smartFactory_preprocessing_Yumin.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "toc_visible": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU",
    "gpuClass": "standard"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "LG AI hackathon Smart Factory\n",
        "[대회 링크](https://dacon.io/competitions/official/236055/overview/description)"
      ],
      "metadata": {
        "id": "7Nq-Y7oIvUIC"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 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",
        "\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": "5664702d-bb1b-419e-d461-37ae362207fb"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 0.Data Load"
      ],
      "metadata": {
        "id": "RYf_ZaTJ9vgg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train_df = pd.read_csv('/content/drive/MyDrive/train.csv')\n",
        "train_df = train_df.drop(columns=['PRODUCT_ID', 'TIMESTAMP'])"
      ],
      "metadata": {
        "id": "kLBoulM6u2Fb"
      },
      "execution_count": 52,
      "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": 53,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 1.Pre-processing"
      ],
      "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": [
        "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 "
      ],
      "metadata": {
        "id": "Xteo4cvno0Ys"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# heatmap: `random X features` and `Y_Quality` correlation \n",
        "import random\n",
        "a = ['Y_Quality']\n",
        "\n",
        "for i in range(20):\n",
        "    num = random.randint(1,2875)\n",
        "    a.append(cols_name[i])\n",
        "\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": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 632
        },
        "id": "E1vfXTKcK9Dt",
        "outputId": "98f09ed0-8d40-40ad-d31f-80ed869aa99f"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x720 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# UNBALANCED data --> metrics: precision, recall, f1-score \n",
        "sns.countplot(x='Y_Class', data=train_df);"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 280
        },
        "id": "Yw54I6dqY1yt",
        "outputId": "42e2ff67-97f3-4f11-c64c-d438fb5c2e29"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 1.2 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', 'Y_Class']\n",
        "train_df['line_pcode_class'] = 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_class'].value_counts())  "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GqgXqn8c12dV",
        "outputId": "d7e34e1a-a185-4e14-8881-ca3d1d5453f2"
      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "T100306:T_31:1    144\n",
            "T100304:T_31:1    141\n",
            "T050304:A_31:1     37\n",
            "T010306:A_31:1     37\n",
            "T010305:A_31:1     35\n",
            "T050304:A_31:0     30\n",
            "T010306:A_31:2     27\n",
            "T050307:A_31:2     22\n",
            "T100304:T_31:0     17\n",
            "T100306:T_31:2     16\n",
            "T100304:T_31:2     14\n",
            "T010305:A_31:0     13\n",
            "T050304:A_31:2     11\n",
            "T010305:A_31:2     11\n",
            "T050307:A_31:0     11\n",
            "T100306:T_31:0     11\n",
            "T050307:A_31:1      9\n",
            "T010306:A_31:0      6\n",
            "T100304:O_31:1      3\n",
            "T100306:O_31:2      2\n",
            "T100306:O_31:1      1\n",
            "Name: line_pcode_class, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Label Encoding \n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "encoder = LabelEncoder()\n",
        "col = ['line_pcode_class']\n",
        "\n",
        "for i in col:\n",
        "    train_df[i] = encoder.fit_transform(train_df[i])\n",
        "\n",
        "# correlation\n",
        "# print(train_x[['Y_Quality', 'line_pcode']].corr()) "
      ],
      "metadata": {
        "id": "wWsCTcAI7gsq"
      },
      "execution_count": 55,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_df.line_pcode_class.unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Z7Mcr_nz3NPa",
        "outputId": "ddc222b3-b43d-4e5b-f20b-5fa0267feb6d"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 7, 11,  8, 13, 14, 19,  5,  0,  1,  6, 10,  4,  2, 15,  3, 18, 20,\n",
              "        9, 12, 17, 16])"
            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ]
    },
    {
      "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": 57,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 1.3 Missing Values"
      ],
      "metadata": {
        "id": "qfIq2KqeFZdt"
      }
    },
    {
      "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": "ff0663d3-55b8-4f9c-e409-1c53f9379c1c",
        "id": "tmQ4DYKrFZdu"
      },
      "execution_count": 58,
      "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": "572964d1-9f75-4c78-e10e-3f263fefe5c0",
        "id": "aE5B1BfPFZdu"
      },
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 2) fillout remaining NULL with 'line_pcdoe' median\n",
        "\n",
        "median_dic = {}\n",
        "for i in range(21): \n",
        "    a = train_df.loc[train_df['line_pcode_class']==i] \n",
        "    m = a.median().median()     \n",
        "    median_dic[i] = m\n",
        "    a.fillna(m, inplace=True)\n",
        "    train_df.loc[train_df['line_pcode_class']==i] = a  \n",
        "\n",
        "print(median_dic)     "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CVc3s9K1I91o",
        "outputId": "32795e97-2793-4402-b9c4-0a8e1b5134d4"
      },
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/pandas/core/frame.py:5176: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  return super().fillna(\n",
            "/usr/local/lib/python3.8/dist-packages/pandas/core/frame.py:5176: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  return super().fillna(\n",
            "/usr/local/lib/python3.8/dist-packages/pandas/core/frame.py:5176: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  return super().fillna(\n",
            "/usr/local/lib/python3.8/dist-packages/pandas/core/frame.py:5176: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  return super().fillna(\n",
            "/usr/local/lib/python3.8/dist-packages/pandas/core/frame.py:5176: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  return super().fillna(\n",
            "/usr/local/lib/python3.8/dist-packages/pandas/core/frame.py:5176: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  return super().fillna(\n",
            "/usr/local/lib/python3.8/dist-packages/pandas/core/frame.py:5176: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  return super().fillna(\n",
            "/usr/local/lib/python3.8/dist-packages/pandas/core/frame.py:5176: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  return super().fillna(\n",
            "/usr/local/lib/python3.8/dist-packages/pandas/core/frame.py:5176: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  return super().fillna(\n",
            "/usr/local/lib/python3.8/dist-packages/pandas/core/frame.py:5176: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  return super().fillna(\n",
            "/usr/local/lib/python3.8/dist-packages/pandas/core/frame.py:5176: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  return super().fillna(\n",
            "/usr/local/lib/python3.8/dist-packages/pandas/core/frame.py:5176: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  return super().fillna(\n",
            "/usr/local/lib/python3.8/dist-packages/pandas/core/frame.py:5176: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  return super().fillna(\n",
            "/usr/local/lib/python3.8/dist-packages/pandas/core/frame.py:5176: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  return super().fillna(\n",
            "/usr/local/lib/python3.8/dist-packages/pandas/core/frame.py:5176: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  return super().fillna(\n",
            "/usr/local/lib/python3.8/dist-packages/pandas/core/frame.py:5176: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  return super().fillna(\n",
            "/usr/local/lib/python3.8/dist-packages/pandas/core/frame.py:5176: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  return super().fillna(\n",
            "/usr/local/lib/python3.8/dist-packages/pandas/core/frame.py:5176: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  return super().fillna(\n",
            "/usr/local/lib/python3.8/dist-packages/pandas/core/frame.py:5176: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  return super().fillna(\n",
            "/usr/local/lib/python3.8/dist-packages/pandas/core/frame.py:5176: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  return super().fillna(\n",
            "/usr/local/lib/python3.8/dist-packages/pandas/core/frame.py:5176: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  return super().fillna(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{0: 20.8, 1: 19.8, 2: 19.2, 3: 20.9791666675, 4: 19.5, 5: 19.0, 6: 48.8, 7: 48.9, 8: 48.9, 9: 51.6, 10: 52.0, 11: 51.2, 12: 9.0, 13: 9.05, 14: 9.0, 15: 9.0, 16: 8.6966666665, 17: 8.75, 18: 9.0, 19: 9.0, 20: 9.0}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 1.4 Scaling\n",
        "\n",
        "[outlier Dection: IsolationForest](https://scikit-learn.org/stable/auto_examples/miscellaneous/plot_anomaly_comparison.html)"
