TFI is the company behind some of the world's most well-known brands; Burger King, Sbarro, Popeyes, Usta Donerci, and Arby’s. With over 1,200 quick service restaurants across the globe, they employ over 20,000 people in Europe and Asia and make significant daily investments in developing new restaurant sites. Since new restaurant sites take large investments of time and capital to get up and running, deciding when and where to open new restaurants is largely a subjective process based on the personal judgement and experience of development teams. Hence, this project will, predict annual restaurant sales based on objective measurements.
Data Description
TFI has provided a dataset with 137 restaurants in the training set, and a test set of 100000 restaurants. The data columns include the open date, location, city type, and three categories of obfuscated data: Demographic data, Real estate data, and Commercial data. The revenue column indicates a (transformed) revenue of the restaurant in a given year and is the target of predictive analysis.
Data fieds
Id : Restaurant id.
Open Date : opening date for a restaurant
City : City that the restaurant is in. Note that there are unicode in the names.
City Group: Type of the city. Big cities, or Other.
Type: Type of the restaurant. FC: Food Court, IL: Inline, DT: Drive Thru, MB: Mobile
P1, P2 - P37: There are three categories of these obfuscated data. Demographic data are gathered from third party providers with GIS systems. These include population in any given area, age and gender distribution, development scales. Real estate data mainly relate to the m2 of the location, front facade of the location, car park availability. Commercial data mainly include the existence of points of interest including schools, banks, other QSR operators.
Revenue: The revenue column indicates a (transformed) revenue of the restaurant in a given year and is the target of predictive analysis. Please note that the values are transformed so they don't mean real dollar values.