click here to download project abstract of data analytics
At DataPro, we provide final year projects with source code in python for computer science students in Hyderabad , Visakhapatnam.
Abstract
The telecommunications industry is undergoing rapid evolution, necessitating innovative solutions to tackle the pervasive issue of customer churn. This research introduces an Intelligent Network Data Analytics Function (INDAF) enriched with advanced Machine Learning (ML) techniques to proactively address and mitigate customer churn in the dynamic landscape of the telecom sector.
In this study, we harness the wealth of network data, so encompassing call records, user behavior patterns, and service utilization metrics, to construct a powerful predictive model for anticipating customer churn. Employing a spectrum of Machine Learning algorithms, including supervised and unsupervised learning, ensemble methods, and deep learning, So that we perform a comprehensive analysis of extensive and dynamic datasets. The objective is to extract meaningful insights that contribute to precise churn prediction.
The proposed INDAF is designed to augment the decision-making processes of telecom operators by furnishing timely and actionable information on potential churners. Thus by leveraging predictive analytics, the system facilitates the formulation of personalized customer retention strategies, thereby cultivating customer loyalty and curtailing churn rates. Both Real-time monitoring and adaptive learning mechanisms integrated into the system ensure continuous refinement, enhancing the model’s accuracy and adaptability to evolving churn patterns.
This research significantly contributes to the burgeoning field of telecommunications analytics by offering both pragmatic and intelligent solution to the challenges posed by customer churn. The proposed INDAF, underpinned by sophisticated machine learning techniques, provides telecom operators with a proactive means of addressing customer retention. The envisaged outcomes include improved customer satisfaction, augmented revenue streams, and sustained business growth, positioning telecom operators competitively in an ever-evolving and highly competitive market.
