Data Analysis Projects in PakistanData Analysis 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. LakeShield - AI-Powered Video Monitoring and Vessel Intelligence Platform
I led the development of LakeShield as the Senior AI/ML Engineer and Lead Developer, taking the platform from initial research and experimentation to a scalable production system.
My responsibilities included:
🔹 Designing the end-to-end AI and video-processing architecture
🔹 Building YOLO-based boat and vehicle detection pipelines
🔹 Developing object tracking and movement-analysis workflows
🔹 Implementing OCR for extracting boat registration information
🔹 Creating scalable pipelines for processing thousands of surveillance videos
🔹 Developing FastAPI backend services and automated data workflows
🔹 Building a Next.js analytics dashboard integrated with Supabase
🔹 Deploying and operating the AI pipeline on cloud GPU infrastructure
🔹 Optimizing model accuracy, inference speed, infrastructure costs, and reliability
🔹 Managing production monitoring, troubleshooting, maintenance, and continuous improvements
The platform transforms raw surveillance footage into structured operational insights, enabling automated vessel monitoring, vehicle activity analysis, registration extraction, and reporting.
This project involved complete technical ownership across Computer Vision, AI/ML, backend development, cloud infrastructure, data engineering, MLOps, and production operations.
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