🚀 ML Explorer

A Streamlit app to explore classification and anomaly-detection pipelines on synthetic datasets! 🧪

🔍 Features

• Synthetic Data Generators:

  • make_classification, make_moons, make_circles, make_blobs, make_gaussian_quantiles

• Feature Selection: VarianceThreshold, SelectKBest (ANOVA F-test, Mutual Information), tree-based importance

• Dimensionality Reduction: PCA, Kernel PCA, UMAP

• Scaling: StandardScaler, MinMaxScaler, RobustScaler

• Classification & Anomaly Detection:

  • Logistic Regression, SVM, k-NN, Decision Trees, Random/Extra Forests, AdaBoost, GradientBoosting, Bagging, GaussianNB, QDA, MLP, SGD, Passive-Aggressive
  • IsolationForest, One-Class SVM, Local Outlier Factor

• Interactive Metrics: Confusion matrix counts (TP, TN, FP, FN), Accuracy, Precision, Recall, F1-score, G-Mean, TPR, TNR, FPR, FNR

• Decision Boundary Visualizations: 2D plots with fullscreen toggle 📈

⚙️ Installation

1. Clone this repo:

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

2. Create a virtual environment (optional but recommended):

   python -m venv venv
   source venv/bin/activate   # macOS/Linux
   venv\Scripts\activate    # Windows

3. Install dependencies:

   pip install -r requirements.txt

🚀 Usage

Run the Streamlit app:

streamlit run app.py

• Use the sidebar to select dataset, preprocessing steps, and models
• View performance metrics in an interactive table (sortable)
• Expand each model's section to see its decision boundary plot
• Toggle Fullscreen plots to enlarge charts inside the main view

🤝 Contributing

Feel free to open issues or PRs! ⭐

📜 License

MIT License © 2025 eldraco