Feature engineering is a course offered on codecademy. This repository is a collection of exercises I completed for the course.

Techniques for transforming categorical data, scaling data, and working with date-time features are applied to a clothing company dataset from Kaggle.

Survey data from a survey conducted by Fabio Mendoza Palechor and Alexis de la Hoz Manotas (sourced from the UCI Machine Learning Repository) are analysed using a regresssion model, with the purpose of predicting whether survey respondents are obese. A number of wrapper methods are testest and compared, including:

• sequential forward selection
• sequential backward floating selection
• recursive feature elimination

A set of logistic regression models were trained on a dataset from the UCI Machine Learning Repository on wine quality using sci-kit learn. The goal of the project was to:

1. implement different logistici classifiers
2. find the best ridge-regularized classifier using hyperparameter tuning
3. implement a tuned lasso-regularised feature selection method

In this project, you will classify particles into gamma(signal) or hadrons(background). Given that the features are correlated, you will perform PCA to get a new set of features, and select the features that contain the most information. The data set was generated by a Monte Carlo program, Corsika, described in D. Heck et al., CORSIKA, A Monte Carlo code to simulate extensive air showers, Forschungszentrum Karlsruhe FZKA 6019 (1998).