{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load\n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# Input data files are available in the read-only \"../input/\" directory\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n for filename in filenames:\n print(os.path.join(dirname, filename))\n\n# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2023-03-28T18:53:31.941604Z","iopub.execute_input":"2023-03-28T18:53:31.942304Z","iopub.status.idle":"2023-03-28T18:53:31.951184Z","shell.execute_reply.started":"2023-03-28T18:53:31.942262Z","shell.execute_reply":"2023-03-28T18:53:31.949255Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"

SUMMARY

","metadata":{}},{"cell_type":"markdown","source":"**The goal:**\n\nPredict student performance during game-based learning in real-time. \n\nThis will help advanced research into knowledge-tracing methods for game-based learning. --> Supporting developers of educational games to create more effective learning experiences for students.\n\n**Important:**\n\nSubmissions will be evaluated based on their F1 score. Submissions file have this format: session_id, correct.\n\n**Deadline:**\n7 june 2023\n\n**Requirement:**\nSumbmissions to this competition must be made through Notebooks. CPU Notebook <= 9 hours run-time. GPU Notebook <= Disabled, Internet access disabled, submissions file must be named \"submission.csv\".","metadata":{}},{"cell_type":"markdown","source":"

Data Exploration

","metadata":{}},{"cell_type":"code","source":"#dataset = pd.read_csv(\"/kaggle/input/predict-student-performance-from-game-play/train.csv\")\n#dataset.head()\n\n#The dataset (4.72GB)is to big for my notebook. \n#My notebook tried to allocate more memory than is available.","metadata":{"execution":{"iopub.status.busy":"2023-03-26T20:24:51.885327Z","iopub.execute_input":"2023-03-26T20:24:51.885791Z","iopub.status.idle":"2023-03-26T20:24:51.890783Z","shell.execute_reply.started":"2023-03-26T20:24:51.885753Z","shell.execute_reply":"2023-03-26T20:24:51.889803Z"},"jupyter":{"source_hidden":true},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"

For this problem we need find a solution, but first let see if there some columns we can throw away from the database. Let's first check the columns, what they all means.

","metadata":{}},{"cell_type":"markdown","source":"### Session_id","metadata":{}},{"cell_type":"code","source":"#Import session_id column\nsession_id = pd.read_csv(\"/kaggle/input/predict-student-performance-from-game-play/train.csv\", usecols = [\"session_id\"])\n#The id of the session the event took place in. \n#This is important because that is the keyid for train_label.csv.\n\n#Categorical value","metadata":{"execution":{"iopub.status.busy":"2023-03-27T14:25:09.636338Z","iopub.execute_input":"2023-03-27T14:25:09.637048Z","iopub.status.idle":"2023-03-27T14:25:41.377790Z","shell.execute_reply.started":"2023-03-27T14:25:09.637004Z","shell.execute_reply":"2023-03-27T14:25:41.376714Z"},"jupyter":{"source_hidden":true},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"session_id.info() #Info about session_id. (Integer)","metadata":{"execution":{"iopub.status.busy":"2023-03-27T14:26:03.613503Z","iopub.execute_input":"2023-03-27T14:26:03.614303Z","iopub.status.idle":"2023-03-27T14:26:03.625413Z","shell.execute_reply.started":"2023-03-27T14:26:03.614247Z","shell.execute_reply":"2023-03-27T14:26:03.624455Z"},"jupyter":{"source_hidden":true,"outputs_hidden":true},"collapsed":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"

Everything in this column is valid. No mismatched and missing. Mean: 21132488b, Std. Deviation: 571295b, Min: 20090312b, Max: 22100221b, unique numbers: 23562

","metadata":{}},{"cell_type":"code","source":"session_id.nunique()","metadata":{"execution":{"iopub.status.busy":"2023-03-27T14:30:55.374010Z","iopub.execute_input":"2023-03-27T14:30:55.374772Z","iopub.status.idle":"2023-03-27T14:30:55.536728Z","shell.execute_reply.started":"2023-03-27T14:30:55.374716Z","shell.execute_reply":"2023-03-27T14:30:55.535646Z"},"jupyter":{"source_hidden":true,"outputs_hidden":true},"collapsed":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### Index","metadata":{}},{"cell_type":"code","source":"#Import index column\nindex = pd.read_csv(\"/kaggle/input/predict-student-performance-from-game-play/train.csv\", usecols = [\"index\"])\n#The index of the even for the session. \n\n#Categorical value","metadata":{"execution":{"iopub.status.busy":"2023-03-27T14:30:57.482614Z","iopub.execute_input":"2023-03-27T14:30:57.483376Z","iopub.status.idle":"2023-03-27T14:31:25.556664Z","shell.execute_reply.started":"2023-03-27T14:30:57.483336Z","shell.execute_reply":"2023-03-27T14:31:25.555377Z"},"jupyter":{"source_hidden":true},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"index.nunique()","metadata":{"execution":{"iopub.status.busy":"2023-03-27T14:33:04.910112Z","iopub.execute_input":"2023-03-27T14:33:04.910605Z","iopub.status.idle":"2023-03-27T14:33:05.113252Z","shell.execute_reply.started":"2023-03-27T14:33:04.910566Z","shell.execute_reply":"2023-03-27T14:33:05.112008Z"},"jupyter":{"source_hidden":true,"outputs_hidden":true},"collapsed":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"

Everything in this column is valid. No mismatched and missing. Mean: 645, Std.Deviation: 546, Min: 0, Max: 20.5k

","metadata":{}},{"cell_type":"markdown","source":"### Elapsed time","metadata":{}},{"cell_type":"code","source":"#Import elapsed_time column\nelapsed_time = pd.read_csv(\"/kaggle/input/predict-student-performance-from-game-play/train.csv\", usecols = [\"elapsed_time\"])\n#How much time has passed between the start of the session and when the event was recorded (millisecondes).\n\n#Continous value","metadata":{"execution":{"iopub.status.busy":"2023-03-27T13:42:23.631925Z","iopub.execute_input":"2023-03-27T13:42:23.632358Z","iopub.status.idle":"2023-03-27T13:42:55.233930Z","shell.execute_reply.started":"2023-03-27T13:42:23.632319Z","shell.execute_reply":"2023-03-27T13:42:55.232906Z"},"jupyter":{"source_hidden":true},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"elapsed_time.info() #Info about elapse_time. (Integer)","metadata":{"execution":{"iopub.status.busy":"2023-03-26T21:22:32.237234Z","iopub.execute_input":"2023-03-26T21:22:32.237660Z","iopub.status.idle":"2023-03-26T21:22:32.250409Z","shell.execute_reply.started":"2023-03-26T21:22:32.237622Z","shell.execute_reply":"2023-03-26T21:22:32.249437Z"},"jupyter":{"source_hidden":true,"outputs_hidden":true},"collapsed":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"

