METHOD FOR PREDICTING SUCCESS OF RESTAURANT

Abstract

ABSTRACT A method 100 for predicting the success of a restaurant, the method 100 comprising the steps of collecting restaurant-related data, including features indicative of the restaurant's location, type, and operational metrics, processing the collected data by handling missing values, transforming categorical variables into numerical representations, and scaling the data to standardize feature values, training a predictive model using a machine learning model on a dataset of historical restaurant data, evaluating the performance of the predictive model using predefined metrics to determine its accuracy in revenue prediction and deploying the trained predictive model in a production environment, the trained predictive model is configured to receive real-time restaurant-related input data and generate revenue predictions based on the input data, wherein the revenue predictions assist restaurant stakeholders in decision-making processes related to resource allocat

Specification

Description:FIELD OF THE DISCLOSURE
[0001] This invention generally relates to revenue prediction systems, specifically to methods and systems for predicting restaurant success by utilizing machine learning models to analyse restaurant-related features, including location, type, and operational metrics, to forecast revenue for strategic decision-making.
BACKGROUND

[0002] The subject matter discussed in the background section should not be assumed to be prior art merely as a result of its mention in the background section. Similarly, a problem mentioned in the background section or associated with the subject matter of the background section should not be assumed to have been previously recognized in the prior art. The subject matter in the background section merely represents different approaches, which in and of themselves may also correspond to implementations of the claimed technology.
[0003] Predicting business success, particularly for restaurants, is challenging due to the high variability in operational condit , Claims:1. A method 100 for predicting the success of a restaurant, the method 100 comprising the steps of:
collecting restaurant-related data, including features indicative of the restaurant's location, type, and operational metrics;
processing the collected data by handling missing values, transforming categorical variables into numerical representations, and scaling the data to standardize feature values;
training a predictive model using a machine learning model on a dataset of historical restaurant data;
evaluating the performance of the predictive model using predefined metrics to determine its accuracy in revenue prediction; and
deploying the trained predictive model in a production environment, the trained predictive model is configured to receive real-time restaurant-related input data and generate revenue predictions based on the input data, wherein the revenue predictions assist restaurant stakeholders in decision-making processes related to resource allocation and strategic planning.

2. The method 100 o

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