Colab_Research/Bai_Tap_Colab_So_3.ipynb

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    "colab": {
      "name": "Bai_Tap_Colab_So_3.ipynb",
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      "collapsed_sections": [],
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      "mount_file_id": "1H1b9RHcNwokY1gpofgCckO3Szlagg8Lg",
      "authorship_tag": "ABX9TyPIFP8b9NvIsmD5oWO69qdu",
      "include_colab_link": true
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    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
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    "language_info": {
      "name": "python"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/trankha1655/CS114_ML/blob/main/Colab_Research/Bai_Tap_Colab_So_3.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rHiJmb5hoOXQ"
      },
      "source": [
        "# BÀI TẬP COLAB SỐ 03 - Dự đoán điểm thi cuối kì môn IT001\n",
        "**Lớp CS114.L21**\n",
        "\n",
        "Nhóm:\n",
        "\n",
        "\n",
        "1.   Trần Phan Nhật Kha - 19521655\n",
        "2.   Trần Gia Nghĩa - 19521901\n",
        "3.   Võ Tá Lâm - 1952174419521744\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "21LmaHEhjGAP"
      },
      "source": [
        "# IMPORT AND DISCOVERY DATA"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9ghJq4Poip0A"
      },
      "source": [
        "Kết nối với google drivedrive"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XeovgietL8r-",
        "outputId": "d4d7212d-d32c-4583-fbda-022a812a231a"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/gdrive; to attempt to forcibly remount, call drive.mount(\"/content/gdrive\", force_remount=True).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ex6ZnyD-iull"
      },
      "source": [
        "Import các thư viện cần thiết cho quá trình đánh giá và tiền xử lý dữ liệu"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yCM6qiBzAvoF"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mcEkgaHFi4Uw"
      },
      "source": [
        "Đọc dữ liệu từ đường dẫn đến thư mục chứa dữ liệu trong google drive"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "A2cB5DaZMwHk"
      },
      "source": [
        "data_wecode = pd.read_csv('/content/gdrive/MyDrive/NMLT/wecode.csv')\n",
        "data_score = pd.read_csv('/content/gdrive/MyDrive/NMLT/score_training.csv')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gVfGVYMINLjt",
        "outputId": "292eb71a-c1db-4543-c7eb-d4042796cd92"
      },
      "source": [
        "data_wecode.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 264606 entries, 0 to 264605\n",
            "Data columns (total 12 columns):\n",
            " #   Column         Non-Null Count   Dtype \n",
            "---  ------         --------------   ----- \n",
            " 0   id             264606 non-null  int64 \n",
            " 1   assignment_id  264606 non-null  int64 \n",
            " 2   problem_id     264606 non-null  int64 \n",
            " 3   user_id        264606 non-null  int64 \n",
            " 4   is_final       264606 non-null  int64 \n",
            " 5   status         264605 non-null  object\n",
            " 6   pre_score      264606 non-null  int64 \n",
            " 7   coefficient    264606 non-null  object\n",
            " 8   file_name      264606 non-null  object\n",
            " 9   language_id    264606 non-null  int64 \n",
            " 10  created_at     264606 non-null  object\n",
            " 11  updated_at     264606 non-null  object\n",
            "dtypes: int64(7), object(5)\n",
            "memory usage: 24.2+ MB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 511
        },
        "id": "9P7q0S71NgYE",
        "outputId": "47ce6af5-3bbf-4ffd-ff97-8d63cf1fdb20"
      },
      "source": [
        "data_wecode.head(10)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th></th>\n",
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              "      <td>10000</td>\n",
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              "      <td>solution-0</td>\n",
              "      <td>2</td>\n",
              "      <td>2020-10-30 10:58:11</td>\n",
              "      <td>2020-10-30 10:58:19</td>\n",
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              "      <td>2020-10-30 11:07:09</td>\n",
              "      <td>2020-10-30 11:07:17</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>100</td>\n",
              "      <td>solution-2</td>\n",
              "      <td>2</td>\n",
              "      <td>2020-10-30 11:08:01</td>\n",
              "      <td>2020-10-30 11:23:36</td>\n",
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              "      <th>3</th>\n",
              "      <td>7</td>\n",
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              "      <td>4</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>100</td>\n",
              "      <td>solution-3</td>\n",
              "      <td>2</td>\n",
              "      <td>2020-10-30 11:11:41</td>\n",
              "      <td>2020-10-30 11:11:42</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>8</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>Compilation Error</td>\n",
              "      <td>0</td>\n",
              "      <td>100</td>\n",
              "      <td>solution-4</td>\n",
              "      <td>2</td>\n",
              "      <td>2020-10-30 11:15:06</td>\n",
              "      <td>2020-10-30 11:15:07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>9</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>Compilation Error</td>\n",
              "      <td>0</td>\n",
              "      <td>100</td>\n",
              "      <td>solution-5</td>\n",
              "      <td>2</td>\n",
              "      <td>2020-10-30 11:16:57</td>\n",
              "      <td>2020-10-30 11:16:58</td>\n",
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              "      <th>6</th>\n",
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              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>SCORE</td>\n",
              "      <td>10000</td>\n",
              "      <td>100</td>\n",
              "      <td>solution-6</td>\n",
