Plot_Test_dataset.ipynb

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    "colab": {
      "name": "Plot Test_dataset.ipynb",
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      "collapsed_sections": []
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      "name": "python3",
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_P32yyGeq2-K"
      },
      "source": [
        "**Members**\n",
        "1.   Nguyễn Trọng Thoại - 19522298\n",
        "2.   Lê Văn Nhân - 19521935\n",
        "3.   Lê Cao Hưng - 17520539"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eWxojmMgKzik"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "\n",
        "datapath = \"https://raw.githubusercontent.com/thoithoi58/CS114.L21/main/Colab/Final_Score_Prediction/wecode.csv\"\n",
        "wecode = pd.read_csv(datapath)\n",
        "datapath = \"https://raw.githubusercontent.com/thoithoi58/CS114.L21/main/Colab/Final_Score_Prediction/score_training.csv\"\n",
        "sc_training = pd.read_csv(datapath) "
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oxUw5O1ZK5KO",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3f097c3e-f5a8-4fc3-ad5f-f40dc08be85b"
      },
      "source": [
        "wc_data = wecode.drop(columns=[\"id\", \"language_id\", \"status\", \"created_at\", \"updated_at\", \"file_name\"])\n",
        "print(wc_data)\n",
        "sc_training = sc_training[['id','CK']]\n",
        "sc_training['CK'] = sc_training['CK'].apply(pd.to_numeric, errors='coerce')\n",
        "print(sc_training)"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "        assignment_id  problem_id  user_id  is_final  pre_score coefficient\n",
            "0                   0           3        4         1      10000         100\n",
            "1                   0           4        4         1      10000         100\n",
            "2                   0           5        4         0          0         100\n",
            "3                   0           5        4         0          0         100\n",
            "4                   0           5        4         0          0         100\n",
            "...               ...         ...      ...       ...        ...         ...\n",
            "264601              0          25     1796         0          0         100\n",
            "264602              0          25     1796         0          0         100\n",
            "264603              0          25     1796         1      10000         100\n",
            "264604              0         150      255         1       6250         100\n",
            "264605             21          24     1568         0      10000         100\n",
            "\n",
            "[264606 rows x 6 columns]\n",
            "       id    CK\n",
            "0      22   8.5\n",
            "1      24   9.5\n",
            "2      27  10.0\n",
            "3      28   9.5\n",
            "4      29   8.5\n",
            "..    ...   ...\n",
            "970  1857   4.0\n",
            "971  1859   1.5\n",
            "972  1862   7.5\n",
            "973  1865   7.5\n",
            "974  1866   5.0\n",
            "\n",
            "[975 rows x 2 columns]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ySWXm1rUMFIR"
      },
      "source": [
        "**Tìm Total Submit**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p9bOX5Lbwb7X",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "outputId": "83010d15-0891-4fef-a0d8-88ae42e0a646"
      },
      "source": [
        "Total_Submit = pd.DataFrame({'Total Submit' : wc_data['user_id'].value_counts()})\n",
        "Total_Submit = Total_Submit.sort_index()\n",
        "Total_Submit"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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            "text/plain": [
              "      Total Submit\n",
              "1                2\n",
              "4               54\n",
              "5                6\n",
              "6              130\n",
              "7                5\n",
              "...            ...\n",
              "1860            76\n",
              "1862            12\n",
              "1864           187\n",
              "1865            99\n",
              "1868            47\n",
              "\n",
              "[1159 rows x 1 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Cn-kJR6evbn9"
      },
      "source": [
        "**Tìm Total Solved**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "81NEErLRub_P",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 450
        },
        "outputId": "64620859-1c1c-43b3-9229-724dd745c5b9"
      },
      "source": [
        "wc_tsolved = pd.DataFrame(wc_data['user_id'])\n",
        "wc_tsolved['Total Solved'] = pd.DataFrame(map(int, (wc_data['pre_score'] == 10000) & (wc_data['is_final'] == 1)))\n",
        "wc_tsolved = wc_tsolved.groupby('user_id').sum()\n",
        "wc_tsolved"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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            "text/plain": [
              "         Total Solved\n",
              "user_id              \n",
              "1                   1\n",
              "4                  37\n",
              "5                   3\n",
              "6                  85\n",
              "7                   0\n",
              "...               ...\n",
              "1860               28\n",
              "1862                4\n",
              "1864               28\n",
              "1865               10\n",
              "1868               27\n",
              "\n",
              "[1159 rows x 1 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Y4Ws2lZWdWid"
      },
      "source": [
        "**Tìm Final Score**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 450
        },
        "id": "Fhhw95KJdWJS",
        "outputId": "5c891d85-a185-4fd9-eedd-7fcaae2aec15"
      },
      "source": [
        "wc_fscore = pd.DataFrame(wc_data['user_id'])\n",
        "wc_fscore['Correct Submits'] = pd.DataFrame(map(int, (wc_data['pre_score'] == 10000)))\n",
        "wc_fscore['Final Score'] = wc_data.loc[wc_data['is_final'] == 1, ['pre_score']]\n",
        "wc_fscore = wc_fscore.groupby('user_id').sum()\n",
        "wc_fscore"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Correct Submits</th>\n",
              "      <th>Final Score</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>user_id</th>\n",
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              "      <td>370000.0</td>\n",
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              "      <th>5</th>\n",
