Update README.md

README.md

@@ -1,19 +1,55 @@
 ---
 title: SimpleML
-emoji: 🚀
-colorFrom: red
-colorTo: red
+emoji: 🤖
+colorFrom: blue
+colorTo: purple
 sdk: docker
 app_port: 8501
 tags:
-- streamlit
+  - streamlit
+  - machine-learning
+  - classification
+  - regression
+  - sklearn
 pinned: false
-short_description: Streamlit template space
+short_description: Train ML models in seconds — no code required.
 ---
 
-# Welcome to Streamlit!
+# SimpleML
 
-Edit `/src/streamlit_app.py` to customize this app to your heart's desire. :heart:
+Interactive Streamlit dashboard to train machine learning models (classification or regression) from CSV files — no code required.
 
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
+## 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!