Prediction of Poisonous Mushrooms

This is a machine learning project that uses Random Forest Classification to determine whether a mushroom is poisonous or edible. The project showcases my skills in data preprocessing, feature selection, and modeling, as well as the interpretability of Random Forest through feature importance analysis.

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Features

• Data Preprocessing: Cleaning and preparing the dataset for machine learning.
• Feature Selection: Identifying the most important features for classification.
• Random Forest Model: Building and training a Random Forest classifier.
• Interpretability: Analyzing feature importance to understand the model's decision-making process.

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Technology Stack

• Python – Core programming language.
• Scikit-learn – Machine learning library for building and evaluating models.
• Pandas – Data manipulation and preprocessing.
• NumPy – Numerical computations and data transformations.
• Matplotlib – Data visualization.