Skip to content
View eatangphd's full-sized avatar

Block or report eatangphd

Block user

Prevent this user from interacting with your repositories and sending you notifications. Learn more about blocking users.

You must be logged in to block users.

Maximum 250 characters. Please don’t include any personal information such as legal names or email addresses. Markdown is supported. This note will only be visible to you.
Report abuse

Contact GitHub support about this user’s behavior. Learn more about reporting abuse.

Report abuse
eatangphd/README.md
  • 👋 Hi, I’m @eatangphd
  • 👀 I’m interested in ... working in FinTech industries as a Data Scientist
  • 🌱 I’m currently learning ... Fraud Detection Analysis, SQL for Data Science
  • 💞️ I’m looking to collaborate on ... Quant Research
  • 📫 How to reach me ... liz21atang@gmail.com
  • 😄 Pronouns: ... she/her
  • ⚡ Fun fact:... I have lived and studied on three different continents (Africa, Europe, & North America).

Popular repositories Loading

  1. eatangphd eatangphd Public

    Config files for my GitHub profile.

  2. Machine_Learning_Basics Machine_Learning_Basics Public

    Taking the Google Machine Learning Crash Course. This repo is to enable me track my activity in the course

    Jupyter Notebook

  3. ComputerVision ComputerVision Public

    Learn Computer vision skills as well as Deep Learning with Computer Vision

  4. spending-anomaly-detector spending-anomaly-detector Public

    A Python-based anomaly detection system for personal financial transactions using statistical methods and machine learning techniques to identify unusual spending patterns and potential fraud. Topi…

  5. Fraud_Detection_Rules_Engine_Project Fraud_Detection_Rules_Engine_Project Public

    A Python-based simple rules engine to flag potential fraudulent personal spending transactions. Topics: python, rules-engine, anomaly-detection, personal-finance, fraud-detection, data-science, mat…

  6. IEEE-CIS-Fraud-Detection IEEE-CIS-Fraud-Detection Public

    A production-ready fraud detection system using Random Forest & SMOTE. Handles severe class imbalance (3.5% fraud rate) and 80%+ missing data. Achieves 0.88 F1-score on IEEE-CIS dataset.