With the ever-increasing number of restaurants and the wide variety of dishes they offer, customers often find it challenging to discover and choose the perfect meal that aligns with their preferences and moods.

The goal of MoodBites is to develop an innovative and user-friendly mobile application that utilizes Sentiment Analysis to cluster and recommend restaurant dishes based on customers' emotions, preferences, and past dining experiences. (This is still an ongoing project)

The model involves comparing actual ratings of the restaurant and the overall ratings from the reviews left by the customers. Bayesian Estimate is used to calculate the overall rating of the restaurant. This Enhanced Rating is shown to the customer while they browse through the restaurants. The sentimental analysis would be carried out by comparing RNN, LSTM, SVM models. The Bayesian Estimate combined with the sentimental analysis model would give the final suggestions to the customers. The model woud also consider the price ratings so that customers select their meals in their desired price range.