Everything in this column is valid. No mismatched and missing. Mean: 4.02m, Std. Deviation 31.3m, Min: 0, Max: 1.99b

","metadata":{}},{"cell_type":"markdown","source":"### Event name","metadata":{}},{"cell_type":"code","source":"#Import event_name column\nevent_name = pd.read_csv(\"/kaggle/input/predict-student-performance-from-game-play/train.csv\", usecols = [\"event_name\"])\n#The name of the event type.\n\n#Categorical value","metadata":{"execution":{"iopub.status.busy":"2023-03-27T13:13:35.652462Z","iopub.execute_input":"2023-03-27T13:13:35.652915Z","iopub.status.idle":"2023-03-27T13:15:00.896284Z","shell.execute_reply.started":"2023-03-27T13:13:35.652870Z","shell.execute_reply":"2023-03-27T13:15:00.894761Z"},"jupyter":{"source_hidden":true},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"event_name.info() #Info about event_name. (Object)","metadata":{"execution":{"iopub.status.busy":"2023-03-26T21:39:26.658607Z","iopub.execute_input":"2023-03-26T21:39:26.659090Z","iopub.status.idle":"2023-03-26T21:39:26.686227Z","shell.execute_reply.started":"2023-03-26T21:39:26.659052Z","shell.execute_reply":"2023-03-26T21:39:26.685304Z"},"jupyter":{"source_hidden":true,"outputs_hidden":true},"collapsed":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"event_name['event_name'].value_counts().plot(kind='bar') #Plot alle the variables.","metadata":{"execution":{"iopub.status.busy":"2023-03-27T13:16:44.308449Z","iopub.execute_input":"2023-03-27T13:16:44.309531Z","iopub.status.idle":"2023-03-27T13:16:46.344095Z","shell.execute_reply.started":"2023-03-27T13:16:44.309486Z","shell.execute_reply":"2023-03-27T13:16:46.342447Z"},"jupyter":{"outputs_hidden":true,"source_hidden":true},"collapsed":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"

Everything in this column is valid. No mismatched and missing. There is 11 unique values and most common event_name is navigate_click 43%

\n\n

The 11 unique values are **navigate_click**, **person_click**, **cutscene_click**, **object_click**, **object_hover**, **map_hover**, **observation_click** and **checkpoint**

","metadata":{}},{"cell_type":"markdown","source":"### Name","metadata":{}},{"cell_type":"code","source":"#Import name column\nname = pd.read_csv(\"/kaggle/input/predict-student-performance-from-game-play/train.csv\", usecols = [\"name\"])\n#The event name (e.g. identifies whether a notebook_click is is opening or closing the notebook)\n\n#Categorical value","metadata":{"execution":{"iopub.status.busy":"2023-03-27T13:19:58.106325Z","iopub.execute_input":"2023-03-27T13:19:58.106735Z","iopub.status.idle":"2023-03-27T13:20:27.609338Z","shell.execute_reply.started":"2023-03-27T13:19:58.106695Z","shell.execute_reply":"2023-03-27T13:20:27.608127Z"},"jupyter":{"source_hidden":true},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"name.info() #Info about name. (object)","metadata":{"execution":{"iopub.status.busy":"2023-03-26T21:44:23.460900Z","iopub.execute_input":"2023-03-26T21:44:23.461473Z","iopub.status.idle":"2023-03-26T21:44:23.477169Z","shell.execute_reply.started":"2023-03-26T21:44:23.461427Z","shell.execute_reply":"2023-03-26T21:44:23.476098Z"},"jupyter":{"source_hidden":true,"outputs_hidden":true},"collapsed":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"name['name'].value_counts().plot(kind='bar') #Plot alle the variables.","metadata":{"execution":{"iopub.status.busy":"2023-03-27T13:21:05.821659Z","iopub.execute_input":"2023-03-27T13:21:05.822099Z","iopub.status.idle":"2023-03-27T13:21:07.787328Z","shell.execute_reply.started":"2023-03-27T13:21:05.822059Z","shell.execute_reply":"2023-03-27T13:21:07.786259Z"},"jupyter":{"source_hidden":true,"outputs_hidden":true},"collapsed":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"

Everything in this column is valid. No mismatched and missing. There is 6 unique values and most common name is undefined 48%

\n\n

The 6 unique values are **undefined**, **basic**, **close**, **open**, **prev** and **next**

","metadata":{}},{"cell_type":"markdown","source":"### Level","metadata":{}},{"cell_type":"code","source":"#Import Level column\nlevel = pd.read_csv(\"/kaggle/input/predict-student-performance-from-game-play/train.csv\", usecols = [\"level\"])\n#What level of the game the event occurred in (0 to 22)\n\n#Categorical value","metadata":{"execution":{"iopub.status.busy":"2023-03-27T14:41:01.053894Z","iopub.execute_input":"2023-03-27T14:41:01.054801Z","iopub.status.idle":"2023-03-27T14:41:29.455600Z","shell.execute_reply.started":"2023-03-27T14:41:01.054747Z","shell.execute_reply":"2023-03-27T14:41:29.454209Z"},"jupyter":{"source_hidden":true},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"level.info() #Info about level (integer)","metadata":{"execution":{"iopub.status.busy":"2023-03-27T06:11:37.350321Z","iopub.execute_input":"2023-03-27T06:11:37.350740Z","iopub.status.idle":"2023-03-27T06:11:37.379420Z","shell.execute_reply.started":"2023-03-27T06:11:37.350707Z","shell.execute_reply":"2023-03-27T06:11:37.378070Z"},"jupyter":{"outputs_hidden":true,"source_hidden":true},"collapsed":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"level.nunique()","metadata":{"execution":{"iopub.status.busy":"2023-03-27T14:41:44.388754Z","iopub.execute_input":"2023-03-27T14:41:44.389173Z","iopub.status.idle":"2023-03-27T14:41:44.545046Z","shell.execute_reply.started":"2023-03-27T14:41:44.389137Z","shell.execute_reply":"2023-03-27T14:41:44.543945Z"},"jupyter":{"source_hidden":true,"outputs_hidden":true},"collapsed":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"

Everything in this column is valid. No mismatched and missing. Mean: 12.2, Std. Deviation: 6.5, Min: 0, Max: 22