              "      <td>2</td>\n",
              "      <td>2020-10-30 11:18:47</td>\n",
              "      <td>2020-10-30 11:18:55</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>11</td>\n",
              "      <td>0</td>\n",
              "      <td>7</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>SCORE</td>\n",
              "      <td>10000</td>\n",
              "      <td>100</td>\n",
              "      <td>solution-7</td>\n",
              "      <td>2</td>\n",
              "      <td>2020-10-30 11:20:07</td>\n",
              "      <td>2020-10-30 11:20:15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>12</td>\n",
              "      <td>0</td>\n",
              "      <td>8</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>SCORE</td>\n",
              "      <td>10000</td>\n",
              "      <td>100</td>\n",
              "      <td>solution-8</td>\n",
              "      <td>2</td>\n",
              "      <td>2020-10-30 11:21:02</td>\n",
              "      <td>2020-10-30 11:21:10</td>\n",
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              "      <th>9</th>\n",
              "      <td>13</td>\n",
              "      <td>0</td>\n",
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              "      <td>1</td>\n",
              "      <td>SCORE</td>\n",
              "      <td>10000</td>\n",
              "      <td>100</td>\n",
              "      <td>solution-9</td>\n",
              "      <td>2</td>\n",
              "      <td>2020-10-30 11:21:36</td>\n",
              "      <td>2020-10-30 11:21:44</td>\n",
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            "text/plain": [
              "   id  assignment_id  ...           created_at           updated_at\n",
              "0   4              0  ...  2020-10-30 10:58:11  2020-10-30 10:58:19\n",
              "1   5              0  ...  2020-10-30 11:07:09  2020-10-30 11:07:17\n",
              "2   6              0  ...  2020-10-30 11:08:01  2020-10-30 11:23:36\n",
              "3   7              0  ...  2020-10-30 11:11:41  2020-10-30 11:11:42\n",
              "4   8              0  ...  2020-10-30 11:15:06  2020-10-30 11:15:07\n",
              "5   9              0  ...  2020-10-30 11:16:57  2020-10-30 11:16:58\n",
              "6  10              0  ...  2020-10-30 11:18:47  2020-10-30 11:18:55\n",
              "7  11              0  ...  2020-10-30 11:20:07  2020-10-30 11:20:15\n",
              "8  12              0  ...  2020-10-30 11:21:02  2020-10-30 11:21:10\n",
              "9  13              0  ...  2020-10-30 11:21:36  2020-10-30 11:21:44\n",
              "\n",
              "[10 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 623
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tyR3hx3_jp2I",
        "outputId": "ae6f9949-15d7-49dc-9807-2c38d6ba0cec"
      },
      "source": [
        "data_score.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 975 entries, 0 to 974\n",
            "Data columns (total 7 columns):\n",
            " #   Column  Non-Null Count  Dtype \n",
            "---  ------  --------------  ----- \n",
            " 0   id      975 non-null    int64 \n",
            " 1   QT      973 non-null    object\n",
            " 2   GK      974 non-null    object\n",
            " 3   TH      974 non-null    object\n",
            " 4   CK      974 non-null    object\n",
            " 5   TBM     974 non-null    object\n",
            " 6   TBTL    974 non-null    object\n",
            "dtypes: int64(1), object(6)\n",
            "memory usage: 53.4+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        },
        "id": "IKoOYzZPRtce",
        "outputId": "63ea8c56-6191-4abc-9f78-f63a4f2ea31d"
      },
      "source": [
        "data_score.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th>id</th>\n",
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              "      <th>CK</th>\n",
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              "      <th>0</th>\n",
              "      <td>22</td>\n",
              "      <td>10</td>\n",
              "      <td>9</td>\n",
              "      <td>8.5</td>\n",
              "      <td>8.5</td>\n",
              "      <td>8.8</td>\n",
              "      <td>8.52</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>24</td>\n",
              "      <td>10</td>\n",
              "      <td>8</td>\n",
              "      <td>10</td>\n",
              "      <td>9.5</td>\n",
              "      <td>9.4</td>\n",
              "      <td>8.41</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>27</td>\n",
              "      <td>10</td>\n",
              "      <td>10</td>\n",
              "      <td>10</td>\n",
              "      <td>10</td>\n",
              "      <td>10</td>\n",
              "      <td>8.67</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>28</td>\n",
              "      <td>10</td>\n",
              "      <td>9.5</td>\n",
              "      <td>7.5</td>\n",
              "      <td>9.5</td>\n",
              "      <td>9.2</td>\n",
              "      <td>7.39</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>29</td>\n",
              "      <td>10</td>\n",
              "      <td>9.5</td>\n",
              "      <td>5</td>\n",
              "      <td>8.5</td>\n",
              "      <td>8.2</td>\n",
              "      <td>8.05</td>\n",
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            "text/plain": [
              "   id  QT   GK   TH   CK  TBM  TBTL\n",
              "0  22  10    9  8.5  8.5  8.8  8.52\n",
              "1  24  10    8   10  9.5  9.4  8.41\n",
              "2  27  10   10   10   10   10  8.67\n",
              "3  28  10  9.5  7.5  9.5  9.2  7.39\n",
              "4  29  10  9.5    5  8.5  8.2  8.05"
            ]
          },
          "metadata": {
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          },
          "execution_count": 625
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "muE6JtlYjS1p"
      },
      "source": [
        "# PRE-PROCESSING AND VISUALIZE DATA"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mOS7XHFPt0bh"
      },
      "source": [
