_model = XGBRegressor(random_state=37).fit(X_train, y_train) \n",
        "print('Done.') "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f3b57215-7ac9-4d7d-f15e-57afb615aae8",
        "id": "loABUoxqlPnx"
      },
      "execution_count": 266,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[12:12:21] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "Done.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "preds=XGB_model.predict(test_x)  \n",
        "print('Done.')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3bfaca71-3d3f-482c-ddd4-c4dacfa1b5fe",
        "id": "Mi39v4bWlPnx"
      },
      "execution_count": 267,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Done.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_x['Y_Quality'] = preds\n",
        "test_x  "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "outputId": "bb6ca2a6-e0b1-4f08-a32e-d71f26669e84",
        "id": "H7XKB3_RlPnx"
      },
      "execution_count": 268,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          X_1       X_2  X_3   X_4        X_5  X_6        X_7        X_8  \\\n",
              "0    2.000000  94.00000  0.0  45.0  10.000000  0.0  51.000000  10.000000   \n",
              "1    2.000000  93.00000  0.0  45.0  11.000000  0.0  45.000000  10.000000   \n",
              "2    2.000000  95.00000  0.0  45.0  11.000000  0.0  45.000000  10.000000   \n",
              "3    4.337449  94.44856  0.0  45.0  10.271605  0.0  48.485597  10.012346   \n",
              "4    4.337449  94.44856  0.0  45.0  10.271605  0.0  48.485597  10.012346   \n",
              "..        ...       ...  ...   ...        ...  ...        ...        ...   \n",
              "305  2.000000  91.00000  0.0  45.0  10.000000  0.0  51.000000  10.000000   \n",
              "306  2.000000  96.00000  0.0  45.0  11.000000  0.0  45.000000  10.000000   \n",
              "307  2.000000  91.00000  0.0  45.0  10.000000  0.0  50.000000  10.000000   \n",
              "308  2.000000  95.00000  0.0  45.0  10.000000  0.0  51.000000  10.000000   \n",
              "309  2.000000  87.00000  0.0  45.0  10.000000  0.0  51.000000  10.000000   \n",
              "\n",
              "           X_9  X_10  ...     X_2868     X_2869     X_2870    X_2871  \\\n",
              "0    52.000000   2.0  ...  49.994412  54.202353  63.806176  0.974359   \n",
              "1    31.000000   2.0  ...  49.994412  54.202353  63.806176  0.974359   \n",
              "2    31.000000   2.0  ...  49.994412  54.202353  63.806176  0.974359   \n",
              "3    42.407407   2.0  ...  49.994412  54.202353  63.806176  0.974359   \n",
              "4    42.407407   2.0  ...  49.994412  54.202353  63.806176  0.974359   \n",
              "..         ...   ...  ...        ...        ...        ...       ...   \n",
              "305  52.000000   2.0  ...  49.994412  54.202353  63.806176  0.974359   \n",
              "306  31.000000   2.0  ...  49.994412  54.202353  63.806176  0.974359   \n",
              "307  52.000000   2.0  ...  49.994412  54.202353  63.806176  0.974359   \n",
              "308  52.000000   2.0  ...  49.994412  54.202353  63.806176  0.974359   \n",
              "309  52.000000   2.0  ...  49.994412  54.202353  63.806176  0.974359   \n",
              "\n",
              "         X_2872      X_2873  X_2874      X_2875  line_pcode  Y_Quality  \n",
              "0    247.988889  251.888889   243.3  228.844444           7   0.524482  \n",
              "1    247.988889  251.888889   243.3  228.844444           5   0.527027  \n",
              "2    247.988889  251.888889   243.3  228.844444           5   0.528316  \n",
              "3    247.988889  251.888889   243.3  228.844444           0   0.524518  \n",
              "4    247.988889  251.888889   243.3  228.844444           1   0.525997  \n",
              "..          ...         ...     ...         ...         ...        ...  \n",
              "305  247.988889  251.888889   243.3  228.844444           7   0.523075  \n",
              "306  247.988889  251.888889   243.3  228.844444           5   0.522320  \n",
              "307  247.988889  251.888889   243.3  228.844444           7   0.523158  \n",
              "308  247.988889  251.888889   243.3  228.844444           7   0.521964  \n",
              "309  247.988889  251.888889   243.3  228.844444           7   0.522379  \n",
              "\n",
              "[310 rows x 2877 columns]"
            ],
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              "    <div class=\"colab-df-container\">\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>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>X_9</th>\n",
              "      <th>X_10</th>\n",
