Food Delivery App Data Analysis
Project Overview
In this project, I conducted a comprehensive analysis of the Zomato dataset to extract valuable insights into restaurant ratings, popularity, and customer preferences. Leveraging Excel for data analysis and visualization, and Python for data cleaning, I uncovered fascinating patterns that offer deep insights into the culinary landscape.
Tools Used
Excel: Used for data analysis and visualization, creating detailed charts and graphs to illustrate key findings.
Python: Utilized for data cleaning tasks, ensuring the dataset was reliable and accurate for analysis.
Key Findings
Performance Dashboard: Identified top-performing restaurant types, such as Pub, Cafe, and Microbrewery, based on ratings and popularity.
Engagement Analysis: Discovered that restaurants offering online ordering and table booking services tend to have higher ratings and more votes.
Flavoronomics Insights: Found the most common cost range for two people dining and popular cuisines like North Indian, Chinese, and South Indian.
Skills Showcased
Data Cleaning: Ensured the dataset was clean and ready for analysis, enhancing its quality and reliability.
Data Analysis: Conducted detailed analysis to uncover trends and patterns, providing valuable insights for decision-making.
Data Visualization: Created visualizations to effectively communicate findings, making complex data easy to understand.
Python Programming: Used Python for data cleaning tasks, showcasing proficiency in programming for data analysis.
Conclusion
The insights from this project can help restaurant owners refine their offerings and marketing strategies, while also assisting consumers in making informed dining decisions. This project highlights my ability to derive meaningful insights from data and communicate them effectively through visualization.