This repository contains a classification project based on the UCI Secondary Mushroom dataset. The project focuses on accurately classifying mushrooms as either poisonous or edible based on their physical characteristics. By leveraging machine learning techniques, we developed a classification model to aid in identifying the toxicity of mushrooms, thereby ensuring public safety.
The dataset consists of various features such as cap diameter, cap shape, cap color, gill attachment, stem height, and habitat, among others. Through extensive analysis and feature selection, we trained and evaluated multiple classification algorithms, including decision trees, random forests, support vector machines (SVM), logistic regression, and naive Bayes.
The project utilizes popular Python libraries such as scikit-learn and pandas for data preprocessing, model training, and evaluation. The code implementation, along with comprehensive documentation, is provided in this repository to facilitate easy replication and understanding of the classification process.
Our goal is to provide an accurate and efficient classification model that enables the identification of poisonous and edible mushrooms, thereby promoting public safety and preventing mushroom-related health hazards. The findings and insights gained from this project contribute to the field of mushroom classification and have implications for mushroom enthusiasts, collectors, and mycologists.
Feel free to explore the code, documentation, and experiment with the dataset to gain a deeper understanding of the classification process and its implications for mushroom identification.