salesanalyzer-mds 2.1.0

A Python package for sales forecasting, statistical analysis, and data-driven insights

salesanalyzer_mds

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_mds aims to streamline the process by offering a suite of pre-built, retail-specific analytical functions.

Installation

$ pip install salesanalyzer_mds

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

1. Set up imports

from salesanalyzer_mds.sales_summary_statistics import sales_summary_statistics
from salesanalyzer_mds.segment_revenue_share import segment_revenue_share
from salesanalyzer_mds.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() function 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',
                      price_thresholds=None)      # replace column names with your data column names

The segment_revenue_share() funtion returns a pandas DataFrame showing the total revenue share for each price segment: 'cheap', 'medium', 'expensive'. Custom price thresholds can be set by the user other set automatically.

• Custom price thresholds can be set using the price_thresholds parameter.
• If not specified, thresholds are automatically determined based on the data.

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() function 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_mds --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_mds was created by Yeji Sohn, Daria Khon, Franklin Aryee. It is licensed under the terms of the MIT license.

Credits

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