              "      <th>...</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",
              "      <th>line_pcode</th>\n",
              "      <th>Y_Quality</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2.000000</td>\n",
              "      <td>94.00000</td>\n",
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              "      <td>45.0</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>51.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>52.000000</td>\n",
              "      <td>2.0</td>\n",
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              "      <td>49.994412</td>\n",
              "      <td>54.202353</td>\n",
              "      <td>63.806176</td>\n",
              "      <td>0.974359</td>\n",
              "      <td>247.988889</td>\n",
              "      <td>251.888889</td>\n",
              "      <td>243.3</td>\n",
              "      <td>228.844444</td>\n",
              "      <td>7</td>\n",
              "      <td>0.524482</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2.000000</td>\n",
              "      <td>93.00000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>45.0</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>45.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>31.000000</td>\n",
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              "      <td>49.994412</td>\n",
              "      <td>54.202353</td>\n",
              "      <td>63.806176</td>\n",
              "      <td>0.974359</td>\n",
              "      <td>247.988889</td>\n",
              "      <td>251.888889</td>\n",
              "      <td>243.3</td>\n",
              "      <td>228.844444</td>\n",
              "      <td>5</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2.000000</td>\n",
              "      <td>95.00000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>45.0</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>45.000000</td>\n",
              "      <td>10.000000</td>\n",
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              "      <td>2.0</td>\n",
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              "      <td>49.994412</td>\n",
              "      <td>54.202353</td>\n",
              "      <td>63.806176</td>\n",
              "      <td>0.974359</td>\n",
              "      <td>247.988889</td>\n",
              "      <td>251.888889</td>\n",
              "      <td>243.3</td>\n",
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              "      <td>247.988889</td>\n",
              "      <td>251.888889</td>\n",
              "      <td>243.3</td>\n",
              "      <td>228.844444</td>\n",
              "      <td>0</td>\n",
              "      <td>0.524518</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4.337449</td>\n",
              "      <td>94.44856</td>\n",
              "      <td>0.0</td>\n",
              "      <td>45.0</td>\n",
              "      <td>10.271605</td>\n",
              "      <td>0.0</td>\n",
              "      <td>48.485597</td>\n",
              "      <td>10.012346</td>\n",
              "      <td>42.407407</td>\n",
              "      <td>2.0</td>\n",
              "      <td>...</td>\n",
              "      <td>49.994412</td>\n",
              "      <td>54.202353</td>\n",
              "      <td>63.806176</td>\n",
              "      <td>0.974359</td>\n",
              "      <td>247.988889</td>\n",
              "      <td>251.888889</td>\n",
              "      <td>243.3</td>\n",
              "      <td>228.844444</td>\n",
              "      <td>1</td>\n",
              "      <td>0.525997</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>305</th>\n",
              "      <td>2.000000</td>\n",
              "      <td>91.00000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>45.0</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>51.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>52.000000</td>\n",
              "      <td>2.0</td>\n",
              "      <td>...</td>\n",
              "      <td>49.994412</td>\n",
              "      <td>54.202353</td>\n",
              "      <td>63.806176</td>\n",
              "      <td>0.974359</td>\n",
              "      <td>247.988889</td>\n",
              "      <td>251.888889</td>\n",
              "      <td>243.3</td>\n",
              "      <td>228.844444</td>\n",
              "      <td>7</td>\n",
              "      <td>0.523075</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>306</th>\n",
              "      <td>2.000000</td>\n",
              "      <td>96.00000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>45.0</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>45.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>31.000000</td>\n",
