Begin by downloading the games.json file from the Steam Games Dataset on Kaggle. This dataset provides comprehensive information about Steam games, which will serve as the foundation for analysis and modeling.

1. Locate the downloaded games.json.zip file.
2. Extract the contents of the .zip archive to retrieve the games.json file.
3. Place the games.json file in the storage/ directory of this repository.

These steps ensure the dataset is properly organized and ready for further processing.

Install the required Python dependencies using the following command:

pip install -r requirements.txt

This will ensure all necessary libraries are installed in your environment. Python version is 3.9

playwright install chromium

Apply the trained model to external data and generate predictions.

Command:

python run.py -mode "inference" -file "./path_to_input.csv"

Arguments:

-mode: Set to "inference" to run inference on best model. -file: Path to the input CSV file. Output:

A new CSV file with predictions added as a predict column, saved in the folder specified by infer_folder in config.yaml.

Fetch the next batch of data, preprocess it, and retrain the model.

Command:

python run.py -mode "update"

Arguments:

-mode: Set to "update" to process the next batch and retrain the model. Output:

Updated model and metrics saved in the storage/results/ folder. Data quality and EDA reports saved in the storage/results/reports and storage/results/eda folders.

Generate a monitoring report summarizing data quality, model metrics, and hyperparameters.

Command:

python run.py -mode "summary"

Arguments:

-mode: Set to "summary" to generate the monitoring report. Output:

A monitoring_report.txt file saved in the report_storage folder.

Access dashboard by

python dashboard/app.py