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 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.

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.

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.

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.

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

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

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.

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

A web-based machine learning app built with Python Flask and Random Forest that predicts whether a telecom customer is likely to churn, showing both prediction and confidence. Perfect for exploring feature engineering, ML deployment, and business analytics.

Developing a machine learning model to analyze subscriber behavior and recommend one of Megaline's newer plans (Smart or Ultra) with at least 75% accuracy.