Just a chill guy...

github

Arittra Bag • Peerlist

High-performance quantitative trading simulator leveraging real-time L2 orderbook data from OKX via WebSocket, with advanced models for slippage, market impact, and fee estimation. Integrated AI market sentiment analysis using Google's Gemini. Built with Python, Dash, and Plotly, featuring latency metrics, backtesting support, and modular architecture.

Quant Trade Simulator

Dementia Dreamscape is a modern web application designed to assist in the early detection of Alzheimer's disease and related dementia conditions using brain MRI analysis. It features a responsive, AI-powered interface that provides real-time classification, confidence scoring, and AI-generated insights. Built using React, TypeScript, and TailwindCSS on the frontend, and FastAPI with TensorFlow on the backend, the platform analyzes uploaded MRI scans and returns predictions with confidence scores and recommendations. It integrates Google Gemini for enhanced interpretability and provides educational content to support dementia awareness. This project is intended for educational and research use only and is not a substitute for professional medical diagnosis.

Dementia Dreamscape | AI-Powered MRI Dementia

DataVizX is a web application that allows users to explore and visualize their data in an Interactive manner. With a sleek and modern user interface, DataVizX makes data visualization a breeze, whether you're a data analyst, a business professional, or just someone who loves playing with data.

DataVizX

Track your Ethereum (ETH) investments and balances with this simple web-based cryptocurrency portfolio tracker. Features: Connect Your Wallet: Easily connect your Ethereum wallet (e.g., MetaMask) to view your account details and balances. Add Multiple Accounts: Keep track of investments across multiple Ethereum accounts. Portfolio Summary: See an overview of your account balances in Ether (ETH). PWA Support: Install the tracker on your device for quick access. Send Cryptocurrency: Send Ether (ETH) to other addresses directly from the tracker.

Cryptocurrency Portfolio Tracker

NeuraScan is a full-stack AI-driven web application designed to classify brain tumors from MRI scans in real time. The model is trained using EfficientNet and implemented with PyTorch, enabling accurate multi-class classification across tumor types such as glioma, meningioma, pituitary, and no tumor. The application features a FastAPI backend that handles image preprocessing, model inference, and secure API interactions. On the frontend, it uses React 18, TypeScript, and TailwindCSS to deliver a smooth, responsive user interface. Animations and UI elements are enhanced with GSAP, Lucide Icons, and Shadcn UI components. To enrich user experience with interpretability, the system integrates Google Gemini to generate AI-powered medical insights and explainable reports based on classification results. The platform emphasizes: 1. Real-time MRI image classification 2. Confidence scoring for each tumor type 3. AI-generated medical recommendations 4. Educational utility in healthcare research and pre-diagnostic support All data is processed in-memory with no storage, ensuring user privacy. The system is CORS-protected, API key–secured, and production-ready. This project demonstrates expertise in deep learning, medical imaging, model deployment, full-stack development, and real-time AI applications in healthcare.

NeuraScan | AI-Powered Brain Tumor Detection 

Developed a machine learning model using XGBoost to predict LN_IC50 for cancer drug responses with an impressive R² score of 99.41%. Leveraged SHAP for model interpretability, providing clear insights into feature contributions. Deployed the solution using Gradio, enabling real-time predictions through an interactive web interface. This project aims to assist researchers and pharmaceutical companies in evaluating drug efficacy and advancing personalized cancer treatment strategies.

GDSC Drug Sensitivity Predictor

AI-powered cardiovascular disease risk predictor using XGBoost, with SHAP for explainability and Gradio for an interactive web app. Achieved 73.66% accuracy, leveraging key health indicators like blood pressure, cholesterol, and lifestyle factors.

Heart Disease Risk Predictor with XGBoost & S

MindScope is an AI-powered tool that predicts depression and anxiety severity using XGBoost models trained on mental health data. It features a Streamlit-based interface for users to input their details and receive instant predictions, enabling self-assessment and awareness.

MindScope: Self-Analysis Mental Health Model

This project provides an interactive interface to classify MRI scans for Alzheimer's disease and brain tumors using transformer-based SegFormer models (nvidia/mit-b1). Each model is fine-tuned for image classification with four output classes specific to the condition. The models were trained on labeled MRI data using data augmentation techniques to improve generalization and reduce overfitting. Both models achieved an average validation accuracy of approximately 99.5%. The system is deployed using Gradio, enabling users to upload an MRI scan, select the condition type, and receive an instant prediction.

MRI Classifier: Alzheimer’s & Tumor Detection

Disease Predictor

TerraBoost

OpsGraph is an open-source, self-hosted workspace for evidence-first PostgreSQL investigations.Ask a question in natural language and let your configured model propose bounded queries. OpsGraph - not the model - controls what can actually execute: approved schemas and tables, read-only access, query count, row limits and timeouts.Every finding remains connected to the SQL and captured records behind it, so you can inspect the evidence instead of blindly trusting a generated answer.<a target="_blank" rel="noopener noreferrer nofollow ugc" class="break-all link-break text-gray-500 hover:underline" href="https://github.com/Arittra-Bag/opsgraph/releases/tag/v0.1.0b1">Beta v0.1.0b1</a> adds saved and reopenable investigations, linked follow-ups, evidence exports, local Ollama / OpenAI-compatible models, Anthropic support, native macOS/Linux/Windows installation and a multi-architecture Docker image.The scope is intentionally narrow for now: PostgreSQL, one operator, read-only investigations.