Unveiling Patterns and Enhancing Recommendations: A Novel Regression Analysis Approach for Yelp Dataset

This research paper presents a novel application of regression analysis on the vast and intricate Yelp dataset, a task that has not been previously explored. With its repository of over 8 million reviews, and 1 million users from 11 metropolitan areas, the dataset poses significant challenges for data analysis. Our study focuses on developing a powerful recommendation system for restaurants by harnessing the potential of regression algorithms. The uniqueness of our research lies in successfully navigating the complexities of the Yelp dataset and effectively applying regression analysis to extract valuable insights. This approach unlocks new possibilities in the realm of restaurant recommendations, enabling users to make informed dining choices based on reliable predictions. Our findings not only contribute to the field of machine learning but also hold substantial practical implications for the restaurant industry and beyond. As data-driven solutions gain prominence in modern society, our work showcases the transformative impact of regression analysis in uncovering patterns and driving innovation in a data-rich world.