","metadata":{}},{"cell_type":"markdown","source":"### Page","metadata":{}},{"cell_type":"code","source":"#Import Page column\npage = pd.read_csv(\"/kaggle/input/predict-student-performance-from-game-play/train.csv\", usecols = [\"page\"])\n#The page number of the event (only for notebook-related events)\n\n#Categorical value","metadata":{"execution":{"iopub.status.busy":"2023-03-27T14:42:21.204529Z","iopub.execute_input":"2023-03-27T14:42:21.205075Z","iopub.status.idle":"2023-03-27T14:42:48.475678Z","shell.execute_reply.started":"2023-03-27T14:42:21.205028Z","shell.execute_reply":"2023-03-27T14:42:48.474416Z"},"jupyter":{"source_hidden":true},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"page.info() #Info about page (float)","metadata":{"execution":{"iopub.status.busy":"2023-03-27T06:14:21.858229Z","iopub.execute_input":"2023-03-27T06:14:21.858766Z","iopub.status.idle":"2023-03-27T06:14:21.872561Z","shell.execute_reply.started":"2023-03-27T06:14:21.858721Z","shell.execute_reply":"2023-03-27T06:14:21.871090Z"},"jupyter":{"source_hidden":true,"outputs_hidden":true},"collapsed":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"page.nunique()","metadata":{"execution":{"iopub.status.busy":"2023-03-27T14:44:54.770138Z","iopub.execute_input":"2023-03-27T14:44:54.770674Z","iopub.status.idle":"2023-03-27T14:44:54.955331Z","shell.execute_reply.started":"2023-03-27T14:44:54.770634Z","shell.execute_reply":"2023-03-27T14:44:54.954044Z"},"jupyter":{"source_hidden":true,"outputs_hidden":true},"collapsed":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"

There is 98% missing and 2% valid. Mean: 2.17, Std. Deviation: 2.06, Min: 0, Max: 6

","metadata":{}},{"cell_type":"markdown","source":"### Room_coor_x","metadata":{}},{"cell_type":"code","source":"#Import Room_coor_x column\nroom_coor_x = pd.read_csv(\"/kaggle/input/predict-student-performance-from-game-play/train.csv\", usecols = [\"room_coor_x\"])\n#The coordinates of the click in reference to the in-game (only for click events)\n\n#Continous value","metadata":{"execution":{"iopub.status.busy":"2023-03-27T06:15:56.964225Z","iopub.execute_input":"2023-03-27T06:15:56.964623Z","iopub.status.idle":"2023-03-27T06:16:26.194347Z","shell.execute_reply.started":"2023-03-27T06:15:56.964591Z","shell.execute_reply":"2023-03-27T06:16:26.193224Z"},"jupyter":{"source_hidden":true},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"room_coor_x.info() #Info abour room_coor_x (float)","metadata":{"execution":{"iopub.status.busy":"2023-03-27T06:16:47.422579Z","iopub.execute_input":"2023-03-27T06:16:47.423024Z","iopub.status.idle":"2023-03-27T06:16:47.437369Z","shell.execute_reply.started":"2023-03-27T06:16:47.422987Z","shell.execute_reply":"2023-03-27T06:16:47.436272Z"},"jupyter":{"source_hidden":true,"outputs_hidden":true},"collapsed":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"

There is 92% valid and 8% missing. Mean: -54.9, Std.Deviation: 520, Min: -1.99k, Max: 1.26k

","metadata":{}},{"cell_type":"markdown","source":"### Room_coor_y","metadata":{}},{"cell_type":"code","source":"#Import Room_coor_y column\nroom_coor_y = pd.read_csv(\"/kaggle/input/predict-student-performance-from-game-play/train.csv\", usecols = [\"room_coor_y\"])\n#The coordinates of the click in reference to the in-game (only for click events)\n\n#Continous value","metadata":{"execution":{"iopub.status.busy":"2023-03-27T06:19:52.220266Z","iopub.execute_input":"2023-03-27T06:19:52.220651Z","iopub.status.idle":"2023-03-27T06:20:21.318630Z","shell.execute_reply.started":"2023-03-27T06:19:52.220617Z","shell.execute_reply":"2023-03-27T06:20:21.317511Z"},"jupyter":{"source_hidden":true},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"room_coor_y.info() #Info abour room_coor_y (float)","metadata":{"execution":{"iopub.status.busy":"2023-03-27T06:20:36.548776Z","iopub.execute_input":"2023-03-27T06:20:36.549218Z","iopub.status.idle":"2023-03-27T06:20:36.563173Z","shell.execute_reply.started":"2023-03-27T06:20:36.549180Z","shell.execute_reply":"2023-03-27T06:20:36.561877Z"},"jupyter":{"source_hidden":true,"outputs_hidden":true},"collapsed":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"

There is 92% valid and 8% missing. Mean: -116, Std.Deviation: 218, Min: -918, Max: 544

","metadata":{}},{"cell_type":"markdown","source":"### Screen_coor_x","metadata":{}},{"cell_type":"code","source":"#Import Sreen_coor_x column\nscreen_coor_x = pd.read_csv(\"/kaggle/input/predict-student-performance-from-game-play/train.csv\", usecols = [\"screen_coor_x\"])\n#The coordinates of the click in reference to the player's screen (only for click events)\n\n#Continous value","metadata":{"execution":{"iopub.status.busy":"2023-03-27T06:22:30.575459Z","iopub.execute_input":"2023-03-27T06:22:30.575904Z","iopub.status.idle":"2023-03-27T06:22:58.445335Z","shell.execute_reply.started":"2023-03-27T06:22:30.575864Z","shell.execute_reply":"2023-03-27T06:22:58.444174Z"},"jupyter":{"source_hidden":true},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"screen_coor_x.info() #Info abour screen_coor_x (float)","metadata":{"execution":{"iopub.status.busy":"2023-03-27T06:23:09.461613Z","iopub.execute_input":"2023-03-27T06:23:09.462049Z","iopub.status.idle":"2023-03-27T06:23:09.473449Z","shell.execute_reply.started":"2023-03-27T06:23:09.462013Z","shell.execute_reply":"2023-03-27T06:23:09.472446Z"},"jupyter":{"source_hidden":true,"outputs_hidden":true},"collapsed":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"

There is 92% valid and 8% missing. Mean: 458, Std.Deviation: 248, Min: 0, Max: 1.92k