              "      <td>2.0</td>\n",
              "      <td>...</td>\n",
              "      <td>49.994412</td>\n",
              "      <td>54.202353</td>\n",
              "      <td>63.806176</td>\n",
              "      <td>0.974359</td>\n",
              "      <td>247.988889</td>\n",
              "      <td>251.888889</td>\n",
              "      <td>243.3</td>\n",
              "      <td>228.844444</td>\n",
              "      <td>5</td>\n",
              "      <td>0.522320</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>307</th>\n",
              "      <td>2.000000</td>\n",
              "      <td>91.00000</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>10.000000</td>\n",
              "      <td>52.000000</td>\n",
              "      <td>2.0</td>\n",
              "      <td>...</td>\n",
              "      <td>49.994412</td>\n",
              "      <td>54.202353</td>\n",
              "      <td>63.806176</td>\n",
              "      <td>0.974359</td>\n",
              "      <td>247.988889</td>\n",
              "      <td>251.888889</td>\n",
              "      <td>243.3</td>\n",
              "      <td>228.844444</td>\n",
              "      <td>7</td>\n",
              "      <td>0.523158</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>308</th>\n",
              "      <td>2.000000</td>\n",
              "      <td>95.00000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>45.0</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>51.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>52.000000</td>\n",
              "      <td>2.0</td>\n",
              "      <td>...</td>\n",
              "      <td>49.994412</td>\n",
              "      <td>54.202353</td>\n",
              "      <td>63.806176</td>\n",
              "      <td>0.974359</td>\n",
              "      <td>247.988889</td>\n",
              "      <td>251.888889</td>\n",
              "      <td>243.3</td>\n",
              "      <td>228.844444</td>\n",
              "      <td>7</td>\n",
              "      <td>0.521964</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>309</th>\n",
              "      <td>2.000000</td>\n",
              "      <td>87.00000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>45.0</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>51.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>52.000000</td>\n",
              "      <td>2.0</td>\n",
              "      <td>...</td>\n",
              "      <td>49.994412</td>\n",
              "      <td>54.202353</td>\n",
              "      <td>63.806176</td>\n",
              "      <td>0.974359</td>\n",
              "      <td>247.988889</td>\n",
              "      <td>251.888889</td>\n",
              "      <td>243.3</td>\n",
              "      <td>228.844444</td>\n",
              "      <td>7</td>\n",
              "      <td>0.522379</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>310 rows × 2877 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-94bf40b6-f6ce-4227-b6b4-12d9e5921df4')\"\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",
              "      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",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-94bf40b6-f6ce-4227-b6b4-12d9e5921df4 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-94bf40b6-f6ce-4227-b6b4-12d9e5921df4');\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",
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              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 268
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.countplot(x=preds);"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "outputId": "6cf5d4d5-7907-473e-ba44-12d8eec19c6d",
        "id": "pFy9yZDSlPnx"
      },
      "execution_count": 269,
      "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": "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": "_pdf8dM-lPny"
      },
      "execution_count": 270,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "158738d0-7834-4d9b-cebd-6d54c7f39ffe",
        "id": "F469MpDQlPny"
      },
      "execution_count": 271,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Y_Quality    0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 271
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from xgboost import XGBClassifier\n",
        "\n",
        "XGB_model =  XGBClassifier(random_state=37).fit(X_train, y_train)\n",
        "print('Done.')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0fa3dd75-1312-445b-d8a0-98ac1a23fcc0",
        "id": "-UlR05R1lPny"
      },
