PCOS Tracker — AI-Powered Wellness & Cycle Companion for Women's Hormonal Health

PCOS Tracker — The Smart Wellness Companion Built for Women's Health

PCOS Tracker is a complete AI-powered wellness application designed to help women understand, monitor, and manage Polycystic Ovary Syndrome through a combination of intelligent tracking, machine learning risk assessment, and personalized lifestyle insights. Built with Flutter for a beautiful cross-platform mobile experience and Django REST Framework for a powerful backend, this project tackles a genuine real-world problem affecting nearly one in five women globally. It's one of the most meaningful and technically rich AI/ML final year projects a student can present in their final semester.

Unlike generic period tracker apps, PCOS Tracker goes much deeper. It combines cycle data, daily symptoms, mood patterns, exercise routines, nutrition, and medication adherence into a single intelligent dashboard that learns from the user's behavior over time. The integrated ML engine, trained on a real clinical PCOS dataset of 540+ samples and 13 medical features, predicts hormonal risk levels and generates personalized health recommendations, making this not just a tracking app but a genuine decision-support system for women.

Why This Project Stands Out

Most final year projects either focus on technology or domain expertise, but rarely both. PCOS Tracker bridges that gap. It uses Random Forest and Logistic Regression models trained from scratch on real medical data, integrates them into a production-grade Django REST API, and delivers everything through a polished Flutter mobile app with dark mode, charts, and offline-capable predictions. Students working on healthtech, women's wellness, or AI-based diagnosis topics will find this project genuinely impressive to demonstrate in front of evaluators and panel members.

Key Project Features

• Intelligent Cycle Tracking: Log menstrual cycles with flow intensity, view historical patterns, and calculate average cycle length and regularity scores automatically.
• ML-Powered PCOS Risk Assessment: Custom-trained Random Forest and Logistic Regression models analyze 13 clinical features to give a risk score, detailed analysis, and personalized recommendations — all working offline once trained.
• Comprehensive Symptom Logging: Track 10+ PCOS-specific symptoms with severity levels including acne, hair fall, weight fluctuation, fatigue, and mood swings.
• Mood and Mental Health Tracking: Daily mood logging with visual distribution analytics that help users connect emotional patterns to their cycle.
• Lifestyle Management: Exercise duration logging, nutrition tracking with calorie counts, and medication adherence with reminders.
• Auto-Generated Insights: Django Signals trigger real-time symptom trend analysis, mood distribution charts, and cycle regularity reports without manual refresh.
• Community Space: A safe peer-support area where users share stories, awareness, and recovery tips.
• Private Journaling: A secure personal space for daily notes, reflections, and self-observations.
• JWT Authentication: Secure token-based login using SimpleJWT for production-grade user session handling.
• Beautiful Dark Mode UI: A premium, fully responsive Flutter interface with smooth charts powered by fl_chart.

Real-World Applications

• Personal Health Management: Women diagnosed with PCOS can monitor symptoms and progress over months for better self-awareness.
• Pre-Diagnosis Screening: The risk assessment helps women identify whether their symptoms warrant a clinical PCOS workup.
• Clinical Companion Tool: Doctors and gynecologists can use exported user reports for faster consultation insights.
• Research and Awareness Platform: The community feature enables peer-driven awareness on a widely under-discussed condition.
• Academic Demonstration: An ideal showcase project for BCA, MCA, BTech CSE, and BSc IT students who want to present a healthtech AI/ML system.

What You Get When You Purchase This Project

The complete PCOS Tracker package includes full Flutter source code, the Django REST backend, pre-trained ML models, the original PCOS clinical dataset, and a polished UI with dark mode already implemented. Students also receive a college-format project report covering abstract, literature review, methodology, system architecture, and testing chapters. Need help setting it up? Our project setup and source code explanation service walks you through the entire codebase line by line until you are confident defending it in your viva.

If you want to extend or customize this idea further — for example, adding fertility tracking, integration with wearable devices, or a doctor consultation module — our custom project development service can handle that for you. We also publish IEEE-format research papers based on this project, which adds enormous value to your final year submission and placement portfolio.

Who Should Buy This Project

This project is perfectly suited for BCA, MCA, BTech CSE, BSc IT, and MTech students looking for a strong, socially impactful, and technically deep Python final year project. It also fits perfectly for students searching for Flutter app development projects or full-stack mobile + backend submissions. The combination of healthcare, machine learning, mobile development, and real dataset training makes it one of the most evaluator-friendly choices available on the CodeAj Marketplace.

Why CodeAj Marketplace

At CodeAj, every project comes with full source code, a ready-made report, a complete installation guide, and mentorship until submission. From debugging to viva preparation, our team supports you end-to-end. Browse more final year projects with source code on our marketplace and pick the one that fits your domain perfectly.