Medicine Demand Prediction System

Leverage advanced machine learning to optimize pharmacy inventory. Predict demand, reduce waste, and ensure medicines are always available when patients need them.

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analytics
Demand Analytics
Medicine demand predictions
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+24%
Efficiency
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Optimized
Stock Levels
About The Project

Why MediPredict?

Revolutionizing pharmacy inventory management with cutting-edge predictive analytics

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The Problem

Most pharmacies lack intelligent systems to classify high-demand medicines, leading to costly operational issues including stockouts, overstocking, and expired inventory. Existing systems don't leverage advanced ML models to categorize medicines into clear demand levels.

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Our Solution

MediPredict implements a secure, easy-to-use prediction platform using XGBoost classification to analyze historical sales data. Our system categorizes medicines into high, medium, or low demand levels, enabling pharmacies to optimize inventory and improve patient care.

Capabilities

Powerful Features

Everything you need to transform your pharmacy inventory management

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CSV Data Upload

Easily upload historical pharmacy sales data in CSV format for seamless processing and comprehensive analysis.

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XGBoost Classification

Advanced machine learning algorithm that accurately classifies medicines into distinct demand levels with high precision.

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Interactive Dashboard

User-friendly interface displaying prediction results with visual charts, graphs, and detailed reports.

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Medicine Search

Quickly find medicines by usage, symptoms, or category with our intelligent search functionality.

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Trend Analysis

Visualize monthly usage patterns and identify seasonal trends in medicine demand over time.

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Secure Access

Role-based authentication ensures secure access to sensitive pharmacy data and predictions.

Our Team

Meet The Developers

BS Information Technology Students at Eastern Visayas State University

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Josua Sapuez

BS Information Technology

Developer & Researcher
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Oliver P. Pomarejos

BS Information Technology

Developer & Researcher
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Rona Mae S. Amistoso

BS Information Technology

Developer & Researcher
Get In Touch

Contact Us

Eastern Visayas State University, Tacloban City

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Eastern Visayas State University
College of Engineering
Department of Information Technology
Tacloban City, Leyte

descriptionProject Details

Capstone Project for
BS Information Technology
Academic Year 2024-2025
Presented: August 2025