","metadata":{}},{"cell_type":"markdown","source":"### Screen_coor_y","metadata":{}},{"cell_type":"code","source":"#Import Sreen_coor_y column\nscreen_coor_y = pd.read_csv(\"/kaggle/input/predict-student-performance-from-game-play/train.csv\", usecols = [\"screen_coor_y\"])\n#The coordinates of the click in reference to the player's screen (only for click events)\n\n#Continous value","metadata":{"execution":{"iopub.status.busy":"2023-03-27T06:23:57.213072Z","iopub.execute_input":"2023-03-27T06:23:57.213529Z","iopub.status.idle":"2023-03-27T06:24:24.991810Z","shell.execute_reply.started":"2023-03-27T06:23:57.213489Z","shell.execute_reply":"2023-03-27T06:24:24.990126Z"},"jupyter":{"source_hidden":true},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"screen_coor_y.info() #Info about screen_coor_y (float)","metadata":{"execution":{"iopub.status.busy":"2023-03-27T06:24:25.123466Z","iopub.execute_input":"2023-03-27T06:24:25.123897Z","iopub.status.idle":"2023-03-27T06:24:25.136701Z","shell.execute_reply.started":"2023-03-27T06:24:25.123863Z","shell.execute_reply":"2023-03-27T06:24:25.135635Z"},"jupyter":{"source_hidden":true,"outputs_hidden":true},"collapsed":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"

There is 92% valid and 8% missing. Mean: 386, Std.Deviation: 130, Min: 0, Max: 1.44k

","metadata":{}},{"cell_type":"markdown","source":"### Hover duration","metadata":{}},{"cell_type":"code","source":"#Import Hover_duration column\nhover_duration = pd.read_csv(\"/kaggle/input/predict-student-performance-from-game-play/train.csv\", usecols = [\"hover_duration\"])\n#How long the hover happend (millliseconds)\n\n#Continous value","metadata":{"execution":{"iopub.status.busy":"2023-03-27T06:26:06.195066Z","iopub.execute_input":"2023-03-27T06:26:06.195495Z","iopub.status.idle":"2023-03-27T06:26:33.695411Z","shell.execute_reply.started":"2023-03-27T06:26:06.195461Z","shell.execute_reply":"2023-03-27T06:26:33.693899Z"},"jupyter":{"source_hidden":true},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"hover_duration.info() #Info about hover_duration (float)","metadata":{"execution":{"iopub.status.busy":"2023-03-27T06:26:55.925848Z","iopub.execute_input":"2023-03-27T06:26:55.926270Z","iopub.status.idle":"2023-03-27T06:26:55.938652Z","shell.execute_reply.started":"2023-03-27T06:26:55.926236Z","shell.execute_reply":"2023-03-27T06:26:55.937193Z"},"jupyter":{"source_hidden":true,"outputs_hidden":true},"collapsed":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"

There is 8% valid and 92% missing. Mean: 3.47k, Std.Deviation: 460k, Min: 0, Max: 245m

","metadata":{}},{"cell_type":"markdown","source":"### Text","metadata":{}},{"cell_type":"code","source":"#Import Text column\ntext = pd.read_csv(\"/kaggle/input/predict-student-performance-from-game-play/train.csv\", usecols = [\"text\"])\n#The text the player sees during this event\n\n#Categorical value","metadata":{"execution":{"iopub.status.busy":"2023-03-27T14:00:13.339857Z","iopub.execute_input":"2023-03-27T14:00:13.340426Z","iopub.status.idle":"2023-03-27T14:00:43.319895Z","shell.execute_reply.started":"2023-03-27T14:00:13.340377Z","shell.execute_reply":"2023-03-27T14:00:43.318324Z"},"jupyter":{"source_hidden":true},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"text.info() #Info about text (object)","metadata":{"execution":{"iopub.status.busy":"2023-03-27T06:33:16.397651Z","iopub.execute_input":"2023-03-27T06:33:16.398065Z","iopub.status.idle":"2023-03-27T06:33:16.433311Z","shell.execute_reply.started":"2023-03-27T06:33:16.398028Z","shell.execute_reply":"2023-03-27T06:33:16.431550Z"},"jupyter":{"source_hidden":true,"outputs_hidden":true},"collapsed":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"

There is 37% valid and 63% missing. There is 597 unique values and the most common is undefined 1%

","metadata":{}},{"cell_type":"markdown","source":"### Fqid","metadata":{}},{"cell_type":"code","source":"#Import Fqid column\nfqid = pd.read_csv(\"/kaggle/input/predict-student-performance-from-game-play/train.csv\", usecols = [\"fqid\"])\n#The fully qualified ID of the event.\n\n#Continous value","metadata":{"execution":{"iopub.status.busy":"2023-03-27T06:33:32.231356Z","iopub.execute_input":"2023-03-27T06:33:32.232642Z","iopub.status.idle":"2023-03-27T06:34:03.256940Z","shell.execute_reply.started":"2023-03-27T06:33:32.232588Z","shell.execute_reply":"2023-03-27T06:34:03.255829Z"},"jupyter":{"source_hidden":true},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"fqid.info() #Info about fqid (object)","metadata":{"execution":{"iopub.status.busy":"2023-03-27T06:39:23.778593Z","iopub.execute_input":"2023-03-27T06:39:23.779054Z","iopub.status.idle":"2023-03-27T06:39:23.792347Z","shell.execute_reply.started":"2023-03-27T06:39:23.779015Z","shell.execute_reply":"2023-03-27T06:39:23.791201Z"},"jupyter":{"source_hidden":true,"outputs_hidden":true},"collapsed":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"

There is 69% valid and 31% missing. There is 128 unique values and the most common is worker (7%)

","metadata":{}},{"cell_type":"markdown","source":"### Room_fqid ","metadata":{}},{"cell_type":"code","source":"#Import Room_fqid column\nroom_fqid = pd.read_csv(\"/kaggle/input/predict-student-performance-from-game-play/train.csv\", usecols = [\"room_fqid\"])\n#The fully qualified ID of the room the event took place in.\n\n#Categorical value","metadata":{"execution":{"iopub.status.busy":"2023-03-27T06:39:25.527054Z","iopub.execute_input":"2023-03-27T06:39:25.527531Z","iopub.status.idle":"2023-03-27T06:39:57.334235Z","shell.execute_reply.started":"2023-03-27T06:39:25.527491Z","shell.execute_reply":"2023-03-27T06:39:57.333029Z"},"jupyter":{"source_hidden":true},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"room_fqid.info() #Info about fqid (object)","metadata":{"execution":{"iopub.status.busy":"2023-03-27T06:43:57.861118Z","iopub.execute_input":"2023-03-27T06:43:57.861591Z","iopub.status.idle":"2023-03-27T06:43:57.878654Z","shell.execute_reply.started":"2023-03-27T06:43:57.861553Z","shell.execute_reply":"2023-03-27T06:43:57.877212Z"},"jupyter":{"source_hidden":true,"outputs_hidden":true},"collapsed":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"

