Data Scientist / Machine Learning Engineer

1) Recommender System : With most modern services and products now being offered predominately online, it can be hard to get to know your customers. Unlike running a local store where you get to see each person, online businesses can struggle to understand their users’ expectations.
The project’s goal is to improve the customer experience by offering what they are looking for, thereby improving the conversion rates for retailers. To achieve these goals, the company plans to develop a Recommender system that predicts whether a particular user would prefer an item or not based on the user’s profile. Here the Recommender System draws on advances in machine learning to deliver personalized recommendations that suit each customer’s tastes and preferences across all your touchpoints.
2) Canada, a resource-rich country, garners a significant portion of the GDP from mining. With that said, most mines are still using legacy technology and facing the growing need to drive operations deeper underground.
Moreover, the industry faces volatile commodity prices and a decline in productivity despite continuous improvements in mining.
While artificial intelligence is still an emerging technology, it enables mining companies to become insight-driven enterprises that utilize data to pinpoint ores and lower costs. The project goal is to use machine learning to deliver value by instantly collecting data and deriving on-site insights that have the potential to vastly improve safety and streamline the workflow and develop an AI model that leverages the data captured from various sources and predicts the locations for ore mining.

Faculty Supervisor:

Sandy Staples

Student:

Partner:

Incorporation.AI

Discipline:

Business

Sector:

Artificial Intelligence

University:

Queen's University

Program: