Predicting NBA Team Performance Using Polynomial Regression: A Data-Driven Economic Approach With Python
Python | Scikit-learn | Machine Learning | Polynomial Regression | Linear Regression | Feature Engineering | Data Preprocessing | Outlier Detection | Data Imputation | Statistical Analysis | R-squared | RMSE | AIC | Predictive Modeling | Seaborn | Matplotlib | Data Visualization | Business Analytics | Economic Modeling | Risk Management | ANOVA | Strategic Decision-Making | Sports Performance Forecasting
Project Description
This project applies advanced data science and machine learning techniques to predict the performance of NBA teams based on various physical attributes and training metrics. Utilizing polynomial regression, the project models the complex, non-linear relationships between points scored and attributes such as weightlifting sessions, yoga sessions, laps run, and water intake. By exploring these variables, the project provides a comprehensive analysis of team performance, revealing economic insights into player productivity and team efficiency.
Through data preprocessing, cleaning, and feature engineering, I developed both linear and polynomial regression models to predict team outcomes with varying degrees of complexity. This comprehensive analysis highlights how data science can be applied to real-world sports economics, using data to inform strategic decisions and optimize resource allocation for better team performance.
Project Skills
Data Science & Machine Learning: Applied advanced polynomial regression techniques to capture non-linear relationships, ensuring a more accurate prediction of NBA team performance.
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Data Analysis & Economic Insight: Conducted thorough data cleaning, imputation, and exploratory analysis to transform raw datasets into actionable insights, modeling how physical activities impact team performance.
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Business Strategy: Modeled economic principles of team management, providing recommendations based on player efficiency and resource allocation to enhance team performance.
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Risk Management: Identified and addressed key outliers in data to minimize prediction errors, ensuring a robust and reliable model that stakeholders can trust for decision-making.
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Technical Acumen: Developed a seamless workflow incorporating data extraction, feature engineering, model training, and evaluation. Combined polynomial regression and machine learning to optimize model accuracy.
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Project Demostrates
In this project, I demonstrated proficiency in Data Science & Machine Learning by utilizing regression analysis, feature engineering, and predictive modeling to assess team performance. My Data Analysis & Economic Insight skills were applied to synthesize large datasets, using business and economic principles to evaluate team efficiency and player contributions. Through Business Strategy, I provided data-driven insights that helped teams optimize training regimens, improving points scored through strategic adjustments. In terms of Risk Management, I addressed model uncertainty by handling outliers and employing imputation techniques for missing data. My Technical Acumen was highlighted by creating efficient models that seamlessly integrated data processing, visualization, and model evaluation, enabling more informed decision-making for sports management teams.