Challenge Telecom X - análisis de datos

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

End-to-End Customer Churn Prediction using Machine Learning

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.

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

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 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.

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

Machine learning project to predict customer churn and support retention strategy using threshold tuning, profit simulation, and model interpretation.

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.

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.

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

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

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

Telecom Customer Churn Analytics project using SQL Server and Power BI to analyze churn drivers, customer risk, and revenue impact.

Predictive churn model using Python & ML to identify high-risk telecom customers (~49%) and key churn drivers, enabling targeted retention strategies and revenue protection.

This project uses real-world telecom customer data to predict churn behavior using machine learning. It includes data cleaning, exploratory data analysis (EDA), feature engineering, model training (Logistic Regression and Random Forest), and strategic business recommendations. The final model is ready for deployment in customer retention systems.

This project leverages Python (Pandas, Prophet, Matplotlib) to track segment-wise performance, calculate kebele-level market penetration rates, and deliver a 12-month forecast. Provides actionable geographic and segment-specific insights for resource allocation and and strategic planning.