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              "      <td>30000.0</td>\n",
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              "      <th>6</th>\n",
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              "      <th>7</th>\n",
              "      <td>0</td>\n",
              "      <td>9209.0</td>\n",
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              "      <td>29</td>\n",
              "      <td>329665.0</td>\n",
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              "      <th>1862</th>\n",
              "      <td>4</td>\n",
              "      <td>40000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1864</th>\n",
              "      <td>30</td>\n",
              "      <td>439741.0</td>\n",
              "    </tr>\n",
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              "      <th>1865</th>\n",
              "      <td>10</td>\n",
              "      <td>116428.0</td>\n",
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              "      <th>1868</th>\n",
              "      <td>38</td>\n",
              "      <td>270000.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1159 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "         Correct Submits  Final Score\n",
              "user_id                              \n",
              "1                      1      10000.0\n",
              "4                     44     370000.0\n",
              "5                      3      30000.0\n",
              "6                     89     850000.0\n",
              "7                      0       9209.0\n",
              "...                  ...          ...\n",
              "1860                  29     329665.0\n",
              "1862                   4      40000.0\n",
              "1864                  30     439741.0\n",
              "1865                  10     116428.0\n",
              "1868                  38     270000.0\n",
              "\n",
              "[1159 rows x 2 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 450
        },
        "id": "lv6TjqoFbPgr",
        "outputId": "46f60450-16cb-437a-fdbe-0dc093248eed"
      },
      "source": [
        "get = pd.DataFrame(wecode['user_id'])\n",
        "get['Correct Submits'] = pd.DataFrame(map(int, (wc_data['pre_score'] == 10000)))\n",
        "get = get.groupby('user_id').sum()\n",
        "get"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>Correct Submits</th>\n",
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              "      <td>4</td>\n",
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              "      <td>30</td>\n",
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              "      <th>1865</th>\n",
              "      <td>10</td>\n",
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              "      <th>1868</th>\n",
              "      <td>38</td>\n",
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              "<p>1159 rows × 1 columns</p>\n",
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            ],
            "text/plain": [
              "         Correct Submits\n",
              "user_id                 \n",
              "1                      1\n",
              "4                     44\n",
              "5                      3\n",
              "6                     89\n",
              "7                      0\n",
              "...                  ...\n",
              "1860                  29\n",
              "1862                   4\n",
              "1864                  30\n",
              "1865                  10\n",
              "1868                  38\n",
              "\n",
              "[1159 rows x 1 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "v_1g8Bkns7Bo"
      },
      "source": [
        "**Số lượng Problems có submit**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 450
        },
        "id": "JnPInPP8s515",
        "outputId": "d3da7305-9478-4636-928f-53f90fbb343e"
      },
      "source": [
        "PHaS = pd.DataFrame(wc_data['user_id'])\n",
        "PHaS['Problems Have Submit'] = pd.DataFrame(wc_data['is_final'])\n",
        "PHaS = PHaS.groupby(['user_id']).sum()\n",
        "PHaS"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th>Problems Have Submit</th>\n",
              "    </tr>\n",
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              "      <td>48</td>\n",
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              "    <tr>\n",
              "      <th>1862</th>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1864</th>\n",
              "      <td>63</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1865</th>\n",
              "      <td>17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1868</th>\n",
              "      <td>27</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1159 rows × 1 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "         Problems Have Submit\n",
              "user_id                      \n",
              "1                           2\n",
              "4                          38\n",
              "5                           5\n",
              "6                          88\n",
              "7                           2\n",
              "...                       ...\n",
              "1860                       48\n",
              "1862                        6\n",
              "1864                       63\n",
              "1865                       17\n",
              "1868                       27\n",
              "\n",
              "[1159 rows x 1 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7fe-Kp2OpPEN"
      },
      "source": [
        "**Tìm Problems Đã Submit**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 450
        },
        "id": "ufKePGSYnLwI",
        "outputId": "993d6545-09cd-46af-8216-ec4afc0d96ae"
      },
      "source": [
        "Prob_Submit = pd.DataFrame(wc_data['assignment_id'])\n",
        "Prob_Submit['user_id'] = pd.DataFrame(wc_data['user_id'])\n",
        "Prob_Submit['Problems Submit'] =  pd.DataFrame(wc_data['problem_id'])\n",
        "Prob_Submit = Prob_Submit.groupby(['assignment_id', 'user_id']).nunique().reset_index()\n",
        "Prob_Submit = Prob_Submit.groupby(['user_id']).sum()\n",
        "Prob_Submit = Prob_Submit.drop(columns= ['assignment_id'])\n",
        "Prob_Submit"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Problems Submit</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>2</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>41</td>\n",
