NBA Game Outcome Prediction with Machine Learning | AthlyticZ | скачать в хорошем качестве

NBA Game Outcome Prediction with Machine Learning | AthlyticZ |

Ever wondered if Python could predict the outcome of an NBA game? In this video, we’ll show you how to use machine learning with Scikit-learn and Pandas to forecast basketball game winners based on real historical data. Here’s what you’ll learn: Clean and explore an NBA dataset to get it ready for ML Select key features like field goal percentage, rebounds, and assists Train a Random Forest Classifier to make predictions Evaluate your model’s accuracy and visualize a confusion matrix Use the model to predict the winner of a new, unseen matchup By the end of the video, you’ll know how to: Prepare sports data for machine learning Train and test classification models Make real-world predictions using Python Whether you’re a sports fan, a data science enthusiast, or a Python learner, this video makes machine learning fun and practical! Resources & Tools Mentioned: NBA dataset: https://www.kaggle.com/datasets/natha... Get started with Python ML: https://athlyticz.com/python Got questions? Drop them in the comments, and don’t forget to Like and Subscribe for more sports data science tutorials! #PythonMachineLearning #NBAData #SportsPredictions #RandomForest #LearnPython #DataScienceProjects #AthlyticZ #SportsAnalytics