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I can help with Data Entry & Product Listing!
Hi everyone! I'm Ranjith from Ajman, UAE.
✅ Excel Data Entry - 100% Accurate
✅ Shopify / Amazon Product Listing - 100 products/day
✅ Data Cleaning & Formatting
Available 30+ hrs/week. Fast delivery!
#DataEntry #Excel #ProductListing
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26 rows came in. Only 16 belonged in the total.
I built a small Python kit that keeps accepted orders, exact duplicates and problem rows separate. The sample ends with $973.69 in accepted orders, and you can trace every number back to the original CSV.
Try the free sample files first
https://contra.com/products/xhHZSOr9-csv-order-report-kit-free-preview
The full kit is $19 with source code and an illustrated guide. It is for people comfortable running or adapting Python. It requires Python 3.10+, a fixed six-column format, USD and three built-in categories.
🚀 𝐁𝟐𝐁 𝐋𝐞𝐚𝐝 𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐨𝐧 𝐢𝐧 𝟐𝟎𝟐𝟔: 𝐓𝐡𝐞 𝐑𝐢𝐠𝐡𝐭 𝐓𝐨𝐨𝐥𝐬 𝐌𝐚𝐤𝐞 𝐚 𝐃𝐢𝐟𝐟𝐞𝐫𝐞𝐧𝐜𝐞!
B2B lead generation has evolved. Today, it's not just about finding contact details - it's about identifying the right prospects, building targeted lead lists, and creating meaningful connections. 🎯
As a B2B Lead Generation & List Building Specialist, I use the right tools and strategies to help businesses connect with their ideal customers.
🔍 𝐌𝐲 𝐂𝐨𝐫𝐞 𝐒𝐞𝐫𝐯𝐢𝐜𝐞𝐬:
✅ Targeted B2B Lead Generation
✅ LinkedIn Sales Navigator Research
✅ Verified Email & Contact List Building
✅ LinkedIn Outreach & Prospecting
✅ Cold Email Outreach & Campaign Management
✅ Data Research & Enrichment
✅ Appointment Setting Support
🛠️ 𝐓𝐨𝐨𝐥𝐬 𝐈 𝐖𝐨𝐫𝐤 𝐖𝐢𝐭𝐡:
Apollo.io | LinkedIn Sales Navigator | Google Sheets | Excel | Smartlead | Gmail | ChatGPT
💡 The goal is simple: better targeting, organized data, personalized outreach, and more opportunities to grow your business.
Are you looking for reliable B2B lead generation support for your business?
Let's connect! 🤝
The Then vs Now toolkit graphic is a fun way to show the stack evolution, Masroor. Better targeting, organized data and personalized outreach is exactly the right formula. Clean presentation.
How I Clean and Validate Messy CSV Data
A CSV can look tidy and still be wrong: extra spaces can split matching values, and treating an identifier as a number can erase its leading zeros.
My approach with FreelanceAgent is to define the rules first, run the transformation, then check the exported file independently.
In this synthetic inventory demo, I trimmed surrounding spaces in item and category, kept only status=active rows, and exported UTF-8 CSV. All four columns stayed strings. No deduplication or inferred corrections were requested.
The result: 6 input rows became 4 active rows. 12 cells were trimmed before filtering, and 2 inactive rows were removed. SKU 001 stayed 001. The product name "Clips, 32 mm" kept its comma, with correct CSV quoting.
A separate validator derived the expected output from the source and agreed rules. All 9 checks passed, covering schema, row count, deduplication behavior, exact values, types, encoding, change accounting, formula safety and the output hash.
Practical takeaway: agree what may change and what must be preserved before cleaning. Keep the source, rules, output and QA report together so the result can be checked.
This is an AI-assisted demonstration using synthetic data, not client work or a performance benchmark.
Full case study and visual comparison:
If you have a CSV that needs similar cleanup, feel free to message me with the required changes and output format.