      ],
      "metadata": {
        "id": "4038bXsY3ngy"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 1.4.1 Outliers"
      ],
      "metadata": {
        "id": "3KvQyKbDHavB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "xs = list(train_df.iloc[:, 6:].columns)"
      ],
      "metadata": {
        "id": "Ynke3TMsTBJ_"
      },
      "execution_count": 61,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# outlier (replace 1Q, 3Q with MEDIAN)\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": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "id": "nJuv_OqajZze",
        "outputId": "8e216800-6d43-43f4-a229-e2dfb4fc3f14"
      },
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    Y_Class  Y_Quality   X_1   X_2   X_3   X_4   X_5   X_6   X_7   X_8  ...  \\\n",
              "0         1   0.533433  48.9  48.9  48.9  48.9  48.9  48.9  48.9  48.9  ...   \n",
              "1         2   0.541819  51.2  51.2  51.2  51.2  51.2  51.2  51.2  51.2  ...   \n",
              "2         1   0.531267  48.9  48.9  48.9  48.9  48.9  48.9  48.9  48.9  ...   \n",
              "3         2   0.537325  51.2  51.2  51.2  51.2  51.2  51.2  51.2  51.2  ...   \n",
              "4         1   0.531590  48.9  48.9  48.9  48.9  48.9  48.9  48.9  48.9  ...   \n",
              "..      ...        ...   ...   ...   ...   ...   ...   ...   ...   ...  ...   \n",
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              "594       0   0.524022  48.8  48.8  48.8  48.8  48.8  48.8  48.8  48.8  ...   \n",
              "595       0   0.521289  48.8  48.8  48.8  48.8  48.8  48.8  48.8  48.8  ...   \n",
              "596       1   0.531375  40.0  94.0   0.0  45.0  11.0   0.0  45.0  10.0  ...   \n",
              "597       1   0.533702  21.0  87.0   0.0  45.0  10.0   0.0  61.0  10.0  ...   \n",
              "\n",
              "        X_2867     X_2868     X_2869     X_2870     X_2871  X_2872  X_2873  \\\n",
              "0    40.890000  32.560000  34.090000  77.770000  48.900000       0       0   \n",
              "1    42.820000  43.920000  35.340000  72.550000  51.200000       0       0   \n",
              "2    36.650000  42.470000  36.530000  78.350000  48.900000       0       0   \n",
              "3    39.170000  52.170000  30.580000  71.780000  51.200000       0       0   \n",
              "4    41.890000  46.930000  33.090000  76.970000  48.900000       0       0   \n",
              "..         ...        ...        ...        ...        ...     ...     ...   \n",
              "593   9.000000   9.000000   9.000000   9.000000   9.000000       0       0   \n",
              "594  53.070000  50.890000  55.100000  66.490000   1.000000       0       0   \n",
              "595  48.800000  48.800000  48.800000  48.800000   1.000000       0       0   \n",
              "596   9.000000   9.000000   9.000000   9.000000   9.000000       0       0   \n",
              "597   8.696667   8.696667   8.696667   8.696667   8.696667       0       0   \n",
              "\n",
              "     X_2874  X_2875  line_pcode_class  \n",
              "0         0       0                 7  \n",
              "1         0       0                11  \n",
              "2         0       0                 7  \n",
              "3         0       0                11  \n",
              "4         0       0                 7  \n",
              "..      ...     ...               ...  \n",
              "593       0       0                19  \n",
              "594       0       0                 6  \n",
              "595       0       0                 6  \n",
              "596       0       0                12  \n",
              "597       0       0                16  \n",
              "\n",
              "[598 rows x 2878 columns]"
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              "      <td>48.8</td>\n",
              "      <td>48.8</td>\n",
              "      <td>48.8</td>\n",
              "      <td>48.8</td>\n",
              "      <td>48.8</td>\n",
              "      <td>...</td>\n",
              "      <td>48.800000</td>\n",
              "      <td>48.800000</td>\n",
              "      <td>48.800000</td>\n",
              "      <td>48.800000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>596</th>\n",
              "      <td>1</td>\n",
              "      <td>0.531375</td>\n",
              "      <td>40.0</td>\n",
              "      <td>94.0</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>...</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>597</th>\n",
              "      <td>1</td>\n",
              "      <td>0.533702</td>\n",
              "      <td>21.0</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>61.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>...</td>\n",
              "      <td>8.696667</td>\n",
              "      <td>8.696667</td>\n",
              "      <td>8.696667</td>\n",
              "      <td>8.696667</td>\n",
              "      <td>8.696667</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>598 rows × 2878 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7ef06734-31b9-4c71-9f78-7da4751f76f3')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "\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-7ef06734-31b9-4c71-9f78-7da4751f76f3');\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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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_df.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        },
        "id": "fAQulBUxkwNq",
        "outputId": "be7d73c3-952c-4923-b2cd-af7a59b5d6de"
      },
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        Y_Quality         X_1         X_2         X_3         X_4         X_5  \\\n",
              "count  598.000000  598.000000  598.000000  598.000000  598.000000  598.000000   \n",
              "mean     0.530896   15.634072   69.742768   14.227717   40.490259   20.292935   \n",
              "std      0.007401   18.991764   31.766601   19.470502   11.110994   15.252952   \n",
              "min      0.500856    1.000000   19.000000    0.000000   19.000000   10.000000   \n",
              "25%      0.527535    2.000000   48.800000    0.000000   45.000000   10.000000   \n",
              "50%      0.530436    2.000000   91.000000    0.000000   45.000000   11.000000   \n",
              "75%      0.533433   19.800000   95.000000   19.800000   45.000000   19.800000   \n",
              "max      0.578841  103.000000  102.000000   52.000000   52.000000   52.000000   \n",
              "\n",
              "              X_6         X_7         X_8         X_9  ...      X_2867  \\\n",
              "count  598.000000  598.000000  598.000000  598.000000  ...  598.000000   \n",
              "mean    14.227717   42.709323   20.092266   38.430059  ...   20.128581   \n",
              "std     19.470502   12.576686   15.380152   13.127259  ...   17.355424   \n",
              "min      0.000000   19.000000   10.000000   19.000000  ...    8.696667   \n",
              "25%      0.000000   45.000000   10.000000   31.000000  ...    9.000000   \n",
              "50%      0.000000   45.000000   10.000000   31.000000  ...    9.000000   \n",
              "75%     19.800000   51.000000   19.800000   52.000000  ...   19.800000   \n",
              "max     52.000000   62.000000   52.000000   52.000000  ...   68.660000   \n",
              "\n",
              "           X_2868      X_2869      X_2870      X_2871  X_2872  X_2873  X_2874  \\\n",
              "count  598.000000  598.000000  598.000000  598.000000   598.0   598.0   598.0   \n",
              "mean    19.459434   19.802846   22.309518   11.412912     0.0     0.0     0.0   \n",
              "std     16.002440   16.795288   21.532037    9.409032     0.0     0.0     0.0   \n",
              "min      8.696667    8.696667    8.696667    1.000000     0.0     0.0     0.0   \n",
              "25%      9.000000    9.000000    9.000000    9.000000     0.0     0.0     0.0   \n",
              "50%      9.000000    9.000000    9.000000    9.000000     0.0     0.0     0.0   \n",
              "75%     19.800000   19.800000   19.800000   16.512500     0.0     0.0     0.0   \n",
              "max     60.410000   59.930000   79.750000   51.200000     0.0     0.0     0.0   \n",
              "\n",
              "       X_2875  line_pcode_class  \n",
              "count   598.0        598.000000  \n",
              "mean      0.0         11.807692  \n",
              "std       0.0          6.200028  \n",
              "min       0.0          0.000000  \n",
              "25%       0.0          6.000000  \n",
              "50%       0.0         14.000000  \n",
              "75%       0.0         19.000000  \n",
              "max       0.0         20.000000  \n",
              "\n",
              "[8 rows x 2877 columns]"
            ],
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></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",
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              "      <th>X_7</th>\n",
              "      <th>X_8</th>\n",
              "      <th>X_9</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",
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              "      <th>X_2875</th>\n",
