You will get DATA CLEANING & PREPROCESSING
Project details
I provide professional data cleaning and preprocessing services that turn messy datasets into accurate, analysis-ready files. With expertise in Excel, Python, SQL, and Power BI, I handle duplicates, missing values, formatting issues, and preprocessing for visualization or machine learning. My focus is delivering high-quality, actionable results quickly and clearly, helping clients save time and make data-driven decisions.
Data Tool
PythonWhat's included
| Service Tiers |
Starter
$10
|
Standard
$25
|
Advanced
$50
|
|---|---|---|---|
| Delivery Time | 2 days | 3 days | 4 days |
Number of Pages Mined/Scraped | 2 | 5 | 10 |
Number of Sources Mined/Scraped | 1 | 3 | 4 |
Number of Revisions | 1 | 2 | 3 |
Optional add-ons
You can add these on the next page.
Fast Delivery
+$5 - $15
Additional Revision
+$15Frequently asked questions
About Roseline
Data Scientist | Data Analyst | Power BI Expert
Ibadan, Nigeria - 1:02 am local time
I specialize in:
• Data Cleaning & Preprocessing
• Business & Sales Data Analysis
• Dashboards (Power BI & Excel)
• Machine Learning & Predictive Modeling
• Forecasting & Trend Analysis
Tools: Excel, SQL, Power BI, Python (Pandas, NumPy, Scikit-Learn)
I help businesses:
• Fix messy, incomplete, and inconsistent data
• Understand sales performance and customer behavior
• Build dashboards that support fast, smart decision-making
• Predict future outcomes with machine learning and forecasting
• Improve performance with clear, actionable insights
If you need accurate analysis, clean data, or a clear report delivered on time, I’m available and ready to help.
Steps for completing your project
After purchasing the project, send requirements so Roseline can start the project.
Delivery time starts when Roseline receives requirements from you.
Roseline works on your project following the steps below.
Revisions may occur after the delivery date.
Receive Dataset
Client provides dataset(s) in Excel, CSV, or other tabular format with a brief description of the data and any known issues.
Review & Assess Data
Examine the dataset to identify duplicates, missing values, inconsistent formats, and potential outliers.