Predict_Student_Performance_From_Game.ipynb

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        "<a href=\"https://colab.research.google.com/github/mohripan/Machine-Learning/blob/main/Predict_Student_Performance_From_Game.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "execution_count": 1,
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        "id": "bBSWtMnWdxYn",
        "outputId": "f2c256d2-e1d6-4426-ac79-a7fc87b97a55"
      },
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          "name": "stdout",
          "text": [
            "Downloading predict-student-performance-from-game-play.zip to /content\n",
            " 99% 481M/484M [00:25<00:00, 21.5MB/s]\n",
            "100% 484M/484M [00:25<00:00, 19.7MB/s]\n"
          ]
        }
      ],
      "source": [
        "!pip install -q kaggle\n",
        "!mkdir ~/.kaggle\n",
        "! cp kaggle.json ~/.kaggle/\n",
        "! chmod 600 ~/.kaggle/kaggle.json\n",
        "!kaggle competitions download -c predict-student-performance-from-game-play"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!unzip /content/predict-student-performance-from-game-play.zip"
      ],
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          "base_uri": "https://localhost:8080/"
        },
        "id": "ev9wjAOBePte",
        "outputId": "b6b7e8c0-ccf5-4118-b5aa-bc3e445cb888"
      },
      "execution_count": 2,
      "outputs": [
        {
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          "name": "stdout",
          "text": [
            "Archive:  /content/predict-student-performance-from-game-play.zip\n",
            "  inflating: jo_wilder/__init__.py   \n",
            "  inflating: jo_wilder/competition.cpython-37m-x86_64-linux-gnu.so  \n",
            "  inflating: sample_submission.csv   \n",
            "  inflating: test.csv                \n",
            "  inflating: train.csv               \n",
            "  inflating: train_labels.csv        \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F"
      ],
      "metadata": {
        "id": "B22m74sxekIj"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_df = pd.read_csv('train.csv')\n",
        "train_df.head()"
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              "4            Can I come, Gramps?  gramps  tunic.historicalsociety.closet   \n",
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              "      <td>cutscene_click</td>\n",
              "      <td>basic</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-413.991405</td>\n",
              "      <td>75.685314</td>\n",
              "      <td>380.0</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>undefined</td>\n",
              "      <td>intro</td>\n",
              "      <td>tunic.historicalsociety.closet</td>\n",
              "      <td>tunic.historicalsociety.closet.intro</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0-4</td>\n",
              "      <td>20090109393214576_0-4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>20090109393214576</td>\n",
              "      <td>1</td>\n",
              "      <td>1965</td>\n",
              "      <td>person_click</td>\n",
              "      <td>basic</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-105.991405</td>\n",
              "      <td>-63.314686</td>\n",
              "      <td>688.0</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Whatcha doing over there, Jo?</td>\n",
              "      <td>gramps</td>\n",
              "      <td>tunic.historicalsociety.closet</td>\n",
              "      <td>tunic.historicalsociety.closet.gramps.intro_0_...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0-4</td>\n",
              "      <td>20090109393214576_0-4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>20090109393214576</td>\n",
              "      <td>2</td>\n",
              "      <td>3614</td>\n",
              "      <td>person_click</td>\n",
              "      <td>basic</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-418.991405</td>\n",
              "      <td>47.685314</td>\n",
              "      <td>375.0</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Just talking to Teddy.</td>\n",
              "      <td>gramps</td>\n",
              "      <td>tunic.historicalsociety.closet</td>\n",
              "      <td>tunic.historicalsociety.closet.gramps.intro_0_...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0-4</td>\n",
              "      <td>20090109393214576_0-4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>20090109393214576</td>\n",
