ddsgithub / python-based-analysis-of-simulated-transaction-and-customer-data

"Business Insights from Transaction Data" is a Python project for e-commerce and financial services to optimize customer funnel and KPIs. It involves creating synthetic data to model realistic transaction and customer data, which is then analyzed and visualized to support data-driven decision making.

Python-based-Analysis-of-Simulated-Transaction-and-Customer-Data

"Business Insights from Transaction Data" is a Python project designed for businesses in industries such as e-commerce and financial services that rely heavily on transaction data. By analyzing customer behavior and transaction trends, businesses can optimize their customer funnel and improve key performance indicators (KPIs) such as customer retention and revenue growth.

This project offers a systematic and organized approach to working with complex data sets by creating and analyzing synthetic transaction and customer data. The project demonstrates the ability to create modular functions for data transformation and analysis, which can be customized and updated to meet the specific needs of a business.

Using the visualizations and analysis generated from this project, businesses can make data-driven decisions related to marketing, promotions, discounts, and inventory management. Moreover, the project provides functions to create realistic synthetic data for testing purposes, incorporating trends such as seasonality, enabling the testing of various data analysis functions.

Overall, "Business Insights from Transaction Data" is a powerful tool for businesses looking to improve their understanding of customer behavior, optimize their customer funnel, and make data-driven decisions to drive growth and success.

Dependencies

The following Python libraries are required to run this project:

• pandas is used for data manipulation and analysis, providing data structures for efficient data handling and processing.
• numpy is used for numerical computation and mathematical operations on arrays and matrices.
• matplotlib is used for creating static, interactive, and animated visualizations.
• seaborn is used for creating enhanced visualizations.
• plotly.express is used for creating interactive visualizations, such as scatter plots, line charts, and bar charts.
• datetime and timedelta are built-in Python modules that provide date and time handling functionality, used for data manipulation and analysis in the project.

Getting Started

To get started with this project, follow these steps:

1. Install the required Python libraries listed above using pip.
2. Clone the repository to your local machine.
3. Open the project in your preferred Python IDE.
4. Run the project using your IDE's run command.

Contributing

If you would like to contribute to this project, please submit a pull request with your changes.

Contact

If you have any questions about this project, please contact Dave Das at [email protected] or connect with him on LinkedIn at https://www.linkedin.com/in/davedas/.