Data Modeling Projects in PakistanData Modeling Projects in PakistanEnd-to-End Telco Customer Churn Prediction System
Developed and deployed a production-grade Machine Learning web application designed to predict telecom customer churn and identify key drivers of customer attrition. This project bridges the gap between data science research and real-world deployment by transforming a Jupyter Notebook workflow into an interactive, cloud-hosted web service.
Key Technical Highlights & Workflow:
Exploratory Data Analysis & Preprocessing: Conducted thorough EDA on customer behavioral data, handled missing values, encoded categorical features, and performed feature scaling to ensure robust model performance.
Model Training & Hyperparameter Tuning: Trained multiple machine learning algorithms (including Random Forest, XGBoost, and Logistic Regression) and optimized hyperparameters to achieve high predictive accuracy and generalization.
Model Explainability (SHAP): Integrated SHAP (SHapley Additive exPlanations) values to make complex model predictions transparent, helping stakeholders easily interpret feature impacts (such as tenure, monthly charges, and contract types) on individual predictions.
Backend & API Architecture: Built a high-performance RESTful API using FastAPI to handle real-time inference requests seamlessly, coupled with a clean, responsive frontend user interface.
Cloud Deployment & DevOps: Successfully packaged the application and trained model (.pkl), navigated serverless deployment constraints by leveraging container-based cloud infrastructure (Render), and ensured continuous availability.
Tech Stack:
Python, Scikit-Learn, XGBoost, Pandas, NumPy, FastAPI, Uvicorn, SHAP, HTML/CSS, Git, Render. Twin Frontend (Lyona App) is a dual-platform conversational AI application designed to power both web and mobile experiences from a single shared codebase. Built with a modern monorepo architecture, the platform enables real-time AI conversations, voice interactions, and dynamic interface rendering, allowing businesses to deploy scalable conversational products quickly. The system addresses the challenge of building and maintaining separate applications by unifying business logic, UI behavior, and communication layers across platforms, reducing development time while ensuring consistent user experiences.
The platform is built for organizations developing AI assistants, customer support tools, SaaS dashboards, or conversational applications that require real-time responsiveness and cross-device synchronization. Users can interact with AI through text or voice, while the interface dynamically adapts based on backend-driven UI configurations. With enterprise-grade performance, secure authentication, and real-time updates, the application supports scalable deployments and continuous feature expansion without rebuilding separate frontends.
The application includes real-time AI chat with streaming responses, voice recording and playback for conversational interaction, dynamic UI rendering with dashboards, forms, tables, charts, and interactive components, cross-platform synchronization between web and mobile, push and in-app notifications, secure authentication and session handling, role-based access control, performance optimization with lazy loading and memoization, automatic reconnection and error handling, and shared state management ensuring consistent behavior across devices.
Built using React, React Native, TypeScript, Tailwind CSS, Ant Design, Material UI, Redux Toolkit, Socket.IO (http://Socket.IO), Docker, Kubernetes, and modern CI/CD infrastructure.
A scalable conversational AI foundation designed to accelerate development of real-time, cross-platform AI applications for enterprise and SaaS environments.
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