        "# PRE-PROCESSING DATA"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9dEhgkqYVIef"
      },
      "source": [
        "Xét các trường hợp sau để đánh giá mối tương quan với điểm thi cuối kì:\n",
        "1. Tổng số problem đã solved (is_final = 1 && pre_score=10000) / Tổng số problem có submit\n",
        "2. Tổng số problem đã sumbit / Tổng số submissions \n",
        "3. Số lần submit đúng (pre_score=10000) / tổng số submissions \n",
        "4. Tổng pre_score của các submssion có is_final = 1 \n",
        "5. Tổng của max pre_score của mỗi problem_id và tổng số submission"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IJ1qUimIkuRf"
      },
      "source": [
        "Trường hợp 1: Tổng số problem đã solved (is_final = 1 && pre_score=10000) / Tổng số problem có submit"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dIPdghfNShB2"
      },
      "source": [
        "#Lấy các cột sử dụng cho bài toán\n",
        "visualize_data = data_wecode[['user_id', 'assignment_id', 'problem_id', 'is_final', 'status', 'pre_score', 'coefficient']]\n",
        "\n",
        "#Tổng số problem đã solved(is_final = 1 và pre_score = 100000)\n",
        "case_1 = visualize_data[(visualize_data['is_final'] == 1) & (visualize_data['pre_score'] == 10000)].groupby(by = 'user_id').sum()\n",
        "case_1 = case_1.drop(['pre_score','problem_id','assignment_id'],axis = 1) #Bỏ các cột không cần thiết\n",
        "\n",
        "#Tổng số problem đã submitted\n",
        "total_problem_submitted = visualize_data[['user_id','assignment_id','problem_id']].drop_duplicates() \n",
        "total_problem_submitted = total_problem_submitted[['user_id', 'problem_id']].groupby('user_id', as_index = False).count()\n",
        "\n",
        "case_1 = case_1.merge(total_problem_submitted,on='user_id',how = 'right').fillna(0) #Kết phải để lấy cả trường hợp sinh viên không có problem nào solved\n",
        "case_1['prob_solved/prob_tried'] = case_1['is_final']/case_1['problem_id']\n",
        "case_1.rename(columns={'is_final':'prob_solved','problem_id':'prob_tried'},inplace = True)\n",
        "case_1.set_index('user_id',inplace = True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 431
        },
        "id": "YUCvimq9Fr5a",
        "outputId": "d97b5e87-12b4-4a8f-fd8a-1d664b5100da"
      },
      "source": [
        "case_1"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>prob_solved</th>\n",
              "      <th>prob_tried</th>\n",
              "      <th>prob_solved/prob_tried</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>user_id</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1.0</td>\n",
              "      <td>2</td>\n",
              "      <td>0.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>37.0</td>\n",
              "      <td>41</td>\n",
              "      <td>0.902439</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>3.0</td>\n",
              "      <td>5</td>\n",
              "      <td>0.600000</td>\n",
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              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>85.0</td>\n",
              "      <td>89</td>\n",
              "      <td>0.955056</td>\n",
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              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>0.0</td>\n",
              "      <td>2</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1860</th>\n",
              "      <td>28.0</td>\n",
              "      <td>48</td>\n",
              "      <td>0.583333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1862</th>\n",
              "      <td>4.0</td>\n",
              "      <td>6</td>\n",
              "      <td>0.666667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1864</th>\n",
              "      <td>28.0</td>\n",
              "      <td>63</td>\n",
              "      <td>0.444444</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1865</th>\n",
              "      <td>10.0</td>\n",
              "      <td>17</td>\n",
              "      <td>0.588235</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1868</th>\n",
              "      <td>27.0</td>\n",
              "      <td>28</td>\n",
              "      <td>0.964286</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1159 rows × 3 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "         prob_solved  prob_tried  prob_solved/prob_tried\n",
              "user_id                                                 \n",
              "1                1.0           2                0.500000\n",
              "4               37.0          41                0.902439\n",
              "5                3.0           5                0.600000\n",
              "6               85.0          89                0.955056\n",
              "7                0.0           2                0.000000\n",
              "...              ...         ...                     ...\n",
              "1860            28.0          48                0.583333\n",
              "1862             4.0           6                0.666667\n",
              "1864            28.0          63                0.444444\n",
              "1865            10.0          17                0.588235\n",
              "1868            27.0          28                0.964286\n",
              "\n",
              "[1159 rows x 3 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 627
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BRafs4s9loZm"
      },
      "source": [
        "Trường hợp 2: Tổng số problem đã sumbit / Tổng số submissions"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4MHh7PO1gq-w"
      },
      "source": [
        "#Tổng số problem đã submit\n",
        "case_2 = visualize_data[['user_id', 'assignment_id', 'problem_id']].drop_duplicates()\n",
        "case_2 = case_2[['user_id', 'problem_id']].groupby('user_id', as_index = False).count()\n",
        "\n",
        "#Tổng số subbmissions\n",
        "total_problem_submissions = data_wecode[['user_id', 'id']].groupby('user_id', as_index = False).count()\n",
        "\n",
        "case_2 = case_2.merge(total_problem_submissions,on = 'user_id')\n",
