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Overview:

This project focuses on classifying poisonous mushrooms based on their features using a Decision Tree model. The goal is to predict whether a mushroom is poisonous or not by analyzing specific characteristics, such as color, shape, and texture. The model’s performance is validated using the AUC (Area Under the Curve) metric to ensure accurate classification.

Screenshots

Key Features:

1. Random Forest Model:

1. Decision Tree Model:

The Decision Tree is employed for classifying mushrooms based on their characteristics. The model is easy to interpret, making it useful for understanding which features most contribute to identifying poisonous mushrooms.

2. Feature Extraction:

The model analyzes critical features like cap color, gill size, spore print color, and habitat to differentiate between poisonous and non-poisonous mushrooms.

3. AUC Validation:

The model’s performance is evaluated using the Area Under the Curve (AUC), which measures its ability to correctly classify poisonous mushrooms.

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Links:

• GitHub