Problem type

Supervised learning classification

Models

Random Forest, SVM, Gradient Boosting Classifier

Project realisation date

June 2019

This project is the first of a series of seven projects to be delivered as part of the Udacity DataScience Nanodegree self-study.

It is a supervised learning classification problem in which you work for a fictional entity named CharityML. The goal is to find, among a given dataset, if people have a sufficient income and then classify them as a potential donor.

Several classification models from the scikit-learn package are trained:

  • Random Forest
  • Support Vector Machines
  • Gradient Boosting Classifier

They are then evaluated and one is chosen for further tuning. Feature importance and feature selection is also performed.

For more details, please refer to my Github repository for this project.