Regression Models for Prediction
Grasp the basics of using different regression models for predictive modeling. Learn how to establish polynomial, lasso and ridge regression models within Python.
Lessons and practices
Lesson 1: Mastering Multiple Linear Regression with Python
Visualizing Multiple Linear Regression in 3D Space
Expanding Dimensions: Introducing More Features
Visualizing Regression with a New Feature Combination
Crafting a Predictor: From Data to 3D Visualization
Navigating the Cosmos with Multiple Linear Regression
Lesson 2: Meeting Polynomial Regression
Visualizing Polynomial Regression
Elevating Polynomial Features to the Third Degree
Adding the Finishing Touches: Print Model Coefficients
Polynomial Degree Adjustment in Regression Model
Crafting the Polynomial Regression Model
Lesson 3: Decoding the Language of Coefficients in Regression Models
Absolute Insights: Modifying Coefficients Display
Debugging Coefficients Fetching
Fetching the Coefficients of a Regression Model
California Housing and Regression Model Coefficients
Lesson 4: Evaluating Model Accuracy: MSE, RMSE, and MAE in Regression Analysis
Measuring Model Accuracy with Metrics
Evaluating Metric Sensitivity
Calculating the Root Mean Squared Error
Calculating Prediction Accuracy Metrics from Scratch
Interested in this course? Learn and practice with Cosmo!
Practice is how you turn knowledge into actual skills.