📊 Telecom User Behavior Analysis

🚀 Featured Project: Telecom Customer Usage Analytics Dashboard

**Live App:**https://denis0242-telecom-analysis-app-odjz0z.streamlit.app

GitHub:Telecom-User-Behavior-Analytics

---

📌 Executive Summary

Exploratory data analysis of telecom user activity to uncover behavioral patterns, usage trends, and anomalies. This project serves as the foundation layer for understanding user behavior before building product analytics and predictive systems.

---

🎯 Business Problem

Telecom providers need to understand how users interact with services to:

• Identify high-usage behaviors
• Detect anomalies or inefficiencies
• Inform downstream analytics and modeling

---

📊 Dataset

• User sessions
• Call duration
• Data usage
• Device types
• Application traffic

---

📈 Key Metrics

• Session count
• Average session duration
• Data consumption per user
• Device distribution
• Traffic by application

---

🔍 Analysis

• Univariate and bivariate analysis
• Distribution analysis (usage patterns)
• Outlier detection
• Device and traffic segmentation

---

💡 Insights

• Heavy users contribute disproportionately to total traffic
• Certain applications dominate bandwidth usage
• Outliers indicate potential misuse or premium users

---

📊 Dashboard Features

• Interactive filtering (device, app, usage)
• Traffic distribution visualization
• Session behavior trends

---

📊 Dashboard Preview

Device Usage Breakdown

Analyzes user activity across different device types.

---

User Activity Distribution

Shows distribution of user sessions and data usage to identify behavioral patterns.

---

Outlier Detection

Identifies abnormal usage patterns for further investigation.

---

⚙️ Tools & Tech Stack

Python, Pandas, Plotly, Streamlit

---

▶️ How to Run

streamlit run app.py

---

📁 Project Structure Telecom_Analysis/ │── app.py │── data/ │── notebooks/ │── README.md

---

📌 Author

Denis Agyapong