I developed a scalable machine learning model tailored to predict sports outcomes, focusing initially on NFL and UFC matches. This project aimed to provide accurate game result predictions and point scores by analyzing a combination of historical data, player statistics, and external factors such as weather and game venues.

Accurate Outcome Predictions: Build a model capable of predicting game winners and scores with a high degree of accuracy.

Scalable Architecture: Ensure the solution could scale to accommodate additional sports and complex betting structures like parlays.

User-Centric Insights: Provide actionable insights for both casual sports fans and professional bettors.

Automated Data Pipeline: Streamline data retrieval, processing, and model deployment using APIs and a robust data pipeline.

Sources: Gathered historical game data, player statistics, team performance metrics, and contextual external factors (e.g., weather conditions, home/away advantage).

APIs: Integrated sports data APIs for real-time updates and seamless data retrieval.

Preprocessing: Cleaned and transformed raw data into structured datasets using Python libraries such as pandas and NumPy.

Extracted meaningful features like:

Designed new composite features, such as team synergy scores and head-to-head advantage metrics, to improve predictive accuracy.

Algorithm Selection: Evaluated various models, including XGBoost, Random Forests, and Neural Networks, selecting the best-performing ones for each sport.

Hyperparameter Tuning: Optimized models using grid search and cross-validation techniques to maximize accuracy.

Performance Metrics: Focused on metrics like F1 Score, Mean Absolute Error (MAE), and Area Under the Curve (AUC) to ensure robust evaluation.

Expandable Framework: Designed the solution to allow seamless addition of other sports and betting structures like parlays and over/under predictions.

Modular Architecture: Implemented a modular codebase with separate components for data ingestion, model training, and prediction serving.

Integrated with real-time sports APIs to update data dynamically before matches.

Built a streamlined pipeline for automated predictions delivered through dashboards and mobile alerts.

Created interactive visualizations using Power BI and Streamlit, allowing users to:

This project demonstrated my ability to integrate machine learning and data engineering to solve real-world problems. By focusing on user needs and scalability, I delivered a solution that met the demands of diverse stakeholders, from casual fans to professional bettors. This experience reinforced my expertise in predictive modeling, modular development, and building robust analytics systems that drive actionable insights.