Predicting Restaurant Ratings: A Comparative Study of Linear Regression and Decision Tree Regression Models in Machine Learning Technology

Are you interested in trying new food and exploring new places, but unsure where to start? This research uses machine learning to predict restaurant ratings. The article compares two options: simple linear regression is good for basic factors like cost or type of food. However, Decision tree regression is better for capturing complex connections among different variables. Consider preparing a data set with diverse characteristics for restaurants in different areas, which can be used in machine learning. After that, the data should be divided understand how to use features to predict ratings while testing datasets verify the accuracy of such predictions. Ratings have a simple linear relationship with features during linear regression. This model is based on R-squared analysis that can measure the relationship between dependent and independent variables in 2 (two) or even more clubs. For instance (Example), it may have an R-squared coefficient of 0.45. However, factors impacting the restaurant industry are more. On the other hand, non-linear patterns are captured by regression trees hence they may have a higher R-squared value of 0.98 than linear models. These models make it possible to understand better what causes the highest satisfaction among customers, thus making informed choices of where best to go when hunger strikes out of the stomach. The achievements of this work include: helping to develop a refined palate so one knows good from bad food joints; and finding out the best places serving the favorite meals using computer algorithms and other similar tools in machine learning.