Idk Who

I’m an Artificial Intelligence & Data Science undergraduate focused on building end-to-end ML pipelines for classification, anomaly detection, and predictive modeling. Through a Python Development Internship, I created reproducible data manipulation and automated reporting workflows with Pandas and NumPy, and supported internal ML demos with ingestion, preprocessing, and evaluation.

I bring strong practical ML skills—feature engineering, model evaluation (accuracy, precision, recall, F1), and interpretability using SHAP—plus data analysis and visualization with NumPy, Pandas, Matplotlib, and Seaborn. My projects include an end-to-end return risk prediction system using NLP (TF-IDF) plus Gradient Boosting, an unsupervised anomaly detection system for network traffic with Isolation Forest and Local Outlier Factor, and an inventory waste prediction approach using exploratory data analysis and tuned classifiers. I’m excited by roles where I can help deploy responsible, measurable ML solutions and keep improving model quality with clean MLOps-ready foundations.