This is data Entry job in my Upwork account my buyer teak me the job

This is my data cleansing project

Hi, I am an expert in data entry and data scraping projects. I do my work with full accuracy. I have experience in data entry platforms. I believe in my clients satisfaction and quality work. I complete my work within the given time.I always show my respect for clients through the services I deliver. My work is characterized by precision and speed. Thank you)The first one is a sample of a PDF file that was converted to a word file with the company logo attached. While the second file was a sample of data entry tasks complied in excel sheet.

This is a data entry project, I make it for one of my client. I work on it data entry ,data scraping ,data mining, web Research and so on.

This is a data scraping job portfolio. To complete this work I applied data entry, data mining, data scraping, web scraping , data analysis, big data skills. This was my a successful job.

For an offline company, I input manual data in an Excel sheet.

The client needed accurate and organized lead generation data for vacant properties in Pasadena, TX. Their goal was to compile property owner details, including names, addresses, phone numbers, and other relevant contact information, into a well structured Excel sheet for marketing and outreach purposes. Client Goals Create a clean, organized database of property leads. Ensure all contact information was accurate to avoid errors in outreach campaigns. Complete the project within a short timeframe to meet their deadlines. Challenges Encountered Data Accuracy: Some phone numbers and addresses were either incomplete or inconsistent. Solution: I conducted additional online research and cross verified details from reliable databases to ensure the information was accurate and complete. Data Formatting: The client needed the data in a specific format compatible with their CRM software.

The goal of this project is to clean and preprocess the employee dataset to ensure accuracy, consistency, and completeness for further analysis. This involves handling missing values, standardizing data formats, correcting inconsistencies, and removing duplicates to improve data quality.