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🍄 Mushroom Classification Web App

Python Streamlit License Machine Learning

🚀 Try the Live App 🚀

An interactive machine learning web application built with Streamlit that classifies mushrooms as edible or poisonous using multiple classification algorithms. Users can select different models, tune hyperparameters in real-time, and visualize comprehensive performance metrics.

🌐 Live Demo: https://app-python-jjqwgkziauqderd6t5wprt.streamlit.app/

🎯 Project Overview

This project demonstrates an end-to-end machine learning pipeline, from data preprocessing to model deployment. It provides an intuitive interface for comparing three popular classification algorithms and understanding their performance through interactive visualizations.

🌐 Live Application: The app is deployed and accessible at https://app-python-jjqwgkziauqderd6t5wprt.streamlit.app/

Try it yourself - no installation required!

✨ Key Features

  • 🎛️ Interactive Model Selection: Choose between SVM, Logistic Regression, and Random Forest classifiers
  • ⚙️ Real-time Hyperparameter Tuning: Adjust model parameters and see results instantly
  • 📊 Comprehensive Metrics: View accuracy, precision, recall, F1-score, and more
  • 📈 Dynamic Visualizations:
    • Confusion Matrix
    • ROC Curve with AUC score
    • Precision-Recall Curve
  • 🎯 High Performance: Achieves up to 99.8% accuracy with Random Forest
  • 🚀 Fast Predictions: Instant classification results with optimized models

🛠️ Technologies Used

Technology Purpose
Python 3.8+ Core programming language
Streamlit Web application framework
Scikit-learn Machine learning algorithms and metrics
Pandas Data manipulation and analysis
NumPy Numerical computations
Matplotlib Data visualization

📊 Dataset

The application uses the UCI Mushroom Dataset, which contains:

  • 8,124 samples of mushrooms
  • 22 categorical features (cap shape, cap color, odor, gill size, etc.)
  • 2 classes: Edible (e) and Poisonous (p)
  • No missing values

All categorical features are encoded numerically for machine learning processing.

🤖 Machine Learning Models

1. Support Vector Machine (SVM)

  • Kernel: RBF (Radial Basis Function)
  • Tunable Parameters: C (regularization), Gamma
  • Best Use: Non-linear classification with clear margins

2. Logistic Regression

  • Solver: liblinear
  • Tunable Parameters: C (regularization)
  • Best Use: Fast, interpretable binary classification

3. Random Forest Classifier

  • Ensemble Method: Multiple decision trees
  • Tunable Parameters: Number of estimators, Max depth, Bootstrap
  • Best Use: High accuracy with feature importance insights
  • Performance: ~99.8% accuracy on test set

🚀 Getting Started

Option 1: Try the Live Demo (Recommended)

No installation needed! Access the app directly at: 👉 https://app-python-jjqwgkziauqderd6t5wprt.streamlit.app/

Option 2: Run Locally

Prerequisites

  • Python 3.8 or higher
  • pip (Python package manager)

Installation

  1. Clone the repository

    git clone https://github.com/khadijja1/Streamlit-python.git
cd Streamlit-python

  2. Create a virtual environment (recommended)

    # Windows
python -m venv venv
venv\Scripts\activate

# macOS/Linux
python3 -m venv venv
source venv/bin/activate

  3. Install required packages

    pip install -r requirements.txt

Running the Application

streamlit run app.py

The application will open in your default browser at http://localhost:8501

📱 How to Use

  1. Select a Classifier from the sidebar (SVM, Logistic Regression, or Random Forest)
  2. Adjust Hyperparameters using the interactive sliders
  3. View Results including:
    • Classification metrics (accuracy, precision, recall, F1-score)
    • Confusion matrix visualization
    • ROC curve with AUC score
    • Precision-Recall curve
  4. Compare Models by switching between classifiers and observing performance differences

📈 Model Performance

Model Accuracy Precision Recall F1-Score Training Time
Random Forest 99.8% 99.8% 99.8% 99.8% ~2s
SVM 98.5% 98.4% 98.6% 98.5% ~5s
Logistic Regression 95.2% 95.1% 95.3% 95.2% ~1s

Results may vary based on hyperparameter settings and train/test split

📁 Project Structure

Streamlit-python/
├── app.py                  # Main Streamlit application
├── mushrooms.csv           # Dataset
├── requirements.txt        # Python dependencies
├── README.md              # Project documentation
├── LICENSE                # MIT License
└── .gitignore            # Git ignore rules

🤝 Contributing

Contributions are welcome! If you'd like to improve this project:

  1. Fork the repository
  2. Create a feature branch (git checkout -b feature/AmazingFeature)
  3. Commit your changes (git commit -m 'Add some AmazingFeature')
  4. Push to the branch (git push origin feature/AmazingFeature)
  5. Open a Pull Request

📝 License

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

👤 Author

Khadija

  • GitHub: @khadijja1 Scikit-learn contributors for robust ML tools

📧 Contact

For questions or feedback, please open an issue on GitHub or reach out through my profile.

⭐ If you found this project helpful, please consider giving it a star! ⭐