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              "      <th>5</th>\n",
              "      <td>5</td>\n",
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              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>89</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1860</th>\n",
              "      <td>48</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1862</th>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1864</th>\n",
              "      <td>63</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1865</th>\n",
              "      <td>17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1868</th>\n",
              "      <td>28</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1159 rows × 1 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "         Problems Submit\n",
              "user_id                 \n",
              "1                      2\n",
              "4                     41\n",
              "5                      5\n",
              "6                     89\n",
              "7                      2\n",
              "...                  ...\n",
              "1860                  48\n",
              "1862                   6\n",
              "1864                  63\n",
              "1865                  17\n",
              "1868                  28\n",
              "\n",
              "[1159 rows x 1 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jhzlCUf6JV7z",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "outputId": "5ad6290c-cf73-4282-a213-2d0dc157f82e"
      },
      "source": [
        "Data = pd.concat([Total_Submit, Prob_Submit, PHaS, wc_tsolved, wc_fscore], axis=1)\n",
        "Data = Data.fillna(0.0)\n",
        "Data"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "    .dataframe tbody tr th:only-of-type {\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>Total Submit</th>\n",
              "      <th>Problems Submit</th>\n",
              "      <th>Problems Have Submit</th>\n",
              "      <th>Total Solved</th>\n",
              "      <th>Correct Submits</th>\n",
              "      <th>Final Score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>10000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>54</td>\n",
              "      <td>41</td>\n",
              "      <td>38</td>\n",
              "      <td>37</td>\n",
              "      <td>44</td>\n",
              "      <td>370000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>6</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>30000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>130</td>\n",
              "      <td>89</td>\n",
              "      <td>88</td>\n",
              "      <td>85</td>\n",
              "      <td>89</td>\n",
              "      <td>850000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>9209.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1860</th>\n",
              "      <td>76</td>\n",
              "      <td>48</td>\n",
              "      <td>48</td>\n",
              "      <td>28</td>\n",
              "      <td>29</td>\n",
              "      <td>329665.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1862</th>\n",
              "      <td>12</td>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>40000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1864</th>\n",
              "      <td>187</td>\n",
              "      <td>63</td>\n",
              "      <td>63</td>\n",
              "      <td>28</td>\n",
              "      <td>30</td>\n",
              "      <td>439741.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1865</th>\n",
              "      <td>99</td>\n",
              "      <td>17</td>\n",
              "      <td>17</td>\n",
              "      <td>10</td>\n",
              "      <td>10</td>\n",
              "      <td>116428.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1868</th>\n",
              "      <td>47</td>\n",
              "      <td>28</td>\n",
              "      <td>27</td>\n",
              "      <td>27</td>\n",
              "      <td>38</td>\n",
              "      <td>270000.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1159 rows × 6 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      Total Submit  Problems Submit  ...  Correct Submits  Final Score\n",
              "1                2                2  ...                1      10000.0\n",
              "4               54               41  ...               44     370000.0\n",
              "5                6                5  ...                3      30000.0\n",
              "6              130               89  ...               89     850000.0\n",
              "7                5                2  ...                0       9209.0\n",
              "...            ...              ...  ...              ...          ...\n",
              "1860            76               48  ...               29     329665.0\n",
              "1862            12                6  ...                4      40000.0\n",
              "1864           187               63  ...               30     439741.0\n",
              "1865            99               17  ...               10     116428.0\n",
              "1868            47               28  ...               38     270000.0\n",
              "\n",
              "[1159 rows x 6 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OQxHa86Vedzo"
      },
      "source": [
        "**Tính Toán Các Nhóm Ý Kiến**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "II_DgngOeixC",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "outputId": "4112a3f1-1dd3-4920-b21e-bc7c2057fc50"
      },
      "source": [
        "Calc_Idea = pd.DataFrame()\n",
        "#O1: Tổng số problem đã solved (is_final = 1 && pre_score=10000) / Tổng số problem có submit\n",
        "Calc_Idea['Opinion 1'] =Data['Total Solved'] / Data['Problems Have Submit']\n",
        "#O2: Tổng số problem đã sumbit / Tổng số submissions \n",
        "Calc_Idea['Opinion 2'] =Data['Problems Have Submit'] / Data['Total Submit']\n",
        "#O3: Số lần submit đúng (pre_score=10000) / tổng số submissions \n",
        "Calc_Idea['Opinion 3'] =Data['Correct Submits'] / Data['Total Submit']\n",
        "#O5: Tổng pre_score của các submssion có is_final = 1\n",
        "Calc_Idea['Opinion 5'] = Data['Final Score']\n",
        "#06: Tổng số submission\n",
        "Calc_Idea['Opinion 6'] = Data['Total Submit']\n",
        "Calc_Idea"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
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              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Opinion 1</th>\n",
