Python Retail Data Analysis Project

The Python Retail Data Analysis Project aims to provide retail businesses with tools for insightful analysis of customer behaviors and sales patterns. By utilizing advanced data manipulation and analysis techniques, this project seeks to unveil hidden insights within retail data, aiding strategic decision-making and fostering business growth.

Source for Data;

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About The Project

This project was made by TU Dortmund University students Emona Bakalova, Mariia Hrechyn, Alican Ohkay for the final project of the Introduction to Python course. Understanding customer preferences and identifying sales trends are crucial components of success in the fast-paced retail industry. This project introduces a robust framework for the analysis of retail data, featuring classes such as Customer and Retail. These classes facilitate the examination of purchase histories, the evaluation of product performance, and the identification of significant sales trends.

• The Customer class delves into individual customer behaviors, revealing insights into purchase patterns, frequencies, and preferences.
• The Retail class offers a comprehensive overview of sales data, encompassing product performance, seasonal trends, and overall sales effectiveness.

Python scripts and Jupyter notebooks are utilized to explore various aspects of retail data, from detailed analyses of customer behaviors to overarching examinations of sales trends.

Getting Started

To begin utilizing the Python Retail Data Analysis Project, follow these steps to set up your environment:

1. Clone the repository:

   git clone https://github.com/EmonaBakalova/Python_Project.git

2. Navigate to the project directory:

   cd Python_Project

3. Install the required dependencies:

   pip install -r requirements.txt

Usage

This project provides functionalities to analyze retail data, simplifying the process of gaining insights into customer behavior and sales performance.

Analyzing Customer Data

Utilize the Customer class to analyze customer data, which enables the loading of customer data, analysis of purchasing patterns, and generation of reports.

from retail.class_customer import Customer

# Load customer data
customer_data = Customer('docs/source/example/Online_Retail.csv')

# Display customer summary
print(customer_data.summary())

Sales Analysis

The Retail class allows for a more extensive analysis of sales data, including the examination of trends and product performance.

from retail.class_retail import Retail

# Load sales data
sales_data = Retail('docs/source/example/Online_Retail.csv')

# Analyze sales performance
print(sales_data.analyze_performance())

Refer to the included Jupyter notebooks for detailed examples and advanced usage scenarios:

• class_customer.ipynb for insights into the Customer class.
• showcase.ipynb for a comprehensive analysis using both the Customer and Retail classes.

Contributing

Contributions to the Python Retail Data Analysis Project are highly appreciated. Follow these steps to contribute:

1. Fork the Project
2. Create your Feature Branch (git checkout -b feature/AmazingFeature)
3. Commit your Changes (`git commit -m 'Add some AmazingFeature'')
4. Push to the Branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

License

This project is licensed under the MIT License - see the LICENSE.txt file for details.