Data analytics project using Excel, Power BI, Tableau, and SQL.
This project focuses on analyzing Zomato restaurant data to understand customer behaviour, restaurant performance, cuisine preferences, pricing patterns and delivery efficiency. The analysis follows a complete data pipeline that includes cleaning the raw data, conducting exploratory analysis using SQL, and developing interactive dashboards in Power BI and Tableau. The objective of the project is to convert raw restaurant data into meaningful insights that support better decision-making and highlight operational and customer-related trends.
The project makes use of Microsoft Excel for data cleaning and preprocessing, SQL (MySQL) for running analytical queries, Power BI for building KPI-driven dashboards with DAX calculations, Tableau for interactive visual storytelling, and GitHub for storing documentation and maintaining version control. These tools collectively support a structured analytical workflow and ensure accuracy, clarity, and effective presentation of insights.
The dataset used for this project is not included in the repository as per academic requirements. However, it contained essential restaurant-level and customer-level attributes such as restaurant names, geographical locations, cuisine types, cost metrics, delivery indicators, user ratings and customer votes. These attributes enabled the identification of customer patterns, performance variations, pricing behaviour and delivery-related insights which formed the basis of the visualizations and analysis.
The study revealed several important findings related to restaurant performance and customer behaviour. Higher-rated restaurants typically maintained faster delivery times, suggesting that efficient service strongly influences positive feedback. Popular cuisines such as North Indian, Chinese and Continental consistently attracted high demand. Budget-friendly restaurants recorded the highest order volumes, while premium restaurants showed steady but lower traffic. Some areas exhibited high demand despite lower average ratings, indicating potential improvement opportunities. Delivery time emerged as a key factor impacting overall customer satisfaction and rating trends.
Planned enhancements for this project include adding machine learning models to predict ratings or customer demand, developing a personalized restaurant recommendation system, and applying sentiment analysis to customer reviews for deeper qualitative understanding. Additional improvements may involve time-series forecasting to study seasonality, geospatial analysis to map high-performance regions, and integrating real-time or API-based Zomato data to make the dashboards more dynamic and actionable.