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The project provides the Apriori algorithm and Market Basket Analysis (MBA) to analyze transactional data, generating personalized recommendations based on Support, Confidence, and Lift metrics to enhance customer experience and boost sales.
Leveraging K-Means clustering, our project categorizes retail customers based on purchasing behaviors and demographics. This provides businesses with actionable insights to tailor marketing efforts, enhancing customer experience and boosting sales.
Leveraging advanced data analysis techniques, this project is strategically designed to contribute to business success by delivering insightful visualizations, accurate sales forecasting, and actionable information.
This repository contains results of the completed tasks for the Quantium Data Analytics Virtual Experience Program by Forage, designed to replicate life in the Retail Analytics and Strategy team at Quantium, using Python.
Leveraging K-Means clustering, our project categorizes retail customers based on purchasing behaviors and demographics. This provides businesses with actionable insights to tailor marketing efforts, enhancing customer experience and boosting sales.
This project looks at the sales pattern of a product category in a retail store, using the store’s transaction dataset and identifying customer purchase behavior, to generate insights and recommendations.