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https://github.com/iama02/vet-medicine-analysis

Analysis of 50,000+ veterinary medicine records with Pandas and NumPy, highlighting dosage forms, manufacturers, indications, and shelf life trends.
https://github.com/iama02/vet-medicine-analysis

numpy pandas python

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Analysis of 50,000+ veterinary medicine records with Pandas and NumPy, highlighting dosage forms, manufacturers, indications, and shelf life trends.

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README

          

🐾 Veterinary Medicines Data Analysis

This repository presents an exploratory data analysis (EDA) of a comprehensive veterinary medicines dataset (50,000+ records) using Python (Pandas & NumPy).

The project demonstrates skills in data cleaning, feature engineering, and preparation for advanced visualization, making it an excellent resource for both data enthusiasts and professionals in the veterinary/healthcare domain.

🚀 Key Analyses

🔬 Data Cleaning & Transformation – handled missing values, standardized formats

💊 Summarization – by dosage form, manufacturer, and indication

🗂️ Feature Engineering – extracted numeric strength (mg) and calculated shelf life (days)

📊 Data Preparation – structured dataset for visualization and trend analysis

📁 Dataset Features

Name – Medicine name

Category – Type of medicine (e.g., antibiotic, antiviral)

Dosage Form – Cream, injection, ointment, etc.

Strength – Dosage strength (mg)

Manufacturer – Company producing the medicine

Indication – Medical use case (infection, wound, etc.)

Classification – Prescription or over-the-counter (OTC)

Manufactured Date – Date of production

Expiry Date – Expiration date

🛠️ Getting Started
1️⃣ Clone the repository
git clone https://github.com/iama02/vet-medicine-analysis.git

2️⃣ Open the notebook

Launch vet_med.ipynb in JupyterLab or VS Code

3️⃣ Install dependencies
pip install pandas numpy

🔮 Next Steps

📊 Add visualizations using Matplotlib & Seaborn

📈 Explore deeper expiry trends, strength variations, and manufacturer distribution

🧠 Extend analysis with predictive modeling (expiry prediction, demand forecasting)

📄 License

This project is licensed under the MIT License.