MACHINE LEARNING-DRIVEN STATISTICAL ANALYSIS OF INDIAN RESTAURANTS: INSIGHTS FROM THE ZOMATO DATASET

Abstract

Advances in technology and web applications, such as Zomato, have significantly transformed the restaurant industry by catering to diverse culinary preferences and offering a wide variety of food options to customers. This platform stores a vast amount of data that can be analyzed for valuable insights. The paper examines dining habits and restaurant performance through exploratory data analysis (EDA) and machine learning (ML) algorithms, helping customers find the best restaurants based on cost, ratings, location, food quality, and service. The study applies several ML models, including Linear Regression (LR), Decision Tree (DT), Random Forest (RF), Gradient Boosting (GB), XGBoost, K-Nearest Neighbors (KNN), and LASSO to the Zomato dataset. The results are evaluated using metrics such as accuracy, mean absolute error (MAE), model fit time, and model prediction time. Among these models, DT and RF show the highest predictive accuracy, with RF achieving 97.86% and outperforming other algorithms. These findings provide restaurant owners with valuable insights to enhance customer satisfaction, optimize pricing, and improve service quality. The study also demonstrates the important role of ML in the restaurant industry and suggests future opportunities for integrating real-time data, deep learning models, and sentiment analysis to offer even more precise predictions and insights.