|Predict annual restaurant sales based on objective measurements||
New restaurant sites take large investments of time and capital to get up and running. When the wrong location for a restaurant brand is chosen, the site closes within 18 months and operating losses are incurred.
Finding a mathematical model to increase the effectiveness of investments in new restaurant sites would allow TFI to invest more in other important business areas, like sustainability, innovation, and training for new employees. Using demographic, real estate, and commercial data, this competition challenges you to predict the annual restaurant sales of 100,000 regional locations.
|Classify products into the correct category||
A consistent analysis of the performance of their products is crucial to them. However, due to their diverse global infrastructure, many identical products get classified differently. Therefore, the quality of the product analysis depends heavily on the ability to accurately cluster similar products. The better the classification, the more insights we can generate about their product range. The objective is to build a predictive model which is able to distinguish between their main product categories.
|Predict how sales of weather-sensitive products are affected by snow and rain||
Walmart challenges participants to accurately predict the sales of 111 potentially weather-sensitive products (like umbrellas, bread, and milk) around the time of major weather events at 45 of their retail locations.