This repository contains seven end-to-end Excel data cleaning projects showcasing my ability to transform raw, unstructured, and inconsistent datasets into clean, analysis-ready data. Each project demonstrates strong skills in Power Query, Advanced Excel, Data Quality Improvement, and ETL workflows used in analytics and business reporting.
Project Overview
Data cleaning is one of the most essential steps in the data lifecycle. In this repository, each file demonstrates a complete cleaning cycle including: Removal of duplicates Fixing inconsistent data types Handling missing values Splitting/merging columns Applying transformation logic Standardizing formats Cleaning textual inconsistencies Using Power Query M-code Building repeatable ETL pipelines Improving data integrity for reporting These files represent practical business scenarios across domains like sales, HR, finance, operations, and inventory.
Files Included (7 Cleaning Projects) Each Excel file contains:
✔ Raw Sheet Original uncleaned data with issues such as: Mixed data types Formatting errors Incorrect decimals Combined columns (e.g., “27Bottle”) Extra spaces & irregular text Nulls and blanks uplicates Incorrect capitalization Unstructured categorical values
✔ Clean Sheet Final cleaned dataset ready for: Power BI SQL upload MIS reporting Machine learning preprocessing Business analysis
Power Query Steps (where applicable) With: Applied Steps documentation Custom M-code Automated refresh logic Transformation rules
✔ Documentation Sheet Contains: Summary of problems identified Step-by-step cleaning approach Validation checks Before/after examples
Techniques & Skills Demonstrated
🔹 Power Query (ETL) Split column by delimiter / by digit to non-digit Text extraction Merge columns Unpivot & pivot transformations Automatic datatype detection Error fixing & custom steps M-code custom logic Append & reference queries
🔹 Advanced Excel
XLOOKUP / VLOOKUP / INDEX-MATCH Pivot tables Text functions Number restructuring Conditional formatting Data validation Formula-driven cleaning Removing inconsistencies
Data Preprocessing Concepts Standardization Normalization Feature extraction Decimal correction Categorical cleaning Handling dirty/missing entries Data integrity assurance
Why This Project Matters This collection of files demonstrates practical, job-ready expertise required for: Data Analyst roles Power BI Analyst roles MIS Executive Reporting Analyst BI Developer Data Cleaning Specialist ETL/Data Operations roles
The ability to clean messy data is one of the most demanded skills in real analytics projects. This repository shows my hands-on experience in solving real world data quality issues.
Project Highlights Cleaned datasets from 7 unique business domains Automated Power Query workflows for reusability Corrected complex pattern issues (e.g., “00.3 → 0.03”) Standardized product units and quantities Removed formatting errors & text inconsistencies Extracted numeric/text values from merged columns Ensured reliable data types for BI dashboards Prepared high-quality data for analysis & ML