Ranjana Rajendran

Tech Stack Advisor is a production-ready web application that leverages cutting-edge AI technology to help developers and architects make informed decisions about their technology stack. The system uses five specialized AI agents working in concert to analyze project requirements and deliver comprehensive recommendations covering conversations, databases, infrastructure, cost optimization, and security. With intelligent multi-turn conversations, long-term memory powered by Qdrant vector search, and semantic query history, the system provides personalized, context-aware recommendations.

When deploying machine learning models in production, choosing the right API protocol can significantly impact performance, scalability, and developer experience. This project aims to provide data-driven insights to help teams make informed decisions.

This project develops an automated system for analyzing collective emotions in group photographs by detecting individual faces, recognizing their emotions, and aggregating them to determine the overall group mood. Built on Google Vertex AI infrastructure for scalable processing and model deployment, the system has practical applications in assessing employee satisfaction at corporate events, measuring student engagement in educational settings, evaluating attendee sentiment at conferences, and understanding collective well-being in social gatherings. Using advanced computer vision and quality-weighted aggregation, this project provides insights into group dynamics without requiring subjective human labeling.

This project develops an intelligent system to automatically detect spoilers in movie reviews using state-of-the-art natural language processing and deep learning techniques. By analyzing review text alongside movie plot information, the system can alert readers before they encounter spoilers, enhancing the user experience on movie review platforms and discussion forums. The project compares multiple deep learning architectures, including LSTM, BERT, and Longformer, to achieve optimal spoiler-detection performance.

This project addresses the critical challenge of predicting volcanic eruptions using time-series sensor data. The system predicts two key metrics: the tilt-erupt (pressure) value at eruption time and the time remaining until eruption occurs. Using advanced feature extraction from temporal patterns and multiple regression models, this project demonstrates how machine learning can contribute to volcanic hazard assessment and early warning systems.

This project explores the structure of Facebook's social network by identifying communities and clusters of connected users. Using graph theory and machine learning, the analysis compares multiple clustering algorithms and community-detection methods to uncover meaningful patterns in how users form groups and interact within the network. The project demonstrates both traditional graph-based community detection and modern machine learning clustering approaches on real-world social network data.