Linear Regression Model Evaluation | RMSE, R-Squared & Residual Analysis

Ready to build and evaluate your house price prediction model? In Part 2 of our California housing series, we implement linear regression, achieve 60% variance explanation, and discover which features really drive $83K price increases! This video is part of the *Machine Learning with Scikit-learn, PyTorch & Hugging Face Professional Certificate* on Coursera. Complete the housing price prediction journey with model building and professional evaluation techniques. You'll learn: *Train/test split and feature scaling with StandardScaler *Linear regression model training and coefficient optimization *Performance metrics: RMSE ($73K average error) and R-squared (60% variance) *Coefficient interpretation: $83K impact of median income increases *Model visualization with scatter plots and regression lines *Comprehensive residual analysis: scatter, histogram, and Q-Q plots *Identifying nonlinear patterns and model limitations *Real-world model interpretation for business decisions 📌 Enroll in the complete *Machine Learning with Scikit-learn, PyTorch & Hugging Face Professional Certificate* → https://bit.ly/4pQwKfo ⬅️ Watch Part 1: Data Exploration: https://youtu.be/V481EKQ0KDw ✅ Subscribe for complete ML certificate tutorials → https://www.youtube.com/@coursera 💬 Comment: Is $73K average error acceptable for house price predictions? What's your threshold? 👇 00:00 Welcome Back – Recap of Part 1 data exploration 00:20 Data Preparation – Train/test split and feature scaling 00:41 Model Training – Linear regression fitting and optimization 00:52 Performance Evaluation – RMSE and R-squared metrics calculation 01:17 Results Analysis – $73K error, 60% variance, coefficient interpretation 02:26 Model Visualization – Median income scatter plot and regression line 02:57 Scatter Plot Insights – Understanding prediction accuracy and limitations 03:26 Residual Analysis – Three-plot diagnostic approach 03:49 Residual Patterns – Identifying curved patterns and nonlinear relationships