Zomato Bengaluru Restaurant Analysis

Welcome to the Zomato Bengaluru Restaurant Analysis repository. This project provides an in-depth analysis of restaurants listed on Zomato in Bengaluru. The dataset comprises over 40,000 records and 9 features, capturing essential details about various restaurants such as their names, locations, types, ratings, and more. You can view Google Collab notebook [here] .

Contents

1. Introduction

   • This section details the scope and objectives of the analysis, focusing on deriving insights from the dataset to understand restaurant trends and customer preferences.

2. Data Exploration and Cleaning

   • Loading the Data: Explanation of how the dataset is loaded into a pandas DataFrame.
   • Initial Exploration: Methods used to examine the dataset's structure and identify unnecessary columns.
   • Cleaning and Wrangling: Steps taken to clean the data, including handling duplicates, null values, and data transformations.

3. Identified Problems, Solutions, and Trends

   • Percentage of Restaurants Offering Online Ordering: Analysis of the prevalence of online ordering and recommendations to increase adoption.
   • Impact of Table Booking: Examination of how table booking affects restaurant ratings and recommendations to encourage more restaurants to offer this service.
   • Restaurant Type and Online Ordering: Insights into which restaurant types are more likely to offer online ordering.
   • Top Locations by Average Rating: Identification of high-rating locations and recommendations for restaurants in lower-rated areas.
   • Most Popular Restaurant Chains: Analysis of popular chains and their strategies.
   • Distribution of Restaurant Ratings: Visualization and interpretation of rating distributions.
   • Correlation Between Votes and Ratings: Analysis of the relationship between votes and ratings.
   • Locations with Most Restaurants: Insights into high-density restaurant locations.
   • Impact of Online Ordering on Votes: Examination of how online ordering influences customer engagement.
   • Top Rated Restaurants: Identification of the top 10 highest-rated restaurants.
   • Location Impact on Votes: Analysis of how location affects vote counts.
   • Bottom Locations by Average Rating: Identification of locations with the lowest average ratings.
   • Expensive Restaurants for 2 People: Analysis of the top 10 most expensive restaurants.
   • Cheapest Restaurants for 2 People: Identification of the 5 cheapest restaurants.

4. Conclusion

   • A summary of key insights and recommendations based on the analysis, highlighting trends and offering actionable advice for restaurant owners and stakeholders.

5. Dashboard

   • Interactive visualizations and dashboards showcasing the analysis results and insights derived from the data.