Hello,
I am a data scientist with extensive experience in machine learning, predictive modeling, and sports analytics, particularly in sports betting. For this project, I will develop a robust predictive model using Python and its advanced libraries like Scikit-learn and TensorFlow, tailored to evaluate NCAA basketball games with accuracy comparable to the closing line value (CLV). My expertise in classification algorithms and data processing ensures a solid foundation for achieving the desired results.
The approach will begin with data collection and preprocessing, including cleaning NCAA game data, player statistics, and betting trends. I will then engineer meaningful features that capture critical game factors and betting market nuances. Using machine learning algorithms like Random Forest, Gradient Boosting, and Logistic Regression, I will train, tune, and evaluate multiple models to determine the most accurate one.
Finally, I will validate the model against historical data and CLV benchmarks, delivering actionable insights and a scalable tool to optimize betting strategies.
'Hire Me' to receive a robust model that meets your needs and exceeds your expectations.
Best Regards,
Aneesa