Everything in this column is valid. No mismatched and missing. There is 19 unique values and the most common is tunic.historicalsociety.entry (14%)

","metadata":{}},{"cell_type":"markdown","source":"### Text_fqid","metadata":{}},{"cell_type":"code","source":"#Import Text_fqid column\ntext_fqid = pd.read_csv(\"/kaggle/input/predict-student-performance-from-game-play/train.csv\", usecols = [\"text_fqid\"])\n#The fully qualified ID of the text the event took place in.\n\n#Categorical value","metadata":{"execution":{"iopub.status.busy":"2023-03-27T06:52:33.949616Z","iopub.execute_input":"2023-03-27T06:52:33.951398Z","iopub.status.idle":"2023-03-27T06:53:58.811166Z","shell.execute_reply.started":"2023-03-27T06:52:33.951340Z","shell.execute_reply":"2023-03-27T06:53:58.809579Z"},"jupyter":{"source_hidden":true},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"text_fqid.info() #Info about text_fqid (object)","metadata":{"execution":{"iopub.status.busy":"2023-03-27T06:54:25.333495Z","iopub.execute_input":"2023-03-27T06:54:25.333987Z","iopub.status.idle":"2023-03-27T06:54:25.367199Z","shell.execute_reply.started":"2023-03-27T06:54:25.333946Z","shell.execute_reply":"2023-03-27T06:54:25.365191Z"},"jupyter":{"source_hidden":true,"outputs_hidden":true},"collapsed":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"

There is 37% valid and 63% missing. There is 126 unique values and the most common is tunic.historicalsociety.cage.confrontation (3%)

","metadata":{}},{"cell_type":"markdown","source":"### Fullscreen","metadata":{}},{"cell_type":"code","source":"#Import Fullscreen column\nfullscreen = pd.read_csv(\"/kaggle/input/predict-student-performance-from-game-play/train.csv\", usecols = [\"fullscreen\"])\n#Whether the player is in fullscreen mode.\n\n#Categorical value","metadata":{"execution":{"iopub.status.busy":"2023-03-27T06:54:28.113283Z","iopub.execute_input":"2023-03-27T06:54:28.116598Z","iopub.status.idle":"2023-03-27T06:54:57.851643Z","shell.execute_reply.started":"2023-03-27T06:54:28.116511Z","shell.execute_reply":"2023-03-27T06:54:57.850342Z"},"jupyter":{"source_hidden":true},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"fullscreen.info() #Info about fullscreen (integer)","metadata":{"execution":{"iopub.status.busy":"2023-03-27T06:55:44.032222Z","iopub.execute_input":"2023-03-27T06:55:44.032707Z","iopub.status.idle":"2023-03-27T06:55:44.048578Z","shell.execute_reply.started":"2023-03-27T06:55:44.032665Z","shell.execute_reply":"2023-03-27T06:55:44.046884Z"},"collapsed":true,"jupyter":{"outputs_hidden":true,"source_hidden":true},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"

Everything in this column is valid. No mismatched and missing. There is 2 unique values and the most common is 0

","metadata":{}},{"cell_type":"markdown","source":"### Hq","metadata":{}},{"cell_type":"code","source":"#Import Hq column\nhq = pd.read_csv(\"/kaggle/input/predict-student-performance-from-game-play/train.csv\", usecols = [\"hq\"])\n#Whether the game is in high-quality\n\n#Categorical value","metadata":{"execution":{"iopub.status.busy":"2023-03-27T06:55:47.328170Z","iopub.execute_input":"2023-03-27T06:55:47.329040Z","iopub.status.idle":"2023-03-27T06:56:16.896111Z","shell.execute_reply.started":"2023-03-27T06:55:47.328987Z","shell.execute_reply":"2023-03-27T06:56:16.894450Z"},"jupyter":{"source_hidden":true},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"hq.info() #Info about hq (integer)","metadata":{"execution":{"iopub.status.busy":"2023-03-27T06:56:44.527710Z","iopub.execute_input":"2023-03-27T06:56:44.528162Z","iopub.status.idle":"2023-03-27T06:56:44.544273Z","shell.execute_reply.started":"2023-03-27T06:56:44.528110Z","shell.execute_reply":"2023-03-27T06:56:44.542974Z"},"collapsed":true,"jupyter":{"outputs_hidden":true,"source_hidden":true},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"

Everything in this column is valid. No mismatched and missing. There is 2 unique values and the most common is 0

","metadata":{}},{"cell_type":"markdown","source":"### Music","metadata":{}},{"cell_type":"code","source":"#Import Music column\nmusic = pd.read_csv(\"/kaggle/input/predict-student-performance-from-game-play/train.csv\", usecols = [\"music\"])\n#Whether the game music is on or off\n\n#Categorical value","metadata":{"execution":{"iopub.status.busy":"2023-03-27T06:57:34.918401Z","iopub.execute_input":"2023-03-27T06:57:34.918854Z","iopub.status.idle":"2023-03-27T06:58:04.980694Z","shell.execute_reply.started":"2023-03-27T06:57:34.918818Z","shell.execute_reply":"2023-03-27T06:58:04.979517Z"},"jupyter":{"source_hidden":true},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"music.info() #Info about hq (integer)","metadata":{"execution":{"iopub.status.busy":"2023-03-27T06:58:29.385095Z","iopub.execute_input":"2023-03-27T06:58:29.385564Z","iopub.status.idle":"2023-03-27T06:58:29.399493Z","shell.execute_reply.started":"2023-03-27T06:58:29.385522Z","shell.execute_reply":"2023-03-27T06:58:29.398176Z"},"collapsed":true,"jupyter":{"outputs_hidden":true,"source_hidden":true},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"

Everything in this column is valid. No mismatched and missing. There is 2 unique values and the most common is 1

