arleeco

Interrogate (Big Data, SQL, Python, NLP, Hugging Face Transformers, Zero-Shot Learning, TensorFlow, PyTorch). Conducted research and development on Zero-Shot Learning models to achieve Authorship Attribution. Performed model selection and testing, leveraging Hugging Face Transformers to compare performance across BERT, deBERTa, RoBERTa, and BART models. Fine-tuned deep learning models. Developed and tested an annotation system and metadata generation to enhance search, aggregation, and document analysis capabilities. Scraped data from X and dataset users construction. Data cleaning, data understanding, data transformation, anomalies detection, uncover patterns, trends, and relationships within the data. Applied unsupervised learning methods, imbalanced learning and feature selection. Trained and evaluated various Machine Learning models: linear & logistic regression, XGBoost, DecisionTree, Random Forest, SVM, AdaBoost, LightGBM, Neural Networks etc. for users classification. Built a classification model based