To visit the interactive dashboard created for this project, click here.

Welcome to my Zomato dataset analysis project! In this project, I dive deep into the world of culinary data to extract valuable insights and trends from the Zomato dataset. Leveraging Excel for data analysis and visualization, and Python for data cleaning, I've uncovered fascinating patterns that shed light on the culinary landscape.

The objective of this project is to analyze the Zomato dataset to understand trends in restaurant ratings, popularity, and customer preferences. By cleaning the dataset meticulously and conducting detailed analysis, I aim to provide actionable insights for restaurant owners and food enthusiasts alike.

1. Excel: Used for data analysis and visualization.
2. Python: Utilized for data cleaning tasks, enhancing the dataset's quality and reliability.

1. Performance Dashboard: Identified top-performing restaurant types such as Pub, Cafe, and Microbrewery based on ratings and popularity.
2. Engagement Analysis: Discovered that restaurants offering online ordering and table booking services tend to have higher ratings and more votes.
3. Flavoronomics Insights: Uncovered the most common cost range for two people dining and popular cuisines like North Indian, Chinese, and South Indian.

1. Data Cleaning: Ensured the dataset was pristine and ready for analysis.
2. Data Analysis: Conducted detailed analysis to uncover trends and patterns.
3. Data Visualization: Created visualizations to present insights effectively.
4. Python Programming: Used Python for data cleaning tasks, enhancing dataset quality.

The insights from this analysis can help restaurant owners refine their offerings, while also assisting diners in discovering new culinary delights. This project showcases the power of data analysis in understanding consumer behavior and market trends in the food industry.