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The csa_prediction_engine library provides a suite of tools and functions for performing relevance-based predictions using the Cambridge Sports Analytics Prediction Engine API. The package is designed to facilitate single and multi-task predictions, allowing for flexible model evaluation and experimentation.

Project description

Cambridge Sports Analytics Prediction Engine

The CSA Prediction Engine is the official Python client for interacting with the Cambridge Sports Analytics (CSA) API. It enables relevance-based predictions using flexible configurations, including support for batch jobs, grid evaluations, and multi-task prediction workflows.

📦 Source code: github.com/CambridgeSportsAnalytics/csa_prediction_engine

🔍 Key Features

  • Single Task Predictions: Support for predictions with one dependent variable and one set of circumstances.
  • Multi-y Predictions: Perform predictions with multiple dependent variables and a single set of circumstances.
  • Multi-theta Predictions: Perform predictions with one dependent variable and multiple sets of circumstances.
  • Relevance-Based Grid Predictions: Generate optimal predictions by evaluating all thresholds and variable combinations.
  • Grid Singularity Predictions: Analyze grid predictions to find the singular optimal solution.
  • MaxFit Predictions: Find the best-fit model based on adjusted relevance.

🚀 Installation

Install via PyPI:

pip install csa-prediction-engine

Requires Python 3.11

📘 Documentation & Examples

For example scripts, OpenAPI specs, and quickstart usage, visit the companion repo: 👉 CSA Prediction Engine Quickstart

🤝 Contributing

We welcome feedback, feature suggestions, and bug reports. Reach out to our team 📧 support@csanalytics.io

⚖️ License

Copyright c) 2023 - 2024 Cambridge Prediction Analytics, LLC. All rights reserved.

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