Text Data Cleaning & Verification

@TeraByte26

Hi Manisha, I will deliver cleaned Excel/CSV files with duplicates removed, standardized text, typo corrections, plus a change log and a summary of unverifiable entries. I can finish up to 5 files within 4 business days for your budget. Shall I start with a sample sheet? Thanks, Amit Waiting for your response in chat! Best Regards.

@masiahm4

Hi! I'm a full-stack developer with 5 years of experience building Python applications and automations. I can turn your Excel and CSV files into clean, consistent datasets by removing duplicates, standardising text, correcting obvious errors, and separating ambiguous records for review. My background includes web-scraping data entry and invoice-processing automation, so I'll combine careful validation with repeatable checks and deliver the cleaned source-format files, a concise change log, and an exceptions summary. Approximately how many files and total rows are included, and do you have reference lists for verification?

@rimiloca

Hi, I can clean the text using agreed rules for spacing, capitalization, duplicates, and obvious typos, while keeping the originals untouched. I will separate exact duplicates from possible matches and flag uncertain entries rather than silently merging or correcting them. INR 15,000 covers up to 10,000 rows across five files in 5 days, with cleaned originals-format files, fix counts, and an exceptions list. Please share a sample, the reference lists, and the fields that define a duplicate.

@manishashree12

Hello there, I understand your need for thorough text data cleaning and verification. I will carefully review each record, remove duplicates, standardize capitalization, correct typos, and flag ambiguous entries for your review. I will prioritize accuracy over speed and can utilize Excel functions, Power Query, or a light Python script if needed. I will deliver cleaned files in Excel/CSV format, along with a change log and a summary of unverified entries. When can we start this meticulous process? Thanks, Manisha

@alluankit01

Hi, I can clean and verify your Excel/CSV text datasets carefully, combining manual review with Excel/Power Query or light Python automation where it improves consistency without sacrificing accuracy. I can handle: • Duplicate detection and removal • Capitalization standardization • Extra-space cleanup • Obvious typo correction • Consistent text formatting • Cross-checking against reference lists • Ambiguous/unverified entries flagged separately • Final record-by-record quality review • Delivery in the original Excel/CSV format I’ll keep the process transparent and avoid making unsupported assumptions. Any doubtful value will be flagged for your review instead of changed silently. Deliverables will include: • Cleaned version of each original file • Change log with duplicate counts and common fixes • Summary of entries that could not be verified I’m comfortable with Excel, CSV data cleansing, Power Query, Python/Pandas, validation workflows, and large text datasets. I can start immediately. Typical turnaround is 1–3 days depending on file size, number of records, and reference data available. Best regards Ankit

@hassam945

Hi, I understand that accuracy is the main priority here, so I would handle the files carefully rather than relying on a quick automated cleanup. I can review the records, remove true duplicates, standardize capitalization and spacing, fix clear typos, and flag anything that is not safe to change. Where useful, I can use Excel, Power Query, or a small Python script to make the process more consistent, followed by careful verification of the results. I will return each file in the same Excel/CSV format and provide a clear change log showing what was changed, including duplicates removed and common corrections. I’ll also provide a list of entries that need your review so nothing uncertain is silently modified. I work carefully and take responsibility for the data I handle. You can first give me a small sample task to check my work, and if you’re satisfied with the result, we can move forward with the complete files. I am confident you will be satisfied with my work, and that is always my first priority.I am also available for long-term.

@bojans9

Hi, I can clean and verify your text datasets with a careful, methodical approach. I’ll go through each record, remove duplicates, standardise capitalization and spacing, correct clear typos, and flag anything ambiguous so you can review it easily. I’m comfortable working directly in Excel/CSV or using light Python tooling when it improves accuracy without risking unintended changes. You’ll receive fully cleaned files in the original formats, plus a concise change log and a short summary of entries that couldn’t be verified with confidence. Once you share the spreadsheets, I can begin right away.

@Cruz1217

Storing the itinerary JSON directly in the database without a proper schema can cause performance hiccups as the tour list grows. I'll design a relational model in PostgreSQL, using its JSONB column to preserve the exact output while enabling indexed queries. Authentication will run on PHP with JWT tokens, email confirmation, password reset flow, and Google OAuth, keeping the current front‑end fetch calls unchanged. A common mistake is tying the password reset token to the user ID without an expiration, which leaves the endpoint open to replay attacks. I'll store a short‑lived, hashed token and validate it before issuing a new password, so the flow stays safe. You can expect a clean README, one‑click deploy script, and a fully protected itinerary API that behaves exactly like the static files today.

@sara0346

Hello, I reviewed your project for cleaning and verifying Excel and CSV text data. Python with pandas will handle duplicate detection and consistent formatting while preserving the original structure. I will standardize spacing and capitalization, correct clear typos, manually review ambiguous records and maintain a detailed change log. The final delivery will include cleaned files and a summary of entries requiring your review. Best Regards, Sara

@ravichedwal

With my 8+ years of Full Stack Development experience, I am more than equipped to carry out your text data cleaning and verification project. My proficiency in PHP, Python, and Power Query aligns well with the job requirements, enabling me to organize and cleanse your data with meticulous attention to detail. As an experienced developer, I understand the importance of not just cleaning your data but also presenting it in an easily understandable format. In line with this, I will provide you with a comprehensive change log documenting every key fix implemented including duplicates removed and common corrections made. This ensures that you can have a complete picture of the process and enables you to double-check anything necessary.

