Trina Mukherjee
Computer Science student, AI/ML enthusiast, and full-stack d
OmniSales — AI-Powered Customer Relationship Management (CRM) and Sales Analytics Platform
OmniSales is an intelligent CRM and sales analytics platform designed to help businesses understand customer behavior, monitor sales performance, and make data-driven decisions. It transforms retail transaction data into actionable business insights through interactive dashboards, predictive analytics, and machine learning.
Built using Python, Streamlit, Pandas, NumPy, and Scikit-learn, the platform leverages K-Means Clustering for customer segmentation and Decision Tree models for purchase prediction. It features dynamic visualizations of revenue, sales trends, and customer purchasing patterns, enabling businesses to identify valuable customer segments and uncover growth opportunities.
By combining machine learning, business intelligence, and interactive data visualization, OmniSales simplifies complex sales data analysis, supports personalized marketing strategies, and helps businesses improve customer engagement and overall decision-making.
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Bank Fraud Detection System — AI-Powered Financial Fraud Detection Platform
The Bank Fraud Detection System is an AI-powered machine learning platform designed to identify potentially fraudulent financial transactions and enhance banking security through intelligent risk analysis. It leverages advanced machine learning algorithms, including XGBoost and CatBoost, along with SMOTE-ENN for handling imbalanced transaction datasets, achieving approximately 98.6% accuracy and 98.9% ROC-AUC in fraud detection.
Built using Python, Flask, Scikit-learn, MySQL, HTML, CSS, and Chart.js, the platform features a secure web application with Role-Based Access Control (RBAC), JWT authentication, and audit logging to ensure controlled access and transaction traceability. It integrates an optimized REST API with approximately 85 ms response latency, enabling fast fraud predictions and risk assessment.
The system combines predictive analytics, secure backend architecture, and interactive data visualizations to support efficient fraud identification, improve financial risk monitoring, and demonstrate the practical application of AI in banking security.
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Style Genie — AI-Powered Personal Fashion and Styling Assistant
Style Genie is an AI-powered fashion assistant designed to transform the online shopping and personal styling experience through intelligent, personalized recommendations. The platform combines artificial intelligence, computer vision, and modern web technologies to help users explore fashion, visualize outfits, and discover styles that suit their preferences.
Built using React.js, Next.js, Python, Flask, Node.js, and Tailwind CSS, Style Genie features AI-powered virtual try-on, personalized color analysis, and an intelligent styling assistant that provides context-aware fashion guidance. It also includes a scalable product catalog, interactive lookbook, and wishlist for a seamless fashion discovery experience.
By integrating OpenAI, Google Gemini, Hugging Face, and FAISS, Style Genie brings together AI-driven personalization and an intuitive user interface to make fashion exploration more interactive, accessible, and engaging.
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AlphaSignal — AI-Powered Real-Time Stock Market Intelligence Platform
AlphaSignal is an AI-powered trading intelligence platform designed to help investors make informed decisions through real-time market insights. It aggregates financial news, social media discussions, and live market data to analyze market sentiment and generate actionable BUY, SELL, and HOLD signals for US and Indian stocks.
Built using React.js, FastAPI, Apache Kafka, Redis, and PostgreSQL, the platform features a scalable, low-latency data pipeline for real-time information processing. It leverages FinBERT and VADER for NLP-based sentiment analysis and provides an interactive dashboard with dynamic visualizations, live signal updates, and market intelligence insights.
AlphaSignal combines artificial intelligence, financial analytics, and modern full-stack development to make complex market information more accessible and actionable.
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