Segmenting Clients using Machine Learning to get the Best Leads in an E-Commerce Business

Authors

  • B Ravinder Reddy  Professor, Department of Computer Science and Engineering, Anurag University, Hyderabad, Telangana, India
  • S Rohith  Department of Computer Science and Engineering, Anurag University, Hyderabad, Telangana, India
  • V Sai Vamshi  Department of Computer Science and Engineering, Anurag University, Hyderabad, Telangana, India
  • O Dheeraj  Department of Computer Science and Engineering, Anurag University, Hyderabad, Telangana, India

DOI:

https://doi.org/10.32628/IJSRST52310220

Keywords:

Customer Segmentation, clustering, classification, SVC Classifier, Adaboost, Confusion Matrix, Gradient Boost, Random Forest, Decision Tree, Word cloud.

Abstract

Customer segmentation is an essential part of modern marketing strategy, as it enables businesses to effectively tailor their marketing efforts and customize their consumer communication. Machine learning algorithms offer a powerful tool for automating the process of customer segmentation by analyzing large amounts of data about customer behavior and identifying patterns that can be used to group customers into segments. We present our experiments and findings for predicting the segment of a customer. We use the Tensorflow library in Python for Machine Learning combined with Pandas for Data frame Manipulation and experiment with various clustering models, including traditional Machine Learning algorithms.

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International Journal of Scientific Research in Science and Technology

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Published

2023-04-30

Issue

Volume 10, Issue 2, March-April-2023

Section

Research Articles

License

Copyright (c) IJSRST

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
B Ravinder Reddy, S Rohith, V Sai Vamshi, O Dheeraj "Segmenting Clients using Machine Learning to get the Best Leads in an E-Commerce Business" International Journal of Scientific Research in Science and Technology(IJSRST), Online ISSN : 2395-602X, Print ISSN : 2395-6011,Volume 10, Issue 2, pp.184-191, March-April-2023. Available at doi : https://doi.org/10.32628/IJSRST52310220