---
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](https://huggingface.co/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!