This repository contains the code and datasets used for a data-driven analysis of Swiggy's delivery operations. The project explores key aspects such as cuisine preferences, rating distribution, and delivery times to uncover insights that can help improve service efficiency and customer satisfaction.
Swiggy DataSet/: Contains the datasets used for analysis, including the scraped data.Swiggy Data Scrape/: Contains the scripts used for scraping data from Swiggy's website.Swiggy Data Analysis/: Jupyter notebooks containing the analysis and visualizations.README.md: Project documentation.
- Python 3.8 or higher
- Google Chrome and ChromeDriver
- Python packages:
pandasnumpyrequestsbeautifulsoup4seleniummatplotlibseabornscikit-learn
The analysis is performed using Jupyter notebooks located in the Swiggy Data Analysis/ directory. The key aspects explored include:
- Cuisine Analysis: Identifying popular food choices among customers.
- Rating Distribution: Understanding customer satisfaction based on restaurant ratings.
- Delivery Time Analysis: Evaluating the efficiency of the delivery service and its impact on ratings.
The analysis revealed insights such as:
- Popular cuisines on Swiggy.
- Distribution of ratings and identification of outliers.
- Correlation between delivery time and customer ratings.
This project highlights the power of data analysis in optimizing food delivery services. By understanding customer preferences and service dynamics, platforms like Swiggy can enhance customer satisfaction and operational efficiency.
- Investigating seasonal trends and customer segmentation.
- Incorporating additional features like weather conditions and promotional offers into the analysis.
- Exploring advanced modeling techniques to improve prediction accuracy.
Contributions are welcome! Please feel free to fork this repository and submit pull requests.
This project is licensed under the MIT License - see the LICENSE file for details.