Accurate Data Cleaning for Up to 500 Rows of Excel or CSV Files
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About this service
Summary
FAQs
What file formats do you accept?
I accept Excel (.xlsx, .xls) and CSV (.csv) files. If you have another format, feel free to ask — I may be able to convert and work with it.
What types of data cleaning do you perform?
I handle missing values, remove duplicates, correct typos, fix formatting issues, standardize data entries (e.g., dates, text), and ensure consistent structure for analysis.
What if my file has more than 500 rows?
This service covers up to 500 rows. If you have more data, I’ll gladly provide a custom quote based on the size and complexity — just message me before booking!
Will I receive a report of what was cleaned?
Yes. I provide a brief summary report detailing the issues found and how they were resolved. Transparency is part of my service.
Can you clean sensitive or confidential data?
Absolutely. I take data privacy seriously and treat all files with complete confidentiality. If needed, I’m happy to sign a basic NDA.
How long will the delivery take?
Typically, I deliver within 1–2 days, depending on data complexity. For faster turnaround, you can message me to check availability.
Do you offer revisions if I need changes?
Yes! I offer one free revision to make sure the cleaned file meets your expectations. Just share your feedback clearly, and I’ll update the file accordingly.
What's included
Initial Data Review & Issue Identification
I will review your Excel or CSV file (up to 500 rows) and identify key data quality issues such as missing values, inconsistent formatting, duplicates, and typos. You’ll receive an outline of problems found before cleaning begins.
Cleaned Excel/CSV Dataset (Up to 500 Rows)
You’ll receive a fully cleaned and organized version of your file with formatting corrections, duplicate removal, null handling, and standardized text or numeric entries — ready for analysis or reporting.
Cleaning Summary Report
A concise summary (PDF or .txt) outlining the steps taken during data cleaning, including resolved issues, assumptions made, and any suggestions for future data consistency.
