simran719 / swiggy_analysis

Swiggy Analysis Project

Overview:

This project is an in-depth exploration and analysis of the popular food delivery platform, Swiggy. With a focus on major cities in India, this project aims to unveil key insights into customer behavior, ordering patterns, and overall service performance.

Tools and Technologies:

• Python: Utilized for data analysis, visualization, and modeling.
• Pandas: Leveraged for data manipulation and numerical operations.
• Matplotlib: Used for creating visualizations to present key findings.
• Jupyter Notebook: Employed for an interactive and collaborative analysis environment.

Project Impact:

The Swiggy Analysis project provides a comprehensive understanding of the dynamic food delivery landscape in India. By extracting actionable insights, the project aims to contribute to the optimization of Swiggy's services, enhance customer experience, and inform strategic decision-making for sustainable growth.

Prerequisites:

1. Python Installation: Ensure that Python is installed on your machine. You can download and install Python from the official Python website.

2. Required Packages: Make sure you have the necessary Python packages installed. You can use the following command in your terminal or command prompt to install them:

  pip install pandas matplotlib jupyter

This command installs Pandas for data manipulation, Matplotlib for visualization, and Jupyter for interactive notebook capabilities.

Steps to Run:

1. Download Code: Download the code and scripts for your Swiggy Analysis project. This might include Jupyter Notebooks (.ipynb), Python scripts (.py), or any other relevant files (preferably Jupyter Notebook).

2. Navigate to Project Directory: Open a terminal or command prompt and navigate to the directory where your project files are located.

3. Run Jupyter Notebook: Run the following command in your terminal:

  jupyter notebook

This command starts the Jupyter Notebook server, and you can access your notebooks through a web browser.

4. Execute Python Scripts or Notebooks: Open your main Python script or Jupyter Notebook and execute the cells or run the script to perform the analysis.

5. Review Outputs: Review the outputs generated by your analysis.

Tips:

1. Environment Considerations: If you're using virtual environments, make sure to activate your environment before running the code.

2. Check Dependencies: Double-check that all dependencies and required libraries are installed and up-to-date.

3. Follow Documentation: Refer to any documentation provided with the project for specific instructions or notes.