              "      <td>3</td>\n",
              "      <td>5330</td>\n",
              "      <td>person_click</td>\n",
              "      <td>basic</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-110.991405</td>\n",
              "      <td>-57.314686</td>\n",
              "      <td>683.0</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>I gotta run to my meeting!</td>\n",
              "      <td>gramps</td>\n",
              "      <td>tunic.historicalsociety.closet</td>\n",
              "      <td>tunic.historicalsociety.closet.gramps.intro_0_...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0-4</td>\n",
              "      <td>20090109393214576_0-4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>20090109393214576</td>\n",
              "      <td>4</td>\n",
              "      <td>6397</td>\n",
              "      <td>person_click</td>\n",
              "      <td>basic</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-110.991405</td>\n",
              "      <td>-57.314686</td>\n",
              "      <td>683.0</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Can I come, Gramps?</td>\n",
              "      <td>gramps</td>\n",
              "      <td>tunic.historicalsociety.closet</td>\n",
              "      <td>tunic.historicalsociety.closet.gramps.intro_0_...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0-4</td>\n",
              "      <td>20090109393214576_0-4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 21 columns</p>\n",
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        "train_labels_df = pd.read_csv('train_labels.csv')\n",
        "train_labels_df.head()"
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              "             session_id  correct\n",
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      "execution_count": 17,
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            "text/plain": [
              "         session_id         index  elapsed_time         level           page  \\\n",
              "count  1.317421e+07  1.317421e+07  1.317421e+07  1.317421e+07  284746.000000   \n",
              "mean   2.113413e+16  6.526426e+02  3.846817e+06  1.219194e+01       3.157930   \n",
              "std    5.665220e+14  6.275818e+02  2.701387e+07  6.499188e+00       2.064042   \n",
              "min    2.009031e+16  0.000000e+00  0.000000e+00  0.000000e+00       0.000000   \n",
              "25%    2.101031e+16  2.890000e+02  4.394300e+05  6.000000e+00       1.000000   \n",
              "50%    2.104022e+16  5.960000e+02  1.013425e+06  1.300000e+01       3.000000   \n",
              "75%    2.110051e+16  8.970000e+02  1.740050e+06  1.800000e+01       5.000000   \n",
              "max    2.210022e+16  2.047300e+04  1.749293e+09  2.200000e+01       6.000000   \n",
              "\n",
              "        room_coor_x   room_coor_y  screen_coor_x  screen_coor_y  \\\n",
              "count  1.213797e+07  1.213797e+07   1.213797e+07   1.213797e+07   \n",
              "mean  -5.493615e+01 -1.162997e+02   4.581144e+02   3.852363e+02   \n",
              "std    5.201468e+02  2.185912e+02   2.472144e+02   1.292879e+02   \n",
              "min   -1.992355e+03 -9.181587e+02   0.000000e+00   0.000000e+00   \n",
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              "50%   -1.116317e+01 -9.778151e+01   4.470000e+02   3.970000e+02   \n",
              "75%    2.963618e+02  2.268531e+01   6.630000e+02   4.710000e+02   \n",
              "max    1.261774e+03  5.436164e+02   1.916000e+03   1.439000e+03   \n",
              "\n",
              "       hover_duration  fullscreen   hq  music  \n",
              "count    1.000737e+06         0.0  0.0    0.0  \n",
              "mean     3.186237e+03         NaN  NaN    NaN  \n",
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              "min      0.000000e+00         NaN  NaN    NaN  \n",
              "25%      1.000000e+02         NaN  NaN    NaN  \n",
              "50%      4.180000e+02         NaN  NaN    NaN  \n",
              "75%      1.266000e+03         NaN  NaN    NaN  \n",
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              "      <th>count</th>\n",
              "      <td>1.317421e+07</td>\n",
              "      <td>1.317421e+07</td>\n",
              "      <td>1.317421e+07</td>\n",
              "      <td>1.317421e+07</td>\n",
              "      <td>284746.000000</td>\n",
              "      <td>1.213797e+07</td>\n",