","metadata":{}},{"cell_type":"markdown","source":"### Level_group","metadata":{}},{"cell_type":"code","source":"#Import Level group column\nlevel_group = pd.read_csv(\"/kaggle/input/predict-student-performance-from-game-play/train.csv\", usecols = [\"level_group\"])\n#Which group of levels - and group of questions - this row belongs to (0-4, 5-12, 13-22)\n\n#Categorical value","metadata":{"execution":{"iopub.status.busy":"2023-03-27T06:58:46.301087Z","iopub.execute_input":"2023-03-27T06:58:46.301642Z","iopub.status.idle":"2023-03-27T06:59:17.236183Z","shell.execute_reply.started":"2023-03-27T06:58:46.301599Z","shell.execute_reply":"2023-03-27T06:59:17.234734Z"},"jupyter":{"source_hidden":true},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"level_group.info() #Info about level_group (Object)","metadata":{"execution":{"iopub.status.busy":"2023-03-27T06:59:17.654319Z","iopub.execute_input":"2023-03-27T06:59:17.654713Z","iopub.status.idle":"2023-03-27T06:59:17.669060Z","shell.execute_reply.started":"2023-03-27T06:59:17.654666Z","shell.execute_reply":"2023-03-27T06:59:17.665560Z"},"collapsed":true,"jupyter":{"outputs_hidden":true,"source_hidden":true},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"

Everything in this column is valid. No mismatched and missing. There is 3 unique values and the most common is 13-22 (51%)

","metadata":{}},{"cell_type":"markdown","source":"### Data exploration summary","metadata":{}},{"cell_type":"markdown","source":"![Schermafbeelding 2023-03-27 om 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"}}},{"cell_type":"markdown","source":"

Removing column with missing values depends on several factors, such as the amount and distribution of missing values, the size of the dataset, the importance of the feature, and the goals of the analysis.

\n\n

In general, removing a column with a high percentage of missing values (50% Missing) may be resonable, especially if the missing values are not informative and the feature is not important for the analysis.

\n\n

source: https://pressbooks.library.upei.ca/montelpare/chapter/working-with-missing-data/

\n\n

I thought self to remove Page (98%), Hover_duration (92%), Text (63%) and Tekst_fqid (63%), but let's see if that's possible.

","metadata":{}},{"cell_type":"markdown","source":"

Data preparation

","metadata":{}},{"cell_type":"code","source":"#Select only five columns: hover_duration, text, text_fqid, page, levelgoup.\ndataset = pd.read_csv(\"/kaggle/input/predict-student-performance-from-game-play/train.csv\", usecols = [\"hover_duration\", \"text\", \"text_fqid\", \"page\", \"level_group\"])","metadata":{"execution":{"iopub.status.busy":"2023-03-28T16:04:34.168876Z","iopub.execute_input":"2023-03-28T16:04:34.169451Z","iopub.status.idle":"2023-03-28T16:06:22.031846Z","shell.execute_reply.started":"2023-03-28T16:04:34.169405Z","shell.execute_reply":"2023-03-28T16:06:22.030253Z"},"jupyter":{"source_hidden":true},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#Let me see how much people in each group.\nprint(len(dataset[dataset['level_group']=='0-4']))\nprint(len(dataset[dataset['level_group']=='5-12']))\nprint(len(dataset[dataset['level_group']=='13-22']))","metadata":{"execution":{"iopub.status.busy":"2023-03-28T16:10:04.460501Z","iopub.execute_input":"2023-03-28T16:10:04.461003Z","iopub.status.idle":"2023-03-28T16:10:11.893881Z","shell.execute_reply.started":"2023-03-28T16:10:04.460963Z","shell.execute_reply":"2023-03-28T16:10:11.892460Z"},"jupyter":{"source_hidden":true,"outputs_hidden":true},"collapsed":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#Let me see what are not NAN in column hover_duration sorted by level_group.\n(dataset.assign(data=dataset['hover_duration'].isna()).groupby(['level_group'])['hover_duration'].agg(['count']).reset_index())","metadata":{"execution":{"iopub.status.busy":"2023-03-28T16:08:28.746167Z","iopub.execute_input":"2023-03-28T16:08:28.748129Z","iopub.status.idle":"2023-03-28T16:08:33.724655Z","shell.execute_reply.started":"2023-03-28T16:08:28.748054Z","shell.execute_reply":"2023-03-28T16:08:33.723086Z"},"jupyter":{"source_hidden":true,"outputs_hidden":true},"collapsed":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#Let me see what are not NAN in column text sorted by level_group.\n(dataset.assign(data=dataset['text'].isna()).groupby(['level_group'])['text'].agg(['count']).reset_index())","metadata":{"execution":{"iopub.status.busy":"2023-03-28T16:08:40.177361Z","iopub.execute_input":"2023-03-28T16:08:40.177901Z","iopub.status.idle":"2023-03-28T16:08:46.057894Z","shell.execute_reply.started":"2023-03-28T16:08:40.177858Z","shell.execute_reply":"2023-03-28T16:08:46.056582Z"},"jupyter":{"source_hidden":true,"outputs_hidden":true},"collapsed":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#Let me see what are not NAN in column text_fqid sorted by level_group.\n(dataset.assign(data=dataset['text_fqid'].isna()).groupby(['level_group'])['text_fqid'].agg(['count']).reset_index())","metadata":{"execution":{"iopub.status.busy":"2023-03-28T16:35:42.232564Z","iopub.execute_input":"2023-03-28T16:35:42.233175Z","iopub.status.idle":"2023-03-28T16:35:48.524102Z","shell.execute_reply.started":"2023-03-28T16:35:42.233117Z","shell.execute_reply":"2023-03-28T16:35:48.522583Z"},"jupyter":{"source_hidden":true,"outputs_hidden":true},"collapsed":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#Let me see what are not NAN in column page sorted by level_group.\n(dataset.assign(data=dataset['page'].isna()).groupby(['level_group'])['page'].agg(['count']).reset_index())","metadata":{"execution":{"iopub.status.busy":"2023-03-28T16:08:57.266408Z","iopub.execute_input":"2023-03-28T16:08:57.266906Z","iopub.status.idle":"2023-03-28T16:09:00.975632Z","shell.execute_reply.started":"2023-03-28T16:08:57.266864Z","shell.execute_reply":"2023-03-28T16:09:00.974442Z"},"jupyter":{"source_hidden":true,"outputs_hidden":true},"collapsed":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"![Schermafbeelding 2023-03-28 om 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"}}},{"cell_type":"markdown","source":"

We can still remove everything because the missing% for each columns divided in level_group are still to high (above 50%)

","metadata":{}},{"cell_type":"markdown","source":"### Reducing dataset","metadata":{}},{"cell_type":"code","source":"#cols_without_missing = [\"session_id\", \"index\", \"elapsed_time\", \"event_name\", \"name\", \"level\", \"room_coor_x\"\n# , \"room_coor_y\", \"screen_coor_x\", \"screen_coor_y\", \"fqid\", \"room_fqid\", \"fullscreen\"\n# , \"hq\", \"music\", \"level_group\"]\n#dataset = pd.read_csv(\"/kaggle/input/predict-student-performance-from-game-play/train.csv\", usecols = cols_without_missing)\n#dataset.head()\n\n#The dataset is still to big for my notebook. \n#My notebook tried to allocate more memory than is available.","metadata":{"execution":{"iopub.status.busy":"2023-03-27T15:37:12.349193Z","iopub.execute_input":"2023-03-27T15:37:12.349700Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"

Making dataframe smaller so that my notebook can run.

