Inspiration
Pharmacies face challenges such as medicine shortages, overstocking, and expiry-related wastage. We wanted to build a smart solution to improve inventory management and ensure better medicine availability.
What it does
PharmaFlow manages medicine inventory and predicts future medicine demand using machine learning. It helps pharmacies monitor stock levels, plan restocking, reduce wastage, and make data-driven decisions.
How we built it
We developed PharmaFlow using Python and Flask for the backend, MySQL for database management, and Linear Regression for demand prediction. We created a user-friendly web interface for managing inventory and viewing relevant information.
Challenges we ran into
Integrating the machine learning model with the web application, managing inventory data accurately, connecting the database, and ensuring reliable demand predictions were our major challenges.
Accomplishments that we're proud of
We successfully developed a web-based pharmacy inventory system that integrates machine learning-based demand prediction with inventory management, providing a practical solution to real-world pharmacy challenges.
What we learned
We gained practical experience in Python, Flask, MySQL, machine learning, database integration, and web development. We also learned how predictive analytics can support smarter inventory decisions.
What's next for PharmaFlow Smart Pharmacy Inventory & Demand Prediction System
We plan to improve prediction accuracy, add expiry-date alerts, implement automated low-stock notifications, and develop intelligent restocking recommendations to make PharmaFlow more efficient and scalable.
Built With
- backend
- frontend
- fullstack
- sql
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