Messy or poorly prepared data can significantly reduce the quality and reliability of machine learning results. Many ML projects fail not because of complex models, but because the underlying data is inconsistent, noisy, or poorly labeled.

I provide structured support for cleaning, labeling, and preparing datasets so your machine learning training and evaluation process becomes smooth and dependable. My focus is on following clear instructions, maintaining data consistency, and ensuring that datasets are ready to be used in ML workflows.

What I will deliver:

• Data cleaning and preprocessing such as handling missing values and formatting issues
• Dataset labeling or label validation based on provided guidelines
• Proper train-test split to support fair evaluation
• Basic model evaluation support when required

I work with common dataset formats including CSV files, Excel sheets, text datasets, and image folders. I do not modify data beyond the approved scope and I do not make unrealistic accuracy claims.

What I need from you:

• Dataset file or access to the data
• Clear instructions or labeling rules
• Expected output format

Throughout the task, I communicate clearly, ask questions when something is unclear, and provide regular updates on progress. My goal is to support your ML project by handling the data preparation work reliably so you can focus on model development.