This project focuses on analyzing Zomato data to uncover insights about customer behavior, restaurant performance, and order patterns. Using Python for data analysis, the following key questions are explored:
- Popular Restaurant Types: What type of restaurant do the majority of customers order from?
- Customer Votes: How many votes has each type of restaurant received from customers?
- Restaurant Ratings: What ratings do the majority of restaurants receive?
- Average Spending by Couples: Zomato observes that most couples order food online. What is their average spending per order?
- Top Rated Order Mode: Which mode (online or offline) has received the highest ratings?
- Offline Order Insights: Which restaurant type receives more offline orders, allowing Zomato to provide targeted offers?
- Python: Core language for analysis.
- NumPy: Numerical operations.
- Pandas: Data manipulation and analysis.
- Seaborn & Matplotlib: Data visualization.
- Restaurant Preferences: A deep dive into the types of restaurants customers prefer.
- Customer Engagement: Votes and feedback metrics by restaurant category.
- Order Trends: Identifying trends in order mode and ratings to help Zomato optimize offers.
Visualizations like heatmaps, bar charts, and histograms are included to provide a clear picture of Zomato's restaurant data.
This analysis helps Zomato better understand its customers' preferences, allowing for data-driven marketing strategies and better user experience.