Welcome to my personal data science portfolio! This repository showcases a collection of data science projects that I work on in my free time, fueled by my passion for solving real-world problems through data. Here, you'll find a variety of projects covering topics like machine learning, deep learning, computer vision, and more. Each project reflects my hands-on experience and curiosity in exploring different aspects of data science.
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This use case aims to identify fraudulent credit card transactions from a highly imbalanced dataset containing 284,807 transactions, of which only 492 are fraudulent (0.172%). The features have been PCA-transformed for confidentiality reasons, with 'Time' and 'Amount' being the only unaltered variables. The focus is on using techniques to handle class imbalance, with the Area Under the Precision-Recall Curve (AUPRC) as the primary evaluation metric for model performance.
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This use case uses unsupervised learning to categorize countries based on socio-economic and health factors to aid HELP International in determining which countries are in the most need of assistance. The goal is to help the CEO allocate $10 million strategically by identifying countries with the greatest need for humanitarian aid, based on development indicators.