\n\n

source: https://www.ritchieng.com/pandas-making-dataframe-smaller-faster/

","metadata":{}},{"cell_type":"code","source":"#Remove columns with missing values above 50%.\ncols_without_missing = [\"session_id\", \"index\", \"elapsed_time\", \"event_name\", \"name\", \"level\", \"room_coor_x\", \"room_coor_y\", \"screen_coor_x\", \"screen_coor_y\", \"fqid\", \"room_fqid\", \"fullscreen\", \n \"hq\", \"music\", \"level_group\"]\n\n#Make dataframe smaller.\ndtypes_smaller = {\"session_id\": np.int64, \"index\": np.int64, \"elapsed_time\": np.int64, \"event_name\": object, \"name\": object, \"level\": np.int8, \"room_coor_x\": np.float32, \"room_coor_y\": np.float32, \n \"screen_coor_x\": np.float32, \"screen_coor_y\": np.float32, \"fqid\": object, \"room_fqid\": object, \"fullscreen\": np.int8, \"hq\": np.int8 , \"music\": np.int8, \"level_group\": object}\n\n#Read the file.\ndataset = pd.read_csv(\"/kaggle/input/predict-student-performance-from-game-play/train.csv\", usecols = cols_without_missing, dtype = dtypes_smaller)\ndataset.head()","metadata":{"execution":{"iopub.status.busy":"2023-03-28T18:54:32.143399Z","iopub.execute_input":"2023-03-28T18:54:32.146190Z","iopub.status.idle":"2023-03-28T18:56:45.421354Z","shell.execute_reply.started":"2023-03-28T18:54:32.146106Z","shell.execute_reply":"2023-03-28T18:56:45.420159Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"dataset.shape # 26296946 rows and 16 columns.","metadata":{"execution":{"iopub.status.busy":"2023-03-28T17:15:18.435205Z","iopub.execute_input":"2023-03-28T17:15:18.435993Z","iopub.status.idle":"2023-03-28T17:15:18.444406Z","shell.execute_reply.started":"2023-03-28T17:15:18.435940Z","shell.execute_reply":"2023-03-28T17:15:18.443014Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"

Next step we need to watch the level group:

\n\n

\"The timeseries API presents the questions and data to you in order of levels - level segments 0-4, 5-12, and 13-22 are each provided in sequence, and you will be predicting the correctness of each segment's questions as they are presented.\"

\n\n

So we gonna split the dataset in three pieces: 0-4, 5-12 and 13-22, because 0-4 is for question 0 till 4, 5-12 is for question 5 till 12 and 13-22 is for question 13 till 22.

\n\n","metadata":{}},{"cell_type":"code","source":"#Split the dataset in three pieces:\ndataset_level_group04 = dataset[dataset['level_group'] == '0-4']\ndataset_level_group512 = dataset[dataset['level_group'] == '5-12']\ndataset_level_group1322 = dataset[dataset['level_group'] == '13-22']","metadata":{"execution":{"iopub.status.busy":"2023-03-28T19:24:53.066703Z","iopub.execute_input":"2023-03-28T19:24:53.067218Z","iopub.status.idle":"2023-03-28T19:25:03.539596Z","shell.execute_reply.started":"2023-03-28T19:24:53.067166Z","shell.execute_reply":"2023-03-28T19:25:03.538419Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"

Let's calculate the missing data for each level group.

\n

But before that let's import a python library Missingo.

\n","metadata":{}},{"cell_type":"code","source":"#Import library missingo\nimport missingno as msno\n%matplotlib inline\n\n#Plot missing values on dataset_level_group04.\nmsno.bar(dataset_level_group04)","metadata":{"execution":{"iopub.status.busy":"2023-03-28T18:57:05.729227Z","iopub.execute_input":"2023-03-28T18:57:05.729713Z","iopub.status.idle":"2023-03-28T18:57:08.919737Z","shell.execute_reply.started":"2023-03-28T18:57:05.729663Z","shell.execute_reply":"2023-03-28T18:57:08.918486Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#Plot missing values on dataset_level_group5-12.\nmsno.bar(dataset_level_group512)","metadata":{"execution":{"iopub.status.busy":"2023-03-28T18:09:25.554318Z","iopub.execute_input":"2023-03-28T18:09:25.554725Z","iopub.status.idle":"2023-03-28T18:09:28.663503Z","shell.execute_reply.started":"2023-03-28T18:09:25.554684Z","shell.execute_reply":"2023-03-28T18:09:28.662445Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#Plot missing values on dataset_level_group13-22.\nmsno.bar(dataset_level_group1322)","metadata":{"execution":{"iopub.status.busy":"2023-03-28T18:19:04.841738Z","iopub.execute_input":"2023-03-28T18:19:04.842145Z","iopub.status.idle":"2023-03-28T18:19:09.310203Z","shell.execute_reply.started":"2023-03-28T18:19:04.842112Z","shell.execute_reply":"2023-03-28T18:19:09.309287Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"

There is something funny! In all the three dataframe we miss values in the same columns: room_coor_x, room_coor_y, screen_coor_y and fqid.

\n\n

We gonna put some restriction for uself. If the row have more than 3 missing value then delete it, because it miss to lot information.

