Machine Learning Data Preparation, Annotation & Model Evaluation
I have hands-on experience working with labeled datasets as part of multiple machine learning and data science projects. My work includes data cleaning, preprocessing, and annotation-like tasks such as categorizing data, handling missing values, and preparing structured datasets for model training. In projects such as fraud detection and telecom data analysis, I worked with large datasets where I evaluated data quality, corrected inconsistencies, and ensured accurate labeling for supervised learning models. I also performed model evaluation by analyzing predictions, identifying misclassifications, and improving model performance. Additionally, I have experience interpreting model outputs and providing structured feedback, which aligns closely with AI training tasks such as response evaluation, ranking outputs, and improving AI-generated results. I am comfortable working with Python (Pandas, NumPy, Scikit-learn) and handling real-world datasets.