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.

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

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.

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

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

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.

A machine learning capstone project predicting telecom customer churn for BSNL. Features an end-to-end data pipeline, Decision Tree modeling, and an interactive React/Flask dashboard.

📊 Customer Segmentation & Churn Analysis project completed as part of a Business Analyst Internship at Saiket Systems.

Machine learning project for predicting telecom customer churn using exploratory data analysis, feature engineering, Logistic Regression, and Random Forest classification.

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.

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.