              "      <th>Opinion 2</th>\n",
              "      <th>Opinion 3</th>\n",
              "      <th>Opinion 5</th>\n",
              "      <th>Opinion 6</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.500000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>10000.0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.973684</td>\n",
              "      <td>0.703704</td>\n",
              "      <td>0.814815</td>\n",
              "      <td>370000.0</td>\n",
              "      <td>54</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0.600000</td>\n",
              "      <td>0.833333</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>30000.0</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>0.965909</td>\n",
              "      <td>0.676923</td>\n",
              "      <td>0.684615</td>\n",
              "      <td>850000.0</td>\n",
              "      <td>130</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.400000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>9209.0</td>\n",
              "      <td>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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1860</th>\n",
              "      <td>0.583333</td>\n",
              "      <td>0.631579</td>\n",
              "      <td>0.381579</td>\n",
              "      <td>329665.0</td>\n",
              "      <td>76</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1862</th>\n",
              "      <td>0.666667</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>40000.0</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1864</th>\n",
              "      <td>0.444444</td>\n",
              "      <td>0.336898</td>\n",
              "      <td>0.160428</td>\n",
              "      <td>439741.0</td>\n",
              "      <td>187</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1865</th>\n",
              "      <td>0.588235</td>\n",
              "      <td>0.171717</td>\n",
              "      <td>0.101010</td>\n",
              "      <td>116428.0</td>\n",
              "      <td>99</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1868</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.574468</td>\n",
              "      <td>0.808511</td>\n",
              "      <td>270000.0</td>\n",
              "      <td>47</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1159 rows × 5 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      Opinion 1  Opinion 2  Opinion 3  Opinion 5  Opinion 6\n",
              "1      0.500000   1.000000   0.500000    10000.0          2\n",
              "4      0.973684   0.703704   0.814815   370000.0         54\n",
              "5      0.600000   0.833333   0.500000    30000.0          6\n",
              "6      0.965909   0.676923   0.684615   850000.0        130\n",
              "7      0.000000   0.400000   0.000000     9209.0          5\n",
              "...         ...        ...        ...        ...        ...\n",
              "1860   0.583333   0.631579   0.381579   329665.0         76\n",
              "1862   0.666667   0.500000   0.333333    40000.0         12\n",
              "1864   0.444444   0.336898   0.160428   439741.0        187\n",
              "1865   0.588235   0.171717   0.101010   116428.0         99\n",
              "1868   1.000000   0.574468   0.808511   270000.0         47\n",
              "\n",
              "[1159 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 436
        },
        "id": "hUrsgKfHXQdX",
        "outputId": "6e757544-2fa5-4a3d-f652-07a3cb9781d9"
      },
      "source": [
        "stack = pd.concat([Data, Calc_Idea], axis=1)\n",
        "stack = stack.reset_index()\n",
        "stack = stack.rename(columns={'index': 'id'})\n",
        "stack"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "    .dataframe thead th {\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>id</th>\n",
              "      <th>Total Submit</th>\n",
              "      <th>Problems Submit</th>\n",
              "      <th>Problems Have Submit</th>\n",
              "      <th>Total Solved</th>\n",
              "      <th>Correct Submits</th>\n",
              "      <th>Final Score</th>\n",
              "      <th>Opinion 1</th>\n",
              "      <th>Opinion 2</th>\n",
              "      <th>Opinion 3</th>\n",
              "      <th>Opinion 5</th>\n",
              "      <th>Opinion 6</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>10000.0</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>10000.0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4</td>\n",
              "      <td>54</td>\n",
              "      <td>41</td>\n",
              "      <td>38</td>\n",
              "      <td>37</td>\n",
              "      <td>44</td>\n",
              "      <td>370000.0</td>\n",
              "      <td>0.973684</td>\n",
              "      <td>0.703704</td>\n",
              "      <td>0.814815</td>\n",
              "      <td>370000.0</td>\n",
              "      <td>54</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>5</td>\n",
              "      <td>6</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>30000.0</td>\n",
              "      <td>0.600000</td>\n",
              "      <td>0.833333</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>30000.0</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>6</td>\n",
              "      <td>130</td>\n",
              "      <td>89</td>\n",
              "      <td>88</td>\n",
              "      <td>85</td>\n",
              "      <td>89</td>\n",
              "      <td>850000.0</td>\n",
              "      <td>0.965909</td>\n",
              "      <td>0.676923</td>\n",
              "      <td>0.684615</td>\n",
              "      <td>850000.0</td>\n",
              "      <td>130</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>7</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>9209.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.400000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>9209.0</td>\n",
              "      <td>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",
              "      <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>76</td>\n",
              "      <td>48</td>\n",
              "      <td>48</td>\n",
              "      <td>28</td>\n",
              "      <td>29</td>\n",
              "      <td>329665.0</td>\n",
              "      <td>0.583333</td>\n",
              "      <td>0.631579</td>\n",
              "      <td>0.381579</td>\n",
              "      <td>329665.0</td>\n",
