Shrooming Prediction and Analysis using Machine Learning Algorithms

In this study, we address the challenges posed by the mathematical complexity and computational requirements of deep learning in interdisciplinary research. Instead of relying on a traditional dataset, we adopt a pragmatic approach by implementing a Python RandomForestClassifier, a versatile machine learning algorithm known for its simplicity and effectiveness. We enhance its performance by incorporating Principal Component Analysis and Dimensionality Reduction techniques. Our goal is to classify mushrooms as either edible or poisonous, leveraging attributes from 23 mushroom species. Through this approach, we contribute to the advancement of AI and machine learning knowledge, specifically in the context of practical mushroom classification. Our findings underscore the practicality of the RandomForestClassifier, offering a feasible and accurate alternative to traditional deep learning methods for interdisciplinary researchers.