Primary supervisor

Jesmin Nahar

This project uses machine learning and predictive analytics to group customers based on their shopping habits using publicly available or synthetic transactional datasets. Students will clean and analyse purchase data, apply clustering algorithms such as K-Means and Hierarchical Clustering, and identify common product purchase patterns using association rule mining. The project aims to show how data-driven methods can help businesses better understand customer behaviour and design targeted marketing strategies.

 Title: Grouping Customers by Shopping Habits with Machine Learning

Description:
This project focuses on analysing customer purchase data to identify distinct groups of customers with similar shopping habits. Students will work with public or synthetic transactional datasets to clean and prepare the data, then apply clustering algorithms such as K-Means and Hierarchical Clustering to discover meaningful customer segments. The project also includes using association rule mining to find patterns in product purchases within each group. The results will help businesses create targeted marketing strategies based on customer behaviour.

Student cohort

Single Semester
Double Semester

Aim/outline

Expected Outcomes:

  • Well-defined customer segments with clear behavioural profiles.
  • A visual dashboard and short report offering recommendations for personalised marketing.

Skills Required:
Basic knowledge of Python, R, or SQL; understanding of clustering methods; interest in data visualisation.

Benefits:
This project helps businesses improve marketing effectiveness by understanding customer groups better, leading to increased customer satisfaction and sales.

Required knowledge

General Project Pre-requisite Skills and/or Knowledge:

  • Basic understanding of machine learning concepts, particularly clustering methods (e.g., K-Means, Hierarchical Clustering).
  • Familiarity with Python, R, or SQL for data handling and analysis.
  • Basic knowledge of data preprocessing (cleaning and preparing datasets).
  • Interest in data visualisation to present customer segments and patterns.