This project focuses on the application of machine learning algorithms—Naive Bayes, SVM, KNN, and Random Forest in the context of sentiment analysis for restaurant reviews. The objective was to discern the most effective algorithm for accurately categorizing sentiments expressed in customer feedback. Through meticulous implementation and thorough evaluation, it was determined that the Naive Bayes algorithm consistently outperformed its counterparts, showcasing superior performance in handling the intricacies of restaurant reviews and sentiment classification. Following this comprehensive analysis, a dedicated model was developed utilizing the Naive Bayes algorithm, leveraging its simplicity and efficiency in handling text-based data. The resulting sentiment analysis model offers a robust solution for extracting valuable insights from restaurant reviews, aiding restaurant owners in understanding customer sentiments and making informed decisions.