              "      <th>line_pcode_class</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>598.000000</td>\n",
              "      <td>598.000000</td>\n",
              "      <td>598.000000</td>\n",
              "      <td>598.000000</td>\n",
              "      <td>598.000000</td>\n",
              "      <td>598.000000</td>\n",
              "      <td>598.000000</td>\n",
              "      <td>598.000000</td>\n",
              "      <td>598.000000</td>\n",
              "      <td>598.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>598.000000</td>\n",
              "      <td>598.000000</td>\n",
              "      <td>598.000000</td>\n",
              "      <td>598.000000</td>\n",
              "      <td>598.000000</td>\n",
              "      <td>598.0</td>\n",
              "      <td>598.0</td>\n",
              "      <td>598.0</td>\n",
              "      <td>598.0</td>\n",
              "      <td>598.000000</td>\n",
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              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>0.530896</td>\n",
              "      <td>15.634072</td>\n",
              "      <td>69.742768</td>\n",
              "      <td>14.227717</td>\n",
              "      <td>40.490259</td>\n",
              "      <td>20.292935</td>\n",
              "      <td>14.227717</td>\n",
              "      <td>42.709323</td>\n",
              "      <td>20.092266</td>\n",
              "      <td>38.430059</td>\n",
              "      <td>...</td>\n",
              "      <td>20.128581</td>\n",
              "      <td>19.459434</td>\n",
              "      <td>19.802846</td>\n",
              "      <td>22.309518</td>\n",
              "      <td>11.412912</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>11.807692</td>\n",
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              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.007401</td>\n",
              "      <td>18.991764</td>\n",
              "      <td>31.766601</td>\n",
              "      <td>19.470502</td>\n",
              "      <td>11.110994</td>\n",
              "      <td>15.252952</td>\n",
              "      <td>19.470502</td>\n",
              "      <td>12.576686</td>\n",
              "      <td>15.380152</td>\n",
              "      <td>13.127259</td>\n",
              "      <td>...</td>\n",
              "      <td>17.355424</td>\n",
              "      <td>16.002440</td>\n",
              "      <td>16.795288</td>\n",
              "      <td>21.532037</td>\n",
              "      <td>9.409032</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>6.200028</td>\n",
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              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.500856</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>19.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>19.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>19.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>19.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>8.696667</td>\n",
              "      <td>8.696667</td>\n",
              "      <td>8.696667</td>\n",
              "      <td>8.696667</td>\n",
              "      <td>1.000000</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",
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              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>0.527535</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>48.800000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>45.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>45.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>31.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>6.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>0.530436</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>91.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>45.000000</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>45.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>31.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>14.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>0.533433</td>\n",
              "      <td>19.800000</td>\n",
              "      <td>95.000000</td>\n",
              "      <td>19.800000</td>\n",
              "      <td>45.000000</td>\n",
              "      <td>19.800000</td>\n",
              "      <td>19.800000</td>\n",
              "      <td>51.000000</td>\n",
              "      <td>19.800000</td>\n",
              "      <td>52.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>19.800000</td>\n",
              "      <td>19.800000</td>\n",
              "      <td>19.800000</td>\n",
              "      <td>19.800000</td>\n",
              "      <td>16.512500</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>19.000000</td>\n",
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              "    <tr>\n",
              "      <th>max</th>\n",
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              "      <td>103.000000</td>\n",
              "      <td>102.000000</td>\n",
              "      <td>52.000000</td>\n",
              "      <td>52.000000</td>\n",
              "      <td>52.000000</td>\n",
              "      <td>52.000000</td>\n",
              "      <td>62.000000</td>\n",
              "      <td>52.000000</td>\n",
              "      <td>52.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>68.660000</td>\n",
              "      <td>60.410000</td>\n",
              "      <td>59.930000</td>\n",
              "      <td>79.750000</td>\n",
              "      <td>51.200000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>20.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>8 rows × 2877 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a16883f3-de32-4c5e-ad18-d0de64b5e0f3')\"\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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              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
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              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
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              "    .colab-df-convert:hover {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
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              "          document.querySelector('#df-a16883f3-de32-4c5e-ad18-d0de64b5e0f3 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-a16883f3-de32-4c5e-ad18-d0de64b5e0f3');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 1.5 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",
        "from sklearn.preprocessing import LabelEncoder\n",
        "encoder = LabelEncoder()\n",
        "col = ['line_pcode']\n",
        "\n",
        "for i in col:\n",
        "    test_df[i] = encoder.fit_transform(test_df[i])"
      ],
      "metadata": {
        "id": "nT3nx4o7B085"
      },
      "execution_count": 64,
      "outputs": []
    },
    {
      "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": "12a3f1c4-380b-40bc-9b4e-66d40b782ed6",
        "id": "9vLY-L_IBQy5"
      },
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(test_df['line_pcode'].unique())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_7VrI1t3C5tK",
        "outputId": "3fb89cd8-3653-43a2-9974-ffb065804322"
      },
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "8"
            ]
          },
          "metadata": {},
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 2) fillout remaining NULL with 'line_pcdoe' median\n",
        "\n",
        "median_dic = {}\n",
        "for i in range(8): \n",
        "    a = test_df.loc[test_df['line_pcode']==i] \n",
        "    m = a.median().median()    \n",
        "    median_dic[i] = m  \n",
        "    a.fillna(m, inplace=True)\n",
        "    test_df.loc[test_df['line_pcode']==i] = a\n",
        "\n",
        "print(median_dic)  "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b341fddd-0c0c-48e1-c1b0-7752b2f27027",
        "id": "7UOy4D0hq7lM"
      },
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/pandas/core/frame.py:5176: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  return super().fillna(\n",
            "/usr/local/lib/python3.8/dist-packages/pandas/core/frame.py:5176: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  return super().fillna(\n",
            "/usr/local/lib/python3.8/dist-packages/pandas/core/frame.py:5176: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  return super().fillna(\n",
            "/usr/local/lib/python3.8/dist-packages/pandas/core/frame.py:5176: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  return super().fillna(\n",
            "/usr/local/lib/python3.8/dist-packages/pandas/core/frame.py:5176: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  return super().fillna(\n",
            "/usr/local/lib/python3.8/dist-packages/pandas/core/frame.py:5176: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  return super().fillna(\n",
            "/usr/local/lib/python3.8/dist-packages/pandas/core/frame.py:5176: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  return super().fillna(\n",
            "/usr/local/lib/python3.8/dist-packages/pandas/core/frame.py:5176: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  return super().fillna(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{0: 0.9, 1: 0.8500000000000001, 2: 7.3306451615, 3: 3.9975, 4: 0.0, 5: 0.0, 6: 0.0, 7: 0.0}\n"
