salesanalyzer-mds 2.0.1

A package for doing great things!

salesanalyzer

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.

Installation

$ pip install salesanalyzer

Functions

• segment_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.

Usage

salesanalyzer can be used to extract sales data insights from available data.

1. Set up imports

from salesanalyzer.sales_summary_statistics import sales_summary_statistics
from salesanalyzer.segment_revenue_share import segment_revenue_share
from salesanalyzer.predict_sales import predict_sales
import pandas as pd     # additional import to handle your sales data

2. Load your sales data as pandas DataFrame

3. Retrieve the insights:

Summary statistics

sales_summary_statistics(your_sales_data)

The sales_summary_statistics returns a pandas DataFrame with:

• 'total_revenue': The total revenue generated by all sales.
• 'unique_customers': The number of unique customers.
• 'average_order_value': The average value of an order (sum of revenue per invoice).
• 'top_selling_product_quantity': The product with the highest quantity sold.
• 'top_selling_product_revenue': The product with the highest total revenue.
• 'average_revenue_per_customer': The average revenue generated by each customer.

Segment revenue share

segment_revenue_share(your_sales_data, 
                      price_col='UnitPrice', 
                      quantity_col='Quantity')      # replace column names with your data column names

The segment_revenue_share returns a pandas DataFrame showing the total revenue share for each price segment: 'cheap', 'medium', 'expensive'.

Predict sales

predict_sales(your_sales_data, 
              new_data,     # new sales data to base the predictions on
              numeric_features = ['UnitPrice'],
              categorical_features = ['Description', 'Country'], 
              target = 'Quantity', 
              date_feature = 'InvoiceDate')

The predict_sales returns a DataFrame with prediction values, and a printed out MSE score.

Developer notes:

Running The Tests

Run the following command in the terminal from the project's root directory to execute the tests:

pytest tests/

To assess the branch coverage for this package:

pytest --cov=salesanalyzer --cov-branch

Dependencies

This package relies on the following dependencies as outlined in pyproject.toml:

• python = ">=3.10"
• scikit-learn = ">=1.6.1"
• pandas = ">=2.2.3"
• pytest = ">=8.3.4"
• jupyter = ">=1.1.1"
• myst-nb = ">=1.1.2"
• sphinx-autoapi = ">=3.4.0"
• sphinx-rtd-theme = ">=3.0.2"

Contributors

• Yeji Sohn
• Daria Khon
• Franklin Aryee

Contributing

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.

License

salesanalyzer was created by Yeji Sohn, Daria Khon, Franklin Aryee. It is licensed under the terms of the MIT license.

Credits

salesanalyzer was created with cookiecutter and the py-pkgs-cookiecutter template.