You will get Professional Data Cleaning, Formatting Using Python
Project details
Hi! My name is Hassan Raza, and I am a Data Analyst with 3+ years of experience working as an accountant at the Kolachi Hotel Head Office. I specialize in cleaning, organizing, and preparing data so it becomes accurate, well-structured, and ready for reporting or analysis. I use Excel, and Python (Pandas) to deliver clean and reliable datasets tailored to your needs.
Data Tool
PythonWhat's included
| Service Tiers |
Starter
$15
|
Standard
$40
|
Advanced
$100
|
|---|---|---|---|
| Delivery Time | 2 days | 3 days | 4 days |
Number of Revisions | 1 | 1 | 2 |
Optional add-ons
You can add these on the next page.
Fast Delivery
+$30 - $25
Additional Revision
+$10Frequently asked questions
About Hassan
Web Scraping | Data Extraction Specialist | Python Dveloper
Karachi, Pakistan - 6:51 pm local time
Whether you need lead generation, competitive price tracking, or large-scale data mining, I deliver clean Excel/CSV files tailored to your specific business logic.
🛠 Technical Toolkit
I utilize the most efficient libraries to ensure speed and bypass anti-scraping measures:
Scraping: Scrapy, Selenium, Requests, BeautifulSoup
Data Processing: Pandas, NumPy
Languages: Python (Advanced)
Outputs: CSV, Excel, JSON, SQL, or Google Sheets
🎯 Why Choose Me?
Accounting-Level Accuracy: My background means I have a zero-tolerance policy for messy or incorrect data.
Speed & Scalability: I build robust scripts that handle thousands of pages without breaking.
Clear Communication: I provide regular updates and am always available to discuss project pivots.
On-Time Delivery: I respect your deadlines and prioritize fast turnaround times without sacrificing quality.
Steps for completing your project
After purchasing the project, send requirements so Hassan can start the project.
Delivery time starts when Hassan receives requirements from you.
Hassan works on your project following the steps below.
Revisions may occur after the delivery date.
Data Audit & Cleaning Plan
Review raw dataset. Identify issues (missing, duplicates, inconsistencies). Finalize cleaning approach.
Data Cleaning & Structuring
Clean and standardize data Handle missing values and duplicates Structure data for analysis