          ]
        }
      ]
    },
    {
      "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": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        },
        "outputId": "2a4a15b3-1024-4250-c55b-71047b48e3e2",
        "id": "skEOybzqBQy6"
      },
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              X_1         X_2         X_3         X_4         X_5         X_6  \\\n",
              "count  310.000000  310.000000  310.000000  310.000000  310.000000  310.000000   \n",
              "mean     4.121721   74.757204    0.721721   35.995914    8.773334    0.721721   \n",
              "std     14.760337   37.749826    1.775472   17.211959    3.095255    1.775472   \n",
              "min      0.850000    0.850000    0.000000    0.850000    0.850000    0.000000   \n",
              "25%      2.000000   87.250000    0.000000   45.000000   10.000000    0.000000   \n",
              "50%      2.000000   93.000000    0.000000   45.000000   10.000000    0.000000   \n",
              "75%      2.000000   96.000000    0.000000   45.000000   10.000000    0.000000   \n",
              "max    154.000000  102.000000    7.330645   45.000000   11.000000    7.330645   \n",
              "\n",
              "              X_7         X_8         X_9        X_10  ...      X_2867  \\\n",
              "count  310.000000  310.000000  310.000000  310.000000  ...  310.000000   \n",
              "mean    38.728172    8.570108   33.963656    2.289463  ...    5.979528   \n",
              "std     18.936568    2.972388   18.620747    1.249758  ...   16.614796   \n",
              "min      0.850000    0.850000    0.850000    0.850000  ...    0.000000   \n",
              "25%     45.000000   10.000000   31.000000    2.000000  ...    0.000000   \n",
              "50%     45.000000   10.000000   31.000000    2.000000  ...    0.000000   \n",
              "75%     50.000000   10.000000   52.000000    2.000000  ...    0.000000   \n",
              "max     67.000000   11.000000   52.000000    7.330645  ...   60.490000   \n",
              "\n",
              "           X_2868      X_2869      X_2870      X_2871      X_2872      X_2873  \\\n",
              "count  310.000000  310.000000  310.000000  310.000000  310.000000  310.000000   \n",
              "mean     5.648270    6.109786    7.163109    0.201613    7.805342    7.918567   \n",
              "std     15.734697   16.959788   19.936360    0.389399   41.834183   42.411567   \n",
              "min      0.000000    0.000000    0.000000    0.000000    0.000000    0.000000   \n",
              "25%      0.000000    0.000000    0.000000    0.000000    0.000000    0.000000   \n",
              "50%      0.000000    0.000000    0.000000    0.000000    0.000000    0.000000   \n",
              "75%      0.000000    0.000000    0.000000    0.000000    0.000000    0.000000   \n",
              "max     60.310000   58.290000   67.240000    1.000000  268.000000  278.600000   \n",
              "\n",
              "           X_2874      X_2875  line_pcode  \n",
              "count  310.000000  310.000000  310.000000  \n",
              "mean     7.669213    7.249535    5.138710  \n",
              "std     41.243141   38.544468    2.064522  \n",
              "min      0.000000    0.000000    0.000000  \n",
              "25%      0.000000    0.000000    5.000000  \n",
              "50%      0.000000    0.000000    5.000000  \n",
              "75%      0.000000    0.000000    7.000000  \n",
              "max    271.400000  245.600000    7.000000  \n",
              "\n",
              "[8 rows x 2876 columns]"
            ],
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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",
              "      <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_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>310.000000</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>...</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>4.121721</td>\n",
              "      <td>74.757204</td>\n",
              "      <td>0.721721</td>\n",
              "      <td>35.995914</td>\n",
              "      <td>8.773334</td>\n",
              "      <td>0.721721</td>\n",
              "      <td>38.728172</td>\n",
              "      <td>8.570108</td>\n",
              "      <td>33.963656</td>\n",
              "      <td>2.289463</td>\n",
              "      <td>...</td>\n",
              "      <td>5.979528</td>\n",
              "      <td>5.648270</td>\n",
              "      <td>6.109786</td>\n",
              "      <td>7.163109</td>\n",
              "      <td>0.201613</td>\n",
              "      <td>7.805342</td>\n",
              "      <td>7.918567</td>\n",
              "      <td>7.669213</td>\n",
              "      <td>7.249535</td>\n",
              "      <td>5.138710</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>14.760337</td>\n",
              "      <td>37.749826</td>\n",
              "      <td>1.775472</td>\n",
              "      <td>17.211959</td>\n",
              "      <td>3.095255</td>\n",
              "      <td>1.775472</td>\n",
              "      <td>18.936568</td>\n",
              "      <td>2.972388</td>\n",
              "      <td>18.620747</td>\n",
              "      <td>1.249758</td>\n",
              "      <td>...</td>\n",
              "      <td>16.614796</td>\n",
              "      <td>15.734697</td>\n",
              "      <td>16.959788</td>\n",
              "      <td>19.936360</td>\n",
              "      <td>0.389399</td>\n",
              "      <td>41.834183</td>\n",
              "      <td>42.411567</td>\n",
              "      <td>41.243141</td>\n",
              "      <td>38.544468</td>\n",
              "      <td>2.064522</td>\n",
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              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.850000</td>\n",
              "      <td>0.850000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.850000</td>\n",
              "      <td>0.850000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.850000</td>\n",
              "      <td>0.850000</td>\n",
              "      <td>0.850000</td>\n",
              "      <td>0.850000</td>\n",
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              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
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              "      <th>25%</th>\n",
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              "      <td>87.250000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>45.000000</td>\n",
              "      <td>10.000000</td>\n",
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              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>5.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>2.000000</td>\n",
              "      <td>93.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>45.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>45.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>31.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</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>0.000000</td>\n",
              "      <td>45.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>50.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>52.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</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>7.330645</td>\n",
              "      <td>45.000000</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>7.330645</td>\n",
              "      <td>67.000000</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>52.000000</td>\n",
              "      <td>7.330645</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",
              "</div>\n",
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              "\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 2.Modeling_ML"
      ],
      "metadata": {
        "id": "7GodRp6O-5TM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X_train = train_df.drop(columns=['Y_Class','Y_Quality'])\n",
        "y_train = train_df['Y_Quality']\n",
        "\n",
        "test_x = test_df   "
      ],
      "metadata": {
        "id": "mtzzkA9n_flg"
      },
      "execution_count": 69,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test_x.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4438a031-1ff7-4056-c2b3-8ac2989dd19f",
        "id": "TTEMi25a_fli"
      },
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "X_1           0\n",
              "X_2           0\n",
              "X_3           0\n",
              "X_4           0\n",
              "X_5           0\n",
              "             ..\n",
              "X_2872        0\n",
              "X_2873        0\n",
              "X_2874        0\n",
              "X_2875        0\n",
              "line_pcode    0\n",
              "Length: 2876, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "MvyJSC-8VU14"
      },
      "execution_count": 70,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Y_Quality 예측"
      ],
      "metadata": {
        "id": "Aveyf4DbuLHD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import GradientBoostingRegressor   \n",
        "\n",
        "RF = GradientBoostingRegressor(random_state=37).fit(X_train, y_train) \n",