      "execution_count": 272,
      "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": "nVsSiMD6lPnz"
      },
      "execution_count": 273,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test_x = pd.DataFrame(test_x)\n",
        "\n",
        "preds = RF.predict(test_x)   \n",
        "print('Done.')   "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c3d83fbc-15f5-4088-a615-b5a76960d67e",
        "id": "SYNJBRSzlPnz"
      },
      "execution_count": 274,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Done.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "preds"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "31724fe0-6e7c-4000-d2f4-0410f75495db",
        "id": "oxJn-HFKlPnz"
      },
      "execution_count": 275,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0., 1., 1., 0., 1., 1., 1., 0., 0., 1., 1., 1., 1., 1., 0., 0., 1.,\n",
              "       0., 0., 1., 0., 1., 0., 0., 0., 0., 1., 0., 0., 1., 0., 1., 0., 0.,\n",
              "       1., 0., 1., 0., 2., 0., 1., 0., 0., 0., 0., 1., 1., 0., 0., 0., 1.,\n",
              "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 0., 0., 0., 1.,\n",
              "       0., 0., 0., 0., 1., 0., 1., 0., 1., 0., 2., 1., 1., 1., 1., 1., 1.,\n",
              "       1., 0., 1., 0., 0., 1., 1., 1., 1., 0., 1., 1., 1., 0., 1., 0., 1.,\n",
              "       1., 1., 1., 1., 1., 0., 1., 1., 1., 0., 0., 1., 1., 1., 0., 0., 0.,\n",
              "       1., 1., 1., 1., 1., 0., 0., 1., 1., 0., 0., 1., 1., 1., 0., 1., 1.,\n",
              "       1., 1., 1., 0., 1., 0., 0., 1., 1., 1., 1., 1., 0., 0., 0., 1., 1.,\n",
              "       1., 0., 0., 0., 0., 1., 0., 2., 1., 1., 1., 0., 1., 0., 1., 0., 1.,\n",
              "       0., 0., 1., 0., 0., 0., 1., 1., 0., 1., 1., 0., 0., 1., 1., 0., 0.,\n",
              "       1., 0., 1., 0., 1., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
              "       1., 0., 1., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0.,\n",
              "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0.,\n",
              "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1., 1., 1., 1.,\n",
              "       1., 0., 0., 0., 0., 2., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
              "       0., 0., 0., 0., 0., 0., 0., 0., 1., 2., 1., 2., 1., 1., 1., 0., 0.,\n",
              "       0., 0., 0., 1., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
              "       0., 0., 0., 0.])"
            ]
          },
          "metadata": {},
          "execution_count": 275
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.countplot(x=preds);"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "outputId": "c0b4abf7-a623-40e0-ca36-e79cc3053a28",
        "id": "C9YYggQ9lPnz"
      },
      "execution_count": 276,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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K7DvEJucB19ec24BjkywbT3c6lKraVVXf6uYfAO4ETjhos4k7foZC/04Avj+wvINH/sNs21TVfuA+4Flj6U6P1TDHVz3rhmRPBb5+0KqJO36GgqSJluRpwAbg7VV1f9/99M1Q6N8wr/Zo2yQ5BvhlYO9YutNj5atbFrEkT2YuED5VVZ+fZ5OJO36GQv+GebXHRuCibv61wL+XTx0eLTYCr+/uYjkduK+qdvXdlObuLAKuAe6sqg8usNnEHb9F9ZqLSbTQqz2SXAHMVNVG5v7hfjLJNuYual7QX8calGQ9cCawNMkO4HLgyQBV9THgJuBcYBvwY+CN/XSqeZwBXAh8J8nmrvYuYDlM7vHzNReSpMbhI0lSYyhIkhpDQZLUGAqSpMZQkCQ1hoIkqTEUJEnN/wNvZANtKPoy1AAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 2.4 RandomForest\n",
        "- Output: class 0 too small"
      ],
      "metadata": {
        "id": "_JA7HNFnmHcw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import RandomForestRegressor\n",
        "\n",
        "RF_model = RandomForestRegressor(random_state=37).fit(X_train, y_train) \n",
        "print('Done.') "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0d4000e7-1509-42c1-9890-5a4be7bb5eee",
        "id": "6bXpl80hmHcw"
      },
      "execution_count": 306,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Done.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "preds=RF_model.predict(test_x)  \n",
        "print('Done.')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "88b84dc4-596f-45b7-8fef-9a7718793a6e",
        "id": "qek-NdR0mHcx"
      },
      "execution_count": 307,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Done.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_x['Y_Quality'] = preds\n",
        "test_x  "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "outputId": "58c6f33a-0225-41fa-dfa7-715e4f3022b8",
        "id": "Gbatqqe8mHcx"
      },
      "execution_count": 308,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          X_1       X_2  X_3   X_4        X_5  X_6        X_7        X_8  \\\n",
              "0    2.000000  94.00000  0.0  45.0  10.000000  0.0  51.000000  10.000000   \n",
