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

"Business Insights from Transaction Data" is a Python project designed for individuals interested in practicing data analysis within the e-commerce and financial services sectors. This project offers a realistic, synthetic dataset and comprehensive analysis tools to help users develop their data skills in a practical and meaningful way.

Key Features:

Realistic Synthetic Data Creation:

Generates random but realistic datasets that simulate customer signups, various stages of the signup process, and customer transactions. This allows users to practice with data that closely resembles what they would encounter in real-world scenarios.

Comprehensive Data Analysis:

Provides all the necessary code to analyze the synthetic dataset, enabling users to explore customer behavior and transaction trends. This helps users gain hands-on experience with data manipulation, transformation, and visualization.

Customer Funnel Analysis:

Creates tables and visualizations to analyze different stages of the customer signup process and subsequent transactions. This practice helps users understand how to identify and optimize key stages in the customer journey.

Modular Functions for Flexibility:

Demonstrates the creation of modular functions for data transformation and analysis. These functions can be customized and adapted, allowing users to practice writing and updating reusable code.

Data-Driven Insights:

Empowers users to make informed decisions based on the insights generated from the data. This practice helps users understand how to derive actionable insights from data analysis.

Testing and Trend Analysis:

Incorporates trends such as seasonality in the synthetic data, providing users with the opportunity to test various data analysis functions and scenarios effectively.

Purpose and Benefits:

"Business Insights from Transaction Data" is designed to give individuals a realistic dataset and the tools needed to practice data analysis in the e-commerce and financial services space. By working with this project, users can:

• Develop a deeper understanding of customer behavior and transaction trends.
• Gain experience in data manipulation, visualization, and analysis.
• Improve their ability to create and work with synthetic datasets.
• Enhance their data-driven decision-making skills.
• This project is an excellent resource for anyone looking to strengthen their data analysis capabilities and apply their skills to scenarios commonly found in e-commerce and financial services.

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 Dave.das92@gmail.com or connect with him on LinkedIn at https://www.linkedin.com/in/davedas/.