        "print('Done.') "
      ],
      "metadata": {
        "id": "U7pCY5HPsv4Q",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "10853de1-7887-4c27-e983-d2c97cc1ea22"
      },
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Done.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "preds=RF.predict(test_x)  \n",
        "print('Done.')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d13562e0-1bbc-42c8-e328-42678f2d512a",
        "id": "Hpn3cF6CVVgM"
      },
      "execution_count": 72,
      "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": "552bbd61-79c6-4950-8630-8a846f32f044",
        "id": "xPXPRUoYVVgM"
      },
      "execution_count": 73,
      "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  ...  \\\n",
              "0    2.00  94.00  0.00  45.00  10.00  0.00  51.00  10.00  52.00  2.00  ...   \n",
              "1    2.00  93.00  0.00  45.00  11.00  0.00  45.00  10.00  31.00  2.00  ...   \n",
              "2    2.00  95.00  0.00  45.00  11.00  0.00  45.00  10.00  31.00  2.00  ...   \n",
              "3    0.90   0.90  0.90   0.90   0.90  0.90   0.90   0.90   0.90  0.90  ...   \n",
              "4    0.85   0.85  0.85   0.85   0.85  0.85   0.85   0.85   0.85  0.85  ...   \n",
              "..    ...    ...   ...    ...    ...   ...    ...    ...    ...   ...  ...   \n",
              "305  2.00  91.00  0.00  45.00  10.00  0.00  51.00  10.00  52.00  2.00  ...   \n",
              "306  2.00  96.00  0.00  45.00  11.00  0.00  45.00  10.00  31.00  2.00  ...   \n",
              "307  2.00  91.00  0.00  45.00  10.00  0.00  50.00  10.00  52.00  2.00  ...   \n",
              "308  2.00  95.00  0.00  45.00  10.00  0.00  51.00  10.00  52.00  2.00  ...   \n",
              "309  2.00  87.00  0.00  45.00  10.00  0.00  51.00  10.00  52.00  2.00  ...   \n",
              "\n",
              "     X_2868  X_2869  X_2870  X_2871  X_2872  X_2873  X_2874  X_2875  \\\n",
              "0      0.00    0.00    0.00    0.00    0.00    0.00    0.00    0.00   \n",
              "1      0.00    0.00    0.00    0.00    0.00    0.00    0.00    0.00   \n",
              "2      0.00    0.00    0.00    0.00    0.00    0.00    0.00    0.00   \n",
              "3      0.90    0.90    0.90    0.90    0.90    0.90    0.90    0.90   \n",
              "4      0.85    0.85    0.85    0.85    0.85    0.85    0.85    0.85   \n",
              "..      ...     ...     ...     ...     ...     ...     ...     ...   \n",
              "305    0.00    0.00    0.00    0.00    0.00    0.00    0.00    0.00   \n",
              "306    0.00    0.00    0.00    0.00    0.00    0.00    0.00    0.00   \n",
              "307    0.00    0.00    0.00    0.00    0.00    0.00    0.00    0.00   \n",
              "308    0.00    0.00    0.00    0.00    0.00    0.00    0.00    0.00   \n",
              "309    0.00    0.00    0.00    0.00    0.00    0.00    0.00    0.00   \n",
              "\n",
              "     line_pcode  Y_Quality  \n",
              "0             7   0.540655  \n",
              "1             5   0.537758  \n",
              "2             5   0.538051  \n",
              "3             0   0.521730  \n",
              "4             1   0.522480  \n",
              "..          ...        ...  \n",
              "305           7   0.540027  \n",
              "306           5   0.540168  \n",
              "307           7   0.539932  \n",
              "308           7   0.539866  \n",
              "309           7   0.539701  \n",
              "\n",
              "[310 rows x 2877 columns]"
            ],
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              "      <th>1</th>\n",
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              "      <td>93.00</td>\n",
              "      <td>0.00</td>\n",
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              "      <td>11.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>45.00</td>\n",
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              "      <td>0.00</td>\n",
              "      <td>45.00</td>\n",
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              "      <td>0.00</td>\n",
              "      <td>45.00</td>\n",
              "      <td>10.00</td>\n",
              "      <td>31.00</td>\n",
              "      <td>2.00</td>\n",
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              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>5</td>\n",
              "      <td>0.538051</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.90</td>\n",
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              "      <td>0.90</td>\n",
              "      <td>0.90</td>\n",
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              "      <td>0.90</td>\n",
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              "      <td>0</td>\n",
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              "      <th>4</th>\n",
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              "      <td>0.85</td>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>305</th>\n",
              "      <td>2.00</td>\n",
              "      <td>91.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>45.00</td>\n",
              "      <td>10.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>51.00</td>\n",
              "      <td>10.00</td>\n",
              "      <td>52.00</td>\n",
              "      <td>2.00</td>\n",
              "      <td>...</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>7</td>\n",
              "      <td>0.540027</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>306</th>\n",
              "      <td>2.00</td>\n",
              "      <td>96.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>45.00</td>\n",
              "      <td>11.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>45.00</td>\n",
              "      <td>10.00</td>\n",
              "      <td>31.00</td>\n",
              "      <td>2.00</td>\n",
              "      <td>...</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>5</td>\n",
              "      <td>0.540168</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>307</th>\n",
              "      <td>2.00</td>\n",
              "      <td>91.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>45.00</td>\n",
              "      <td>10.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>50.00</td>\n",
              "      <td>10.00</td>\n",
              "      <td>52.00</td>\n",
              "      <td>2.00</td>\n",
              "      <td>...</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>7</td>\n",
              "      <td>0.539932</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>308</th>\n",
              "      <td>2.00</td>\n",
              "      <td>95.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>45.00</td>\n",
              "      <td>10.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>51.00</td>\n",
              "      <td>10.00</td>\n",
              "      <td>52.00</td>\n",
              "      <td>2.00</td>\n",
              "      <td>...</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>7</td>\n",
              "      <td>0.539866</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>309</th>\n",
              "      <td>2.00</td>\n",
              "      <td>87.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>45.00</td>\n",
              "      <td>10.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>51.00</td>\n",
              "      <td>10.00</td>\n",
              "      <td>52.00</td>\n",
              "      <td>2.00</td>\n",
              "      <td>...</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>7</td>\n",
              "      <td>0.539701</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-2c5f3dfc-aaed-4950-b7ed-0301500229e7')\"\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-2c5f3dfc-aaed-4950-b7ed-0301500229e7 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-2c5f3dfc-aaed-4950-b7ed-0301500229e7');\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": 73
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.countplot(x=preds);"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "bP9e3szwGvDU",
        "outputId": "0ef0b9a1-1349-4413-9bfe-e303e3749d7d"
      },
      "execution_count": 74,
      "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": "5s6xZc33ZgBc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 다시 Y_Class 예측"
      ],
      "metadata": {
        "id": "UgNEMkg7uGNe"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# X_train = train_df\n",
        "X_train = train_df.drop(columns=['Y_Class'])\n",
        "y_train = train_df['Y_Class']"
      ],
      "metadata": {
        "id": "XyiKQJUeuHOu"
      },
      "execution_count": 116,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train = pd.DataFrame(train_df['Y_Quality'])\n",
        "y_train = train_df['Y_Class']"
      ],
      "metadata": {
        "id": "FajYD-pCuHT-"
      },
      "execution_count": 117,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WV4E6K-JyShA",
        "outputId": "fa6c899e-6ddc-406e-adcf-63369fbce82c"
      },
      "execution_count": 118,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Y_Quality    0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 118
