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Keep shoppingNBA Prediction AI: From Data to Deployment Step by Step
16 students
NBA Prediction AI: From Data to Deployment Step by Step
End-to-End Machine Learning for Sports Analytics: Data Prep, Modeling, Testing, and Deployment
Created byAykut Onat
Last updated 9/2025
English
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
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Course content
7 sections • 45 lectures • 2h 54m total length
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 complete NBA Prediction Engine from 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 full end-to-end pipeline, transforming raw basketball data into a fully deployed AI app.
You’ll begin with data preparation, learning how to clean, organize, and explore NBA game data. Then we’ll move into feature engineering, creating powerful indicators like rolling averages, home/away splits, and opponent trends to give your model the edge. Next, you’ll tackle modeling, training and evaluating machine learning algorithms while avoiding common pitfalls like overfitting. We’ll cover testing and evaluation metrics such as accuracy, precision, recall, and log loss, so you can measure real performance.
But we won’t stop at numbers. You’ll also build visualizations in Tableau to communicate insights and trends, making your results clear and interactive. Finally, you’ll package everything into a Streamlit app and deploy it on Hugging 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 a portfolio-ready sports analytics project that proves you can take an AI idea from data 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 a portfolio-ready AI project from 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