              "      <td>1.213797e+07</td>\n",
              "      <td>1.213797e+07</td>\n",
              "      <td>1.213797e+07</td>\n",
              "      <td>1.000737e+06</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>2.113413e+16</td>\n",
              "      <td>6.526426e+02</td>\n",
              "      <td>3.846817e+06</td>\n",
              "      <td>1.219194e+01</td>\n",
              "      <td>3.157930</td>\n",
              "      <td>-5.493615e+01</td>\n",
              "      <td>-1.162997e+02</td>\n",
              "      <td>4.581144e+02</td>\n",
              "      <td>3.852363e+02</td>\n",
              "      <td>3.186237e+03</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>5.665220e+14</td>\n",
              "      <td>6.275818e+02</td>\n",
              "      <td>2.701387e+07</td>\n",
              "      <td>6.499188e+00</td>\n",
              "      <td>2.064042</td>\n",
              "      <td>5.201468e+02</td>\n",
              "      <td>2.185912e+02</td>\n",
              "      <td>2.472144e+02</td>\n",
              "      <td>1.292879e+02</td>\n",
              "      <td>3.692265e+05</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>2.009031e+16</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-1.992355e+03</td>\n",
              "      <td>-9.181587e+02</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>2.101031e+16</td>\n",
              "      <td>2.890000e+02</td>\n",
              "      <td>4.394300e+05</td>\n",
              "      <td>6.000000e+00</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-3.529376e+02</td>\n",
              "      <td>-2.128361e+02</td>\n",
              "      <td>2.690000e+02</td>\n",
              "      <td>3.040000e+02</td>\n",
              "      <td>1.000000e+02</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>2.104022e+16</td>\n",
              "      <td>5.960000e+02</td>\n",
              "      <td>1.013425e+06</td>\n",
              "      <td>1.300000e+01</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>-1.116317e+01</td>\n",
              "      <td>-9.778151e+01</td>\n",
              "      <td>4.470000e+02</td>\n",
              "      <td>3.970000e+02</td>\n",
              "      <td>4.180000e+02</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>2.110051e+16</td>\n",
              "      <td>8.970000e+02</td>\n",
              "      <td>1.740050e+06</td>\n",
              "      <td>1.800000e+01</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>2.963618e+02</td>\n",
              "      <td>2.268531e+01</td>\n",
              "      <td>6.630000e+02</td>\n",
              "      <td>4.710000e+02</td>\n",
              "      <td>1.266000e+03</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>2.210022e+16</td>\n",
              "      <td>2.047300e+04</td>\n",
              "      <td>1.749293e+09</td>\n",
              "      <td>2.200000e+01</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>1.261774e+03</td>\n",
              "      <td>5.436164e+02</td>\n",
              "      <td>1.916000e+03</td>\n",
              "      <td>1.439000e+03</td>\n",
              "      <td>2.199078e+08</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3c8b1118-3e50-4328-ae17-7a9f07ba362b')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-3c8b1118-3e50-4328-ae17-7a9f07ba362b button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-3c8b1118-3e50-4328-ae17-7a9f07ba362b');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_df.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 364
        },
        "id": "-0VecoKJgPs2",
        "outputId": "7d74fcf6-15b2-4a1c-923e-b662e00be99d"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         session_id        index  elapsed_time        level        page  \\\n",
              "count  3.728000e+03  3728.000000  3.728000e+03  3728.000000  153.000000   \n",
              "mean   2.009024e+16   670.427039  2.065026e+06    12.171137    3.405229   \n",
              "std    9.599042e+10   411.203747  2.079750e+06     6.282262    2.037277   \n",
              "min    2.009011e+16     0.000000  0.000000e+00     0.000000    0.000000   \n",
              "25%    2.009011e+16   320.000000  5.708750e+05     7.000000    2.000000   \n",
              "50%    2.009031e+16   642.000000  1.174912e+06    11.000000    4.000000   \n",
              "75%    2.009031e+16   983.250000  2.481634e+06    18.000000    5.000000   \n",
              "max    2.009031e+16  1563.000000  6.524025e+06    22.000000    6.000000   \n",