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "96Be5f46uHZf",
        "outputId": "98219d92-220a-4d53-b1e4-6428efaced73"
      },
      "execution_count": 119,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Y_Quality\n",
              "0     0.533433\n",
              "1     0.541819\n",
              "2     0.531267\n",
              "3     0.537325\n",
              "4     0.531590\n",
              "..         ...\n",
              "593   0.526546\n",
              "594   0.524022\n",
              "595   0.521289\n",
              "596   0.531375\n",
              "597   0.533702\n",
              "\n",
              "[598 rows x 1 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-49a7e01d-3ca4-4314-823a-54f547c25b6d\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Y_Quality</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.533433</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.541819</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.531267</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.537325</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.531590</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>593</th>\n",
              "      <td>0.526546</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>594</th>\n",
              "      <td>0.524022</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>595</th>\n",
              "      <td>0.521289</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>596</th>\n",
              "      <td>0.531375</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>597</th>\n",
              "      <td>0.533702</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>598 rows × 1 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-49a7e01d-3ca4-4314-823a-54f547c25b6d')\"\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-49a7e01d-3ca4-4314-823a-54f547c25b6d 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-49a7e01d-3ca4-4314-823a-54f547c25b6d');\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": 119
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import GradientBoostingClassifier\n",
        "\n",
        "RF =  GradientBoostingClassifier(random_state=37).fit(X_train, y_train)\n",
        "print('Done.')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ov8uVwu2uHfF",
        "outputId": "51a3e08d-6cd1-4fab-a791-b1bc647561b1"
      },
      "execution_count": 120,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Done.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# test_x\n",
        "# test_x = test_x['Y_Quality']\n",
        "# test_x = test_x.drop(columns=['LINE','PRODUCT_CODE'])\n",
        "test_x = test_x['Y_Quality']  "
      ],
      "metadata": {
        "id": "b4_T3w6sv3SA"
      },
      "execution_count": 124,
      "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/"
        },
        "id": "mncvR7m1wE-3",
        "outputId": "b4c8085b-aa63-41b5-f85d-6f715d64782a"
      },
      "execution_count": 125,
      "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": "fd9718a1-b7c8-41fe-cd13-37585b0b5e59"
      },
      "execution_count": 126,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['2', '2', '2', '0', '0', '0', '1', '1', '1', '0', '1', '1', '0',\n",
              "       '1', '1', '2', '2', '2', '2', '2', '2', '2', '2', '2', '2', '2',\n",
              "       '2', '2', '2', '2', '2', '2', '2', '2', '2', '1', '1', '0', '2',\n",
              "       '0', '0', '1', '1', '2', '2', '2', '2', '2', '2', '2', '2', '2',\n",
              "       '1', '1', '1', '2', '2', '2', '2', '2', '2', '1', '1', '1', '1',\n",
              "       '0', '1', '2', '2', '2', '2', '0', '0', '2', '2', '2', '2', '2',\n",
              "       '2', '0', '2', '2', '2', '2', '2', '2', '0', '0', '0', '0', '2',\n",
              "       '2', '2', '2', '2', '2', '2', '2', '2', '2', '2', '2', '2', '2',\n",
              "       '2', '2', '2', '2', '2', '0', '2', '2', '2', '2', '0', '0', '0',\n",
              "       '0', '2', '2', '2', '2', '2', '2', '2', '2', '2', '2', '0', '0',\n",
              "       '1', '1', '1', '2', '2', '2', '2', '2', '2', '2', '2', '0', '0',\n",
              "       '2', '2', '2', '2', '2', '2', '2', '2', '2', '2', '2', '2', '2',\n",
              "       '2', '2', '2', '2', '2', '2', '2', '2', '2', '2', '2', '2', '2',\n",
              "       '2', '2', '2', '2', '2', '2', '2', '2', '2', '2', '2', '2', '2',\n",
              "       '2', '2', '2', '2', '2', '2', '2', '2', '2', '2', '2', '2', '2',\n",
              "       '2', '2', '2', '2', '2', '2', '2', '2', '2', '2', '2', '2', '2',\n",
              "       '2', '2', '2', '2', '2', '2', '2', '2', '2', '2', '2', '2', '2',\n",
              "       '2', '2', '2', '2', '2', '2', '2', '2', '2', '2', '2', '2', '2',\n",
              "       '2', '2', '2', '2', '2', '2', '2', '2', '2', '2', '2', '2', '2',\n",
              "       '2', '1', '1', '1', '1', '1', '1', '0', '1', '2', '2', '2', '2',\n",
              "       '1', '2', '2', '1', '2', '2', '2', '2', '2', '2', '2', '2', '2',\n",
              "       '2', '2', '2', '2', '2', '2', '2', '1', '1', '1', '1', '1', '1',\n",
              "       '1', '2', '2', '2', '2', '2', '1', '0', '2', '2', '2', '2', '2',\n",
              "       '2', '2', '2', '2', '2', '2', '2', '2', '2', '2', '2'],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 126
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.countplot(x=preds);"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "5ajeXCsP9Awa",
        "outputId": "d9f25ddd-c541-42f5-b331-f3001db3e786"
      },
      "execution_count": 127,
      "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": "q5miG4FKZgP8"
      },
      "execution_count": 127,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "l9KnrCyaZgWD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 2.1 Random Forest"
      ],
      "metadata": {
        "id": "CTdljE99teLX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# RF = RandomForestClassifier(random_state=37).fit(train_x, train_y)\n",
        "# print('Done')"
      ],
      "metadata": {
        "id": "0hMK7g6jtn09"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y,\n",
        "                                                  test_size=0.3,\n",
        "                                                  random_state=37)"
      ],
      "metadata": {
        "id": "Ye7HGX9nCJT5"
      },
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "RF_model = RandomForestClassifier()   "
      ],
      "metadata": {
        "id": "RDzDLlMEEcQK"
      },
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "id": "AOlcQ7iC6lu9",
        "outputId": "3f94e38d-1a2f-4a00-d6c0-6bf3b78e4a98"
      },
      "execution_count": 39,
      "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_2867  \\\n",
              "240  19.0   19.0  19.0  19.0  19.0  19.0  19.0  19.0  19.0  19.0  ...   19.00   \n",
              "185   2.0   97.0   0.0  45.0  11.0   0.0  45.0  10.0  31.0   2.0  ...    9.00   \n",
              "315  48.9   48.9  48.9  48.9  48.9  48.9  48.9  48.9  48.9  48.9  ...   61.84   \n",
              "410  20.2   20.2  20.2  20.2  20.2  20.2  20.2  20.2  20.2  20.2  ...   20.20   \n",
              "396   2.0   96.0   0.0  45.0  10.0   0.0  51.0  10.0  52.0   2.0  ...    9.00   \n",
              "..    ...    ...   ...   ...   ...   ...   ...   ...   ...   ...  ...     ...   \n",
              "451   1.0   95.0   0.0  45.0  10.0   0.0  51.0  10.0  52.0   2.0  ...    9.00   \n",
              "22    2.0  102.0   0.0  45.0  11.0   0.0  45.0  10.0  31.0   2.0  ...    9.00   \n",
              "437  19.0   19.0  19.0  19.0  19.0  19.0  19.0  19.0  19.0  19.0  ...   19.00   \n",
              "251   2.0  101.0   0.0  45.0  11.0   0.0  45.0  10.0  31.0   2.0  ...    9.00   \n",
              "363  48.9   48.9  48.9  48.9  48.9  48.9  48.9  48.9  48.9  48.9  ...   54.98   \n",
              "\n",
              "     X_2868  X_2869  X_2870  X_2871  X_2872  X_2873  X_2874  X_2875  \\\n",
              "240   19.00   19.00   19.00    19.0       0       0       0       0   \n",
              "185    9.00    9.00    9.00     9.0       0       0       0       0   \n",
              "315   40.60   56.59   65.12     1.0       0       0       0       0   \n",
              "410   20.20   20.20   20.20    20.2       0       0       0       0   \n",
              "396    9.00    9.00    9.00     9.0       0       0       0       0   \n",
              "..      ...     ...     ...     ...     ...     ...     ...     ...   \n",
              "451    9.00    9.00    9.00     9.0       0       0       0       0   \n",
              "22     9.00    9.00    9.00     9.0       0       0       0       0   \n",
              "437   19.00   19.00   19.00    19.0       0       0       0       0   \n",
              "251    9.00    9.00    9.00     9.0       0       0       0       0   \n",
              "363   56.42   58.47   63.77     1.0       0       0       0       0   \n",
              "\n",
              "     line_pcode  \n",
              "240           1  \n",
              "185           5  \n",
              "315           2  \n",
              "410           0  \n",
              "396           7  \n",
              "..          ...  \n",
              "451           7  \n",
              "22            5  \n",
              "437           1  \n",
              "251           5  \n",
