A python package that helps with the analysis on a sales data. The packagage will contain functions to be used as tools for identifying market segment, predicting future sales and analyzing seasonal revenue trends.
The sales_analyzer package will be an addition to the Python ecosystem as a specialized tool for analyzing retail sales data, targeting small to medium-sized businesses that may not have the resources for an in-house data analytics team and who could benefit from ready-to-use functions for common sales-related tasks. While existing packages such as Pandas and Scikit-learn provide general tools for data manipulation and machine learning predictions, salesanalyzer aims to streamline the process by offering a suite of pre-built, retail-specific analytical functions.
$ pip install salesanalyzersegment_revenue_share: Segments products into three categories: cheap, medium, expensive, based on price, and calculates their respective share in total revenue.predictSales: Predicts future sales based on the provided historical data and the target.sales_summary_statistics: Calculates a variety of summary statistics that provide insights into overall sales performance, customer behavior, and product performance.
- TODO
- Yeji Sohn
- Daria Khon
- Franklin Aryee
Interested in contributing? Check out the contributing guidelines. Please note that this project is released with a Code of Conduct. By contributing to this project, you agree to abide by its terms.
salesanalyzer was created by Yeji Sohn, Daria Khon, Franklin Aryee. It is licensed under the terms of the MIT license.
salesanalyzer was created with cookiecutter and the py-pkgs-cookiecutter template.