Statistical Analysis and Machine Learning Approach to Retail Sales Forecasting Based on Localized Weather Features

In recent years, weather has been recognized as an important factor that can have a significant impact on
consumer behavior in certain industries. Predictive models that incorporate weather data can help industries to
adjust their inventory and marketing strategies to optimize sales.
This research project focuses on using machine learning and data analytics to determine any weather-related
signals on retail sales based on historical data and then to predict sales using weather information. The project
will involve literature review, developing complex SQL queries, engineering features, analyzing weather and
retail sales databases, and developing machine learning algorithms. The ultimate goal of this end-to-end project
is to create an accurate and scalable sales forecasting model that can be used by industries to optimize their
inventory and marketing strategies based on weather patterns.

Faculty Supervisor:

Nathan Taback

Student:

Partner:

Pelmorex

Discipline:

Computer science

Sector:

Information and cultural industries; Professional, scientific and technical services

University:

University of Toronto

Program: