An in-depth analysis of Swiggy's Data for GFG Data Science 🍽️
- How many cities (including subregions) is Swiggy serving with its restaurants?
- How many cities (excluding subregions) have Swiggy-listed restaurants?
- Which subregion of Delhi boasts the maximum number of restaurants on Swiggy?
- What are the top 5 most expensive cities in the dataset?
- Discover the top 5 restaurants with the highest and lowest ratings across the dataset.
- Identify the top 5 cities with the greatest number of listed restaurants.
- Get a list of the top 10 cities based on the number of listed restaurants.
- Uncover the top 5 most popular restaurants in Pune 🍕.
- Which subregion in Delhi features the most affordable restaurant in terms of cost?
- Explore the 5 most popular restaurant chains in India 🍽️.
- What restaurant in Pune attracts the highest number of visitors?
- Check out the top 10 restaurants with the maximum ratings in Bangalore.
- Delve into the top 10 restaurants in Patna based on ratings 🌟.
I successfully tackled all these intriguing problems using the power of Pandas and Numpy libraries 🐼🧠.
Feel free to explore my detailed analysis and code in the Jupyter Notebook provided in this repository.
Let's dig into the delicious world of Swiggy's data together! 🍽️📈