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

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

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.

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.

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

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

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.

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

Strategic Intelligence Agent (SIA) is an autonomous multi-agent framework powered by LangGraph and Llama-3.3-70B. It proactively identifies at-risk telecom subscribers and deploys hyper-personalized retention protocols to mitigate churn and protect recurring revenue.

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.

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

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

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.

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

Machine learning-based customer churn prediction system to identify high-risk telecom customers and enable data-driven retention strategies.

Power BI dashboard analyzing WaveCon’s post-5G performance—revenue impact, churn signals, city trends, plan-wise performance, and strategic recommendations.

Analyzing telecom customer churn using Python, SQL, and Power BI. Interactive dashboard and charts showcase insights to reduce churn.

this project focuses on predicting telecom customer churn using supervised machine learning models. by analyzing historical data such as contract type, internet service usage, and billing method, we aim to identify customers who are at risk of leaving the company.

SQL + Excel project analyzing telecom customer churn behavior and identifying high-risk customer segments.

Telco Customer Churn is an end-to-end data science project that models customer churn and evaluates decisions using cost-aware metrics, uplift analysis, and business-driven profit optimization.