Comprehensive SQL-based analytics solution for RFQ (Request for Quote) and Quote conversion analysis. Includes sales performance reports, price competitiveness analysis, conversion rate calculations, and vendor response time metrics. Features multiple SQL queries for dashboard visualization and business intelligence.

Interactive dashboard for vendor and buyer analytics with comprehensive data visualization. Built using Python and Jupyter notebooks, featuring exploratory data analysis, Matomo analytics integration, and RFQ data processing. Provides insights into vendor performance and buyer behavior patterns.

Data-driven customer retention analysis project focusing on identifying churn patterns, retention strategies, and customer lifetime value. Utilizes machine learning techniques to predict customer behavior and provide actionable insights for improving retention rates.

Advanced conversational AI system for e-commerce powered by knowledge graphs. Enables intelligent product recommendations, natural language queries, and context-aware customer interactions. Combines graph neural networks with NLP for enhanced user experience.

Machine learning project for customer segmentation using clustering algorithms and unsupervised learning techniques. Identifies distinct customer groups based on behavior, preferences, and purchasing patterns to enable targeted marketing strategies and personalized experiences.

Comprehensive exploratory data analysis (EDA) project with recommendation systems and forecasting capabilities. Includes data preprocessing, feature engineering, recommendation algorithms, and time series forecasting models for business intelligence and decision support.

Machine learning system that detects diagrams in exam questions, extracts text and mathematical expressions, and converts them into clean, searchable MathJax format. Features Streamlit UI, FastAPI backend, and optional Azure OpenAI integration for educational content processing.

Automated web scraping system for extracting and organizing question banks from educational websites. Implements efficient data collection, parsing, and storage mechanisms for building comprehensive question databases for educational platforms.

Fine-tuned Segment Anything Model (SAM) for semantic segmentation tasks. Leverages Meta's pretrained SAM architecture with custom fine-tuning techniques to achieve high-precision pixel-level segmentation across various domains and datasets.

Augmented Reality tutoring system powered by Whisper speech recognition and Large Language Models. Provides interactive, voice-guided learning experiences with real-time AR visualization, enabling immersive educational interactions through natural language conversations.