README.md

metadata

title: SimpleML
emoji: 🤖
colorFrom: blue
colorTo: purple
sdk: docker
app_port: 8501
tags:
  - streamlit
  - machine-learning
  - classification
  - regression
  - sklearn
pinned: false
short_description: Train ML models in seconds — no code required.

SimpleML

Interactive Streamlit dashboard to train machine learning models (classification or regression) from CSV files — no code required.

Author

Eduardo Nacimiento García
📧 enacimie@ull.edu.es
📜 Apache 2.0 License

Features

• Upload CSV or use built-in classification/regression demo datasets
• Auto-detect task type (classification vs regression)
• Encode categorical variables automatically
• Choose between models:
  • Classification: Random Forest, Logistic Regression
  • Regression: Random Forest, Linear Regression
• View performance metrics
• Confusion matrix (classification) or Predicted vs Actual plot (regression)
• Feature importance (for tree-based models)
• Interactive prediction form

Demo Datasets

Two built-in demos:

• Classification: Predict “Purchase” (0/1) based on age, income, education, etc.
• Regression: Predict “Salary” based on experience, age, education, etc.

Deployment

Ready for Hugging Face Spaces (free tier).

⚠️ Uses sdk: docker — include Dockerfile.

Requirements

• Python 3.8+
• Streamlit, scikit-learn, pandas, numpy, plotly

---

💡 Tip: After uploading your CSV, select target variable → features → model → see results + make predictions!