              "1    2.000000  93.00000  0.0  45.0  11.000000  0.0  45.000000  10.000000   \n",
              "2    2.000000  95.00000  0.0  45.0  11.000000  0.0  45.000000  10.000000   \n",
              "3    4.337449  94.44856  0.0  45.0  10.271605  0.0  48.485597  10.012346   \n",
              "4    4.337449  94.44856  0.0  45.0  10.271605  0.0  48.485597  10.012346   \n",
              "..        ...       ...  ...   ...        ...  ...        ...        ...   \n",
              "305  2.000000  91.00000  0.0  45.0  10.000000  0.0  51.000000  10.000000   \n",
              "306  2.000000  96.00000  0.0  45.0  11.000000  0.0  45.000000  10.000000   \n",
              "307  2.000000  91.00000  0.0  45.0  10.000000  0.0  50.000000  10.000000   \n",
              "308  2.000000  95.00000  0.0  45.0  10.000000  0.0  51.000000  10.000000   \n",
              "309  2.000000  87.00000  0.0  45.0  10.000000  0.0  51.000000  10.000000   \n",
              "\n",
              "           X_9  X_10  ...     X_2868     X_2869     X_2870    X_2871  \\\n",
              "0    52.000000   2.0  ...  49.994412  54.202353  63.806176  0.974359   \n",
              "1    31.000000   2.0  ...  49.994412  54.202353  63.806176  0.974359   \n",
              "2    31.000000   2.0  ...  49.994412  54.202353  63.806176  0.974359   \n",
              "3    42.407407   2.0  ...  49.994412  54.202353  63.806176  0.974359   \n",
              "4    42.407407   2.0  ...  49.994412  54.202353  63.806176  0.974359   \n",
              "..         ...   ...  ...        ...        ...        ...       ...   \n",
              "305  52.000000   2.0  ...  49.994412  54.202353  63.806176  0.974359   \n",
              "306  31.000000   2.0  ...  49.994412  54.202353  63.806176  0.974359   \n",
              "307  52.000000   2.0  ...  49.994412  54.202353  63.806176  0.974359   \n",
              "308  52.000000   2.0  ...  49.994412  54.202353  63.806176  0.974359   \n",
              "309  52.000000   2.0  ...  49.994412  54.202353  63.806176  0.974359   \n",
              "\n",
              "         X_2872      X_2873  X_2874      X_2875  line_pcode  Y_Quality  \n",
              "0    247.988889  251.888889   243.3  228.844444           7   0.533170  \n",
              "1    247.988889  251.888889   243.3  228.844444           5   0.535017  \n",
              "2    247.988889  251.888889   243.3  228.844444           5   0.534964  \n",
              "3    247.988889  251.888889   243.3  228.844444           0   0.532341  \n",
              "4    247.988889  251.888889   243.3  228.844444           1   0.532555  \n",
              "..          ...         ...     ...         ...         ...        ...  \n",
              "305  247.988889  251.888889   243.3  228.844444           7   0.532656  \n",
              "306  247.988889  251.888889   243.3  228.844444           5   0.532410  \n",
              "307  247.988889  251.888889   243.3  228.844444           7   0.532898  \n",
              "308  247.988889  251.888889   243.3  228.844444           7   0.532914  \n",
              "309  247.988889  251.888889   243.3  228.844444           7   0.533151  \n",
              "\n",
              "[310 rows x 2877 columns]"
            ],
            "text/html": [
              "\n",
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              "    <div class=\"colab-df-container\">\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>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>X_9</th>\n",
              "      <th>X_10</th>\n",
              "      <th>...</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",
              "      <th>line_pcode</th>\n",
              "      <th>Y_Quality</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2.000000</td>\n",
              "      <td>94.00000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>45.0</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>51.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>52.000000</td>\n",
              "      <td>2.0</td>\n",
              "      <td>...</td>\n",
              "      <td>49.994412</td>\n",
              "      <td>54.202353</td>\n",
              "      <td>63.806176</td>\n",
              "      <td>0.974359</td>\n",
              "      <td>247.988889</td>\n",
              "      <td>251.888889</td>\n",
              "      <td>243.3</td>\n",
              "      <td>228.844444</td>\n",
              "      <td>7</td>\n",
              "      <td>0.533170</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2.000000</td>\n",
