NBA Prediction AI: From Data to Deployment Step by Step

End-to-End Machine Learning for Sports Analytics: Data Prep, Modeling, Testing, and Deployment

What you'll learn
- Build an end-to-end machine learning project using real-world NBA data
- Engineer meaningful features like team momentum, game context, and performance trends
- Train, evaluate, and tune models like Logistic Regression and XGBoost
- Simulate an entire NBA season and forecast match outcomes with batch predictions
- Visualize insights and predictions using Tableau dashboards
- Deploy your final app on Hugging Face using Streamlit and Docker

Requirements
- Basic knowledge of Python and pandas (helpful but not required — we cover it as we go)
- No prior machine learning experience needed — perfect for first-time builders
- You’ll need a computer with internet access and a willingness to experiment and learn
- Free accounts for GitHub, Hugging Face, and optionally Tableau Public

Description
Sports meet AI in this hands-on course where you’ll build a completeNBA Prediction Enginefrom scratch. If you’ve ever wanted to see how data science, machine learning, and real sports analytics come together, this is your chance. Instead of theory alone, you’ll walk through a fullend-to-end pipeline, transforming raw basketball data into a fully deployed AI app.

You’ll begin withdata preparation, learning how to clean, organize, and explore NBA game data. Then we’ll move intofeature engineering, creating powerful indicators like rolling averages, home/away splits, and opponent trends to give your model the edge. Next, you’ll tacklemodeling, training and evaluating machine learning algorithms while avoiding common pitfalls like overfitting. We’ll covertesting and evaluation metricssuch as accuracy, precision, recall, and log loss, so you can measure real performance.

But we won’t stop at numbers. You’ll also buildvisualizations in Tableauto communicate insights and trends, making your results clear and interactive. Finally, you’ll package everything into aStreamlit appand deploy it onHugging Face Spaces, so your project lives online — ready to share with employers, colleagues, or fans.

By the end, you’ll not only understand the machine learning process — you’ll walk away with aportfolio-ready sports analytics projectthat proves you can take an AI idea fromdata to deployment.

What You’ll Learn

Collect, clean, and prepare real NBA game data for machine learning

Engineer powerful features like rolling averages, opponent stats, and home/away effects

Build and train machine learning models to predict NBA game outcomes

Evaluate models using accuracy, log loss, precision, recall, and AUC metrics

Avoid pitfalls like overfitting and data leakage in sports analytics projects

Visualize insights and performance trends with interactive Tableau dashboards

Create a user-friendly interface for your prediction model using Streamlit

Deploy your NBA prediction app to Hugging Face Spaces for public access

Showcase aportfolio-ready AI projectfrom data to deployment

Apply the same workflow to other sports or real-world business analytics problems

Who this course is for:
- Beginner to intermediate Python developers curious about real-world machine learning
- Aspiring data scientists and analysts who want a hands-on portfolio project
- Sports and NBA enthusiasts interested in applying ML to game predictions
- Students or professionals looking to break into data-driven product development
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