Projects using Dash PlotlyProjects using Dash Plotly
Projects
People
Cover image for Saving Lives through Early Clinical
Saving Lives through Early Clinical Risk Detection Application is live at: https://495a-35-237-151-197.ngrok-free.app The Problem: Heart failure is a leading cause of global mortality; the difference between survival and fatality often depends on how early a high-risk patient is identified. The Solution: I developed a clinical diagnostic tool that analyzes patient health markers to forecast mortality risk, assisting medical professionals in prioritizing life-saving care. Key Impacts: Early Intervention: The model identified that follow-up time is the single most critical factor in reducing fatalities, emphasizing the need for early diagnosis and consistent monitoring. Precision Diagnostics: By analyzing heart efficiency (ejection fraction) and chemical markers like serum creatinine, the tool provides a high-accuracy (84.49%) risk score for every patient. Clinical Support: The system helps doctors look past "statistical flukes" by accounting for outliers in medical data, ensuring that extreme clinical cases are caught rather than ignored. Actionable Health Insights: Demonstrated a clear link between age, heart efficiency, and chemical abundance, giving providers a data-driven framework to improve long-term patient outcomes
1
3
772
Cover image for Everyone's talking about quantum computing.
Everyone's talking about quantum computing. Nobody's using it to feed farmers. India loses 20–30% of its crop yield every year to diseases and pests. Not because farmers don't care — but because early detection is hard, expensive, and inaccessible to the people who need it most. The existing solutions? Either a basic image classifier trained on lab-perfect photos that fail in real field conditions, or an agronomist visit that costs time and money most small farmers don't have. So I built QuantumEdge AgriGuard — a hybrid Quantum Neural Network app where a farmer can photograph a diseased leaf on their phone and get an instant diagnosis in under 5 seconds. Here's what makes it different from just another plant disease detector: Instead of a pure classical CNN, I built a hybrid architecture — a ResNet/EfficientNet backbone extracts visual features, then passes them into a Variational Quantum Circuit (VQC) for the final classification. The quantum layer uses angle embedding + StronglyEntanglingLayers, which gives it a measurable edge on small, noisy datasets — exactly the kind of data you get from Indian field conditions. The app doesn't just tell you what disease it is. It gives you: → Confidence score → Organic + chemical remedies (India-specific) → Yield impact estimate → A live classical vs quantum accuracy comparison so you can see the difference yourself I tested the quantum advantage claim honestly — ran both models on the same downsampled PlantVillage dataset and tracked accuracy, F1-score, and inference time side by side. The results are on the dashboard. No hand-waving. Built with PennyLane + PyTorch + Plotly Dash. Designed to run on simulators today and on QpiAI-Indus 25-qubit hardware tomorrow.
1
0
19
Cover image for Smart Data Analyst — AI-Powered
Smart Data Analyst — AI-Powered Data Analysis App Built a fully working AI-powered data analysis app from scratch using Streamlit and Gemini API. The problem: Most business owners have data in Excel or CSV files but no easy way to analyze it without hiring a full-time analyst or learning Python. So I built a tool where you just upload your file — and the app does the rest. → Auto-detects KPIs from any dataset → Generates charts automatically → Ask questions in plain English — get instant answers with tables and charts → Downloads full PDF report with KPIs and charts → RAG memory — remembers past questions Tech: Streamlit · Gemini API · Python · Pandas · Plotly 🔗 Live: https://smart-data-analyst-ahkz32vjd6dzvhmexkrhdm.streamlit.app/ (https://smart-data-analyst-ahkz32vjd6dzvhmexkrhdm.streamlit.app/)⭐ GitHub: https://github.com/AbuAsifAnsari/smart-data-analyst (https://github.com/AbuAsifAnsari/smart-data-analyst)
0
42