Week 6 - Baseline Models and Linear Regression

This week we will...

• get to know about the importance of baseline models

• learn about naïve forecasting

• see how linear regressions are defined

• understand the role of cost functions

• get an introduction into optimization functions

Learning Resources

8MB

250522_Intro ML and Linear Regression.pptx

Open

527KB

IntroMLandLinReg.ipynb

Open

• DataCamp Tutorial on Linear Regression

Until next week you should...

• Watch the videos in the section "The problem of overfitting" from Week 3 of the course "Supervised Machine Learning: Regression and Classification" on Coursera.

• Use a simple linear model to make a prediction for the test dataset and evaluate it here on Kaggle. (Ensure that the number of rows and the defined IDs match those in the sample_submission file!)

• Further enrich the dataset with additional variables that may be relevant for estimating revenue and formulate a linear model equation that maximizes the adjusted R² for your training dataset.

• Document the linear regression calculations in the “Baseline Model” directory of your team repository.