AI Health Prediction Web App
Budget: ₹12,500 – ₹37,500 INR
I am building a web-based system that lets ordinary users input a few clinical values and instantly see risk predictions for Diabetes, Heart Disease, Parkinson’s, Liver Disorder, Kidney Disorder, several common Cancers, and Thyroid conditions. The core must run on Python with either Flask or Django and use scikit-learn models—specifically Logistic Regression and Random Forest—trained on reputable data sets that you will document.
For the interface, stick to Bootstrap so the layout is both desktop- and mobile-friendly. I want a light/dark toggle, clean forms for each disease, and a dashboard that shows results history plus simple charts and analytic summaries. Standard email-and-password authentication is enough; an admin account should be able to review all stored predictions and manage user records.
Please wire the app to SQLite for local development, but keep the ORM layer flexible so it can shift to MySQL in production. Code quality matters: structure the project cleanly, comment the training scripts, and separate the machine-learning logic from the views and routing.
Deliverables must include:
• Full source code with virtual-environment or requirements.txt
• Clear documentation covering model training, setup, and deployment steps
• Help getting the app running on a fresh Linux VPS (gunicorn + Nginx is fine)
If something in this brief is unclear, flag it early so we can keep the timeline tight.
For the interface, stick to Bootstrap so the layout is both desktop- and mobile-friendly. I want a light/dark toggle, clean forms for each disease, and a dashboard that shows results history plus simple charts and analytic summaries. Standard email-and-password authentication is enough; an admin account should be able to review all stored predictions and manage user records.
Please wire the app to SQLite for local development, but keep the ORM layer flexible so it can shift to MySQL in production. Code quality matters: structure the project cleanly, comment the training scripts, and separate the machine-learning logic from the views and routing.
Deliverables must include:
• Full source code with virtual-environment or requirements.txt
• Clear documentation covering model training, setup, and deployment steps
• Help getting the app running on a fresh Linux VPS (gunicorn + Nginx is fine)
If something in this brief is unclear, flag it early so we can keep the timeline tight.