DATA MINING IN TELECOM CALL RECORDS

The Data Mining in Telecom Call Records project highlights how large-scale telecom datasets can be effectively analysed to generate valuable insights The implementation of the Telecom Data Mining and Predictive Analytics framework underscores a fundamental shift in how modern telecommunications entities manage high-velocity data. This research confirms that automated data mining is not merely an optional enhancement but a critical prerequisite for navigating the complexity of contemporary Call Detail Record (CDR) environments.   The project successfully demonstrated a robust Analytical Pipeline, transitioning from raw data extraction to sophisticated reporting. A primary achievement of this architecture is the establishment of Data Fidelity through rigorous preprocessing. By systematically eliminating noise, redundancies, and fragmented logs, the system ensures that the downstream mining algorithms operate on a high- integrity substrate, leading to more accurate and reliable operational insights.   Heuristic Pattern Recognition: The system accurately identified diurnal usage peaks and longitudinal subscriber trends, allowing for more precise network load balancing. Security Resilience: Through Anomaly Detection, the framework successfully isolated the "digital fingerprints" of fraudulent activity and SIM-misuse, providing an essential layer of Proactive Risk Mitigation.

[1]       Margaret H. Dunham (2003). Data Mining: Introductory and Advanced Topics. Pearson Education. [2]         Pang-Ning Tan, Michael Steinbach, and Vipin Kumar (2005). Introduction to Data Mining. Addison-Wesley. [3]       Ian H. Witten, Eibe Frank, and Mark A. Hall (2011). Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann. [4]       Trevor Hastie, Robert Tibshirani, and Jerome Friedman (2009). The Elements of Statistical Learning. Springer. [5]       Telecom Regulatory Authority of India (TRAI). Official Reports and Telecom Statistics. (Accessed from TRAI official publications). [6]       Kononenko, Igor (2001). “Machine Learning for Data Mining: A Comprehensive Overview.” Journal of Intelligent Systems. [7]       Python Software Foundation. Python Documentation for Pandas, NumPy, and Matplotlib. (Used for data preprocessing and visualization). [8]       Fayyad, Usama; Piatetsky-Shapiro, Gregory; Smyth, Padhraic (1996). “From Data Mining to Knowledge Discovery in Databases.” AI Magazine.