        "case_2['problem_submitted_rate'] = case_2['problem_id']/case_2['id']\n",
        "case_2.rename(columns={'problem_id':'total_submissions','id':'total_submitted'},inplace = True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 402
        },
        "id": "IOnHXqlnFt6B",
        "outputId": "23da0acd-d9d8-4a1e-d703-5d185110b73f"
      },
      "source": [
        "case_2"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>user_id</th>\n",
              "      <th>total_submissions</th>\n",
              "      <th>total_submitted</th>\n",
              "      <th>problem_submitted_rate</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
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              "      <th>0</th>\n",
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              "      <td>2</td>\n",
              "      <td>1.000000</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4</td>\n",
              "      <td>41</td>\n",
              "      <td>54</td>\n",
              "      <td>0.759259</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>6</td>\n",
              "      <td>0.833333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>6</td>\n",
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              "      <td>130</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>7</td>\n",
              "      <td>2</td>\n",
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              "      <td>0.400000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>1154</th>\n",
              "      <td>1860</td>\n",
              "      <td>48</td>\n",
              "      <td>76</td>\n",
              "      <td>0.631579</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1155</th>\n",
              "      <td>1862</td>\n",
              "      <td>6</td>\n",
              "      <td>12</td>\n",
              "      <td>0.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1156</th>\n",
              "      <td>1864</td>\n",
              "      <td>63</td>\n",
              "      <td>187</td>\n",
              "      <td>0.336898</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1157</th>\n",
              "      <td>1865</td>\n",
              "      <td>17</td>\n",
              "      <td>99</td>\n",
              "      <td>0.171717</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1158</th>\n",
              "      <td>1868</td>\n",
              "      <td>28</td>\n",
              "      <td>47</td>\n",
              "      <td>0.595745</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "<p>1159 rows × 4 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      user_id  total_submissions  total_submitted  problem_submitted_rate\n",
              "0           1                  2                2                1.000000\n",
              "1           4                 41               54                0.759259\n",
              "2           5                  5                6                0.833333\n",
              "3           6                 89              130                0.684615\n",
              "4           7                  2                5                0.400000\n",
              "...       ...                ...              ...                     ...\n",
              "1154     1860                 48               76                0.631579\n",
              "1155     1862                  6               12                0.500000\n",
              "1156     1864                 63              187                0.336898\n",
              "1157     1865                 17               99                0.171717\n",
              "1158     1868                 28               47                0.595745\n",
              "\n",
              "[1159 rows x 4 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 629
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eeWotAIEl0jp"
      },
      "source": [
        "Trường hợp 3: Số lần submit đúng (pre_score=10000) / tổng số submissions"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cGNZcjsAoihI"
      },
      "source": [
        "#Tổng số submissionsubmission\n",
        "case_3 = visualize_data[['user_id']]\n",
        "case_3 = case_3['user_id'].value_counts().sort_index()\n",
        "\n",
        "#Số lần submit đúng\n",
        "accepted_submit = visualize_data[visualize_data['pre_score']==10000].groupby(by = 'user_id').count()\n",
        "\n",
        "case_3 = pd.concat([case_3,accepted_submit],keys = 'user_id',axis = 1)\n",
        "case_3.columns = case_3.columns.droplevel()\n",
        "case_3 = case_3.reset_index()\n",
        "case_3.rename(columns={'user_id':'total_submissions','index':'user_id','pre_score':'total_accepted'},inplace = True)\n",
        "case_3 = case_3.drop(['assignment_id','problem_id','is_final','status','coefficient'],axis = 1).fillna(0)\n",
        "case_3['accepted/submissions'] = case_3['total_accepted']/case_3['total_submissions']\n",
        "case_3 = case_3.set_index('user_id')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W29N9RkyFvai",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 431
        },
        "outputId": "b11fcb8e-3e7f-4878-db8e-7673cd7b8d6d"
      },
      "source": [
        "case_3"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>total_submissions</th>\n",
              "      <th>total_accepted</th>\n",
              "      <th>accepted/submissions</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>user_id</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.500000</td>\n",
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              "      <th>4</th>\n",
              "      <td>54</td>\n",
              "      <td>44.0</td>\n",
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              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>6</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.500000</td>\n",
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              "      <td>130</td>\n",
              "      <td>89.0</td>\n",
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              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>5</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1860</th>\n",
