During my six-month internship at STB Bank, I had the opportunity to work on an exciting project aimed at developing a credit card fraud detection system using machine learning and deep learning techniques. Here is a summary of my contributions:- Data Collection and Preparation: I supervised the collection of transaction data from STB Bank, covering one year. I then cleaned the data, handled missing values, and balanced the classes using SMOTE.-Machine Learning Models: I developed and implemented several models, including logistic regression, random forests, voting models, and artificial neural networks. The models achieved accuracies of up to 99.40%.- Model Optimization: I used advanced techniques to optimize the models, improving the efficiency and accuracy of fraud detection. This included feature selection and hyperparameter tuning.- REST API Development: I developed a REST API to interact with the models, allowing external systems to submit transactions for analysis and receive real-time predictions.- Web Application Development: I led the development of a user-friendly web application using Streamlit, enabling users to explore data interactively, visualize fraud predictions, and understand transaction trends.- Deployment on Docker and TensorFlow Serving: I used Docker and TensorFlow Serving to deploy the models in production, ensuring optimal accessibility and scalability.My experience at STB Bank has been invaluable, allowing me to apply my data science knowledge to real-world challenges and further develop my skills in a dynamic and innovative environment.

Design of a robust web application to efficiently manage theregistration files and documents of the National Office for DataProtection.

hierarchical structure of a subscriber line network and the MSANsolution