","metadata":{}},{"cell_type":"code","source":"#Remove row more than 3 missing value.\ndataset_level_group04 = dataset_level_group04[dataset_level_group04.isnull().sum(axis=1) < 3]\nmsno.bar(dataset_level_group04)","metadata":{"execution":{"iopub.status.busy":"2023-03-28T19:25:09.191974Z","iopub.execute_input":"2023-03-28T19:25:09.192599Z","iopub.status.idle":"2023-03-28T19:25:13.218854Z","shell.execute_reply.started":"2023-03-28T19:25:09.192544Z","shell.execute_reply":"2023-03-28T19:25:13.217669Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#Remove row more than 3 missing value.\ndataset_level_group512 = dataset_level_group512[dataset_level_group512.isnull().sum(axis=1) < 3]\nmsno.bar(dataset_level_group512)","metadata":{"execution":{"iopub.status.busy":"2023-03-28T19:25:19.273991Z","iopub.execute_input":"2023-03-28T19:25:19.274870Z","iopub.status.idle":"2023-03-28T19:25:25.995167Z","shell.execute_reply.started":"2023-03-28T19:25:19.274814Z","shell.execute_reply":"2023-03-28T19:25:25.993456Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#Remove row more than 3 missing value.\ndataset_level_group1322 = dataset_level_group1322[dataset_level_group1322.isnull().sum(axis=1) < 3]\nmsno.bar(dataset_level_group1322)","metadata":{"execution":{"iopub.status.busy":"2023-03-28T19:25:30.866969Z","iopub.execute_input":"2023-03-28T19:25:30.867979Z","iopub.status.idle":"2023-03-28T19:25:40.850300Z","shell.execute_reply.started":"2023-03-28T19:25:30.867920Z","shell.execute_reply":"2023-03-28T19:25:40.849065Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"

Now we can see that fqid is still missing value! Maybe we can fill that, with help from other columns (machine learning), but for this time I am gonna leave it to you!. So we gonna remove 2 columns: index and fqid. The reason that I remove index is that it have no function for our target feature.

","metadata":{}},{"cell_type":"code","source":"dataset_level_group04 = dataset_level_group04.drop(['index', 'fqid'], axis =1)\ndataset_level_group512 = dataset_level_group512.drop(['index', 'fqid'], axis =1)\ndataset_level_group1322 = dataset_level_group1322.drop(['index', 'fqid'], axis =1)","metadata":{"execution":{"iopub.status.busy":"2023-03-28T19:25:47.247730Z","iopub.execute_input":"2023-03-28T19:25:47.248631Z","iopub.status.idle":"2023-03-28T19:25:49.374894Z","shell.execute_reply.started":"2023-03-28T19:25:47.248581Z","shell.execute_reply":"2023-03-28T19:25:49.373230Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"

Now we are done with that! The next step is feature selection.

","metadata":{}},{"cell_type":"markdown","source":"### Feature selection","metadata":{}},{"cell_type":"code","source":"#Import the necessary modules and libraries.\nimport seaborn as sns\nfrom matplotlib import pyplot\n\n\npyplot.figure(figsize=(20,10))\ncor = dataset_level_group04.corr()\nsns.heatmap(cor, annot=True, cmap=pyplot.cm.Reds)\npyplot.show()","metadata":{"execution":{"iopub.status.busy":"2023-03-28T19:32:50.321608Z","iopub.execute_input":"2023-03-28T19:32:50.322457Z","iopub.status.idle":"2023-03-28T19:32:52.677086Z","shell.execute_reply.started":"2023-03-28T19:32:50.322403Z","shell.execute_reply":"2023-03-28T19:32:52.675501Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"pyplot.figure(figsize=(20,10))\ncor = dataset_level_group512.corr()\nsns.heatmap(cor, annot=True, cmap=pyplot.cm.Reds)\npyplot.show()","metadata":{"execution":{"iopub.status.busy":"2023-03-28T19:33:32.481378Z","iopub.execute_input":"2023-03-28T19:33:32.481909Z","iopub.status.idle":"2023-03-28T19:33:36.309631Z","shell.execute_reply.started":"2023-03-28T19:33:32.481865Z","shell.execute_reply":"2023-03-28T19:33:36.308302Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"pyplot.figure(figsize=(20,10))\ncor = dataset_level_group1322.corr()\nsns.heatmap(cor, annot=True, cmap=pyplot.cm.Reds)\npyplot.show()","metadata":{"execution":{"iopub.status.busy":"2023-03-28T19:33:47.802698Z","iopub.execute_input":"2023-03-28T19:33:47.803381Z","iopub.status.idle":"2023-03-28T19:33:53.019883Z","shell.execute_reply.started":"2023-03-28T19:33:47.803325Z","shell.execute_reply":"2023-03-28T19:33:53.016152Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"

All the dataframe have almost the same correlation.
I will select features that above 0.6 correlation, because feature with high correlation are more linearly dependent and hence have almost the same effect on the dependent variable.
So, when two features have high correlation, I can drop on of the two features:

\n\n

I would say remove room_coor_x and remove room_coor_y

","metadata":{}},{"cell_type":"code","source":"dataset_level_group04 = dataset_level_group04.drop(['room_coor_x', 'room_coor_y'], axis =1)\ndataset_level_group512 = dataset_level_group512.drop(['room_coor_x', 'room_coor_y'], axis =1)\ndataset_level_group1322 = dataset_level_group1322.drop(['room_coor_x', 'room_coor_y'], axis =1)","metadata":{"execution":{"iopub.status.busy":"2023-03-28T19:40:24.727218Z","iopub.execute_input":"2023-03-28T19:40:24.729198Z","iopub.status.idle":"2023-03-28T19:40:26.644974Z","shell.execute_reply.started":"2023-03-28T19:40:24.729129Z","shell.execute_reply":"2023-03-28T19:40:26.643444Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"dataset_level_group04.head()","metadata":{"execution":{"iopub.status.busy":"2023-03-28T19:41:10.770612Z","iopub.execute_input":"2023-03-28T19:41:10.771157Z","iopub.status.idle":"2023-03-28T19:41:10.792791Z","shell.execute_reply.started":"2023-03-28T19:41:10.771100Z","shell.execute_reply":"2023-03-28T19:41:10.791506Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"dataset_level_group512.head()","metadata":{"execution":{"iopub.status.busy":"2023-03-28T19:41:29.897237Z","iopub.execute_input":"2023-03-28T19:41:29.897777Z","iopub.status.idle":"2023-03-28T19:41:29.918912Z","shell.execute_reply.started":"2023-03-28T19:41:29.897731Z","shell.execute_reply":"2023-03-28T19:41:29.917979Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"dataset_level_group1322.head()","metadata":{"execution":{"iopub.status.busy":"2023-03-28T19:41:41.283414Z","iopub.execute_input":"2023-03-28T19:41:41.284297Z","iopub.status.idle":"2023-03-28T19:41:41.305555Z","shell.execute_reply.started":"2023-03-28T19:41:41.284245Z","shell.execute_reply":"2023-03-28T19:41:41.304144Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"

Well guys that's it! Have fun with this! :)

","metadata":{}}]}