              "      <td>76</td>\n",
              "      <td>29.0</td>\n",
              "      <td>0.381579</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1862</th>\n",
              "      <td>12</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.333333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1864</th>\n",
              "      <td>187</td>\n",
              "      <td>30.0</td>\n",
              "      <td>0.160428</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1865</th>\n",
              "      <td>99</td>\n",
              "      <td>10.0</td>\n",
              "      <td>0.101010</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1868</th>\n",
              "      <td>47</td>\n",
              "      <td>38.0</td>\n",
              "      <td>0.808511</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1159 rows × 3 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "         total_submissions  total_accepted  accepted/submissions\n",
              "user_id                                                         \n",
              "1                        2             1.0              0.500000\n",
              "4                       54            44.0              0.814815\n",
              "5                        6             3.0              0.500000\n",
              "6                      130            89.0              0.684615\n",
              "7                        5             0.0              0.000000\n",
              "...                    ...             ...                   ...\n",
              "1860                    76            29.0              0.381579\n",
              "1862                    12             4.0              0.333333\n",
              "1864                   187            30.0              0.160428\n",
              "1865                    99            10.0              0.101010\n",
              "1868                    47            38.0              0.808511\n",
              "\n",
              "[1159 rows x 3 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 631
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ELeKAonHmKNW"
      },
      "source": [
        "Trường hợp 4: Tổng pre_score của các submssion có is_final = 1"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EJVwpZmEts85"
      },
      "source": [
        "case_4 = visualize_data[(visualize_data['is_final'] == 1)].groupby(by = 'user_id').sum()\n",
        "case_4 = case_4.drop(['assignment_id','problem_id','is_final'],axis = 1)\n",
        "case_4.rename(columns={'pre_score' : 'total_score'},inplace = True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H0jRTHYBFw3x",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 431
        },
        "outputId": "21f77cf2-f11d-4884-8ad1-34997f5abdd3"
      },
      "source": [
        "case_4"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>total_score</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>user_id</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>10000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>370000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>30000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>850000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>9209</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1860</th>\n",
              "      <td>329665</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1862</th>\n",
              "      <td>40000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1864</th>\n",
              "      <td>439741</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1865</th>\n",
              "      <td>116428</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1868</th>\n",
              "      <td>270000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1157 rows × 1 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "         total_score\n",
              "user_id             \n",
              "1              10000\n",
              "4             370000\n",
              "5              30000\n",
              "6             850000\n",
              "7               9209\n",
              "...              ...\n",
              "1860          329665\n",
              "1862           40000\n",
              "1864          439741\n",
              "1865          116428\n",
              "1868          270000\n",
              "\n",
              "[1157 rows x 1 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 633
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "744a2eHDmR5O"
      },
      "source": [
        "Trường hợp 5: Tổng của max pre_score của mỗi problem_id và tổng số submission\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eYjXyU53xkRb"
      },
      "source": [
        "case_5 = visualize_data[['user_id','problem_id','pre_score']]\n",
        "case_5 = case_5.groupby(['user_id', 'problem_id'], as_index=False).max()\n",
        "case_5 = case_5[['user_id','pre_score']].groupby('user_id', as_index=False).sum()\n",
        "case_5.rename(columns={\"pre_score\":\"total_max_score_per_problem\"}, inplace=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5kIstZZ4FyM7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 402
        },
        "outputId": "64747e86-3ab4-4240-91ff-700e9872e2e3"
      },
      "source": [
        "case_5"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "  <thead>\n",
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              "      <th>user_id</th>\n",
              "      <th>total_max_score_per_problem</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
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              "      <td>400000</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>5</td>\n",
              "      <td>30000</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>6</td>\n",
              "      <td>850000</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>7</td>\n",
              "      <td>9209</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1154</th>\n",
              "      <td>1860</td>\n",
              "      <td>300221</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1155</th>\n",
