Project 1: Data Cleaning & Preparation

A comprehensive data cleaning and preparation pipeline that processes 1,200 records with zero errors and produces production-ready datasets.

📊 Project Overview

This project demonstrates best practices in data quality management across three critical phases:

• Phase 1: Strategic Imputation of Missing Values
• Phase 2: Integrity Audit for Duplicate Detection
• Phase 3: Format Standardization (Dates, Currency, Text)

✅ Audit Results

Metric	Result
Total Records Processed	1,200
Missing Values Found	309
Duplicate Records	0
Changes Applied	5
Final Status	APPROVED

🔍 Verification Gates

All verification criteria passed with zero error rates:

Criteria	Status	Error Rate
Unique Identifiers	✅ PASS	0.00%
Date Format	✅ PASS	0.00%
Data Type Consistency	✅ PASS	N/A
Missing Value Handling	✅ PASS	N/A

📝 Change Log Summary

Change ID	Description	Resolution	Impact
CR001	Missing CouponCode Values	Replaced NULLs with NO_COUPON	309 records
CR002	Duplicate Row Detection	Verified zero duplicates	0 records
CR003	Date Format Validation	Standardized to ISO 8601	1,200 rows
CR004	Currency Formatting	Applied ₹ symbol formatting	1,200 rows
CR005	Unique ID Audit	Confirmed 100% uniqueness	1,200 IDs

📂 Repository Structure

project-1-data-cleaning/
├── README.md
├── CHANGELOG.md
├── data/
│   ├── raw/
│   │   └── source_data.xlsx
│   ├── processed/
│   │   └── cleaned_data.xlsx
│   └── data_dictionary.md
├── scripts/
│   ├── data_cleaning.py
│   └── validation.py
├── documentation/
│   ├── data_cleaning_process.md
│   ├── quality_standards.md
│   └── verification_gates.md
├── reports/
│   └── audit_report_2026-05-08.md
├── .gitignore
└── LICENSE

🛠️ Tools & Technologies

• Excel/Spreadsheet: Data manipulation and validation
• Python (optional): Automation and scaling scripts
• Git: Version control and documentation

🚀 Getting Started

1. Clone this repository
2. Review the CHANGELOG.md for detailed changes
3. Check documentation/data_cleaning_process.md for methodology
4. Access the cleaned datasets in the data/processed/ directory

📋 Data Quality Standards

All data in this project meets the following standards:

• ✅ 100% unique identifiers with no conflicts
• ✅ Standardized date formats (ISO 8601)
• ✅ Consistent data types across all fields
• ✅ Strategic handling of missing values
• ✅ Zero duplicate records

📊 Phase Documentation

Phase 1: Strategic Imputation

Missing CouponCode values were identified and replaced with a "NO_COUPON" placeholder to maintain data integrity while preserving analytical capability.

Phase 2: Integrity Audit

Comprehensive duplicate detection was performed across all 1,200 records, confirming zero duplicate entries and data uniqueness.

Phase 3: Standardization

All data formats were standardized:

• Dates: ISO 8601 format (YYYY-MM-DD)
• Currency: Indian Rupee (₹) formatting
• Text: Consistent casing and encoding

✨ Status: APPROVED FOR SUBMISSION

Submitted by: Nimra Youns
Date: May 08, 2026
Project: DecodeLabs Project 1

All verification gates passed. Dataset is production-ready.

📄 License

[Add appropriate license - e.g., MIT, Apache 2.0]

📞 Contact

For questions about this data cleaning project, contact: Nimra Youns

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Generated: May 08, 2026 | Last Updated: May 23, 2026