Mushroom Classification Using Decision Trees

The goal of this project is to develop a machine learning model that can accurately classify mushrooms into edible vs poisonous based on their features. The model is built using Decision Tree classifier.

Dataset

• Source: The dataset used is sourced from UCI Machine Learning Repository
• Description: The dataset contains details about mushroom characteristics, such as cap shape, odor, gill color, etc.
• Target Variable: class - Whether the mushroom is edible (e) or poisonous (p).

Features

The dataset includes the following key features:

• Cap Shape: bell, conical, convex, flat, etc.
• Odor: almond, anise, foul, none, etc.
• Gill Spacing: close, crowded, distant.
• Stalk Shape: enlarging,tapering
• Other Features: Cap surface, stalk root, population, habitat, etc.

Requirements

The following Python libraries are required to run the notebook:

• pandas
• numpy
• matplotlib
• seaborn
• scikit-learn

Steps in the Notebook

1. Data Loading:

   • Load the dataset and inspect the structure.

2. Exploratory Data Analysis (EDA):

   • Check for missing values (if any) and perform feature selection.

3. Data Preprocessing:

   • Encode categorical variables using one-hot encoding.
   • Split the data into training and testing sets.

4. Model Building:

   • Train a Decision Tree Classifier on the training set using Decision Tree, Random Forest and XGBoost.
   • Visualize the accuracy score for a range of various hyperparameters like min_samples_split, max_depth, n_estimators.

5. Model Evaluation:

   • Evaluate the model's accuracy on the test set.

License

This project is licensed under the MIT License. See the LICENSE file for details.