Mr Clean Data is a Python application designed to analyze datasets in CSV or XLSX format to identify if they require cleaning. The core functionality includes:
- Missing Data Detection: Checks for null or empty values in the dataset.
- Data Type Consistency: Verifies if column data types are uniform or if there's inconsistency.
- Duplicate Detection: Identifies any duplicate rows in the dataset.
- Outlier Analysis: Uses basic statistical methods (Z-score) to find potential outliers in numeric columns.
This initial implementation provides a foundation for a user-friendly tool that helps in preliminary data quality assessment, setting the stage for more advanced data cleaning operations.
Roadmap for Mr Clean Data Here's a roadmap detailing what we aim to achieve with Mr Clean Data:
Phase 1: Core Functionality ✓ Implement basic data checks (missing data, type consistency, duplicates, outliers). ✓ Basic Flask API for file upload and result presentation.
Phase 2: User Interface Improvements Develop a more intuitive UI with drag-and-drop file upload. Add visual feedback for data issues (e.g., color coding, charts). Implement responsive design for mobile and tablet use.
Phase 3: Advanced Analysis Enhance outlier detection with more sophisticated statistical methods or ML models. Add custom checks based on user-defined rules or thresholds. Support for additional file formats like JSON, SQL databases.
Phase 4: Automated Cleaning Suggestions Develop algorithms to suggest or automatically apply simple cleaning operations (e.g., filling null values, correcting data types). Add a module for data transformation suggestions (e.g., normalization, encoding).
Phase 5: Performance and Scalability Implement asynchronous processing for handling large datasets. Use caching to speed up repeated analyses. Refactor code for better modularity and maintainability, possibly using microservices for scalability.
Phase 6: User Experience Enhancements Create a tutorial or guide for both end-users and developers. Introduce user accounts for saving configurations or preferences. Implement a feedback mechanism for user suggestions or bug reports.
Phase 7: Security and Compliance Enhance data privacy measures, ensuring no user data is stored unnecessarily. Implement authentication for premium features or API access. Ensure compliance with data protection regulations like GDPR if applicable.
Phase 8: Integration and API Develop a robust RESTful API for external integration. Integrate with popular data platforms or tools (e.g., Google Sheets, BI tools).
Phase 9: Community and Documentation Write comprehensive documentation for users and developers. Set up community forums or channels for support and feature discussion. Encourage contributions by setting up clear guidelines for pull requests.
Phase 10: Accessibility and Internationalization Make the app accessible according to WCAG guidelines. Support for multiple languages to cater to a global audience.
Phase 11: Future Innovations Explore the use of machine learning for predictive data cleaning. Consider real-time data cleaning capabilities for streaming data applications.
This roadmap is intended to guide development efforts, ensuring that Mr Clean Data grows from a basic tool into a comprehensive data quality management platform. Contributions, feedback, and additional ideas are always welcome as we aim to make data cleaning an intuitive and powerful process for everyone.