Week 5 - Introduction to Machine Learning and Linear Regression

This week we will...

cover the following topics:

• Procedure for training machine learning algorithms

• Definition of linear regression

• Cost functions

• Optimization functions

• Testing your model on Kaggle

Learning Resources

8MB

250522_Intro ML and Linear Regression.pptx

527KB

IntroMLandLinReg.ipynb

• 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.

• Split your dataset into a training set from 01.07.2013 to 31.07.2017, a validation set from 01.08.2017 to 31.07.2018, and a test set from 01.08.2018 to 31.07.2019.

• Create an account on Kaggle (using the same email address you used for EduHub).

• 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.