telecom-analytics

An end-to-end telecom data analytics project processing 19M+ records using SQL, DuckDB, Python, and Power BI to analyze CRM, device, and revenue insights.

Customer churn prediction system using XGBoost, SHAP explainability, and Streamlit for real-time telecom retention analysis.

Challenge Telecom X - análisis de datos

End-to-End Customer Churn Prediction using Machine Learning

📡 Multimodal AI system for Telecom Customer Churn Prediction using ML, DL + Sentiment Analysis. Includes Business Dashboard, SHAP Explainability, PDF Reports & Batch Processing.

A machine learning solution for churn prediction using CatBoost, achieving a 0.8464 AUC-ROC through feature engineering and hyperparameter optimization.

A machine learning project that predicts customer churn for a telecommunications company using Random Forest and XGBoost models. It analyzes customer demographics, account details, and service usage data to identify customers at risk of leaving and support proactive retention strategies.

A SAS-based statistical analysis project identifying the key drivers of customer churn for a telecom provider, covering data cleaning, missing value treatment, outlier detection, descriptive statistics, and hypothesis testing.

Python, machine learning, and Power BI project predicting late-delivery risk, identifying key risk drivers, and quantifying sales exposure.

Business-oriented SQL patterns for KPI analytics, customer behavior modeling, anomaly detection, and decision-support workflows.

End-to-end Power BI project analyzing telecom customer churn, customer retention, tenure, revenue, and contract behavior with actionable business insights.

SQL and Power BI project analyzing telecom customer churn, revenue risk, churn drivers, and high-risk customer segments.

Evaluación de KPIs y rendimiento operativo para identificar áreas de mejora en servicios de telecomunicaciones.

End-to-End Customer Retention & Revenue Intelligence Project using Python, SQL, and Power BI. Includes churn analysis, revenue risk assessment, customer loyalty analytics, KPI framework, and an interactive business intelligence dashboard.

Telecom Tariff Analysis | Analyzing customer behavior, revenue, and plan profitability using Python, statistical hypothesis testing, and data visualization.

The project demonstrates an end-to-end analytics workflow that converts raw customer data into a decision-ready BI solution. It also highlights the importance of validating dashboard calculations and relationships rather than assuming that technically successful transformations automatically produce accurate analytical results.

Interactive Excel dashboard and end-to-end data analysis identifying telecom churn drivers, customer lifetime value, and retention strategies.

A full data analytics case study that identifies why telecom customers churn, predicts future churn with machine learning, and visualizes actionable business insights in Power BI dashboards.

Enterprise-grade Telecom Customer Churn Prediction system blending advanced machine learning (XGBoost), real-time Flask API deployment, and interactive Streamlit dashboards to enable data-driven customer retention strategies.