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| title: SimpleClean | |
| emoji: 🧹 | |
| colorFrom: yellow | |
| colorTo: pink | |
| sdk: docker | |
| app_port: 8501 | |
| tags: | |
| - streamlit | |
| - data-cleaning | |
| - preprocessing | |
| - imputation | |
| - encoding | |
| pinned: false | |
| short_description: Clean your data interactively — no code required. | |
| # SimpleClean | |
| Interactive Streamlit dashboard to clean and preprocess your datasets: handle missing values, encode categories, scale features, remove duplicates. | |
| ## 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! |