Hybrid Machine Learning Models for Predictive Maintenance in Cloud-Based Infrastructure for SaaS Applications
Sunil Raj Thota, Saransh Arora, Sandeep Gupta
Abstract
It is crucial for SaaS providers to have predictive maintenance as one of the most cost-effective ways for them to successfully maintain service consistency and high satisfaction among their customers. The present research article renders an original technique to forecast the drop-offs of the clients in Customer Relationship Management (CRM) systems through the infrastructure of SaaS in the cloud. Working with a big churn dataset obtained from Kaggle dataset created via a telecom provider and then carefully investigating the feature engineering, preprocessing, and data collecting. Next, some machine learning methods are deployed with SVM + Naive Bayes model, KNN, DT, RF, and ANN. The model of Hybrid SVM + Bayes performed better compared to the individual models being based on the study results, the accuracy was 95.67%. It is revealed that the hybrid ML model shows a significantly higher level of precision, accuracy, recall, and F1-score than individual models do when compared through thorough and methodological model training and evaluation. The outcome emphasizes how effectual hybrid machine learning algorithms are for SaaS arrangements to accomplish better retention tactics in the dynamic world. This framework provides a basis for future projects involving prediction maintenance that are cloud-based SaaS systems as well as beneficial intelligence to enterprises.