3-10-2025, 16:36 | view 👀:26 | 🙍 oneddl | redaktor: Baturi | Rating👍:

NBA Prediction AI From Data to Deployment Step by Step

Name: NBA Prediction AI From Data to Deployment Step by Step
Author: Baturi

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 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 LearnCollect, clean, and prepare real NBA game data for machine learningEngineer powerful features like rolling averages, opponent stats, and home/away effectsBuild and train machine learning models to predict NBA game outcomesEvaluate models using accuracy, log loss, precision, recall, and AUC metricsAvoid pitfalls like overfitting and data leakage in sports analytics projectsVisualize insights and performance trends with interactive Tableau dashboardsCreate a user-friendly interface for your prediction model using StreamlitDeploy your NBA prediction app to Hugging Face Spaces for public accessShowcase a portfolio-ready AI project from data to deploymentApply 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
Homepage
https://www.udemy.com/course/nba-prediction-ai-from-data-to-deployment-step-by-step/

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NBA Prediction AI From Data to Deployment Step by Step

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