Classification of Drug Types using Decision Tree Algorithm

Abstract

The accurate classification of drugs plays a crucial role in various areas of pharmaceutical research and development. In recent years, machine learning techniques have emerged as powerful tools for drug classification tasks. This paper presents a study on drug classification using machine learning techniques implemented in Python. The objective of this research is to explore the effectiveness of different machine learning algorithms in accurately classifying drugs based on their molecular properties and characteristics. The dataset used in this study consists of a diverse collection of drug compounds with annotated class labels. Several popular machine learning algorithms, including decision trees are implemented and evaluated using Python's extensive libraries such as scikit-learn. The dataset is pre-processed to handle missing values, normalize features, and reduce dimensionality using appropriate techniques. Experimental results demonstrate the performance of each algorithm in terms of accuracy, precision, recall, and F1-score. The findings of this study highlight the potential of machine learning techniques in accurately classifying drugs and provide valuable insights into the selection and optimization of algorithms for drug classification tasks. The Python implementation serves as a practical guide for researchers and practitioners interested in applying machine learning for drug classification purposes.