Zomato-Rating-Prediction-Datascience-Project

The Zomato Rating Prediction project aims to develop a Machine Learning based system for the prediction of rating for the Restaurants.

Observation:

1. Cuisine Diversity: The dataset showcases a diverse range of cuisines offered by restaurants, highlighting the multicultural nature of dining options available in different cities
2. Online Delivery and Table Booking: The presence of attributes like online delivery and table booking indicates the increasing trend of convenience in the restaurant industry.
3. Aggregate Rating Distribution: The aggregate rating distribution reveals that ratings tend to follow a normal distribution, with a concentration around the average rating value
4. Customer Engagement: The dataset includes the number of votes received by each restaurant, indicating customer engagement and participation in reviewing and rating dining experiences.

Insights:

The analysis of the Zomato dataset leads to the following insights:

1. Popular Cuisines: By examining the frequency of different cuisines, it is possible to identify popular cuisines in each city, which can guide restaurant owners and investors in understanding local food preferences.
2. Online Presence: Understanding the relationship between online delivery availability, table booking, and restaurant ratings can help in evaluating the significance of these services in enhancing customer satisfaction and overall restaurant performance.
3. Customer Preferences: By considering the aggregate ratings, customer reviews, and votes, it is possible to identify factors that contribute to higher ratings and customer satisfaction, such as excellent service, quality food, or unique dining experiences.
4. Market Opportunities: The dataset analysis may reveal untapped market opportunities, such as cuisines that are underrepresented or emerging trends that can be capitalized upon by restaurant owners and entrepreneurs.

Findings:

Based on the analysis of the Zomato dataset, the following findings can be derived:

1. Cuisine Popularity: Indian, Chinese, and Italian cuisines are popular across multiple cities, indicating a widespread preference for these types of food.
2. Online Services: Restaurants offering online delivery and table booking tend to have higher ratings, indicating the importance of convenience and accessibility in customer satisfaction.
3. Positive Customer Engagement: The dataset showcases active customer engagement through votes and reviews, highlighting the significance of customer opinions in determining restaurant success.
4. Differentiation Factors: Unique and innovative dining experiences, along with excellent service and quality food, contribute to higher ratings and customer satisfaction.

These findings provide valuable insights for restaurant owners, investors, and industry professionals to make informed decisions, improve customer experiences, and drive business growth.

Conclusion:

Achieved in developing a model to predict the rating of a restaurant based on the given data with high accuracy of 93.37% (Extra Trees Regressor)