Customer Segmentation using Machine Learning Techniques

Abstract

The objective of this customer segmentation endeavor is to utilize machine learning methodologies, Python programming, and the Streamlit framework in order to deliver individualized suggestions pertaining to savings plans, loans, and strategies for wealth management. Through the division of customers according to their demographic data, purchase patterns, and online navigation tendencies, this initiative empowers financial establishments to gain deeper insights into their customer demographics and customize their services to cater to specific requirements.
 The project involves several stages, including data collection, data preprocessing, feature selection, model training, and evaluation. The acquired customer data is refined to resolve any inconsistencies or missing information. Important characteristics are identified, and an exploratory analysis of the data is conducted to uncover hidden patterns and relationships. We then select an appropriate machine learning algorithm, such as K-means clustering, hierarchical clustering, or Gaussian Mixture Models, to categorize the customer base into distinct clusters.
 After the completion of model training and assessment, a Streamlit-based web application is constructed, offering an interactive platform for users. This application permits users to input their particulars and obtain tailored suggestions grounded in their designated segment. The suggestions encompass fitting savings schemes, loan alternatives, and wealth management approaches that harmonize with their financial objectives and risk appetite.
 The deployed application facilitates a seamless user experience, providing real-time recommendations and insights. Continuous improvement of the model and application is encouraged through user feedback, allowing for refinement and better customization of recommendations over time.