Data Validation Tool

Skill-building project demonstrating AI-assisted Python for structured data quality validation and automated error detection across high-volume tabular datasets.

Performs rule-based validation on CSV records, identifies data quality issues, and generates an error report for structured review workflows.

🔍 What It Does

• Scan CSV datasets for missing fields, duplicates, and formatting issues
• Flag invalid or inconsistent values (e.g., missing dependencies, malformed dates)
• Generate error_report.csv with detected data quality issues
• Support structured QA and audit preparation workflows

🧰 Tools Used

• Python 3 (AI-assisted)
• pandas
• csv

📁 File Overview

• data_validation_tool.py — Loads CSV input, applies validation rules, outputs issue report
• sample_data.csv — Test dataset with intentional data errors
• error_report.csv — Generated output report of flagged records

🧪 How to Use It

1. Install dependencies: pip install pandas
2. Run data_validation_tool.py
3. Provide or reference input CSV dataset
4. Review terminal output and error_report.csv

✅ Use Case

Designed for structured data environments requiring automated validation of high-volume records to detect missing, inconsistent, or invalid values prior to QA review or downstream processing.

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Made with 💡 by Joseph Netherland (TheRealDjElite)
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