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

📡 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.

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

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

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 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.

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.

An end-to-end telecom data analytics project processing 17M+ records using SQL, DuckDB, Python, and Power BI to analyze CRM, device, and revenue insights.

Customer churn and retention analysis using Power BI. Identifies churn patterns, high-risk segments, and actionable strategies to improve customer retention.

Random Forest Classifier for Customer Churn Prediction