You will get a clean analysis-ready dataset with full documentation
Project details
Most data analysts skip straight to charts and dashboards without fixing what is underneath. Bad data in means bad insights out no matter how good the visualization looks.
I clean data the way an ML engineer would. Not just removing blanks, but thinking about what the data needs to actually be useful downstream correct types, consistent formatting, outlier treatment, and a full log of every change made so you know exactly what was done and why.
I have cleaned datasets ranging from 1,000 to 100,000+ records across e-commerce, telecom, real estate, and customer analytics including a 20,770-record Airbnb NYC dataset with missing values, duplicates, type errors, and extreme price outliers, all documented and resolved.
You get back a clean file plus a transformation log not just a fixed dataset but a clear record of every issue found and every action taken.
I clean data the way an ML engineer would. Not just removing blanks, but thinking about what the data needs to actually be useful downstream correct types, consistent formatting, outlier treatment, and a full log of every change made so you know exactly what was done and why.
I have cleaned datasets ranging from 1,000 to 100,000+ records across e-commerce, telecom, real estate, and customer analytics including a 20,770-record Airbnb NYC dataset with missing values, duplicates, type errors, and extreme price outliers, all documented and resolved.
You get back a clean file plus a transformation log not just a fixed dataset but a clear record of every issue found and every action taken.
Data Tool
PythonWhat's included
| Service Tiers |
Starter
$40
|
Standard
$85
|
Advanced
$155
|
|---|---|---|---|
| Delivery Time | 2 days | 3 days | 6 days |
Number of Revisions | 1 | 2 | 3 |
Number of Pages Mined/Scraped | 0 | 0 | 0 |
Number of Sources Mined/Scraped | 1 | 2 | 3 |
Optional add-ons
You can add these on the next page.
Additional Revision
+$20
EDA report on cleaned data
(+ 2 Days)
+$35Frequently asked questions
About M Wajeeh
ML Engineer | Python | Predictive Modeling | Data Analytics
Islamabad, Pakistan - 7:55 pm local time
I’m an AI Systems & Machine Learning Engineer focused on building practical, production-ready applications with Python and modern backend technologies.
I can help turn existing Excel-based logic, business rules, or calculation workflows into secure web applications where the core logic stays server-side and users only interact with a clean, simple interface.
My experience includes:
• Python & FastAPI backend development
• React frontend development
• PostgreSQL and database-driven applications
• Authentication and role-based access
• REST APIs and server-side business logic
• Docker, AWS, GitHub Actions, and CI/CD
• Automated testing and validation
• PDF/report generation and data export
• Machine learning model development and deployment
• MLflow and DVC for reproducible ML workflows
I’ve built end-to-end systems involving predictive models, APIs, databases, RAG pipelines, computer vision, AI agents, and deployment. I’m comfortable taking an existing workflow, understanding how it works, translating the underlying logic into maintainable code, and building the application around it.
For projects like yours, I can work across the full stack -- reviewing the existing Excel logic, implementing the calculations server-side, building authentication and user roles, creating the input/output screens, adding audit logging and exports, and deploying the application securely.
I care about clean code, clear communication, and building systems that are reliable and maintainable after delivery.
Steps for completing your project
After purchasing the project, send requirements so M Wajeeh can start the project.
Delivery time starts when M Wajeeh receives requirements from you.
M Wajeeh works on your project following the steps below.
Revisions may occur after the delivery date.
Dataset review and issue assessment
I open your file, profile the data, and document every quality issue found nulls, duplicates, wrong types, outliers before touching anything.
Cleaning and transformation
I fix all issues systematically and log every change made what was found, what action was taken, and how many rows were affected.