              "      <td>76</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1155</th>\n",
              "      <td>1862</td>\n",
              "      <td>12</td>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>40000.0</td>\n",
              "      <td>0.666667</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>40000.0</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1156</th>\n",
              "      <td>1864</td>\n",
              "      <td>187</td>\n",
              "      <td>63</td>\n",
              "      <td>63</td>\n",
              "      <td>28</td>\n",
              "      <td>30</td>\n",
              "      <td>439741.0</td>\n",
              "      <td>0.444444</td>\n",
              "      <td>0.336898</td>\n",
              "      <td>0.160428</td>\n",
              "      <td>439741.0</td>\n",
              "      <td>187</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1157</th>\n",
              "      <td>1865</td>\n",
              "      <td>99</td>\n",
              "      <td>17</td>\n",
              "      <td>17</td>\n",
              "      <td>10</td>\n",
              "      <td>10</td>\n",
              "      <td>116428.0</td>\n",
              "      <td>0.588235</td>\n",
              "      <td>0.171717</td>\n",
              "      <td>0.101010</td>\n",
              "      <td>116428.0</td>\n",
              "      <td>99</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1158</th>\n",
              "      <td>1868</td>\n",
              "      <td>47</td>\n",
              "      <td>28</td>\n",
              "      <td>27</td>\n",
              "      <td>27</td>\n",
              "      <td>38</td>\n",
              "      <td>270000.0</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.574468</td>\n",
              "      <td>0.808511</td>\n",
              "      <td>270000.0</td>\n",
              "      <td>47</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1159 rows × 12 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "        id  Total Submit  Problems Submit  ...  Opinion 3  Opinion 5  Opinion 6\n",
              "0        1             2                2  ...   0.500000    10000.0          2\n",
              "1        4            54               41  ...   0.814815   370000.0         54\n",
              "2        5             6                5  ...   0.500000    30000.0          6\n",
              "3        6           130               89  ...   0.684615   850000.0        130\n",
              "4        7             5                2  ...   0.000000     9209.0          5\n",
              "...    ...           ...              ...  ...        ...        ...        ...\n",
              "1154  1860            76               48  ...   0.381579   329665.0         76\n",
              "1155  1862            12                6  ...   0.333333    40000.0         12\n",
              "1156  1864           187               63  ...   0.160428   439741.0        187\n",
              "1157  1865            99               17  ...   0.101010   116428.0         99\n",
              "1158  1868            47               28  ...   0.808511   270000.0         47\n",
              "\n",
              "[1159 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "H6QrS7pksWp9"
      },
      "source": [
        "**Merge Data**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "zE5wNExjYnz8",
        "outputId": "9701ae47-ee37-42c5-b998-612c56805ff1"
      },
      "source": [
        "sc_train = sc_training.dropna(how='any')\n",
        "sc_train.head()"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>CK</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>22</td>\n",
              "      <td>8.5</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>24</td>\n",
              "      <td>9.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>27</td>\n",
              "      <td>10.0</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>28</td>\n",
              "      <td>9.5</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>29</td>\n",
              "      <td>8.5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   id    CK\n",
              "0  22   8.5\n",
              "1  24   9.5\n",
              "2  27  10.0\n",
              "3  28   9.5\n",
              "4  29   8.5"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "40qHx8g1Y8XF",
        "outputId": "4cd28827-f2cf-4816-8dc3-a25cd28a25a4"
      },
      "source": [
        "sc_predict = sc_training[sc_training.isna().any(axis=1)]\n",
        "sc_predict.head()"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>CK</th>\n",
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              "  <tbody>\n",
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              "      <th>19</th>\n",
              "      <td>51</td>\n",
              "      <td>NaN</td>\n",
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              "      <th>127</th>\n",
              "      <td>240</td>\n",
              "      <td>NaN</td>\n",
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              "      <th>211</th>\n",
              "      <td>391</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>292</th>\n",
              "      <td>513</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>333</th>\n",
              "      <td>586</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      id  CK\n",
              "19    51 NaN\n",
              "127  240 NaN\n",
              "211  391 NaN\n",
              "292  513 NaN\n",
              "333  586 NaN"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        },
        "id": "bpRA5X4maQmb",
        "outputId": "dec6520b-0879-491a-f379-474d51c9b374"
      },
      "source": [
        "training_data = pd.merge(sc_train, stack, how='inner', on='id')\n",
        "training_data.head()"
      ],
      "execution_count": 14,
      "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>id</th>\n",
              "      <th>CK</th>\n",
              "      <th>Total Submit</th>\n",
              "      <th>Problems Submit</th>\n",
              "      <th>Problems Have Submit</th>\n",
              "      <th>Total Solved</th>\n",
              "      <th>Correct Submits</th>\n",
              "      <th>Final Score</th>\n",
              "      <th>Opinion 1</th>\n",
              "      <th>Opinion 2</th>\n",
              "      <th>Opinion 3</th>\n",
              "      <th>Opinion 5</th>\n",
              "      <th>Opinion 6</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>24</td>\n",
              "      <td>9.5</td>\n",
