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Restaurant Revenue Prediction Applying Supervised Learning Methods |
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PP: 1-10 |
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doi:10.18576/jsap/120101
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Author(s) |
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Md Yasin Ali Parh,
Mst Sharmin Akter Sumy,
Most Sifat Muntaha Soni,
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Abstract |
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| In the competitive world, it is difficult to make a decision where to open a restaurant outlet that produces maximum revenue. Especially, it is difficult to accurately extrapolate across geographies and culture based on the personal judgement and experiences. Supervised learning approach may play a vital role to determine the feasibility of a new outlet with the prediction of revenue. The goal of this study was to predict restaurant revenue of 100,000 regional tab food investment (TFI) restaurant locations across Turkey. Several supervised learning techniques were used to select the optimal model for prediction. The LASSO method was selected as the best supervised method for the prediction of revenue as determined by lowest test error. Other models were employed, but LASSO outperformed all other models and had the added benefit of simplicity and interpretability. The LASSO model was used to predict the revenue of 100,000 new restaurant site locations based on the coefficients termed using the training data.
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