This challenge aims to help CatsAi better serve their client (a large wholesaler) to estimate bakery orders to reduce waste and under delivery. The main tasks were to predict high-street sales based on meteorological factors and apply explainability techniques to effectively communicate their outputs to the client.
During the challenge, we explored data relating to sales for a key client operating in a single country. The data comprised four different sections: location, products, weather and product sales, our target variables. Each group of variables provided several details about particular weather conditions or location (maximum temperature, visibility, competitor index, etc.), providing fine-grained information about sales.
The document is here: https://www.turing.ac.uk/research/publications/data-study-group-final-report-catsai