🏥 Smart Pharmacy: Sales Intelligence & Supply Chain Optimization

Predicting the Future of Healthcare Retail with Machine Learning

📝 Project Overview

This project transforms raw Point-of-Sale (POS) transaction data into a strategic Demand Forecasting System. By analyzing 6 years of pharmaceutical sales (2014-2019), we developed a model capable of predicting future demand for 8 critical drug categories.

🎯 Business Impact

• Waste Reduction: Minimizes financial loss due to expired medications.
• Stock-out Prevention: Ensures life-saving drugs are always available for patients.
• Sales Intelligence: Provides data-driven insights for better marketing and procurement strategies.

🛠️ Key Features

• Advanced Feature Engineering: Creating "AI Memory" from historical sales trends.
• Time-Series Analysis: Analyzing 600,000+ transactions over a 6-year period.
• Multi-Category Forecasting: Specialized predictions for various drug groups.

💻 Tech Stack

• Language: Python
• Libraries: Pandas, NumPy, Scikit-Learn, Matplotlib, Seaborn

📊 How to Use

1. Clone this repository.
2. Install dependencies: pip install pandas scikit-learn matplotlib seaborn.
3. Open smartpharma-ai-demand-forecaster.ipynb in Jupyter Notebook or Google Colab.