AI Data Quality Desktop Tool
Budget / Salary£20–250
TypeFreelance project
LocationRemote
Posted2 hours ago
AI-Powered Data Quality Assessment and Cleansing Tool
Project Overview
We are looking for an experienced AI developer to build a desktop-based data quality and validation application that uses AI and automated data-processing techniques to identify, investigate, and resolve issues in business datasets.
The application should allow users to import Excel and CSV files and automatically inspect their contents to identify missing values, duplicate records, inconsistent formatting, invalid entries, and unusual data patterns. The objective is to reduce manual data-cleaning efforts and improve the accuracy and reliability of business data.
Project Requirements
The application should provide an automated data inspection process that evaluates uploaded datasets and generates a structured report highlighting detected issues. It should identify potential data inconsistencies, explain the nature of each issue, and use an AI model to provide recommendations for resolving them.
Users should be able to review suggested corrections before applying them. The application should support basic automated cleaning operations, including duplicate removal, standardization of date and text formats, handling of missing values, and correction of common formatting inconsistencies.
It should also provide a comparison between the original and cleaned datasets, showing the changes made and the remaining issues. Users should be able to export the cleaned dataset in Excel or CSV format and download a summary report of the validation results.
Technical Requirements
Python, Pandas, AI/LLM API integration, data validation, data-cleaning techniques, desktop application development using PySide6 or Tkinter, and Excel/CSV processing.
Deliverables
A functional desktop application, Excel and CSV import support, automated data-quality inspection, AI-generated recommendations, data-cleaning functionality, review and approval of suggested corrections, before-and-after comparison, export functionality, testing, and setup documentation.
Project Overview
We are looking for an experienced AI developer to build a desktop-based data quality and validation application that uses AI and automated data-processing techniques to identify, investigate, and resolve issues in business datasets.
The application should allow users to import Excel and CSV files and automatically inspect their contents to identify missing values, duplicate records, inconsistent formatting, invalid entries, and unusual data patterns. The objective is to reduce manual data-cleaning efforts and improve the accuracy and reliability of business data.
Project Requirements
The application should provide an automated data inspection process that evaluates uploaded datasets and generates a structured report highlighting detected issues. It should identify potential data inconsistencies, explain the nature of each issue, and use an AI model to provide recommendations for resolving them.
Users should be able to review suggested corrections before applying them. The application should support basic automated cleaning operations, including duplicate removal, standardization of date and text formats, handling of missing values, and correction of common formatting inconsistencies.
It should also provide a comparison between the original and cleaned datasets, showing the changes made and the remaining issues. Users should be able to export the cleaned dataset in Excel or CSV format and download a summary report of the validation results.
Technical Requirements
Python, Pandas, AI/LLM API integration, data validation, data-cleaning techniques, desktop application development using PySide6 or Tkinter, and Excel/CSV processing.
Deliverables
A functional desktop application, Excel and CSV import support, automated data-quality inspection, AI-generated recommendations, data-cleaning functionality, review and approval of suggested corrections, before-and-after comparison, export functionality, testing, and setup documentation.
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