              "      <td>93.00000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>45.0</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>45.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>31.000000</td>\n",
              "      <td>2.0</td>\n",
              "      <td>...</td>\n",
              "      <td>49.994412</td>\n",
              "      <td>54.202353</td>\n",
              "      <td>63.806176</td>\n",
              "      <td>0.974359</td>\n",
              "      <td>247.988889</td>\n",
              "      <td>251.888889</td>\n",
              "      <td>243.3</td>\n",
              "      <td>228.844444</td>\n",
              "      <td>5</td>\n",
              "      <td>0.535017</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2.000000</td>\n",
              "      <td>95.00000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>45.0</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>45.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>31.000000</td>\n",
              "      <td>2.0</td>\n",
              "      <td>...</td>\n",
              "      <td>49.994412</td>\n",
              "      <td>54.202353</td>\n",
              "      <td>63.806176</td>\n",
              "      <td>0.974359</td>\n",
              "      <td>247.988889</td>\n",
              "      <td>251.888889</td>\n",
              "      <td>243.3</td>\n",
              "      <td>228.844444</td>\n",
              "      <td>5</td>\n",
              "      <td>0.534964</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4.337449</td>\n",
              "      <td>94.44856</td>\n",
              "      <td>0.0</td>\n",
              "      <td>45.0</td>\n",
              "      <td>10.271605</td>\n",
              "      <td>0.0</td>\n",
              "      <td>48.485597</td>\n",
              "      <td>10.012346</td>\n",
              "      <td>42.407407</td>\n",
              "      <td>2.0</td>\n",
              "      <td>...</td>\n",
              "      <td>49.994412</td>\n",
              "      <td>54.202353</td>\n",
              "      <td>63.806176</td>\n",
              "      <td>0.974359</td>\n",
              "      <td>247.988889</td>\n",
              "      <td>251.888889</td>\n",
              "      <td>243.3</td>\n",
              "      <td>228.844444</td>\n",
              "      <td>0</td>\n",
              "      <td>0.532341</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4.337449</td>\n",
              "      <td>94.44856</td>\n",
              "      <td>0.0</td>\n",
              "      <td>45.0</td>\n",
              "      <td>10.271605</td>\n",
              "      <td>0.0</td>\n",
              "      <td>48.485597</td>\n",
              "      <td>10.012346</td>\n",
              "      <td>42.407407</td>\n",
              "      <td>2.0</td>\n",
              "      <td>...</td>\n",
              "      <td>49.994412</td>\n",
              "      <td>54.202353</td>\n",
              "      <td>63.806176</td>\n",
              "      <td>0.974359</td>\n",
              "      <td>247.988889</td>\n",
              "      <td>251.888889</td>\n",
              "      <td>243.3</td>\n",
              "      <td>228.844444</td>\n",
              "      <td>1</td>\n",
              "      <td>0.532555</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>305</th>\n",
              "      <td>2.000000</td>\n",
              "      <td>91.00000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>45.0</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>51.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>52.000000</td>\n",
              "      <td>2.0</td>\n",
              "      <td>...</td>\n",
              "      <td>49.994412</td>\n",
              "      <td>54.202353</td>\n",
              "      <td>63.806176</td>\n",
              "      <td>0.974359</td>\n",
              "      <td>247.988889</td>\n",
              "      <td>251.888889</td>\n",
              "      <td>243.3</td>\n",
              "      <td>228.844444</td>\n",
              "      <td>7</td>\n",
              "      <td>0.532656</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>306</th>\n",
              "      <td>2.000000</td>\n",
              "      <td>96.00000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>45.0</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>45.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>31.000000</td>\n",
              "      <td>2.0</td>\n",
              "      <td>...</td>\n",
              "      <td>49.994412</td>\n",
              "      <td>54.202353</td>\n",
              "      <td>63.806176</td>\n",
              "      <td>0.974359</td>\n",
              "      <td>247.988889</td>\n",
              "      <td>251.888889</td>\n",
