End-to-end fraud intelligence system for telecom SMS traffic. Combines quantile-based EDA, Isolation Forest anomaly discovery, clustering exploration, Random Forest classification, and a production-ready Streamlit dashboard for operator-level fraud detection.
# 🚨 SMS Fraud Intelligence Framework
**Orange Egypt – Big Data & AI Internship Project**
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An end-to-end system for detecting and analyzing fraudulent SMS traffic at telecom scale.
This project combines exploratory analysis, anomaly detection, supervised learning, and a production-ready dashboard — designed not just as a prototype, but as an **operator-level fraud intelligence tool**.
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## 📌 Overview
Billions of SMS messages flow through telecom networks daily. Hidden within them lies a fraction of traffic that represents fraud — small in volume, but significant in impact.
This framework was built to uncover those invisible patterns using a **multi-layered approach**:
- **Quantile-based Exploratory Data Analysis (EDA)** to identify anomaly thresholds.
- **Isolation Forest** for unsupervised outlier discovery.
- **Clustering** as a diagnostic instrument to validate anomaly structures.
- **Random Forest** classifier trained on labeled anomalies, optimized for high recall.
- **Streamlit dashboard** to operationalize detection with scalability and usability.
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## 🔬 Methodology
1. **EDA as Foundation**
- Performed quantile drilling (every 5%) across millions of SMS logs.
- Determined 0.1% (≈0.001) as the logical anomaly ratio.
2. **Anomaly Discovery & Labeling**
- Detected anomalies via Isolation Forest.
- Used clustering to interpret anomaly distribution.
- Converted signals into labeled fraud datasets.
3. **Modeling**
- Trained a Random Forest classifier for supervised detection.
- Prioritized **high recall** to minimize missed fraud cases.
4. **Deployment**
- Built a Streamlit app with:
- Multi-format support (CSV, Parquet, ZIP).
- Sender-level aggregation & ranking.
- Exportable anomaly reports.
- Interactive charts for anomaly distributions.
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## ⚡ Features
- Scalable scoring for **multi-million row datasets**.
- Hybrid fraud detection: **Isolation Forest + Random Forest**.
- Operator-grade interface wi …