A machine learning application designed to predict mushroom edibility based on morphological features. This project demonstrates a complete binary classification pipeline using the Scikit-learn library.
- Feature Engineering: Implemented One-Hot Encoding to transform categorical environmental data into a machine-readable numerical format.
- Model Selection: Utilized a DecisionTreeClassifier to create an interpretable logic path for classification, prioritizing safety-critical accuracy.
- Data Preprocessing: Managed a complex dataset with missing values, ensuring a balanced distribution to prevent model bias.
- Language: Python
- Libraries: Scikit-learn, Pandas, NumPy