This project is focused on analyzing data related to the food delivery service provided by Swiggy. The project aims to provide insights into the trends and patterns in food delivery orders, customer behavior, and restaurant performance on the Swiggy platform.
The project uses data from Swiggy's food delivery platform to analyze key metrics such as order volume, delivery time, customer ratings, and restaurant ratings. The analysis is performed using Python programming language and various data analysis libraries such as Pandas, NumPy, and Matplotlib, Seaborn
The data used in this project is obtained from Swiggy's website requested paged by performing web-scarpping, which includes data related to orders, customers, and restaurants. The data is pre-processed and cleaned to remove any duplicates, missing values, or irrelevant information.
The project outputs include various visualizations, such as graphs and charts, which provide insights into the trends and patterns in food delivery orders, customer behavior, and restaurant performance. These outputs are included in the Jupyter Notebook used for the analysis.
To use this project, you need to have Python 3 installed on your machine, along with various data analysis libraries such as Pandas, NumPy, and Matplotlib. You can simply download or clone the project repository from GitHub and run the Jupyter Notebook file to perform the analysis.
This project provides valuable insights into the food delivery service provided by Swiggy. The analysis can be used to improve the service provided by Swiggy to customers and restaurants, and to help in making data-driven decisions.