Data Preparation and Feature Engineerin

[Mostafa044]

Regardless of the model, the quality and relevance of features derived from mobile number data are paramount.Usage Patterns: Average daily call duration, number of unique contacts called, SMS sent/received count, data consumption (2G/3G/4G/5G), time of day/week usage peaks.
Recharge Behavior: Average recharge amount, frequency, last recharge date, consistency of recharges.
MFS Transactions: Transaction count, average value, types of transactions (cash-in/out, P2P, bill payments), frequency, geographical patterns.
Customer Service Interactions: Number of complaints, type of query, resolution time.
Demographics: Age, gender, location (e.g., Bogra city vs. rural areas), income group (inferred).
Network Features: Number of unique cell towers connected to, roaming activity.
Engineered Features: Ratios (e.g., calls to unique numbers vs. total calls), velocity features (e.g., sudden increase in data usage), recency (time since last activity), frequency.
Important Considerations
Data Privacy (Ethical & Legal): Given the sensitivity of mobile number data and recent data leaks in Bangladesh, privacy is paramount.
Anonymization/Pseudonymization: Sensitive identifiers must be masked.
Consent: Ensure users have explicitly consented to their data being taiwan phone number list used for predictive modeling.
Federated Learning: Explore FL for on-device model training, especially for personalized features, to keep raw data decentralized.
Data Quality & Imbalance: Mobile data can be messy. Cleaning, handling missing values, and addressing class imbalance (e.g., fraud cases are rare) are crucial.
Model Interpretability: Especially for fraud or credit scoring, understanding why a model made a certain prediction is vital for trust and actionability.
Real-time vs. Batch Prediction: Some predictions (fraud detection, NBO) require near real-time inference, while others (churn, CLTV) can be run in batches.
Compute Resources: Training complex ML models on massive mobile datasets requires significant computational power.
By carefully selecting and applying appropriate ML models, combined with robust data engineering and a strong commitment to privacy, MNOs and MFS providers in Bangladesh can unlock immense value from their mobile number data to enhance customer experience, boost revenue, and secure their operations.