Update README.md

README.md

@@ -1,19 +1,56 @@
 ---
 title: SimpleClean
-emoji: 🚀
-colorFrom: red
-colorTo: red
+emoji: 🧹
+colorFrom: yellow
+colorTo: pink
 sdk: docker
 app_port: 8501
 tags:
-- streamlit
+  - streamlit
+  - data-cleaning
+  - preprocessing
+  - imputation
+  - encoding
 pinned: false
-short_description: Streamlit template space
+short_description: Clean your data interactively — no code required.
 ---
 
-# Welcome to Streamlit!
+# SimpleClean
 
-Edit `/src/streamlit_app.py` to customize this app to your heart's desire. :heart:
+Interactive Streamlit dashboard to clean and preprocess your datasets: handle missing values, encode categories, scale features, remove duplicates.
 
-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 demo dataset
+- Data quality report: missing values, duplicates, data types
+- Interactive cleaning:
+  - 🧹 Remove duplicate rows
+  - 🩹 Impute missing values (Mean, Median, Mode, Constant, KNN)
+  - 🔠 Encode categorical variables (Label Encoding, One-Hot Encoding)
+  - 📏 Scale numeric variables (StandardScaler, MinMaxScaler)
+- Visualize missing data with Plotly
+- Download cleaned dataset as CSV
+- Reset to original anytime
+
+## Demo Dataset
+Includes sample data with:
+- Numeric columns: Age, Income, Satisfaction
+- Categorical columns: City, Gender, Has_Children
+- Intentional missing values and duplicates
+
+## Deployment
+Ready for [Hugging Face Spaces](https://huggingface.co/spaces) (free tier).
+
+> ⚠️ Uses `sdk: docker` — include `Dockerfile`.
+
+## Requirements
+- Python 3.8+
+- Streamlit, pandas, numpy, scikit-learn, plotly
+
+---
+
+💡 Tip: Clean step-by-step → preview changes → download when ready!