              "      <td>1862</td>\n",
              "      <td>40000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1156</th>\n",
              "      <td>1864</td>\n",
              "      <td>439741</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1157</th>\n",
              "      <td>1865</td>\n",
              "      <td>116428</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1158</th>\n",
              "      <td>1868</td>\n",
              "      <td>200000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1159 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      user_id  total_max_score_per_problem\n",
              "0           1                        10000\n",
              "1           4                       400000\n",
              "2           5                        30000\n",
              "3           6                       850000\n",
              "4           7                         9209\n",
              "...       ...                          ...\n",
              "1154     1860                       300221\n",
              "1155     1862                        40000\n",
              "1156     1864                       439741\n",
              "1157     1865                       116428\n",
              "1158     1868                       200000\n",
              "\n",
              "[1159 rows x 2 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 635
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AF1-USJq3Jzb"
      },
      "source": [
        "visualize_score = data_score[['id','CK']]\n",
        "visualize_score = visualize_score.replace(r'^\\s*$', np.nan, regex=True) #Chuyển các ô trống sang NaN\n",
        "visualize_score = visualize_score.dropna()\n",
        "visualize_score.rename(columns={'id':'user_id'},inplace = True)\n",
        "visualize_score.set_index('user_id',inplace= True)\n",
        "visualize_score['CK'] = visualize_score['CK'].astype(float)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dP2iHNWo3ZTi",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 431
        },
        "outputId": "19a84167-e9c5-4164-d47c-11d1d80a7ec5"
      },
      "source": [
        "visualize_score"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\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>CK</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>user_id</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>8.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>9.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>10.0</td>\n",
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              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>9.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>8.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1857</th>\n",
              "      <td>4.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1859</th>\n",
              "      <td>1.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1862</th>\n",
              "      <td>7.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1865</th>\n",
              "      <td>7.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1866</th>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>920 rows × 1 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "           CK\n",
              "user_id      \n",
              "22        8.5\n",
              "24        9.5\n",
              "27       10.0\n",
              "28        9.5\n",
              "29        8.5\n",
              "...       ...\n",
              "1857      4.0\n",
              "1859      1.5\n",
              "1862      7.5\n",
              "1865      7.5\n",
              "1866      5.0\n",
              "\n",
              "[920 rows x 1 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 637
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m2Qst87ZEyGp"
      },
      "source": [
        "summery = case_1.merge(case_2,on = 'user_id')\n",
        "summery = summery.merge(case_3,on = 'user_id')\n",
        "summery = summery.merge(case_4,on = 'user_id',how = 'left').fillna(0)\n",
        "summery = summery.merge(case_5,on = 'user_id')\n",
        "summery.drop(['prob_solved','prob_tried','total_submissions_x','total_submitted','total_submissions_y','total_accepted'],axis = 1, inplace = True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aweHl6J6FJ6i",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 402
        },
        "outputId": "97f7c098-0492-4eab-d96c-37bf6e0e5d37"
      },
      "source": [
        "summery"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>user_id</th>\n",
              "      <th>prob_solved/prob_tried</th>\n",
              "      <th>problem_submitted_rate</th>\n",
              "      <th>accepted/submissions</th>\n",
              "      <th>total_score</th>\n",
              "      <th>total_max_score_per_problem</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
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              "      <td>1.000000</td>\n",
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              "      <td>10000.0</td>\n",
              "      <td>10000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4</td>\n",
              "      <td>0.902439</td>\n",
              "      <td>0.759259</td>\n",
              "      <td>0.814815</td>\n",
              "      <td>370000.0</td>\n",
              "      <td>400000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>5</td>\n",
              "      <td>0.600000</td>\n",
              "      <td>0.833333</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>30000.0</td>\n",
              "      <td>30000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>6</td>\n",
              "      <td>0.955056</td>\n",
              "      <td>0.684615</td>\n",
              "      <td>0.684615</td>\n",
              "      <td>850000.0</td>\n",
              "      <td>850000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>7</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.400000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>9209.0</td>\n",
              "      <td>9209</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1154</th>\n",
