Topic: Predicting Credit Card Fraudulent Transactions**
Credit card fraud is a significant global issue, posing challenges for financial institutions due to the low incidence of fraud amid a high volume of legitimate transactions. This project aims to develop a machine learning model to predict fraudulent transactions using a publicly available dataset. The dataset includes transaction records with features derived from PCA, transaction time, and amount. Our approach involves several key steps: preprocessing the data to handle missing values and normalize features, conducting thorough exploratory data analysis to understand data distributions and relationships, engineering new features to enhance model accuracy, and experimenting with a range of machine learning algorithms such as logistic regression, decision trees, random forests, gradient boosting, and neural networks. The project goals are to create a robust model for fraud detection, evaluate its performance using metrics such as precision. We will employ hyper parameter tuning and cross-validation to optimize model performance and ensure generalizability. Expected outcomes include an effective predictive model. This project aims to significantly enhance fraud detection capabilities, aiding financial institutions in minimizing fraudulent activities and protecting customers from financial losses. This project aims to significantly enhance fraud detection capabilities, aiding financial institutions in minimizing fraudulent activities and protecting customers from financial losses. Ultimately, the objective is to provide a reliable tool that improves financial security and fosters greater trust between customers and financial institutions, reducing the overall impact of credit card fraud.