              "      <td>243.3</td>\n",
              "      <td>228.844444</td>\n",
              "      <td>5</td>\n",
              "      <td>0.532410</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>307</th>\n",
              "      <td>2.000000</td>\n",
              "      <td>91.00000</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>10.000000</td>\n",
              "      <td>52.000000</td>\n",
              "      <td>2.0</td>\n",
              "      <td>...</td>\n",
              "      <td>49.994412</td>\n",
              "      <td>54.202353</td>\n",
              "      <td>63.806176</td>\n",
              "      <td>0.974359</td>\n",
              "      <td>247.988889</td>\n",
              "      <td>251.888889</td>\n",
              "      <td>243.3</td>\n",
              "      <td>228.844444</td>\n",
              "      <td>7</td>\n",
              "      <td>0.532898</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>308</th>\n",
              "      <td>2.000000</td>\n",
              "      <td>95.00000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>45.0</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>51.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>52.000000</td>\n",
              "      <td>2.0</td>\n",
              "      <td>...</td>\n",
              "      <td>49.994412</td>\n",
              "      <td>54.202353</td>\n",
              "      <td>63.806176</td>\n",
              "      <td>0.974359</td>\n",
              "      <td>247.988889</td>\n",
              "      <td>251.888889</td>\n",
              "      <td>243.3</td>\n",
              "      <td>228.844444</td>\n",
              "      <td>7</td>\n",
              "      <td>0.532914</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>309</th>\n",
              "      <td>2.000000</td>\n",
              "      <td>87.00000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>45.0</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>51.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>52.000000</td>\n",
              "      <td>2.0</td>\n",
              "      <td>...</td>\n",
              "      <td>49.994412</td>\n",
              "      <td>54.202353</td>\n",
              "      <td>63.806176</td>\n",
              "      <td>0.974359</td>\n",
              "      <td>247.988889</td>\n",
              "      <td>251.888889</td>\n",
              "      <td>243.3</td>\n",
              "      <td>228.844444</td>\n",
              "      <td>7</td>\n",
              "      <td>0.533151</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>310 rows × 2877 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a6af05ce-aa69-458d-880c-774bd76921ea')\"\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",
              "      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",
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              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-a6af05ce-aa69-458d-880c-774bd76921ea 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-a6af05ce-aa69-458d-880c-774bd76921ea');\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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              "          element.appendChild(docLink);\n",
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              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 308
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.countplot(x=preds);"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "outputId": "d7ca2855-bbc0-4d77-9bc2-d72ee5af711b",
        "id": "BdTa6wmvmHcx"
      },
      "execution_count": 309,
      "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": "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": "3CLUxMwnmHcx"
      },
      "execution_count": 310,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7dcfd452-d247-4e78-abdd-030623e4e73a",
        "id": "I4oUj_iNmHcy"
      },
      "execution_count": 311,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Y_Quality    0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 311
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "\n",
        "RF_model =  RandomForestClassifier(random_state=37).fit(X_train, y_train)\n",
        "print('Done.')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "cedda7bc-6949-4d00-ad36-96ba7be2afb4",
        "id": "QNxn6BzmmHcy"
      },
      "execution_count": 312,
      "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": "ZitUPecemHcy"
      },
      "execution_count": 313,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test_x = pd.DataFrame(test_x)\n",
        "\n",
        "preds = RF.predict(test_x)   \n",
