Packages Used

Common packages used across the Betting Oracle projects, with a short description and typical usage.

Python / Machine Learning

• pandas: data manipulation and I/O for CSV/JSON/SQL.
• numpy: numerical arrays and fast computations.
• scikit-learn: model building, validation, and preprocessing utilities.
• xgboost: gradient-boosted trees for classification/regression.
• lightgbm: fast gradient boosting optimized for large datasets.
• catboost: gradient boosting that handles categorical features.
• streamlit: build and run interactive dashboards and apps.
• tensorflow / torch: optional deep learning frameworks for neural models.

Data & Web

• requests: HTTP requests for fetching APIs and pages.
• beautifulsoup4: HTML parsing and scraping utilities.
• fastf1: F1 telemetry and session data (used in F1 projects).
• sqlalchemy: database access and ORM helpers.

Utilities & Testing

• pytest: tests and assertions.
• black / ruff: code formatting and linting.

If you want a requirements file for a specific project, check that repository's README or ask me to generate one.