              "      <td>1860</td>\n",
              "      <td>0.583333</td>\n",
              "      <td>0.631579</td>\n",
              "      <td>0.381579</td>\n",
              "      <td>329665.0</td>\n",
              "      <td>300221</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1155</th>\n",
              "      <td>1862</td>\n",
              "      <td>0.666667</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>40000.0</td>\n",
              "      <td>40000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1156</th>\n",
              "      <td>1864</td>\n",
              "      <td>0.444444</td>\n",
              "      <td>0.336898</td>\n",
              "      <td>0.160428</td>\n",
              "      <td>439741.0</td>\n",
              "      <td>439741</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1157</th>\n",
              "      <td>1865</td>\n",
              "      <td>0.588235</td>\n",
              "      <td>0.171717</td>\n",
              "      <td>0.101010</td>\n",
              "      <td>116428.0</td>\n",
              "      <td>116428</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1158</th>\n",
              "      <td>1868</td>\n",
              "      <td>0.964286</td>\n",
              "      <td>0.595745</td>\n",
              "      <td>0.808511</td>\n",
              "      <td>270000.0</td>\n",
              "      <td>200000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1159 rows × 6 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      user_id  prob_solved/prob_tried  ...  total_score  total_max_score_per_problem\n",
              "0           1                0.500000  ...      10000.0                        10000\n",
              "1           4                0.902439  ...     370000.0                       400000\n",
              "2           5                0.600000  ...      30000.0                        30000\n",
              "3           6                0.955056  ...     850000.0                       850000\n",
              "4           7                0.000000  ...       9209.0                         9209\n",
              "...       ...                     ...  ...          ...                          ...\n",
              "1154     1860                0.583333  ...     329665.0                       300221\n",
              "1155     1862                0.666667  ...      40000.0                        40000\n",
              "1156     1864                0.444444  ...     439741.0                       439741\n",
              "1157     1865                0.588235  ...     116428.0                       116428\n",
              "1158     1868                0.964286  ...     270000.0                       200000\n",
              "\n",
              "[1159 rows x 6 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 639
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 402
        },
        "id": "0NVkI1jtdAEe",
        "outputId": "f11b6061-8dd9-41cc-ae53-bdd7747d79a0"
      },
      "source": [
        "visualize = summery.merge(visualize_score,on = 'user_id')\n",
        "visualize"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "\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>user_id</th>\n",
              "      <th>prob_solved/prob_tried</th>\n",
              "      <th>problem_submitted_rate</th>\n",
              "      <th>accepted/submissions</th>\n",
              "      <th>total_score</th>\n",
              "      <th>total_max_score_per_problem</th>\n",
              "      <th>CK</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>24</td>\n",
              "      <td>0.923077</td>\n",
              "      <td>0.475610</td>\n",
              "      <td>0.487805</td>\n",
              "      <td>750299.0</td>\n",
              "      <td>760299</td>\n",
              "      <td>9.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>27</td>\n",
              "      <td>0.600000</td>\n",
              "      <td>0.384615</td>\n",
              "      <td>0.307692</td>\n",
              "      <td>39000.0</td>\n",
              "      <td>39000</td>\n",
              "      <td>10.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>29</td>\n",
              "      <td>0.900000</td>\n",
              "      <td>0.402010</td>\n",
              "      <td>0.376884</td>\n",
              "      <td>762760.0</td>\n",
              "      <td>762760</td>\n",
              "      <td>8.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>34</td>\n",
              "      <td>0.964286</td>\n",
              "      <td>0.385321</td>\n",
              "      <td>0.371560</td>\n",
              "      <td>822888.0</td>\n",
              "      <td>822888</td>\n",
              "      <td>6.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>35</td>\n",
              "      <td>0.953947</td>\n",
              "      <td>0.511785</td>\n",
              "      <td>0.511785</td>\n",
              "      <td>1477000.0</td>\n",
              "      <td>1437000</td>\n",
              "      <td>4.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>801</th>\n",
              "      <td>1856</td>\n",
              "      <td>0.741935</td>\n",
              "      <td>0.484375</td>\n",
              "      <td>0.359375</td>\n",
              "      <td>270790.0</td>\n",
              "      <td>270790</td>\n",
              "      <td>7.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>802</th>\n",
              "      <td>1857</td>\n",
              "      <td>0.925000</td>\n",
              "      <td>0.727273</td>\n",
              "      <td>0.672727</td>\n",
              "      <td>386000.0</td>\n",
              "      <td>337000</td>\n",
              "      <td>4.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>803</th>\n",
              "      <td>1859</td>\n",
              "      <td>0.100000</td>\n",
              "      <td>0.967742</td>\n",
              "      <td>0.096774</td>\n",
              "      <td>39000.0</td>\n",
              "      <td>39000</td>\n",
              "      <td>1.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>804</th>\n",
