Feature selection Impact on Models’ Performance

Data is rapidly growing in most of the applications and fields. Thus, data is becoming big data as it meets the 5V model of big data: volume, velocity, variety, veracity, and value. As a result, dimensionality reduction and feature selection become mandatory to overcome ML models’ performance and explainability issues. This project looks at feature selection as a crucial step in the preprocessing data phase and reducing the number of features to be considered by an ML model and, in turn, reduces the complexity of the problem.

Faculty Supervisor:

Jian Tang

Student:

Partner:

NASDAQ Canada Inc

Discipline:

Computer science

Sector:

Finance and Insurance; Health and Related Sciences & Technology; Information and Communications Technology

University:

Memorial University of Newfoundland

Program: