BSc IT Project Guide: Deduplication Tool
1. Introduction
The Deduplication Tool aims to identify and remove duplicate records from structured datasets. This is essential in maintaining data quality, improving analysis accuracy, and ensuring data consistency in systems such as customer databases, transaction logs, and inventory management systems.
2. Objectives
• Identify duplicate records based on defined criteria (e.g., exact match, fuzzy match).
• Remove or merge duplicates without losing important information.
• Provide a user-friendly interface for loading and cleaning datasets.
• Generate reports showing duplicates found and actions taken.
3. Tools and Technologies
• Programming Language: Python
• Libraries: Pandas, NumPy, FuzzyWuzzy, Streamlit (for GUI)
• Database: SQLite or CSV files
• IDE: Visual Studio Code, Jupyter Notebook
4. Functional Requirements
• Upload dataset (CSV format).
• Choose deduplication criteria (e.g., column-based, similarity threshold).
• Preview duplicates before deletion.
• Export cleaned dataset.
• Generate summary report.
5. Non-Functional Requirements
• Easy-to-use GUI for non-technical users.
• Efficient processing for large datasets.
• Maintain original data integrity.
6. System Architecture
The system follows a simple architecture where the frontend allows users to upload files, choose deduplication options, and view results. The backend processes the data using matching algorithms and returns cleaned results and statistics.
7. Methodology
• Data Preprocessing
• Duplicate Detection (Exact and Fuzzy Matching)
• Record Merging or Deletion
• Result Visualization and Export
8. Future Enhancements
• Support for multiple file formats (Excel, JSON).
• Integration with cloud-based storage systems.
• Advanced machine learning-based deduplication techniques.
9. Conclusion
This project provides a practical solution to the widespread issue of duplicate data records. It supports cleaner, more reliable datasets for various analytical and operational tasks.