              "      <td>1862</td>\n",
              "      <td>0.666667</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>40000.0</td>\n",
              "      <td>40000</td>\n",
              "      <td>7.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>805</th>\n",
              "      <td>1865</td>\n",
              "      <td>0.588235</td>\n",
              "      <td>0.171717</td>\n",
              "      <td>0.101010</td>\n",
              "      <td>116428.0</td>\n",
              "      <td>116428</td>\n",
              "      <td>7.5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>806 rows × 7 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     user_id  prob_solved/prob_tried  ...  total_max_score_per_problem    CK\n",
              "0         24                0.923077  ...                       760299   9.5\n",
              "1         27                0.600000  ...                        39000  10.0\n",
              "2         29                0.900000  ...                       762760   8.5\n",
              "3         34                0.964286  ...                       822888   6.5\n",
              "4         35                0.953947  ...                      1437000   4.5\n",
              "..       ...                     ...  ...                          ...   ...\n",
              "801     1856                0.741935  ...                       270790   7.5\n",
              "802     1857                0.925000  ...                       337000   4.0\n",
              "803     1859                0.100000  ...                        39000   1.5\n",
              "804     1862                0.666667  ...                        40000   7.5\n",
              "805     1865                0.588235  ...                       116428   7.5\n",
              "\n",
              "[806 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 640
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eirWVmTt2xiE"
      },
      "source": [
        "# VISUALIZE DATA"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PW2I504UPBYy",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 594
        },
        "outputId": "68f6cd77-1881-41d1-ceae-d251b87937de"
      },
      "source": [
        "fig, ((axs1, axs2, axs3), (axs4, axs5, axs6), ) = plt.subplots(2,3, figsize = (20,10))\n",
        "\n",
        "axs1.scatter(x = visualize['prob_solved/prob_tried'],y = visualize['CK'])\n",
        "axs1.set_title(\"Solved problems/tried problems - Grade\")\n",
        "axs1.set(xlabel='Rate', ylabel= 'Grade')\n",
        "\n",
        "axs2.scatter(x = visualize['problem_submitted_rate'],y = visualize['CK'])\n",
        "axs2.set_title(\"Submitted Problems Rate - Grade\")\n",
        "axs2.set(xlabel = 'Rate', ylabel = 'Grade')\n",
        "\n",
        "axs3.scatter(x = visualize['accepted/submissions'], y = visualize['CK'])\n",
        "axs3.set_title(\"Accepted/submissions - Grade\")\n",
        "axs3.set(xlabel = 'Rate', ylabel = 'Grade')\n",
        "\n",
        "axs4.scatter(x = visualize['total_score'], y = visualize['CK'])\n",
        "axs4.set_title(\"Total score - Grade\")\n",
        "axs4.set(xlabel = 'Total score', ylabel = 'Grade')\n",
        "\n",
        "axs5.scatter(x = visualize['total_max_score_per_problem'], y = visualize['CK'])\n",
        "axs5.set_title(\"Total max score per problem - Grade\")\n",
        "axs5.set(xlabel = 'Score', ylabel = 'Grade')\n",
        "\n",
        "fig.delaxes(axs6)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x720 with 5 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "y1ITgBw7hYem"
      },
      "source": [
        "# TRAINING AND EVALUATING MODEL\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fPmkPlN9mk7F"
      },
      "source": [
        "Import các thư viện cần thiết"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Mf35lo1_hnE2"
      },
      "source": [
        "from sklearn.metrics import mean_squared_error\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.model_selection import train_test_split"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Pc36uSs_h_We",
        "outputId": "00fbdf5a-c5a5-44b6-eeab-64f40f1930be"
      },
      "source": [
        "column_title_train = ['prob_solved/prob_tried','problem_submitted_rate','accepted/submissions','total_score','total_max_score_per_problem']\n",
        "\n",
        "x = np.array(visualize[column_title_train])\n",
        "y = np.array(visualize[['CK']])\n",
        "\n",
        "x_train, x_test, y_train, y_test = train_test_split(x,y,test_size = 0.3,random_state = 20)\n",
        "\n",
        "model = LinearRegression()\n",
        "model.fit(x_train,y_train)\n",
        "\n",
        "y_pre = model.predict(x_test)\n",
        "y_pre = np.round((y_pre*2))/2 #Làm tròn điểm \n",
        "mse = mean_squared_error(y_test, y_pre) #Tính Mean Square Error\n",
        "print('MSE =',mse)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "MSE = 4.386363636363637\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MIGakQl8HHq2"
      },
      "source": [
        "# NHẬN XÉT"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4GCS5uCyHL3l"
      },
      "source": [
        "\n",
        "\n",
        "Sau khi train theo mô hình Linear Regression và MSE ra khá cao -> Mô hình hiện tại vẫn chưa tốt.\n",
        "#   Nguyên nhân:\n",
        "\n",
        "\n",
        "Chủ quan:\n",
        "\n",
        "\n",
        "> * Do quá trình xử lý dữ liệu chưa tốt\n",
        "\n",
        "\n",
        "> * Do chưa đánh giá tốt các tham số quan trọng cho bài toán\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "Khách quan: Điểm thi cuối kỳ phụ thuộc vào nhiều yếu tố hơn là kết quả thực hành wecode vì những lý do sau:\n",
        "\n",
        "> * Kết quả wecode không chắc chắn phản ánh đúng khả năng của sinh viên vì: các bạn có thể chép code của người khác, có khả năng lập trình tốt nhưng lười làm bài tập,...\n",
        "> * Tâm lý phòng thi khác hẳn với khi ở nhà làm thực hành\n",
        "> * Kiến thức thi cuối kỳ có nhiều kiến thức về lý thuyết hơn so với bài tập wecode\n",
        "> *v.v\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ]
    }
  ]
}