Customer Churn Prediction by Using Machine Learning

Customer Churn Prediction by Using Machine Learning

Harshal Takade, Shreeyash Kotgiri, Kartikeyan Charkupalli, Vinayak Swami

Dept. of Computer Engineering

Sinhgad College of Engineering, Vadgaon bk, Pune, Maharashatra, India

ABSTRACT:

The telecommunications industry, characterized by rapid technological advancements and fierce market competition, grapples with the imperative of customer retention [1]. In response, this study focuses on crafting a robust churn prediction model tailored to the telecom sector. The research employs a comprehensive suite of models, including support vector machine (SVM), logistic regression, random forest, and K-nearest neighbors (KNN) [2]. The investigation unfolds against the backdrop of a dynamic telecommunications landscape, where customer behaviors are influenced by multifaceted factors. Notably, we introduce SVM alongside conventional models, emphasizing the role of logistic regression, random forest, and KNN in the churn prediction paradigm. Through meticulous parameter optimization and performance evaluation, our findings showcase the effectiveness of these models in discerning patterns indicative of potential churn. The comparative analysis reveals nuances in predictive capabilities, with SVM demonstrating robust performance, followed closely by random forest and KNN [2]. This study highlights the importance of embracing a diverse set of models to accommodate the intricacies of telecommunications data [2]. The predictive accuracy and versatility of SVM, random forest, and KNN advocate for a nuanced, data-driven approach to churn prediction, transcending the limitations of individual models [2]. As telecommunications providers navigate a dynamic customer landscape, the insights derived from this study contribute to the evolution of targeted retention strategies, guiding industry practitioners towards proactive and effective customer management practices.