Telecom — Telecom Feature Engineering Pipeline for Predictive Models
PopularThis DAG automates the creation of feature pipelines for predictive fraud and anomaly detection models in the telecom industry. It processes both historical and real-time data to enhance model accuracy and operational efficiency.
Overview
The Telecom Feature Engineering Pipeline for Predictive Models is designed to facilitate the development of predictive analytics capabilities within the telecom sector, focusing on fraud and anomaly detection. Triggered by data updates, this DAG integrates multiple data sources, including historical transaction logs and real-time call detail records. The ingestion pipeline begins with data extraction from these sources, followed by a series of transformation steps that include feature extraction
The Telecom Feature Engineering Pipeline for Predictive Models is designed to facilitate the development of predictive analytics capabilities within the telecom sector, focusing on fraud and anomaly detection. Triggered by data updates, this DAG integrates multiple data sources, including historical transaction logs and real-time call detail records. The ingestion pipeline begins with data extraction from these sources, followed by a series of transformation steps that include feature extraction, normalization, and aggregation. Each feature is meticulously validated to ensure quality and reliability before being stored in a centralized data warehouse. This storage solution allows data science teams to access and utilize the features for model training and refinement. Monitoring key performance indicators (KPIs) such as feature significance and model performance metrics ensures continuous improvement and alignment with business objectives. The business value of this DAG lies in its ability to enhance predictive accuracy, reduce fraud losses, and optimize operational workflows, ultimately leading to increased customer trust and satisfaction.
Part of the Fraud & Anomaly Analytics solution for the Telecom industry.
Use cases
- Improved accuracy of fraud detection models
- Faster response times to emerging anomalies
- Enhanced operational efficiency through automation
- Increased trust and satisfaction among customers
- Data-driven insights for strategic decision-making
Technical Specifications
Inputs
- • Historical transaction logs
- • Real-time call detail records
- • Customer account information
- • Network usage statistics
Outputs
- • Validated feature sets for predictive models
- • Performance reports on feature impact
- • Data warehouse updates with new features
Processing Steps
- 1. Extract historical transaction logs
- 2. Ingest real-time call detail records
- 3. Perform feature extraction and transformation
- 4. Validate features for accuracy and relevance
- 5. Store features in centralized data warehouse
- 6. Generate performance reports for data science teams
Additional Information
DAG ID
WK-0415
Last Updated
2026-02-02
Downloads
73