              "363           2  \n",
              "\n",
              "[448 rows x 2876 columns]"
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              "      <th>240</th>\n",
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              "      <td>48.9</td>\n",
              "      <td>48.9</td>\n",
              "      <td>48.9</td>\n",
              "      <td>48.9</td>\n",
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              "      <td>61.84</td>\n",
              "      <td>40.60</td>\n",
              "      <td>56.59</td>\n",
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              "      <th>410</th>\n",
              "      <td>20.2</td>\n",
              "      <td>20.2</td>\n",
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              "      <th>363</th>\n",
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              "      <td>58.47</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>2</td>\n",
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              "<p>448 rows × 2876 columns</p>\n",
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fitted = RF_model.fit(X_train, y_train) "
      ],
      "metadata": {
        "id": "zOitf3hTCWb7"
      },
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "\n",
        "RandomForest_model = RandomForestClassifier(n_estimators = 10,\n",
        "                                            oob_score=False)\n",
        "RandomForest_model.fit(X_train, y_train)\n",
        "RF_predict = tf.cast(RandomForest_model.predict(X_test) > 0.5, \n",
        "                     dtype = tf.float32)\n",
        "\n",
        "print('Accuracy = ', accuracy_score(RF_predict, y_test))\n",
        "print(\"Test Report = \\n\", classification_report(RF_predict, y_test))"
      ],
      "metadata": {
        "id": "6NU83x3q-YAO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(fitted.score(X_train, y_train))\n",
        "print(fitted.score(X_test, y_test))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NusO0ggVBZ1i",
        "outputId": "845f4e78-52c4-450c-9492-3e2974f0f661"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1.0\n",
            "0.7533333333333333\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# from sklearn.ensemble import RandomForestClassifier\n",
        "# from sklearn.datasets import make_classification\n",
        "\n",
        "# X, y = make_classification(n_samples=1000, n_features=4,\n",
        "#                            n_informative=2, n_redundant=0,\n",
        "#                            random_state=37, shuffle=False)\n",
        "\n",
        "# clf = RandomForestClassifier(max_depth=2, random_state=37)\n",
        "# clf.fit(X,y)\n",
        "# print(clf.predict([[0,0,0,0]]))"
      ],
      "metadata": {
        "id": "XT-ZYSwF8xJI"
      },
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "samQfNa3ExTK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 3.Modeling_DL"
      ],
      "metadata": {
        "id": "Vl05J9tpExbR"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "IFIeaDruExgK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Rut5hjM1Exna"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 4.Model Comparison"
      ],
      "metadata": {
        "id": "1YwpCO-LHGWh"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "_51zBFdKHGcp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "3s2Na48HHGhp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "gSZlZQe8HGno"
      },
      "execution_count": null,
      "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": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        },
        "id": "o6wlxZ63-aor",
        "outputId": "32f17b5c-c4eb-40c2-8206-40e971365b07"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              X_1         X_2         X_3         X_4         X_5         X_6  \\\n",
              "count  310.000000  310.000000  310.000000  310.000000  310.000000  310.000000   \n",
              "mean     4.121721   74.757204    0.721721   35.995914    8.773334    0.721721   \n",
              "std     14.760337   37.749826    1.775472   17.211959    3.095255    1.775472   \n",
              "min      0.850000    0.850000    0.000000    0.850000    0.850000    0.000000   \n",
              "25%      2.000000   87.250000    0.000000   45.000000   10.000000    0.000000   \n",
              "50%      2.000000   93.000000    0.000000   45.000000   10.000000    0.000000   \n",
              "75%      2.000000   96.000000    0.000000   45.000000   10.000000    0.000000   \n",
              "max    154.000000  102.000000    7.330645   45.000000   11.000000    7.330645   \n",
              "\n",
              "              X_7         X_8         X_9        X_10  ...      X_2867  \\\n",
              "count  310.000000  310.000000  310.000000  310.000000  ...  310.000000   \n",
              "mean    38.728172    8.570108   33.963656    2.289463  ...    5.979528   \n",
              "std     18.936568    2.972388   18.620747    1.249758  ...   16.614796   \n",
              "min      0.850000    0.850000    0.850000    0.850000  ...    0.000000   \n",
              "25%     45.000000   10.000000   31.000000    2.000000  ...    0.000000   \n",
              "50%     45.000000   10.000000   31.000000    2.000000  ...    0.000000   \n",
              "75%     50.000000   10.000000   52.000000    2.000000  ...    0.000000   \n",
              "max     67.000000   11.000000   52.000000    7.330645  ...   60.490000   \n",
              "\n",
              "           X_2868      X_2869      X_2870      X_2871      X_2872      X_2873  \\\n",
              "count  310.000000  310.000000  310.000000  310.000000  310.000000  310.000000   \n",
              "mean     5.648270    6.109786    7.163109    0.201613    7.805342    7.918567   \n",
              "std     15.734697   16.959788   19.936360    0.389399   41.834183   42.411567   \n",
              "min      0.000000    0.000000    0.000000    0.000000    0.000000    0.000000   \n",
              "25%      0.000000    0.000000    0.000000    0.000000    0.000000    0.000000   \n",
              "50%      0.000000    0.000000    0.000000    0.000000    0.000000    0.000000   \n",
              "75%      0.000000    0.000000    0.000000    0.000000    0.000000    0.000000   \n",
              "max     60.310000   58.290000   67.240000    1.000000  268.000000  278.600000   \n",
              "\n",
              "           X_2874      X_2875  line_pcode  \n",
              "count  310.000000  310.000000  310.000000  \n",
              "mean     7.669213    7.249535    5.138710  \n",
              "std     41.243141   38.544468    2.064522  \n",
              "min      0.000000    0.000000    0.000000  \n",
              "25%      0.000000    0.000000    5.000000  \n",
              "50%      0.000000    0.000000    5.000000  \n",
              "75%      0.000000    0.000000    7.000000  \n",
              "max    271.400000  245.600000    7.000000  \n",
              "\n",
              "[8 rows x 2876 columns]"
            ],
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              "  <div id=\"df-c9f1e4d9-1b5b-49ae-a050-489d1d2c3a61\">\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_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",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\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",
              "      <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>4.121721</td>\n",
              "      <td>74.757204</td>\n",
              "      <td>0.721721</td>\n",
              "      <td>35.995914</td>\n",
              "      <td>8.773334</td>\n",
              "      <td>0.721721</td>\n",
              "      <td>38.728172</td>\n",
              "      <td>8.570108</td>\n",
              "      <td>33.963656</td>\n",
              "      <td>2.289463</td>\n",
              "      <td>...</td>\n",
              "      <td>5.979528</td>\n",
              "      <td>5.648270</td>\n",
              "      <td>6.109786</td>\n",
              "      <td>7.163109</td>\n",
              "      <td>0.201613</td>\n",
              "      <td>7.805342</td>\n",
              "      <td>7.918567</td>\n",
              "      <td>7.669213</td>\n",
              "      <td>7.249535</td>\n",
              "      <td>5.138710</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>14.760337</td>\n",
              "      <td>37.749826</td>\n",
              "      <td>1.775472</td>\n",
              "      <td>17.211959</td>\n",
              "      <td>3.095255</td>\n",
              "      <td>1.775472</td>\n",
              "      <td>18.936568</td>\n",
              "      <td>2.972388</td>\n",
              "      <td>18.620747</td>\n",
              "      <td>1.249758</td>\n",
              "      <td>...</td>\n",
              "      <td>16.614796</td>\n",
              "      <td>15.734697</td>\n",
              "      <td>16.959788</td>\n",
              "      <td>19.936360</td>\n",
              "      <td>0.389399</td>\n",
              "      <td>41.834183</td>\n",
              "      <td>42.411567</td>\n",
              "      <td>41.243141</td>\n",
              "      <td>38.544468</td>\n",
              "      <td>2.064522</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.850000</td>\n",
