This repository is a demonstration of some feature generation and feature selection methods using a portion of the Avazu click-through rate data from the 2015 Kaggle competition "Click-Through Rate Prediction". The dataset consists of 500000 examples, each characterized by 23 features with each example having a label of either 0 or 1 (binary classification). Only part of the dataset is provided in this repository - the full dataset can be found here. This repository focuses on feature generation and feature selection and how these can improve the performance of a machine learning model. The feature generation methods covered include count encoding of categorical data and creation of interaction features among an original feature set. The feature selection methods covered include L1-regularization, a univariate method that uses the f_classif scoring function, and a random forest for feature selection. The Jupyter Notebook is more tutorial-like and has lots of supplementary text whereas the .py code is just the code used in the Notebook with few comments.

January 2023 update: this repository has been archived