              "\n",
              "       room_coor_x  room_coor_y  screen_coor_x  screen_coor_y  hover_duration  \\\n",
              "count  3366.000000  3366.000000    3366.000000    3366.000000      353.000000   \n",
              "mean    -14.706281  -131.060856     463.707665     394.447415     1415.631728   \n",
              "std     511.111044   224.031904     243.673841     133.453547     2664.616915   \n",
              "min   -1913.116237  -908.325270       0.000000      10.000000       15.000000   \n",
              "25%    -290.094620  -232.803150     278.250000     320.250000       83.000000   \n",
              "50%      16.843882  -109.000000     460.000000     408.000000      416.000000   \n",
              "75%     355.095522     1.006296     680.000000     486.000000     1316.000000   \n",
              "max    1214.941972   489.514956     879.000000     658.000000    23451.000000   \n",
              "\n",
              "       fullscreen   hq  music  \n",
              "count         0.0  0.0    0.0  \n",
              "mean          NaN  NaN    NaN  \n",
              "std           NaN  NaN    NaN  \n",
              "min           NaN  NaN    NaN  \n",
              "25%           NaN  NaN    NaN  \n",
              "50%           NaN  NaN    NaN  \n",
              "75%           NaN  NaN    NaN  \n",
              "max           NaN  NaN    NaN  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-797f2c83-3b7d-4496-89bf-e1278e30f455\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .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>session_id</th>\n",
              "      <th>index</th>\n",
              "      <th>elapsed_time</th>\n",
              "      <th>level</th>\n",
              "      <th>page</th>\n",
              "      <th>room_coor_x</th>\n",
              "      <th>room_coor_y</th>\n",
              "      <th>screen_coor_x</th>\n",
              "      <th>screen_coor_y</th>\n",
              "      <th>hover_duration</th>\n",
              "      <th>fullscreen</th>\n",
              "      <th>hq</th>\n",
              "      <th>music</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>3.728000e+03</td>\n",
              "      <td>3728.000000</td>\n",
              "      <td>3.728000e+03</td>\n",
              "      <td>3728.000000</td>\n",
              "      <td>153.000000</td>\n",
              "      <td>3366.000000</td>\n",
              "      <td>3366.000000</td>\n",
              "      <td>3366.000000</td>\n",
              "      <td>3366.000000</td>\n",
              "      <td>353.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>2.009024e+16</td>\n",
              "      <td>670.427039</td>\n",
              "      <td>2.065026e+06</td>\n",
              "      <td>12.171137</td>\n",
              "      <td>3.405229</td>\n",
              "      <td>-14.706281</td>\n",
              "      <td>-131.060856</td>\n",
              "      <td>463.707665</td>\n",
              "      <td>394.447415</td>\n",
              "      <td>1415.631728</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>9.599042e+10</td>\n",
              "      <td>411.203747</td>\n",
              "      <td>2.079750e+06</td>\n",
              "      <td>6.282262</td>\n",
              "      <td>2.037277</td>\n",
              "      <td>511.111044</td>\n",
              "      <td>224.031904</td>\n",
              "      <td>243.673841</td>\n",
              "      <td>133.453547</td>\n",
              "      <td>2664.616915</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>2.009011e+16</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-1913.116237</td>\n",
              "      <td>-908.325270</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>15.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>2.009011e+16</td>\n",
              "      <td>320.000000</td>\n",
              "      <td>5.708750e+05</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>-290.094620</td>\n",
              "      <td>-232.803150</td>\n",
              "      <td>278.250000</td>\n",
              "      <td>320.250000</td>\n",
              "      <td>83.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>2.009031e+16</td>\n",
              "      <td>642.000000</td>\n",
              "      <td>1.174912e+06</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>16.843882</td>\n",
              "      <td>-109.000000</td>\n",
              "      <td>460.000000</td>\n",
              "      <td>408.000000</td>\n",
