End-to-end machine learning exerice for data regression tasks using sklearn library.
The exercise discusses
- understading the data
- frame the problem with objectives, assumptions, performance measure, etc.
- create test dataset by considering sampling noise, handing categorical data
- select different models and train
- find optimal hyperparamters
- evaluate test set
This is based on the book Chapter 2 of Hands on Machine Learning with Sckit-learn, Keras, and Tensorflow by Aurelien Geron.