Drug Classification Using Machine Learning

This project implements a machine learning approach to classify drugs based on biomarkers related to epileptic seizures. The model utilizes a Support Vector Classifier (SVC) and employs various visualization techniques to interpret the results.

Table of Contents

• Features
• Technologies Used
• Installation
• Usage
• Model Evaluation
• SHAP Analysis
• Contributing
• License

Features

• Classification of drugs based on biomarkers using machine learning.
• Visualization of model performance using confusion matrix.
• Interpretation of model predictions using SHAP values.
• Comprehensive data analysis and visualization using Matplotlib and Seaborn.

Technologies Used

• Python: Programming language used for the implementation.
• Pandas: For data manipulation and analysis.
• NumPy: For numerical operations.
• Scikit-learn: For machine learning algorithms, including SVC and confusion matrix.
• Matplotlib: For creating static, animated, and interactive visualizations.
• Seaborn: For statistical data visualization.
• SHAP: For interpreting machine learning model predictions.

Installation

To set up the project locally, follow these steps:

1. Clone the repository:

   git clone https://github.com/yourusername/drug-classification.git
   cd drug-classification