Machine Learning vs. Feature Engineering: What should the Focus be in Attempting to Predict Customer Behaviour

Abstract: The use of machine learning is a common theme in organizations today, yet most people still struggle with its definition given its many different levels. In this session, we attempt to eliminate this confusion by exploring a number of machine learning algorithms ranging from the simple to the more complex. We observe the use of these algorithms across a variety of industries as well as different behaviours such as customer response and customer risk. Alongside the comparison of machine learning algorithms, we also look at the impact of the data and how feature engineering impacts a given solution.

Bio: Richard Boire's experience in data science dates back to 1983, when he received an MBA from Concordia University in Finance and Statistics complemented with career experience at leading-edge organizations such as Reader’s Digest and American Express.
Through his work at Boire Filler Group and most recently at Environics Analytics, Richard has become a recognized authority on predictive analytics and is among a very select few experts in this field in Canada that have vast expertise and knowledge across virtually all business sectors. Richard published a book in 2014 entitled “Data Mining for Managers:How to use data(big and small) to solve business problems” which was published by Palgrave McMillian of New York City.

Open Data Science Conference