              "      <td>164</td>\n",
              "      <td>78</td>\n",
              "      <td>77</td>\n",
              "      <td>72</td>\n",
              "      <td>80</td>\n",
              "      <td>750299.0</td>\n",
              "      <td>0.935065</td>\n",
              "      <td>0.469512</td>\n",
              "      <td>0.487805</td>\n",
              "      <td>750299.0</td>\n",
              "      <td>164</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>27</td>\n",
              "      <td>10.0</td>\n",
              "      <td>13</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>39000.0</td>\n",
              "      <td>0.600000</td>\n",
              "      <td>0.384615</td>\n",
              "      <td>0.307692</td>\n",
              "      <td>39000.0</td>\n",
              "      <td>13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>29</td>\n",
              "      <td>8.5</td>\n",
              "      <td>199</td>\n",
              "      <td>80</td>\n",
              "      <td>80</td>\n",
              "      <td>72</td>\n",
              "      <td>75</td>\n",
              "      <td>762760.0</td>\n",
              "      <td>0.900000</td>\n",
              "      <td>0.402010</td>\n",
              "      <td>0.376884</td>\n",
              "      <td>762760.0</td>\n",
              "      <td>199</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>34</td>\n",
              "      <td>6.5</td>\n",
              "      <td>218</td>\n",
              "      <td>84</td>\n",
              "      <td>84</td>\n",
              "      <td>81</td>\n",
              "      <td>81</td>\n",
              "      <td>822888.0</td>\n",
              "      <td>0.964286</td>\n",
              "      <td>0.385321</td>\n",
              "      <td>0.371560</td>\n",
              "      <td>822888.0</td>\n",
              "      <td>218</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>35</td>\n",
              "      <td>4.5</td>\n",
              "      <td>297</td>\n",
              "      <td>152</td>\n",
              "      <td>151</td>\n",
              "      <td>145</td>\n",
              "      <td>152</td>\n",
              "      <td>1477000.0</td>\n",
              "      <td>0.960265</td>\n",
              "      <td>0.508418</td>\n",
              "      <td>0.511785</td>\n",
              "      <td>1477000.0</td>\n",
              "      <td>297</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   id    CK  Total Submit  ...  Opinion 3  Opinion 5  Opinion 6\n",
              "0  24   9.5           164  ...   0.487805   750299.0        164\n",
              "1  27  10.0            13  ...   0.307692    39000.0         13\n",
              "2  29   8.5           199  ...   0.376884   762760.0        199\n",
              "3  34   6.5           218  ...   0.371560   822888.0        218\n",
              "4  35   4.5           297  ...   0.511785  1477000.0        297\n",
              "\n",
              "[5 rows x 13 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        },
        "id": "ZktDQ_AuW_Cj",
        "outputId": "839a3bad-4ca4-4140-b9c6-cbe07938f545"
      },
      "source": [
        "predict_data = pd.merge(sc_predict, stack, how='inner', on='id')\n",
        "predict_data.head()"
      ],
      "execution_count": 15,
      "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></th>\n",
              "      <th>id</th>\n",
              "      <th>CK</th>\n",
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              "      <th>Problems Submit</th>\n",
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              "      <th>Total Solved</th>\n",
              "      <th>Correct Submits</th>\n",
              "      <th>Final Score</th>\n",
              "      <th>Opinion 1</th>\n",
              "      <th>Opinion 2</th>\n",
              "      <th>Opinion 3</th>\n",
              "      <th>Opinion 5</th>\n",
              "      <th>Opinion 6</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>51</td>\n",
              "      <td>NaN</td>\n",
              "      <td>86</td>\n",
              "      <td>39</td>\n",
              "      <td>39</td>\n",
              "      <td>38</td>\n",
              "      <td>40</td>\n",
              "      <td>380000.0</td>\n",
              "      <td>0.974359</td>\n",
              "      <td>0.453488</td>\n",
              "      <td>0.465116</td>\n",
              "      <td>380000.0</td>\n",
              "      <td>86</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>240</td>\n",
              "      <td>NaN</td>\n",
              "      <td>623</td>\n",
              "      <td>138</td>\n",
              "      <td>138</td>\n",
              "      <td>126</td>\n",
              "      <td>133</td>\n",
              "      <td>1309761.0</td>\n",
              "      <td>0.913043</td>\n",
              "      <td>0.221509</td>\n",
              "      <td>0.213483</td>\n",
              "      <td>1309761.0</td>\n",
              "      <td>623</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>391</td>\n",
              "      <td>NaN</td>\n",
              "      <td>379</td>\n",
              "      <td>113</td>\n",
              "      <td>113</td>\n",
              "      <td>102</td>\n",
              "      <td>104</td>\n",
              "      <td>1059240.0</td>\n",
              "      <td>0.902655</td>\n",
              "      <td>0.298153</td>\n",
              "      <td>0.274406</td>\n",
              "      <td>1059240.0</td>\n",
              "      <td>379</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>513</td>\n",
              "      <td>NaN</td>\n",
              "      <td>672</td>\n",
              "      <td>139</td>\n",
              "      <td>140</td>\n",
              "      <td>130</td>\n",
              "      <td>135</td>\n",
              "      <td>1319307.0</td>\n",
              "      <td>0.928571</td>\n",
              "      <td>0.208333</td>\n",
              "      <td>0.200893</td>\n",
              "      <td>1319307.0</td>\n",
              "      <td>672</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>586</td>\n",
              "      <td>NaN</td>\n",
              "      <td>296</td>\n",
              "      <td>127</td>\n",
              "      <td>127</td>\n",
              "      <td>123</td>\n",
              "      <td>127</td>\n",
              "      <td>1238947.0</td>\n",
              "      <td>0.968504</td>\n",
              "      <td>0.429054</td>\n",
              "      <td>0.429054</td>\n",
              "      <td>1238947.0</td>\n",
              "      <td>296</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    id  CK  Total Submit  ...  Opinion 3  Opinion 5  Opinion 6\n",
              "0   51 NaN            86  ...   0.465116   380000.0         86\n",
              "1  240 NaN           623  ...   0.213483  1309761.0        623\n",
              "2  391 NaN           379  ...   0.274406  1059240.0        379\n",
              "3  513 NaN           672  ...   0.200893  1319307.0        672\n",
              "4  586 NaN           296  ...   0.429054  1238947.0        296\n",
              "\n",
              "[5 rows x 13 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nhW9evdvkPhD"
      },
      "source": [
        "**Plot Data**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p4P1KAxejzS1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "outputId": "5bbd16ff-86b2-408c-a107-3879ad6b11ed"
      },
      "source": [
        "plt.scatter(x=training_data['Opinion 1'],y=training_data['CK'],c='blue')\n",
        "plt.xlabel('Opinion 1')\n",
        "plt.ylabel('Final')\n",
        "plt.show"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<function matplotlib.pyplot.show>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "kCTwQ9CxbApR",
        "outputId": "0455b4cf-8316-4b57-a4c6-e84fcb5c91bd"
      },
      "source": [
        "plt.scatter(x=training_data['Opinion 2'],y=training_data['CK'],c='red')\n",
        "plt.xlabel('Opinion 2')\n",
        "plt.ylabel('Final')\n",
        "plt.show"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<function matplotlib.pyplot.show>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "hMmcA4xZbM0d",
        "outputId": "8e673030-db1f-4ae0-ec7a-2847207dca99"
      },
      "source": [
        "plt.scatter(x=training_data['Opinion 3'],y=training_data['CK'],c='violet')\n",
        "plt.xlabel('Opinion 3')\n",
        "plt.ylabel('Final')\n",
        "plt.show"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<function matplotlib.pyplot.show>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "xNCZRjPebkcK",
        "outputId": "1d293928-5b0b-4bae-81c9-d7d9a03ae728"
      },
      "source": [
        "plt.scatter(x=training_data['Opinion 5'],y=training_data['CK'],c='purple')\n",
        "plt.xlabel('Opinion 5')\n",
        "plt.ylabel('Final')\n",
        "plt.show"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<function matplotlib.pyplot.show>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "vKxrt5bZboUe",
        "outputId": "22aaaa77-ca9a-4112-90d2-5c184f9e9ad4"
      },
      "source": [
        "plt.scatter(x=training_data['Opinion 6'],y=training_data['CK'],c='green')\n",
        "plt.xlabel('Opinion 6')\n",
        "plt.ylabel('Final')\n",
        "plt.show"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<function matplotlib.pyplot.show>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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jsXLJSqy9aC3uffJe35qikIR9S4OX5J1crim4mZalQgmqWmc5W9m5d3//7gbTFTh76+UoL9pFo4i5hbm6W1+0M6VCCR+47AOuY2DPMB7fPY479t4R+fhRMpfDECVXOai/qPYtDV7SadBoNolirzb7l3oe8BsDy07t+VRP7HGKkrkc93hBJGHf0uAlnQYXmk2iGJXdPiEA/mNgjWUz4xQlcznu8eK2iXIsGrykG8jlpBDFqPTKzu0m/MbAGstmxilK5nLc48VtE+VYNHhJN5DLScHNtCwVSg2Ws5Wd62a6AtUXwqgvhkWj2LKIzVZQKpQ8x8CeYTy6ZjTW8aNkLochSq5yUH9R7VsavKQbyOWk4GZaTq2fwvSGadfsXKfpClSvdJm5YQYzN8z4Gs4FFOrs3+kN07hn4z2umcdWdi+AOnu5GQpSwAX9F/juY4iBynDF9Ryn1k+5joEzw3hy3WSdjWzfz8pitvcHIHTmsnUe9q9WjrRfTVFIwr6lwUu6gVwuNBNCCOFCMyGEkCbJrbxmJyia0y5Zje8ex/Z92+tiMO0yllOosiIp3SQuL5wRnnFwHqNklFAsFGvCnJdAZhfQ/GIvl/YurTsfZ3Slm3DmrGmgdwDvfeN78cCzD9Rkr7UXra37Pqz8Faa2bvgYh/IcaTW5//gobDRnX7EPV664Ent+sidWP+2MJVg9cugRVwHNK/bSiVt0ZVK1+b2whRHd4kptnQTlORIVymsudHM0p52VS1bi8MuHXX0Dv9jLNAiSv8L+DPMukVGeI1FhHKcLFIuqHDp+yPMjK7/YyzQI6jtsbXn/WVOeI2mQ+4VmikVVBpcMel7+6hd7mQZBfYetLe8/a8pzJA1yPymEjebsK/ahMlxJoaL0sQQrLwHNK/bSiVt0ZVK1+RGmtrhSWydBeY6kQe4nhTDRnJaE9ODmBzE2MtYQg2mXsZxClWUvu0lcXiRhPDuPUTJKdcKcm0DmFND8Yi+d5+OMrrTa2786axroHcDYyFid7OX8PswiaZja8r7IDFCeI+mQ+4VmQgjpViivEUIIaQpOCoQQQmpkckmqiJwD4C4A/xaAAtiqqt9Ouh+nBQugFpvpNJOB6mfT8wvz+MWpX9S2XdB/AZ7/k+frjju+exx37r2zwd61jF+3KM7FhcV4bf4133qttYpjJ4/V1VsulX1NaafR62Zl2/c9r3wenvr5U7Vtq5etxokzJ3yjS1ctXYWHnnuodsxysYzNl252tZODolCdP4skrVwav4Q0RyZrCiIyA+CbqnqXiJQA9KnqL7z2j7OmEDfu0Q37xOAXS2nAgJr/pY1l9HpZy1GPJSKRzWUr3nTmiZnAKFRnuyQWTGn8ElJPRxjNIrIEwOMALtSQnceZFJI2mfWT1VKbiaVsNX7WclrEjTdNwsql8UtIPZ2y0DwM4AiAaRH5vojcJSIN4QMiMioie0Vk75EjRyJ30irLs10nBKB6zlnXF7f/JH5eNH4JaZ4sJoUeAJcDuENVLwNwAsDHnDup6g5VHVHVkeXLl0fupFWWZzvHd/pZy2kRt/8kfl40fglpniwmhcMADqvqo+b3n0d1kkiUuHGPbtiTzfxiKQ0YmUVxWkZv3NhM57HimMtWvGmYKFRnuySsXBq/hDRP6pOCqr4I4KcicrG5qQLgKZ8msXCzYO2xmU4zGahemXPOonPqtjmvPrKsYDd7d9fGXZ5RnIsLiwNrttdofxxkStuNXqs+p5Vt33f1stV121YvW11nyU5vmK6Zy9a2ynCl7pjlYtnVTp5cNxkYher8WSS1EEzjl5DmyerqozejeklqCcC/ALhJVf+f1/40mgkhJDodc+tsVX0cQKRCCSGEtB4azYQQQmrkPmTHwpnT7JY57MQr+9duzRaMAuYW5iLX45ZfvGrpKjz83MN1+ckA6jKVLx64GM8cfabu0k8rj9lZt3Vuh44fQl+xDyfnTjZYzuViGduv3+75uXsUQ/htu95WF2daGa7gpstuqmvvltFs1emW+2zlYieR80wICaYr7pI6u38WW+/f2mDoFqQAQwzf/GZn9m+SpnQrCXNu9n1nbphxnfzCGsLOCcHCOWE5CbKdw0BrmRB3OsJojkOzk0KzdrPdiM1r5rOb9RvFEJZbs7kU14LWMiGNdIrRnDrNGq329nm1Y93Oq5MM4XasiZBOpCsmhWaNVnv7vNqxbufVSYZwO9ZESCfiOymIyEa/f2kV2SwTlQlXQ7cghcD8Zmf2b5KmdCsJc272fd2s3yiGsFe+tZdEZxFkO4eB1jIhyRH0TuF6n3/vbm1pybHpkk2YWj9VZzAP9A5g5oaZmmnrhlv2r9Oa7THiXcDlll9cGa405Cc7M5VXL1vdcH8h5wuv89wEgnKx7PoCXS6WXReZ3c7VzxB+cPODDRNDZbiCXTfs8s1ottvO1jnav7q1iZPzTAgJR1csNBNCSDfSUqNZRNYBeCOA2k18VPVTUTojhBDS3oRaaBaROwH8HoA/AiAAfheA+2cuhBBCOpaw7xTeqqpvEpEfqOqtIvIXAL7WysKSZnz3eJ0ZPLpmFFcNXuVrOfsZs5bpa7dwnQgECq2tZRw9ebRB5rL2iYIhBm5eczMm103W1WE/lp80Zn/Oblbbz2XlkpUN551W/nGUfpjJTEiyhFpTEJFHVfU3ROQ7ADYCOArgSVVd1eoCgebXFLxyld1ekHuMHgikzrB1GrPtYjVXhiv49uFvt6wO+3mnlX8cpR9mMhPiT8uMZhH5OID/gWr2wV8DUAB3qerH4xQalWYnhSRylbvBanbDOu+08o+j9MNMZkL8adlCs6r+F/PhF0TkqwAWq+rxqAVmRRK5xd1gNbthnWtadnOUfjrJuCakUwhtNIvIW0Xk91FdcF4vIptbV1ayJJFb3A1WsxvWuaZlN0fpp5OMa0I6hbBXH90D4LMArgbwFvNfx4TkeOUWu+Up9xg9DYat05htF6u5MlxpaR32804r/zhKP8xkJiR5wr5TGAFwlaqOq+ofmf/+uJWFJYmVW+y0he/ZeE+D5bxzw846E9jNmLWbvtbx3LAmHSuTGGi0j90mpiAMMTA2MoYHNz9YV4f9WH63l7A/Zzer7efiPO+08o+j9MNMZkKSJ+xC898B+GNVfaH1JTVCo5kQQqLTSqN5GYCnROQxAKesjar6niidEUIIaW/CTgp/3soiCCGEtAdhL0n9h1YX0irsxuvS3qUAgGMnj9U9dpqwfpas8zlnrvI1Q9fgwLEDrnnDQdGUPUYPDBg4vXDa9XmBoFQo4dT8qbptloBXMkqY0zks6AIMMdAjPXXHCpMLbdUfxuaOahz7jTkhpD3wXVMQkW+p6tUi8kugTv0VAKqqr291gUD8NYUo5rFlwgLwtGTdnss7YWzuKMax37EJIcmSuNEsIitVNXN1N+6kENU8tq7A8bJkvZ7LO2Fs7ijGcVA7QkgytGKh+UsALjcP/gVV/e24xWVBVLPVb/9utmTD2NxRjOOo+xBC0iPIU7BfRH9hKwtpBVHN1sElg76WbLeasmFs7ijGcdR9CCHpETQpqMfjjiCKeWyZsH6WbLuYzGkSxuaOYhyHaUcIyY6gSeFSEXnZXGh+k/n4ZRH5pYi8nEaBzeA0Xi2z2PnYbsL6WbJuzzlzlSvDFc+84aAQ+x6jByWj5Pm8QLCosKhhm0XJKNX6MMRoOFaYXGir/jA2d1Tj2GvMCSHtAzOaCSEkp8RZaA59l1RCCCH5h5MCIYSQGmFvc5E