@ZohaibSathio

Hi, cleaned and verified copies of your spreadsheets in the same format, plus a change log. The risk with any bulk pass is that it quietly mangles a value that was fine. How I'd do it: load each file in pandas, normalise case and spacing, then fuzzy match near duplicates into a review list instead of deleting them outright. Ambiguous rows get flagged, not guessed. Every edit is logged with its original value. Roughly how many rows in total? I did the same cleanup on a supplier catalogue of about 40k rows. Regards, Zohaib

@SoneJ

Hello, I understand the importance of accuracy and thoroughness in cleaning and verifying your text data. My approach will involve a meticulous review of each record, using Excel functions and Power Query to standardize formatting, remove duplicates, and correct any obvious typos. I will ensure to flag ambiguous entries for your review, providing you with a comprehensive change log and summary of unverified entries. To tailor my process effectively, could you share the total number of records in your files? Additionally, do you have any specific guidelines for standardization that I should follow? Studio SJ operates on paid fixed-price or hourly engagements. Is that acceptable for your project? Looking forward to our conversation. Sone Joubert Studio SJ

@NikosAnast

Hi, I am a professional in the fields of software development and data-entry. I am interested in participating in this project. Visit my profile to explore my projects and discover my work. Get in touch for more information, Nikos

@maliijaz2002

Hi, Your emphasis on accuracy over speed matches how I approach data work — I'd rather flag something ambiguous than "clean" it wrong. Happy to take this on. How I'd handle it: - Review the raw files and any reference lists carefully before starting, so I understand the shape of the data and your standards - Work through each file methodically: deduplication (with a check for near-duplicates, not just exact matches), consistent capitalisation and spacing, and correction of obvious typos - Anything ambiguous — unclear entries, values that don't match your reference lists, or judgement calls — goes into a flagged list for your review rather than a guess from me - Use a light Python/Pandas assist where it genuinely helps (bulk pattern fixes, duplicate detection) but keep the careful manual pass as the primary check Deliverables: cleaned files in the same format, a clear change log of fixes applied and counts, and the flagged-entries summary. Availability: I can start as soon as you send the files. Timeline depends on total row count — happy to give a firm estimate once I see the volume, but for a typical multi-file batch, 2–4 days is usually realistic. Best, Ali

@abdurrehman373

Accuracy and attention to detail are my priorities for this data verification task. I understand that you need a methodical approach to ensure your datasets are clean, consistent, and reliable, rather than just a rushed automated process. I will approach your request by: • Standardizing records: Applying consistent formatting for capitalization and spacing across all fields. • Deduplication: Identifying and removing redundant entries while maintaining data integrity. • Error Correction: Fixing typos and inconsistencies, while flagging ambiguous entries that require your professional input. • Reporting: Providing a clear change log and a summary of flagged items for your review. I am proficient in Python and advanced Excel/Power Query, which allows me to build a repeatable, reliable workflow for these files. I can ensure that the output remains in your original format while providing the clean, verified results you need. I am ready to start immediately and can provide a sample of the first file to ensure my approach aligns with your expectations. What is the approximate number of rows or total file size to be processed?

@abhilashr53

I can deliver 'Text Data Cleaning & Verification' end to end — clean architecture, tests, and deployment. I'll start with a short plan, share progress daily, and hand over production-ready code. Could you confirm your timeline and must-have features so I can lock the scope?

@Lennyfixesall

Accuracy mattering more than speed is the right call for duplicate removal, capitalization, spacing, typos, and ambiguous entries. Messy text files can be draining to review, so I’ll return clean versions in the same formats, a concise change log, and a short list of anything that needs your eye. I keep a 100% CSV cleanup benchmark on file, so this is work I take carefully. INR 12,500, done in 3 days once I have the spreadsheets and reference lists.

@lisneya

Hello, I can clean the text fields in your Excel/CSV files carefully: identify and remove true duplicates, standardize capitalization and whitespace, correct clear typos, and flag ambiguous entries instead of guessing. I can use Excel/Power Query or a small Python check where useful, followed by record-level review. You will receive each file in its original format, a concise changelog with duplicate counts and typical fixes, and a separate list of unresolved items. I can begin after receiving the files and reference lists. My ₹18,000 / 5-day estimate is for the set described; I would confirm the number of records and files before starting.

@petropankovdev

Hello, Your files are text fields in Excel/CSV: duplicates, mixed capitalisation and spacing, obvious typos, plus a set of records that need a human decision rather than a rule. I read the "accuracy matters more than speed" line as the actual spec, so here is how I would honour it. Nothing gets silently "fixed". A deterministic pass handles only the changes that are provably safe (exact and near-duplicate merge, whitespace and case normalisation, encoding artefacts), and every single change lands in the change log with the before and after value. Anything the rules are not confident about is never edited: it goes to the flag list with a reason, so you review a short file instead of re-checking my work. Per original file you get back the cleaned Excel/CSV in the same format and column order, a change log with counts and the recurring correction patterns, and a separate list of entries I could not verify. First step, before you commit to anything: send one file, or just 200 rows of it. I return the cleaned version and the change log within a day, at no cost. If the output is not at the level you want, you have lost nothing. Background: two completed projects on this account, both rated 5 out of 5, delivered on time and on budget. I have also delivered a large contact-base build where deduplication, field normalisation and verification of scraped text was the whole job, so this is work I have shipped rather than a first attempt. Outside client work, 16 merged pull requests into third-party open-source projects, mostly a Go security tool. Price 18,000 INR, 4 working days from the moment the raw files arrive. If the volume turns out to be far larger than the brief suggests, I will tell you before starting, not after. One thing I need to know: roughly how many rows and how many files, and is there a reference list I should validate against? Petro Pankov, BotCraft Group