Customer Churn Prediction Using Machine Learning Algorithms

Shantanu Sonune; Abhijeet Mohite; Anjali Borhude; Yash Patil; Prof. Anuradha Thorat

doi:10.32628/IJSRST52411140

Authors

Shantanu Sonune B.E(Information Technology), Savitribai Phule Pune University, Zeal College of Engineering and Research, Pune, Maharashtra, India Author
Abhijeet Mohite B.E(Information Technology), Savitribai Phule Pune University, Zeal College of Engineering and Research, Pune, Maharashtra, India Author
Anjali Borhude B.E(Information Technology), Savitribai Phule Pune University, Zeal College of Engineering and Research, Pune, Maharashtra, India Author
Yash Patil B.E(Information Technology), Savitribai Phule Pune University, Zeal College of Engineering and Research, Pune, Maharashtra, India Author
Prof. Anuradha Thorat Asst.Professor. Department of Information Technology, Zeal College of Engineering and Research, Pune, Maharashtra, India Author

DOI:

https://doi.org/10.32628/IJSRST52411140

Keywords:

Customer Churn Prediction, Industry, Machine Learning Algorithms, Predictive Analytics

Abstract

In today’s highly competitive industries, retaining customers is vital for sustaining business growth and profitability. Customer churn, the phenomenon where customers switch from one service provider to another, poses a significant challenge for different companies. Predicting churn can help these companies take proactive measures to retain valuable customers. This study explores the application of machine learning algorithms for predicting customer churn in different industries like Telecom Industry, IT and Banking Sector. Additionally, the research contributes to the existing body of knowledge in the field of customer churn prediction, showcasing the potential of machine learning algorithms in addressing complex business challenges.
The project begins with data collection and preprocessing, involving the extraction and cleaning of relevant features from diverse sources such as customer interactions, transaction history, and demographic information.The predictive modeling phase employs state-of-the-art machine learning algorithms, including but not limited to logistic regression, decision trees and random forest, feature engineering is employed to enhance the model's ability to capture nuanced customer behavior. The dataset is split into training and testing sets to evaluate model performance accurately.
The outcomes of this project have practical implications for businesses aiming to proactively manage customer retention strategies. By identifying potential churners early, companies can implement targeted interventions, personalized marketing strategies, and loyalty programs to mitigate the risk of customer loss and foster long-term relationships.

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