4IlIAsBfA86r67jT7djNygcZcZgB1Gc4A0F/qx5n5MzWrWCC4dvhafPPgNxtMZCvn+JFDj9TyoS38spkXFRahv9RfM69PnD6B1+Zfq9vHspPvffLeuvqc+7z5V99cZyyPrhnF5LpJ37Gw4jfD5FQ7M66dxvTai9bWfZ83i5kZ0SRvZLamICIfRfWW3K8PmhSSXFNws2yLRhEigtPzZ1/US4US5hfmm05tM2BgAd63tsiCsZExTK6b9LSTt1y6BTNPzPhay7P7Z7H1/q11YxaGPFnMzIgm7U7LMpqTRkRWAJgBMAHgo2lOCt2Ur+xFQQqY+8Sc51hY92pyklROdV4sZmZEk3ankxaabwPwp4D3n9AiMioie0Vk75EjRxLrmAbt2cxqr7HweneUVE51Xn4GzIgmeST1SUFE3g3gJVXd57efqu5Q1RFVHVm+fHli/dOgPetNeI2FV5JcUjnVefkZMCOa5JEs3ilcBeA9IvIcgL8BcK2I/K+0OnezbItGEaVCvehVKpQ8XxyjYLThBV5WZrWXnTy6ZjTQWp6oTDSMWRjyZDEzI5rkkdRfsVT1FlVdoapDAN4H4O9V9Q/S6t/NyJ3eMI2p9VN126bWT2Hmhpm6DGegevWR3SoWCCrDFVcTeeWSldi1cVddPrS9nReLCovqzOvFhcUN+1h2srM+5z5OY9laZPYaix3X78DkuslAa3nTJZswtX6qIePaaUw7v8/TIiwzokkeydRoFpFrAPynNBeaCSGkW2hlRnNLUNWHATycZQ2EEELO0n4feBNCCMmMTN8ppEFU49S+f1+xDyfOnPA9vmUtA432sxsFFDCP6iWf9kxlC+szemeOcZTc6LCZyXEMXBq8hOSbXN8lNapxGiXT2U7RKEKhmFuYi1xjEEGGMeCdKR0mMzmKgUuDl5DOomOM5qgkndHsZZy2q+3sZxgD3pnSYTOTwxq4NHgJ6Sw6bqG51UQ1TtvVRA1jGId5rtnz7rRxI4REJ9cLzVGN03Y1Uf0M4yQyk8Oed6eNGyEkOrmeFKIap3EzmItGET1Ga950BRnGzWYmRzFwafASkn9yPSlENU6d+5eL5cA+LCN654advnaxRQFn/+q3ZypbuOUY+xnGzWQmRzVwafASkn9yvdBMCCHdTCfdOpsQQkgbwkmBEEJIjVxfkuqG01g+OXcSC7pQl18cxh52u17fK3d5oHcAt7/rdleZzDqW5SI4v1rGtL1tVKt4fPc47tx7Z602q063YxNCupuuWlMIYyxXhiv49uFvh7aHw1IqlDC1fqpucgl7LLs1HNUqHt89jjv23hHq2ISQfEGjOYBmc4UBd3s4yjHiZhxbbaNaxT2f6vGU34LaEkI6GxrNAWSdK9xMxrG1f1SrOGhCiFMLISS/dNVCc7O5ws2au81kHFv7R7WKw0SK0kgmhFh01aQQxliuDFci2cNhKRVKDRnHYY9lt4ajWsVWHnOYYxNCSFdNCm7GsmUUW/nFD25+MJQ97IZX7vJA70DdIrOzFqt/t69OaziqVTy5bhJjI2N1tVmPaSQTQpx01UIzIYR0EzSaCSGENEVurz6a3T9bF4/pJZA52zhlsrDYj+8muBWkgGuGrsGBYweajtQc3z2OHft21NXnJaK5HRdAbdvS3qUAGuM/CSHdSS4/PprdP4ut92/F6fnTdduLRhHTG6YTjeK0UyqU8IHLPtAQnelFnEhNPxnN2cbtnIpGESLSMDZB/RJCOg/KayZ+YlirozijvsOIGqkZJKM1I8j59UsI6Twor5lEjakMahOFKBNCUL9uzwUdvxlBrtl2hJDOJ5cLzX4yVqsjJcPIYs5+o9QUdPxmBLlm2xFCOp9cTgoTlQmUCqWG7UWjmHgUp51SoeQanelFnEhNPxnN2cbtuEWj6Do2Qf0SQrqDXE4Kmy7ZhKn1U3XxmAO9A56LzFYbN5ksLJagZo/OtFOQAirDlaYjNS0ZzVmfWxu3405vmMbU+qnaNrf4Ty4yE9K95HKhmRBCCOU1QgghTcJJgRBCSI3UJwUReYOIPCQiT4nIkyLyobRrcDK7fxZDtw3BuNXA0G1DmN0/67vvss8sg9wqkFsFyz6zDLP7Z123j+8ed923FXV6PRfl3AghJPU1BRE5H8D5qvo9EXkdgH0ANqjqU15tWrmmECXe0suUNmDAMAzMLcwF9udnVcetE3A3ordcuqXBrqaxTEj30JFGs4jcD+B/quo3vPZp5aQQJd4yKes5jjHsVyfgbkR72dU0lgnpDjrOaBaRIQCXAXjU5blRAKMAMDjYOpkqiuGclOkb5zhxTGwv+5nGMiHEi8wWmkWkH8AXAHxYVV92Pq+qO1R1RFVHli9f3rI6otjESZm+cY7jV2fUKE4ayw4B2NYAAAxnSURBVIQQLzKZFESkiOqEMKuqX8yiBosoNrGXKW3AQI8R7k2Xn1Udt06v59zsahrLhBA/srj6SADcDeBpVf3LtPt3EsUm9jKld23chZ0bdjZsHxsZi2RVx63T6zm7XU1jmRAShiyuProawDcB7AewYG7+M1V9wKsNjWZCCIlORyw0q+q3AI+Ee0IIIZlCo5kQQkiNrpwU4li+Vhu5VdDzqR7IrRKqrZcB3ep6CSEkDrlMXvPDaQYfPH4Qo1+pZhR4LcA621jX/we1dTOgj548ipvuu8m3v2brJYSQuGRuNIchyYXmKAZzUJugtnGyopOolxBCAN46OxRxzOAgA7gVx2zm2IQQEpeumxTiZDQHGcCtOGYzxyaEkLh03aQQxWD2axOmbZys6CTqJYSQuHTdpBDFYHZrA5y9p1BQ2zhZ0UnUSwghcem6hWZCCOkWuNBMCCGkKTgpEEIIqdG1k0KQJRw2D3nZZ5Zh2WeW1fYb3z0eyz6mtUwIaQe6ck0hKJc5ah6yH2EykaPkRBNCSFg6MqM5DElPCkGWcJw8ZD+C7GNay4SQVtARt85uB4Is4aQt4lYY0YQQ0gq6ck0hyBKOk4ccp7+w9RBCSFp05aQQZAlHzUP2I4x9TGuZENIudOWkEGQJR8lDHugdwEDvQG2/sZGxyPYxrWVCSLvQlQvNhBDSDdBoJoQQ0hScFAghhNTI7aSQliHcDiZyO9RACMkHufQU0so1bof85HaogRCSH3K50JyWIdwOJnI71EAIaU+40GySliHcDiZyO9RACMkPuZwU0jKE28FEbocaCCH5IZeTQlqGcDuYyO1QAyEkP+RyUkjLEG4HE7kdaiCE5IdcLjQTQgjhQjMhhJAm4aRACCGkRibymohcB+B2AAUAd6nqf0u6j9n9s9i2ZxsOHj+IghQwr/PoL/XjxOkTUDR+ZGaIgQVdaNg+0DuA2991OwBg255tOHT8EAaXDGLV0lV46LmHam3KxTK2X7+97rN8qwarzURlohb36bbdrX6/fcKcf9z2hJDuJPU1BREpAPgxgLcDOAzguwBuVNWnvNpEXVNwyzxuBgMGego9OD1/2ne/ghQwc8OMb87zlku3YOaJGd885mYzm5n5TAgBOiSjWUSuBPDnqvpO8/tbAEBV/6tXm6SM5jQIynm23rV4tQOat5RpORNCgM5ZaP41AD+1fX/Y3FaHiIyKyF4R2XvkyJFIHWRp8wblPLtNCM79m7WUaTkTQuLStgvNqrpDVUdUdWT58uWR2mZp8wblPBek4NvOr23Y86LlTAiJSxaTwvMA3mD7foW5LTGi5igHYcBAqVAK3K8ghcCc59E1o4EGcrOWMi1nQkhcspgUvgvgIhEZFpESgPcB+HKSHdgtX+DsX+f9pX4IxLWNIe5DMdA7gF0bd2Fq/VSdNVwZrtS1KRfLtUVmZw1203hy3WSggdyspUzLmRASl0yMZhFZC+A2VC9JnVJV3z9haTQTQkh04iw0Z+IpqOoDAB7Iom9CCCHetO1CMyGEkPThpEAIIaQGJwVCCCE1OCkQQgip0RF5CiJyBEDc+1YsA/DzBMtJg06rudPqBVhzWrDmdPCqeaWqRrJ/O2JSaAYR2Rv1kqys6bSaO61egDWnBWtOhyRr5sdHhBBCanBSIIQQUqMbJoUdWRcQg06rudPqBVhzWrDmdEis5tyvKRBCCAlPN7xTIIQQEhJOCoQQQmrkdlIQketE5BkROSAiH8u6HgsReYOIPCQiT4nIkyLyIXP7UhH5hog8a34919wuIvJX5nn8QEQuz7D2goh8X0S+an4/LCKPmrX9rXkrdIjIIvP7A+bzQxnVe46IfF5EfiQiT4vIle08ziLyEfN34oci8jkRWdyOYywiUyLykoj80LYt8riKyBZz/2dFZEvK9f538/fiByLyJRE5x/bcLWa9z4jIO23bU3tNcavZ9tyfiIiKyDLz+2THWFVz9w/VW3L/M4ALAZQAPAFgddZ1mbWdD+By8/HrAPwYwGoAnwHwMXP7xwB82ny8FsDXAAiAKwA8mmHtHwXwvwF81fz+XgDvMx/fCWDMfDwO4E7z8fsA/G1G9c4A+KD5uATgnHYdZ1QjaX8CoNc2tu9vxzEG8JsALgfwQ9u2SOMKYCmAfzG/nms+PjfFet8BoMd8/GlbvavN14tFAIbN15FC2q8pbjWb298A4OuoyrzLWjHGqf3Sp/kPwJUAvm77/hYAt2Rdl0et9wN4O4BnAJxvbjsfwDPm4+0AbrTtX9sv5TpXANgD4FoAXzV/AX9u+x+rNubmL+2V5uMecz9Jud4l5ousOLa35TjjbHb5UnPMvgrgne06xgCGHC+ykcYVwI0Attu21+3X6nodz90AYNZ8XPdaYY1zFq8pbjUD+DyASwE8h7OTQqJjnNePj6z/wSwOm9vaCvMt/2UAHgVwnqq+YD71IoDzzMftci63AfhTAAvm9wMAfqGqcy511Wo2nz9u7p8mwwCOAJg2P/K6S0TKaNNxVtXnAXwWwCEAL6A6ZvvQ3mNsJ+q4tsvvNQBsRfUvbaCN6xWR9QCeV9UnHE8lWnNeJ4W2R0T6AXwBwIdV9WX7c1qd1tvmWmEReTeAl1R1X9a1RKAH1bffd6jqZQBOoPqxRo12GmfzM/j1qE5mFwAoA7gu06Ji0k7jGoSIbAMwB2A261r8EJE+AH8G4BOt7iuvk8LzqH72ZrHC3NYWiEgR1QlhVlW/aG7+mYicbz5/PoCXzO3tcC5XAXiPiDwH4G9Q/QjpdgDniIiV3mevq1az+fwSAEfTLBjVv4oOq+qj5vefR3WSaNdxfhuAn6jqEVU9A+CLqI57O4+xnajjmvV4Q0TeD+DdADaZExl86sq63n+D6h8MT5j/H64A8D0R+VWf2mLVnNdJ4bsALjKv3CihuhD35YxrAlC9UgDA3QCeVtW/tD31ZQDW1QFbUF1rsLZvNq8wuALAcdvb9FRQ1VtUdYWqDqE6ln+vqpsAPATgdzxqts7ld8z9U/3LUVVfBPBTEbnY3FQB8BTad5wPAbhCRPrM3xGr3rYdYwdRx/XrAN4hIuea75LeYW5LBRG5DtWPQ9+jqq/anvoygPeZV3cNA7gIwGPI+DVFVfer6q+o6pD5/+FhVC9YeRFJj3ErF0qy/IfqivyPUb1iYFvW9djquhrVt9Y/APC4+W8tqp8H7wHwLIAHASw19xcAf22ex34AIxnXfw3OXn10Iar/wxwA8HcAFpnbF5vfHzCfvzCjWt8MYK851vehegVG244zgFsB/AjADwHcg+oVMG03xgA+h+q6xxnzxekDccYV1c/yD5j/bkq53gOoft5u/T94p23/bWa9zwB4l217aq8pbjU7nn8OZxeaEx1j3uaCEEJIjbx+fEQIISQGnBQIIYTU4KRACCGkBicFQgghNTgpEEIIqcFJgeQeEVkhIvebd4r8ZxG53bzWPKjdP4XY5y4RWZ1QndeIyONSvVPqPyRxTEKiwktSSa4xRbBHUb3dxbSIFFCNLjymqv852+rOYt66+Z8AXKeqh0TkV1T1paB2hCQN3ymQvHMtgNdUdRoAVHUewEcAbDXt4feb7yIeNt9JfNJqKCKvmF+vMZ+3shlmzckG5vYR8/GNIrJfqnkIn7YfR0QmROQJEfmOiJyHRn4fwBdV9ZBZJycEkgmcFEjeeSOqdxutodUbEB4CsMrc9O8A/DaANwH4XetF3sFlAD6M6v32L0T1vkQ1ROQCVO/Lfy2qJvVbRGSD+XQZwHdU9VIA/wjgP7gc/9cBnGtOMvtEZHPUEyUkCTgpEAJ8Q1WPqupJVG9Ed7XLPo+p6mFVXUD1tghDjuffAuBhrd7Qzrrr5m+az51GNR8BqE5QzrZA9a6uawCsQzVH4eMi8uvxT4mQeHBSIHnnKVRfbGuIyOsBDKJ6Pxig8TbPbgttp2yP51F9EQ/LGT27eOfV9jCqIS4nVPXnqL6juDRCH4QkAicFknf2AOizPo4xF5r/AsBOPXt3zLdLNWO4F8AGAI/E6OcxAP9eRJaZfdwIIMoVRPcDuFpEesx75/8GgKdj1EFIU3BSILnG/Av9BlTXCp5F9S6Xr6EaWGLxGKr5Fj8A8AVV3RujnxdQDfF5CNX83n2qer9/q7r2TwP4P2YNjwG4S1UbQtsJaTW8JJV0NWbQyoiq/sesayGkHeA7BUIIITX4ToEQQkgNvlMghBBSg5MCIYSQGpwUCCGE1OCkQAghpAYnBUIIITX+PyVWIypl4dGDAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}