You will get Data Preprocessing & Cleaning | Feature Engineering | Data Transformation
Project details
Transform your messy, raw data into a clean, analysis-ready dataset.
The Problem:
Raw data is rarely in perfect condition. Missing values, inconsistent formats,
duplicates, and outliers waste time and hurt model accuracy. Manual cleaning
is tedious and error-prone.
My Solution:
Systematic data preprocessing that gets your data ready for modeling in days,
not weeks.
What You Get:
✓ Cleaned dataset (CSV, Excel, or database format)
✓ Python preprocessing script (reproducible and automated)
✓ Data quality report (before/after metrics)
✓ Complete documentation of all transformations
Process:
1. Data assessment and quality check
2. Missing value analysis and imputation
3. Outlier detection and handling
4. Categorical encoding and numerical scaling
5. Feature engineering and transformation
6. Final validation and delivery
Perfect for: Teams with data quality problems who need to move fast.
Delivery: Clean, production-ready dataset + reproducible code
Timeline: 3-5 days
The Problem:
Raw data is rarely in perfect condition. Missing values, inconsistent formats,
duplicates, and outliers waste time and hurt model accuracy. Manual cleaning
is tedious and error-prone.
My Solution:
Systematic data preprocessing that gets your data ready for modeling in days,
not weeks.
What You Get:
✓ Cleaned dataset (CSV, Excel, or database format)
✓ Python preprocessing script (reproducible and automated)
✓ Data quality report (before/after metrics)
✓ Complete documentation of all transformations
Process:
1. Data assessment and quality check
2. Missing value analysis and imputation
3. Outlier detection and handling
4. Categorical encoding and numerical scaling
5. Feature engineering and transformation
6. Final validation and delivery
Perfect for: Teams with data quality problems who need to move fast.
Delivery: Clean, production-ready dataset + reproducible code
Timeline: 3-5 days
Data Tool
PythonWhat's included
| Service Tiers |
Starter
$30
|
Standard
$60
|
Advanced
$120
|
|---|---|---|---|
| Delivery Time | 1 day | 2 days | 3 days |
Number of Revisions | Unlimited | Unlimited | Unlimited |
Number of Pages Mined/Scraped | 5 | 10 | 20 |
Number of Sources Mined/Scraped | 1 | 5 | 8 |
Optional add-ons
You can add these on the next page.
Fast Delivery
+$20 - $50About Affan
Machine Learning Engineer | Data Science & Agentic AI
Karachi, Pakistan - 7:39 am local time
I help small businesses and startups turn messy data into working, deployable ML and analytics solutions not just a Jupyter notebook, but something you can actually use.
What I can do for you:
✓ Clean and analyze your data (Python, SQL, Pandas) to answer specific business questions
✓ Build predictive models (regression, classification) with clear accuracy metrics
✓ Turn a model into something usable: a Flask/Streamlit app, dashboard, or simple API
✓ Deploy it to the cloud (AWS) so it's live, not stuck on my laptop
I'm newer to Upwork but not new to building I've completed several full pipelines end-to-end (data → model → cloud deployment), including a CI/CD deployment system and a student performance prediction app deployed on AWS. You can see the code and a full write-up on my GitHub.
Because I'm building my Upwork track record, I'm offering my first few clients a lower rate and extra attention to make sure you're fully happy with the result before anything is marked complete.
Let's talk about what you're trying to solve happy to give you a quick, honest read on scope before you commit to anything.
Steps for completing your project
After purchasing the project, send requirements so Affan can start the project.
Delivery time starts when Affan receives requirements from you.
Affan works on your project following the steps below.
Revisions may occur after the delivery date.
Data Assessment & Quality Check
I receive your raw dataset and conduct a thorough quality assessment. analyze missing values, data types, duplicates, outliers. You'll get a report of issues found and recommended solutions. This ensures we understand the scope before starting work.
Data Cleaning & Transformation
I systematically clean your data: handle missing values through imputation, remove duplicates, detect and treat outliers, standardize formats, and fix inconsistencies. Each transformation is documented and reproducible via Python script.