Building and Evaluating Models: Linear Regression, Decision Trees, and Validation

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📊 Learn how to build, train, and evaluate machine learning models with this hands-on tutorial! We dive into two fundamental algorithms — Linear Regression and Decision Trees — and walk through how to train, test, and validate models effectively using Python and Scikit-Learn.

✅ What You’ll Learn:

How to implement Linear Regression and Decision Trees in Python

The difference between training, testing, and validation datasets

Evaluating model performance with accuracy, R² score, confusion matrix, and more

Best practices for avoiding overfitting and underfitting

Real-world examples using Scikit-Learn and real datasets
🎓 Level: Beginner to Intermediate
Perfect for: Students, data science beginners, and anyone learning ML model development and evaluation.

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Видео Building and Evaluating Models: Linear Regression, Decision Trees, and Validation канала GeeksforGeeks