Interesting Retail Data Science Challenges on Kaggle

Noticed some interesting Data Science challenges posted on Kaggle related to Retail. Curious if anyone out there plans to tackle this with Azure ML

Predict annual restaurant sales based on objective measurements

With over 1,200 quick service restaurants across the globe, TFI is the company behind some of the world's most well-known brands: Burger King, Sbarro, Popeyes, Usta Donerci, and Arby’s. They employ over 20,000 people in Europe and Asia and make significant daily investments in developing new restaurant sites.

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

The Otto Group is one of the world’s biggest e-commerce companies, with subsidiaries in more than 20 countries, including Crate & Barrel (USA), (Germany) and 3 Suisses (France). We are selling millions of products worldwide every day, with several thousand products being added to our product line.

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 operates 11,450 stores in 27 countries, managing inventory across varying climates and cultures. Extreme weather events, like hurricanes, blizzards, and floods, can have a huge impact on sales at the store and product level. 

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. 

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