This project aims to provide a comprehensive analysis of retail sales data, focusing on trends in monthly sales, demographic distributions, product performance, and customer segmentation. The analysis leverages statistical methods like clustering and fitting techniques to uncover insights that could guide business strategies and marketing decisions.
The retail sales dataset includes the following fields:
- Transaction ID: A unique identifier for each transaction.
- Date: The date on which the transaction occurred.
- Customer ID: A unique identifier for each customer.
- Gender: The gender of the customer.
- Age: The age of the customer.
- Product Category: Category of the purchased product.
- Quantity: Number of units purchased.
- Price per Unit: Price of one unit of the product.
- Total Amount: Total amount spent in the transaction.
The dataset is used to perform various analyses, including trend analysis, demographic profiling, product category performance, and clustering to identify customer segments.
This project requires the following Python libraries:
- numpy
- pandas
- matplotlib
- seaborn
- scikit-learn
- scipy
You can install them using pip:
pip install numpy pandas matplotlib seaborn scikit-learn scipyContributions to this project are welcome. You can contribute in the following ways:
- Submit bugs and feature requests.
- Review code and improve documentation.
- Add new analysis features or improve existing ones.
For major changes, please open an issue first to discuss what you would like to change.
This project is licensed under the MIT License - see the LICENSE.md file for details.