In this short Jupyter Book, we will try to predict Online Shoppers Intentions from data. The dataset is taken from the uci webpage.
This work is a required essay for the Mathematics in Machine Learning course by prof. Gasparini & Vaccarino @ PoliTO, a nice gym to exercise my machine learning & python skills, and an excuse to try out jupyter book. I hope this will be a useful platfrom for anybody to hopefully learn something new.
The book is divided in 4 chapters:
- Data Exploration: here we go through some preliminary analysis of our data, plot it, and figure out what issues we will have to tackle further on in the analysis
- Pipelines: in this section we merge the data preparation part of the analysis with the sklearn's pipelines, to show how easy is to build modular code, where we can plug in and out different modules without almost any overhead.
- Model Exploration: The name Exploration is purposefully used to show that our aim is not to find the best model for our problem, because our aim is not to achieve a competitive score. Our goal, instead, is to show how to deal with this kind of problem, and to take a deeper approach to our models with respect to the typical machine learning tutorial.
- Appendix: here we will put all the extra information about the topics that we will go through. We also find a learning theory section, used to give a background to some of the claims we will make in our analysis.
This web page is hosted by GitHub Pages. The source code for this build htmls can be found in here.