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Improvement Plan for Data Preprocessing Project

Logical Steps:

  1. Update requirements.txt: Add new dependencies for advanced features (plotly for interactive charts, openpyxl for Excel support, scikit-learn for preprocessing like outlier removal and scaling).

  2. Enhance app.py:

    • Add support for multiple file formats (CSV and Excel).
    • Implement advanced preprocessing options: outlier removal (IQR), normalization (MinMaxScaler), categorical encoding (one-hot), duplicate removal.
    • Generate summary statistics tables for original and cleaned data.
    • Switch chart generation to Plotly for interactive visualizations (comparison subplots).
    • Add file validation (size limit 10MB, check if CSV/Excel).
    • Save both original and cleaned datasets for download.
    • Improve error handling.

  3. Update index.html: Add form elements for advanced preprocessing options (checkboxes for outliers, normalize, encode, duplicates). Update file input to accept .csv and .xlsx.

  4. Update result.html: Add sections for summary statistics (HTML tables), download links for original and cleaned files, embed Plotly interactive charts (using plotly.js CDN).

  5. Update styles.css: Add styles for new UI elements (preprocessing options, summary tables, interactive chart containers).

  6. Update README.md: Expand with setup instructions, new features list, usage guide.

  7. Install dependencies: Run pip install -r requirements.txt.

  8. Test the application:

    • Run Flask app.
    • Upload sample CSV/Excel files.
    • Select preprocessing options and charts.
    • Verify outputs: cleaned data, summaries, interactive charts, downloads.
    • Use browser to interact and confirm.

  9. Handle any issues: Debug errors from testing, update files as needed.