You will get a cleaned and analyzed CSV or Excel dataset using Python
Project details
You will receive a clean, organized, and analysis-ready CSV or Excel dataset processed with Python.
I can identify and correct duplicate records, missing values, inconsistent categories, incorrect data types, invalid dates, formatting problems, and other data-quality issues. Depending on the selected package, I can also merge files or worksheets, perform exploratory data analysis, create summary statistics and charts, and provide a reproducible Jupyter Notebook.
Your cleaned dataset will be delivered in CSV or Excel format with the original structure preserved whenever possible. I will clearly document important corrections and avoid removing or changing critical information without an agreed cleaning rule.
This service is suitable for structured tabular data. One page refers to one worksheet or data table, while one source refers to one uploaded CSV or Excel file.
Please contact me before ordering if your files contain highly sensitive data, require web scraping, database integration, advanced dashboards, or complex automation.
I can identify and correct duplicate records, missing values, inconsistent categories, incorrect data types, invalid dates, formatting problems, and other data-quality issues. Depending on the selected package, I can also merge files or worksheets, perform exploratory data analysis, create summary statistics and charts, and provide a reproducible Jupyter Notebook.
Your cleaned dataset will be delivered in CSV or Excel format with the original structure preserved whenever possible. I will clearly document important corrections and avoid removing or changing critical information without an agreed cleaning rule.
This service is suitable for structured tabular data. One page refers to one worksheet or data table, while one source refers to one uploaded CSV or Excel file.
Please contact me before ordering if your files contain highly sensitive data, require web scraping, database integration, advanced dashboards, or complex automation.
Data Tool
PythonWhat's included
| Service Tiers |
Starter
$35
|
Standard
$75
|
Advanced
$140
|
|---|---|---|---|
| Delivery Time | 2 days | 3 days | 5 days |
Number of Revisions | 1 | 2 | 3 |
Number of Pages Mined/Scraped | 1 | 2 | 6 |
Number of Sources Mined/Scraped | 0 | 1 | 3 |
Optional add-ons
You can add these on the next page.
Additional Revision
+$10
Additional Page Mined/Scraped
(+ 1 Day)
+$10
Additional Source Mined/Scraped
(+ 1 Day)
+$20Frequently asked questions
About Emre
Machine Learning & Data Science Specialist | Python, Computer Vision
Ankara, Turkey - 3:14 am local time
I can support your project with:
• Machine learning classification and regression models
• Data cleaning, preprocessing, and exploratory data analysis
• Feature engineering and model selection
• Model training, validation, and performance optimization
• Accuracy, precision, recall, F1-score, and confusion matrix analysis
• Computer vision model and dataset preparation
• Image classification and damage-detection workflows
• Jupyter Notebook and Google Colab development
• Python, Pandas, NumPy, Scikit-learn, Matplotlib, and OpenCV
• Existing machine learning code and notebook debugging
• Dataset quality checks and class-balance analysis
• Clear technical reports, documentation, and reproducible source code
You will receive organized code, transparent results, and deliverables that are easy to understand and continue developing.
I focus on practical solutions, clear communication, realistic expectations, and on-time delivery.
Please send me your dataset, project objective, target variable, current code, and expected output so I can recommend the most suitable approach.
Steps for completing your project
After purchasing the project, send requirements so Emre can start the project.
Delivery time starts when Emre receives requirements from you.
Emre works on your project following the steps below.
Revisions may occur after the delivery date.
Step 1 — Review files and requirements
I review the uploaded files, project objective, requested output, and any cleaning rules provided by the client.
Step 2 — Inspect data quality
I check the dataset for duplicates, missing values, incorrect formats, inconsistent categories, invalid records, and data-type problems.