              "      <td>0.850000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.850000</td>\n",
              "      <td>0.850000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.850000</td>\n",
              "      <td>0.850000</td>\n",
              "      <td>0.850000</td>\n",
              "      <td>0.850000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>2.000000</td>\n",
              "      <td>87.250000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>45.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>45.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>31.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>5.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>2.000000</td>\n",
              "      <td>93.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>45.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>45.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>31.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</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>0.000000</td>\n",
              "      <td>45.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>50.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>52.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</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>7.330645</td>\n",
              "      <td>45.000000</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>7.330645</td>\n",
              "      <td>67.000000</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>52.000000</td>\n",
              "      <td>7.330645</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",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c9f1e4d9-1b5b-49ae-a050-489d1d2c3a61')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
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              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
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              "      border-radius: 50%;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      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-c9f1e4d9-1b5b-49ae-a050-489d1d2c3a61 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-c9f1e4d9-1b5b-49ae-a050-489d1d2c3a61');\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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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "preds = RF_model.predict(test_x)\n",
        "print('Done.')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sUcI9qjRtyU2",
        "outputId": "c694331b-5751-4ec6-a1ef-c778d7832eac"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Done.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_x['Y_Class']=preds\n",
        "preds"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IYsJ59R8DvKQ",
        "outputId": "b1369c63-9d87-4ba4-d6e2-4a9d9fa04986"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([2, 2, 2, 1, 1, 2, 1, 1, 2, 2, 1, 1, 2, 2, 0, 2, 2, 2, 2, 2, 2, 2,\n",
              "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 0, 2,\n",
              "       2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0,\n",
              "       0, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 0, 1,\n",
              "       0, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
              "       2, 2, 2, 2, 2, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
              "       2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
              "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
              "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
              "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
              "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
              "       2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 2, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
              "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
              "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
              "       2, 2])"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.countplot(x=preds);"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "TPukl7Nk2bH1",
        "outputId": "819848de-402a-4f15-9dba-bca0f78a0fbf"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAD4CAYAAAAD6PrjAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAANYUlEQVR4nO3cf6jd9X3H8edL4zo2HVVyl8UkLlLSQrof2l2czDFcZasKW2zpRKGaOUf6hxYFGdj+sUpBKEwtrRtCilYdTmdRZzZkmw0yafFHb0TUJHMNrc6EaFIt1VHqSPbeH/ebjwe9ieeq3/O9N/f5gMP9ns/5nnPfcDVPvud8vydVhSRJAMcMPYAkaeEwCpKkxihIkhqjIElqjIIkqVk29ADvx/Lly2vt2rVDjyFJi8q2bdt+XFVTcz22qKOwdu1aZmZmhh5DkhaVJC8e7jHfPpIkNUZBktQYBUlSYxQkSY1RkCQ1RkGS1BgFSVJjFCRJjVGQJDWL+opmSYvDWTefNfQIR73vfeF7H8jreKQgSWqMgiSpMQqSpMYoSJIaoyBJaoyCJKkxCpKkxihIkhqjIElqjIIkqTEKkqTGKEiSGqMgSWqMgiSpMQqSpMYoSJIaoyBJaoyCJKkxCpKkxihIkpreopBkTZJHkuxIsj3JVd36dUn2JHm6u50/8pwvJtmV5Pkkn+prNknS3Jb1+NoHgGuq6qkkJwDbkjzcPfa1qrphdOck64GLgI8DJwPfSfLRqjrY44ySpBG9HSlU1d6qeqrbfgPYCaw6wlM2APdU1ZtV9SNgF3BGX/NJkt5pIp8pJFkLnA480S1dmeSZJLclObFbWwW8NPK03cwRkSSbkswkmdm/f3+PU0vS0tN7FJIcD9wHXF1VrwO3AB8BTgP2AjfO5/WqanNVTVfV9NTU1Ac+ryQtZb1GIclxzAbhrqq6H6CqXqmqg1X1f8A3eestoj3AmpGnr+7WJEkT0ufZRwFuBXZW1U0j6ytHdvs08Fy3vQW4KMmHkpwKrAOe7Gs+SdI79Xn20VnAJcCzSZ7u1r4EXJzkNKCAF4DPA1TV9iT3AjuYPXPpCs88kqTJ6i0KVfVdIHM89NARnnM9cH1fM0mSjswrmiVJjVGQJDVGQZLUGAVJUmMUJEmNUZAkNUZBktQYBUlSYxQkSY1RkCQ1RkGS1BgFSVJjFCRJjVGQJDVGQZLUGAVJUmMUJEmNUZAkNUZBktQYBUlSYxQkSY1RkCQ1RkGS1BgFSVJjFCRJjVGQJDVGQZLU9BaFJGuSPJJkR5LtSa7q1k9K8nCSH3Q/T+zWk+QbSXYleSbJJ/qaTZI0tz6PFA4A11TVeuBM4Iok64Frga1VtQ7Y2t0HOA9Y1902Abf0OJskaQ69RaGq9lbVU932G8BOYBWwAbij2+0O4IJuewNwZ816HPhwkpV9zSdJeqeJfKaQZC1wOvAEsKKq9nYPvQys6LZXAS+NPG13t/b219qUZCbJzP79+3ubWZKWot6jkOR44D7g6qp6ffSxqiqg5vN6VbW5qqaranpqauoDnFSS1GsUkhzHbBDuqqr7u+VXDr0t1P3c163vAdaMPH11tyZJmpA+zz4KcCuws6puGnloC7Cx294IPDiyfml3FtKZwE9H3maSJE3Ash5f+yzgEuDZJE93a18Cvgrcm+Ry4EXgwu6xh4DzgV3Az4DLepxNkjSH3qJQVd8FcpiHz5lj/wKu6GseSdK784pmSVJjFCRJjVGQJDVGQZLUGAVJUmMUJEmNUZAkNUZBktQYBUlSYxQkSY1RkCQ1RkGS1BgFSVJjFCRJjVGQJDVGQZLUGAVJUmMUJEmNUZAkNUZBktQYBUlSM1YUkmwdZ02StLgtO9KDSX4R+CVgeZITgXQP/QqwqufZJEkTdsQoAJ8HrgZOBrbxVhReB/62x7kkSQM4YhSq6uvA15N8oapuntBMkqSBvNuRAgBVdXOS3wPWjj6nqu7saS5J0gDGikKSvwc+AjwNHOyWCzAKknQUGSsKwDSwvqqqz2EkScMa9zqF54Bfm88LJ7ktyb4kz42sXZdkT5Knu9v5I499McmuJM8n+dR8fpck6YMx7pHCcmBHkieBNw8tVtWfHuE5tzN7htLb32L6WlXdMLqQZD1wEfBxZs90+k6Sj1bVQSRJEzNuFK6b7wtX1aNJ1o65+wbgnqp6E/hRkl3AGcBj8/29kqT3btyzj/7jA/ydVya5FJgBrqmqnzB7IdzjI/vs5jAXxyXZBGwCOOWUUz7AsSRJ437NxRtJXu9uP09yMMnr7+H33cLsWUynAXuBG+f7AlW1uaqmq2p6amrqPYwgSTqccY8UTji0nSTMvt1z5nx/WVW9MvI63wT+pbu7B1gzsuvqbk2SNEHz/pbUmvVPwLzPEEqycuTup5k9qwlgC3BRkg8lORVYBzw539eXJL0/41689pmRu8cwe93Cz9/lOXcDZzP7ZXq7gS8DZyc5jdkL315g9ruVqKrtSe4FdgAHgCs880iSJm/cs4/+ZGT7ALP/oG840hOq6uI5lm89wv7XA9ePOY8kqQfjfqZwWd+DSJKGN+7ZR6uTPNBdobwvyX1JVvc9nCRpssb9oPlbzH4YfHJ3++duTZJ0FBk3ClNV9a2qOtDdbge8SECSjjLjRuHVJJ9Lcmx3+xzwap+DSZImb9wo/AVwIfAys1cifxb4855mkiQNZNxTUr8CbOy+p4gkJwE3MBsLSdJRYtwjhd86FASAqnoNOL2fkSRJQxk3CsckOfHQne5IYdyjDEnSIjHuP+w3Ao8l+XZ3/8/w6mNJOuqMe0XznUlmgE92S5+pqh39jSVJGsLYbwF1ETAEknQUm/dXZ0uSjl5GQZLUGAVJUmMUJEmNUZAkNUZBktQYBUlSYxQkSY1RkCQ1RkGS1BgFSVJjFCRJjVGQJDVGQZLUGAVJUtNbFJLclmRfkudG1k5K8nCSH3Q/T+zWk+QbSXYleSbJJ/qaS5J0eH0eKdwOnPu2tWuBrVW1Dtja3Qc4D1jX3TYBt/Q4lyTpMHqLQlU9Crz2tuUNwB3d9h3ABSPrd9asx4EPJ1nZ12ySpLlN+jOFFVW1t9t+GVjRba8CXhrZb3e3JkmaoME+aK6qAmq+z0uyKclMkpn9+/f3MJkkLV2TjsIrh94W6n7u69b3AGtG9lvdrb1DVW2uqumqmp6amup1WElaaiYdhS3Axm57I/DgyPql3VlIZwI/HXmbSZI0Icv6euEkdwNnA8uT7Aa+DHwVuDfJ5cCLwIXd7g8B5wO7gJ8Bl/U1lyTp8HqLQlVdfJiHzplj3wKu6GsWSdJ4vKJZktQYBUlSYxQkSY1RkCQ1RkGS1BgFSVJjFCRJjVGQJDVGQZLUGAVJUmMUJEmNUZAkNUZBktQYBUlSYxQkSY1RkCQ1RkGS1BgFSVJjFCRJjVGQJDVGQZLUGAVJUmMUJEmNUZAkNUZBktQYBUlSYxQkSY1RkCQ1y4b4pUleAN4ADgIHqmo6yUnAPwJrgReAC6vqJ0PMJ0lL1ZBHCn9YVadV1XR3/1pga1WtA7Z29yVJE7SQ3j7aANzRbd8BXDDgLJK0JA0VhQL+Pcm2JJu6tRVVtbfbfhlYMdcTk2xKMpNkZv/+/ZOYVZKWjEE+UwB+v6r2JPlV4OEk/zn6YFVVkprriVW1GdgMMD09Pec+kqT3ZpAjhara0/3cBzwAnAG8kmQlQPdz3xCzSdJSNvEoJPnlJCcc2gb+GHgO2AJs7HbbCDw46dkkaakb4u2jFcADSQ79/n+oqn9N8n3g3iSXAy8CFw4wmyQtaROPQlX9EPjtOdZfBc6Z9DySpLcspFNSJUkDMwqSpMYoSJIaoyBJaoyCJKkxCpKkxihIkhqjIElqjIIkqTEKkqTGKEiSGqMgSWqMgiSpMQqSpMYoSJIaoyBJaoyCJKkxCpKkxihIkhqjIElqlg09gDSO//7Kbw49wlHvlL9+dugRtAB4pCBJapbMkcLv/NWdQ4+wJGz7m0uHHkHS++CRgiSpMQqSpMYoSJIaoyBJaoyCJKlZcFFIcm6S55PsSnLt0PNI0lKyoKKQ5Fjg74DzgPXAxUnWDzuVJC0dCyoKwBnArqr6YVX9L3APsGHgmSRpyUhVDT1Dk+SzwLlV9Zfd/UuA362qK0f22QRs6u5+DHh+4oNOznLgx0MPoffMv9/idbT/7X69qqbmemDRXdFcVZuBzUPPMQlJZqpqeug59N7491u8lvLfbqG9fbQHWDNyf3W3JkmagIUWhe8D65KcmuQXgIuALQPPJElLxoJ6+6iqDiS5Evg34FjgtqraPvBYQ1oSb5Mdxfz7LV5L9m+3oD5oliQNa6G9fSRJGpBRkCQ1RmEB8qs+FrcktyXZl+S5oWfR/CRZk+SRJDuSbE9y1dAzTZqfKSww3Vd9/BfwR8BuZs/Iuriqdgw6mMaW5A+A/wHurKrfGHoejS/JSmBlVT2V5ARgG3DBUvr/zyOFhcev+ljkqupR4LWh59D8VdXeqnqq234D2AmsGnaqyTIKC88q4KWR+7tZYv9RSgtBkrXA6cATw04yWUZBkt4myfHAfcDVVfX60PNMklFYePyqD2lASY5jNgh3VdX9Q88zaUZh4fGrPqSBJAlwK7Czqm4aep4hGIUFpqoOAIe+6mMncO8S/6qPRSfJ3cBjwMeS7E5y+dAzaWxnAZcAn0zydHc7f+ihJslTUiVJjUcKkqTGKEiSGqMgSWqMgiSpMQqSpMYoSJIaoyBJav4flRs3OO06b1kAAAAASUVORK5CYII=\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "OhYKzO279lgJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "jKpDM1Q29llQ"
      },
      "execution_count": null,
      "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": 129,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# THE END\n"
      ],
      "metadata": {
        "id": "hcNFz5zguHMQ"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "l4x3CNe09oiT"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}