Call to Action: Strengthen Mobile Data Protection Legislation

[Mostafa044]

Logistic Regression: A good baseline for binary classification (churn/no-churn, fraud/no-fraud). Simple, interpretable.
Decision Trees / Random Forests: Excellent for handling mixed data types and identifying important features. Random Forests are robust and perform well.
Gradient Boosting Machines (GBMs - e.g., XGBoost, LightGBM, CatBoost): Often top performers in structured data prediction competitions. Highly effective for complex patterns and high accuracy.
Support Vector Machines (SVMs): Good for complex decision boundaries, effective when data is not linearly separable.
Neural Networks (Deep Learning): For very complex patterns, especially if integrating unstructured data like text from SMS (using RNNs or Transformers) or voice (using CNNs/RNNs).
Regression Models (for CLTV):

Linear Regression: A simple baseline for predicting continuous values.
Random Forests / GBMs: Can also be used for regression tasks and south korea phone number list often provide higher accuracy than linear models.
Neural Networks: For highly non-linear relationships.
Clustering Models (for Segmentation):

K-Means Clustering: A popular algorithm for grouping data points into 'k' clusters based on similarity.
DBSCAN: Good for finding clusters of varying shapes and densities.
Hierarchical Clustering: Creates a hierarchy of clusters, useful for exploring different levels of segmentation.
Graph Neural Networks (GNNs - for Social Network Analysis & Advanced Fraud):

Application: When relationships between mobile numbers (call graphs, shared contacts) are key. Can identify communities, central figures, or anomalous network patterns (e.g., groups of fraudsters coordinating).
Why it matters: Provides deeper insights into relational data that traditional models miss, highly relevant for uncovering sophisticated fraud rings in Bangladesh.