Day 3 — Moving to Multiple Linear Regression

1. 1Build a simple linear regression model using a single feature to understand the basics
2. 2Extend the model to Multiple Linear Regression by adding more features
3. 3Run a regression analysis using a library like scikit-learn to see the coefficients and R-squared value
4. 4Configure and test the model with different combinations of features to observe the impact on predictions
5. 5Apply cross-validation to evaluate the model's performance on unseen data

Data scientists and machine learning engineers benefit from understanding Multiple Linear Regression to build more accurate predictive models, while data analysts can apply this knowledge to improve data insights

💡 Multiple Linear Regression allows you to predict a continuous outcome based on multiple features, increasing model accuracy

Boost your ML skills with Multiple Linear Regression! 🚀