Chukwuka Anyasi

I’m a detail-oriented Data Annotator with a strong foundation in data handling, analysis, and quality review. I complete structured data science training through the YouThrive Tech Scholarship, building hands-on experience across labeling logic, data cleaning, and model evaluation tasks.

I’m known for accuracy, consistency, and the ability to follow detailed guidelines—skills I bring to every annotation and review cycle. I aim to contribute to AI model improvement by making sure datasets are accurate, complete, and fit for training and assessment.

In my projects, I cleaned and reviewed structured datasets to improve accuracy, missing-value handling, and consistency before model training and evaluation. I also examined transaction records to identify errors, duplicates, and inconsistent entries, then organized clean datasets for reporting.

Technically, I work confidently with Python and core data/ML libraries, including Pandas, NumPy, Matplotlib, and Seaborn. I’ve also trained using Scikit-Learn, PyTorch, and OpenCV, and I’m familiar with tools for deployment and version control such as Docker, Kubernetes, and Git.