              "      <td>416.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>2.009031e+16</td>\n",
              "      <td>983.250000</td>\n",
              "      <td>2.481634e+06</td>\n",
              "      <td>18.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>355.095522</td>\n",
              "      <td>1.006296</td>\n",
              "      <td>680.000000</td>\n",
              "      <td>486.000000</td>\n",
              "      <td>1316.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>2.009031e+16</td>\n",
              "      <td>1563.000000</td>\n",
              "      <td>6.524025e+06</td>\n",
              "      <td>22.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>1214.941972</td>\n",
              "      <td>489.514956</td>\n",
              "      <td>879.000000</td>\n",
              "      <td>658.000000</td>\n",
              "      <td>23451.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-797f2c83-3b7d-4496-89bf-e1278e30f455 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-797f2c83-3b7d-4496-89bf-e1278e30f455');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YZELhELAeyhf",
        "outputId": "48850e9c-ce5e-4b61-d838-a7666596aa5e"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 13174211 entries, 0 to 13174210\n",
            "Data columns (total 20 columns):\n",
            " #   Column          Dtype  \n",
            "---  ------          -----  \n",
            " 0   session_id      int64  \n",
            " 1   index           int64  \n",
            " 2   elapsed_time    int64  \n",
            " 3   event_name      object \n",
            " 4   name            object \n",
            " 5   level           int64  \n",
            " 6   page            float64\n",
            " 7   room_coor_x     float64\n",
            " 8   room_coor_y     float64\n",
            " 9   screen_coor_x   float64\n",
            " 10  screen_coor_y   float64\n",
            " 11  hover_duration  float64\n",
            " 12  text            object \n",
            " 13  fqid            object \n",
            " 14  room_fqid       object \n",
            " 15  text_fqid       object \n",
            " 16  fullscreen      float64\n",
            " 17  hq              float64\n",
            " 18  music           float64\n",
            " 19  level_group     object \n",
            "dtypes: float64(9), int64(4), object(7)\n",
            "memory usage: 2.0+ GB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KrUZvDO9gSrz",
        "outputId": "3cbe518b-35e8-4ea4-a361-0a861fb62773"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 3728 entries, 0 to 3727\n",
            "Data columns (total 21 columns):\n",
            " #   Column          Non-Null Count  Dtype  \n",
            "---  ------          --------------  -----  \n",
            " 0   session_id      3728 non-null   int64  \n",
            " 1   index           3728 non-null   int64  \n",
            " 2   elapsed_time    3728 non-null   int64  \n",
            " 3   event_name      3728 non-null   object \n",
            " 4   name            3728 non-null   object \n",
            " 5   level           3728 non-null   int64  \n",
            " 6   page            153 non-null    float64\n",
            " 7   room_coor_x     3366 non-null   float64\n",
            " 8   room_coor_y     3366 non-null   float64\n",
            " 9   screen_coor_x   3366 non-null   float64\n",
            " 10  screen_coor_y   3366 non-null   float64\n",
            " 11  hover_duration  353 non-null    float64\n",
            " 12  text            1162 non-null   object \n",
            " 13  fqid            2505 non-null   object \n",
            " 14  room_fqid       3728 non-null   object \n",
            " 15  text_fqid       1162 non-null   object \n",
            " 16  fullscreen      0 non-null      float64\n",
            " 17  hq              0 non-null      float64\n",
            " 18  music           0 non-null      float64\n",
            " 19  level_group     3728 non-null   object \n",
            " 20  session_level   3728 non-null   object \n",
            "dtypes: float64(9), int64(4), object(8)\n",
            "memory usage: 611.8+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_df.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZM7StlQPfG07",
        "outputId": "feab8bab-2260-4d77-9332-2d3eeca0c202"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "session_id               0\n",
              "index                    0\n",
              "elapsed_time             0\n",
