This project is a machine learning model that predicts the type of events in the game Egg, Inc. based on historical event data.

- src/
    |--- data/
    |       |--- events.json
    |--- feature_extraction.py
    |--- ml.py
    |--- predict.py
    |--- vis.py

• events.json: This file contains the raw event data.
• events_data.csv: This file contains the processed event data used for training the model.
• feature_extraction.py: This script reads the raw event data, extracts relevant features, and saves the processed data to a CSV file.
• ml.py: This script reads the processed event data, trains a Random Forest Classifier, and evaluates its performance.
• predict.py: This script uses the trained model to make predictions on new data.
• vis.py: This script is used for visualizing the data and the results.

1. Run feature_extraction.py to process the raw event data.
2. Run ml.py to train and evaluate the model.
3. Use predict.py to make predictions on new data.

The model uses the following features for prediction:

• dayOfMonth: Day of the month when the event starts.
• dayOfWeekEncoded: Day of the week when the event starts, encoded as an integer.
• typeEncoded: Type of the event, encoded as an integer.
• timeBetweenEvents: Time between this event and the previous event of the same type, in seconds.
• weekOfYear: Week of the year of the event start (attempting to capture holidays)
• eventFrequency: How often different event types occur

The model is a Random Forest Classifier trained on 80% of the data, with the remaining 20% used for testing. The model's performance is evaluated based on its accuracy, confusion matrix, and classification report.