        "print('Done.')   "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "827518b1-e2e0-44db-c633-92ff0928ea83",
        "id": "BwTORrAdmHcz"
      },
      "execution_count": 314,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Done.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "preds"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "22d6d442-d5c8-4088-e544-13c04a665534",
        "id": "DbNwsGI2mHcz"
      },
      "execution_count": 315,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1., 2., 2., 1., 1., 1., 2., 1., 1., 1., 2., 2., 1., 1., 1., 1., 1.,\n",
              "       1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
              "       1., 1., 1., 0., 2., 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.,\n",
              "       1., 1., 1., 0., 1., 1., 1., 1., 1., 1., 2., 1., 1., 1., 1., 1., 2.,\n",
              "       1., 0., 1., 0., 1., 2., 1., 2., 2., 1., 2., 1., 2., 1., 2., 1., 2.,\n",
              "       1., 1., 1., 1., 2., 1., 2., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
              "       2., 1., 1., 1., 2., 1., 1., 2., 1., 1., 1., 1., 1., 1., 1., 2., 2.,\n",
              "       1., 2., 1., 1., 1., 1., 1., 1., 2., 2., 1., 1., 1., 1., 1., 1., 1.,\n",
              "       1., 1., 1., 1., 1., 2., 1., 2., 2., 1., 2., 1., 1., 1., 1., 1., 2.,\n",
              "       1., 1., 1., 1., 1., 1., 2., 1., 1., 2., 1., 1., 1., 2., 1., 1., 1.,\n",
              "       2., 1., 2., 1., 2., 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.,\n",
              "       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.,\n",
              "       1., 1., 1., 1., 1., 2., 1., 1., 2., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
              "       1., 1., 1., 1., 1., 1., 1., 1., 1., 2., 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.,\n",
              "       1., 1., 1., 1.])"
            ]
          },
          "metadata": {},
          "execution_count": 315
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.countplot(x=preds);"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "outputId": "244a9392-2175-472b-d964-27313685c7bd",
        "id": "xOUunqOEmHcz"
      },
      "execution_count": 316,
      "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": [
        "# 3.Modeling_DL"
      ],
      "metadata": {
        "id": "Vl05J9tpExbR"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "IFIeaDruExgK"
      },
      "execution_count": 144,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Rut5hjM1Exna"
      },
      "execution_count": 144,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 4.Model Comparison"
      ],
      "metadata": {
        "id": "1YwpCO-LHGWh"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "_51zBFdKHGcp"
      },
      "execution_count": 144,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "3s2Na48HHGhp"
      },
      "execution_count": 144,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "gSZlZQe8HGno"
      },
      "execution_count": 144,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 5.Inference\n",
        "- finally, performs perdiction for test set"
      ],
      "metadata": {
        "id": "ex7768-Ktt2u"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# test_x.describe()"
      ],
      "metadata": {
        "id": "o6wlxZ63-aor"
      },
      "execution_count": 145,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# preds = RF_model.predict(test_x)\n",
        "# print('Done.')"
      ],
      "metadata": {
        "id": "sUcI9qjRtyU2"
      },
      "execution_count": 146,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# test_x['Y_Class']=preds\n",
        "# preds"
      ],
      "metadata": {
        "id": "IYsJ59R8DvKQ"
      },
      "execution_count": 147,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "OhYKzO279lgJ"
      },
      "execution_count": 147,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 6.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('smartFactory_submission.csv', index=False)  "
      ],
      "metadata": {
        "id": "Cyfb21qOt4dn"
      },
      "execution_count": 235,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sns.countplot(x=preds, order=[0,1,2]);"
      ],
      "metadata": {
        "id": "l4x3CNe09oiT",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "outputId": "63362b4b-07bb-44ee-978d-61bb02953c43"
      },
      "execution_count": 236,
      "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": [
        "# THE END\n"
      ],
      "metadata": {
        "id": "hcNFz5zguHMQ"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "5asYFRqp7j0f"
      },
      "execution_count": 149,
      "outputs": []
    }
  ]
}