Best Deep Dive Zomato Data Analysis Project Using Python & Power BI

Zomato Data Analysis

Overview

This is a project called “Zomato Data Analysis with Python and Power BI” made by UPDATEGADH. It’s all about using real Zomato data to find useful info about restaurants around the world. It’s a good project for students or anyone trying to learn data analysis, make dashboards, or get into business intelligence stuff using Python and Power BI.The project includes a clean and interactive dashboard that shows cool insights like ratings, locations, food types, delivery options, and more. It’s perfect if you wanna add something solid to your data science portfolio or show off your skills to clients or for job stuff.

Project Details

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Project Objective

The core objective of this project is to investigate and analyze a global restaurant dataset from Zomato. The goal is to apply data cleaning, descriptive statistics, and visualization techniques using Python and Power BI, eventually delivering insights on food trends, restaurant performance, and customer behavior.

Data Analysis Steps

The analysis followed a structured pipeline:

1. Load Raw Data: Raw .csv files are imported using pandas.
2. Data Cleaning:
   • Dropping unnecessary columns and duplicates.
   • Cleaning rows and correcting values.
3. Feature Engineering:
   • Merged country codes with main dataset.
   • Converted columns for meaningful aggregation.
4. Descriptive Analysis: Focused on restaurants, ratings, cuisines, delivery types, and country-based data.
5. Interactive Dashboard Development: Used Power BI to design visuals and KPIs for stakeholders.

Analytical Questions Covered

This analysis answers several business-relevant questions:

• How many total restaurants and cuisines are listed worldwide?
• Which countries have the most restaurants listed on Zomato?
• What Indian cities have the highest number of value-for-money restaurants?
• What are the top 10 most-voted cuisines in India?
• Which countries feature restaurants that offer online delivery?
• Which localities have the highest number of restaurants?
• Which restaurants offer good cuisines and maintain strong average ratings?

Dashboard Snapshot

A comprehensive interactive dashboard was developed using Power BI that displays the most relevant insights for easy interpretation. Below is a snapshot:

Available Features

This project provides the following complete and functional modules:

• Clean and structured CSV data files
• Python script for data preprocessing and analysis
• Power BI dashboard file (.pbix)
• Cleaned CSV output ready for visual analysis
• Country-code mapping and integration
• Filtering logic for cuisines, cost, delivery options
• Questions-to-insight mapping in analysis

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Conclusion

From this Zomato dataset, we were able to:

• Identify high-cost restaurants and value-for-money options.
• Pinpoint cities and countries with a high restaurant density.
• Understand cuisine popularity and service availability (e.g., online delivery).
• Evaluate countries based on their restaurant rating averages.
• Suggest actionable insights for restaurant owners to improve offerings and engagement:
  • Introduce table booking and online delivery where feasible.
  • Broaden cuisine offerings to appeal to diverse customer groups.
  • Leverage customer feedback to improve ratings and value propositions.

This project highlights the importance of effective data visualization and analysis in the food and hospitality industry, providing tangible insights to both data analysts and restaurant stakeholders.