Restaurant Revenue Prediction using Machine Learning

Personal judgment and experience will influence the location and release date of a restaurant. It can be challenging to extrapolate subjective data across cultures and geographical areas. A supervised learning algorithm can construct complicated representations of complex variables using simple inputs such as the opening date, city and type of the restaurant (Food Court, Inline, Drive-Thru, Mobile), Demographic data (population in any given area, age, and gender distribution, development scales), Real estate data (front facade of the location, car park availability), and points of interest including schools, banks. Machine learning concepts such as catboost and random forests can enable food chains to predict the annual revenue of a new restaurant, which can help them determine the viability of a news outlet