              "event_name               0\n",
              "name                     0\n",
              "level                    0\n",
              "page              12889465\n",
              "room_coor_x        1036240\n",
              "room_coor_y        1036240\n",
              "screen_coor_x      1036240\n",
              "screen_coor_y      1036240\n",
              "hover_duration    12173474\n",
              "text               8367320\n",
              "fqid               4159648\n",
              "room_fqid                0\n",
              "text_fqid          8367266\n",
              "fullscreen        13174211\n",
              "hq                13174211\n",
              "music             13174211\n",
              "level_group              0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_df['session_id'].nunique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UzBD0RudfNRu",
        "outputId": "a0b013e2-c9d4-4b36-b3ba-6e5eaa1fc39e"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "11779"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def reduce_memory_usage(df, df_name):\n",
        "    start_mem = df.memory_usage(index=True, deep=True).sum() / 1024**2\n",
        "    print(f'Initial memory usage of {df_name} dataframe is {round(start_mem, 2)} MB')    \n",
        "    for col in df.columns:\n",
        "        col_type = df[col].dtype.name\n",
        "        if ((col_type != 'datetime64[ns]') & (col_type != 'category')):\n",
        "            if (col_type != 'object'):\n",
        "                c_min = df[col].min()\n",
        "                c_max = df[col].max()\n",
        "                if str(col_type)[:3] == 'int':\n",
        "                    if c_min > np.iinfo(np.int8).min and c_max < np.iinfo(np.int8).max:\n",
        "                        df[col] = df[col].astype(np.int8)\n",
        "                    elif c_min > np.iinfo(np.int16).min and c_max < np.iinfo(np.int16).max:\n",
        "                        df[col] = df[col].astype(np.int16)\n",
        "                    elif c_min > np.iinfo(np.int32).min and c_max < np.iinfo(np.int32).max:\n",
        "                        df[col] = df[col].astype(np.int32)\n",
        "                    elif c_min > np.iinfo(np.int64).min and c_max < np.iinfo(np.int64).max:\n",
        "                        df[col] = df[col].astype(np.int64)\n",
        "                else:\n",
        "                    if c_min > np.finfo(np.float16).min and c_max < np.finfo(np.float16).max:\n",
        "                        df[col] = df[col].astype(np.float16)\n",
        "                    elif c_min > np.finfo(np.float32).min and c_max < np.finfo(np.float32).max:\n",
        "                        df[col] = df[col].astype(np.float32)\n",
        "                    else:\n",
        "                        pass\n",
        "            else:\n",
        "                df[col] = df[col].astype('category')\n",
        "    mem_usg = df.memory_usage(index=True, deep=True).sum() / 1024**2 \n",
        "    print(f\"Memory usage of {df_name} became: {round(mem_usg, 2)} MB\")    \n",
        "    return df\n",
        "\n",
        "train = reduce_memory_usage(train_df, 'train')\n",
        "test = reduce_memory_usage(test_df, 'test')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tauV4UqshboN",
        "outputId": "3c418c34-9aea-45fd-d370-055e97955f8e"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Initial memory usage of train dataframe is 6908.33 MB\n",
            "Memory usage of train became: 779.06 MB\n",
            "Initial memory usage of test dataframe is 2.21 MB\n",
            "Memory usage of test became: 0.3 MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_labels_df.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a_AgPA5KiVZf",
        "outputId": "3e31c273-fc61-467c-f0a3-de65ddf5cfd8"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['session_id', 'correct'], dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print('Label 0: '+ str(len(train_labels_df[train_labels_df['correct'] == 0])))\n",
        "print('Label 1: '+ str(len(train_labels_df[train_labels_df['correct'] == 1])))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wfHWi48hisYv",
        "outputId": "622ce31d-2040-4413-c839-d3eb4abc6089"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Label 0: 62770\n",
            "Label 1: 149252\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "H1BRfLJMiwni"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}