ML Model Explorer

Overview

ML Model Explorer is an interactive web application built with Streamlit that allows users to experiment with different machine learning classifiers and understand their performance characteristics. The app provides a user-friendly interface for exploring popular datasets, testing various classification algorithms, and visualizing their results through multiple performance metrics.

Try it out live at ml-model-explorer.streamlit.app

Features

Dataset Selection

• Choose from classic machine learning datasets:
  • Iris Dataset
  • Breast Cancer Dataset
  • Wine Dataset

Supported Classifiers

• Logistic Regression
• K-Nearest Neighbors (KNN)
• Support Vector Machines (SVM)
• Decision Trees
• Random Forest
• Gradient Boosting
• Naive Bayes

Interactive Model Tuning

• Real-time hyperparameter adjustment via intuitive sliders
• Classifier-specific parameter controls:
  • Logistic Regression: C parameter
  • KNN: number of neighbors (K)
  • SVM: C parameter
  • Decision Tree: maximum depth
  • Random Forest: number of estimators and maximum depth
  • Gradient Boosting: number of estimators and maximum depth

Performance Analytics

• Comprehensive model evaluation metrics:
  • Accuracy Score
  • Precision Score
  • Recall Score
  • F1 Score
• Visual performance analysis:
  • Interactive Confusion Matrix
  • Detailed Classification Report
  • ROC Curve (for binary classification)

Quick Start

Using the Live App

Visit ml-model-explorer.streamlit.app to try the application instantly in your browser.

Running Locally

1. Clone the repository:

git clone https://github.com/yourusername/ml-model-explorer.git
cd ml-model-explorer

2. Install required dependencies:

pip install -r requirements.txt

3. Launch the application:

streamlit run src/main.py

Dependencies

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

Usage

1. Select a dataset from the sidebar dropdown menu
2. Choose a classifier type
3. Adjust the hyperparameters using the interactive sliders
4. Click the "Predict" button to see the results
5. Explore the various performance metrics and visualizations

Contributing

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

License

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

Acknowledgments

• Built with Streamlit
• Uses scikit-learn's built-in datasets and classifiers
• Visualization powered by matplotlib and seaborn