Objective The basic idea of analyzing the Zomato dataset is to get a fair idea about the factors affecting the establishment of different types of restaurants at different places in Bengaluru. This Zomato data aims at analyzing demography of the location. Most importantly it will help new restaurants in deciding their theme, menus, cuisine, cost, etc for a particular location. It also aims at finding similarity between neighborhoods of Bengaluru on the basis of food.

Data Analysis Using Python Work flow of process: 1. Data Collection 2. Data Cleaning 3. Performing EDA 4. Performing Geospatial Analysis 5. Performing Sentiment Analysis

Tools Used: Jupyter Notebook is used as IDE. Among the Python libraries, Pandas and NumPy are used for handling data, preprocessing, and mathematical operations, respectively. Plotly, Seaborn, and Matplotlib are used for visualizing plots.

Conclusion: Cafe Coffee Day dominates the restaurant chain landscape followed by Onesta and then Empire. Online orders are accepted by 64.4% of restaurants, whereas 35.6% of restaurants do not accept them. The city of Bangalore is known as a high-tech hub of India, and people who live a busy and modern life are inclined to choose Quick Bites. The most common cuisines are North Indian, Chinese, and South Indian. Bangalore is therefore influenced more by the cultures of the north than those of the south. Having reviewed the above scatterplot, we can conclude that most of the highest-rated restaurants accept online orders and are budget-friendly as well.

Please refer to the Github Repository for this project "here"