The basic idea of analyzing the Zomato dataset is to get a fair idea about the factors affecting the establishment of different types of the restaurant at different places in Bengaluru, aggregate rating of each restaurant, Bengaluru being one such city has more than 12,000 restaurants with restaurants serving dishes from all over the world. With each day new restaurants opening the industry hasn’t been saturated yet and the demand is increasing day by day. In spite of the increasing demand, however, has become difficult for new restaurants to compete with established restaurants. Most of them serving the same food. Bengaluru being an IT capital of India. Most of the people here are dependent mainly on the restaurant food as they don’t have time to cook for themselves.
- Python
- Numpy
- Pandas
- Matplotlib
- Seaborn
For this analysis, we will be using Zomato Bangalore Restaurants dataset present on kaggle. The dataset contains all the details of the restaurants listed on Zomato website as of 15th March 2019.
- The variable dish_liked as more than 54 % of missing data. If I drop the missing data, I would lose more than 50% of the data. To simplify the analysis, I drop some of the columns that are not very useful like url, address and phone.
- I then analyze the data by creating multiple visualizations using seaborn and matplotlib.
- What is the most liked Dish type?
- How many types of Restaurant types are there?
- What is the most liked Restaurant type?
- What is the Average cost for 2 persons?
- Which franchise is the most popular and which has the highest no. of Restaurants?
- Is there Delivery option available or not?
- Does location affect the rating?
- How many have a book table facility?