This project aims to detect fraudulent credit card transactions using a Logistic Regression model. The dataset used is highly imbalanced, containing both legitimate and fraudulent transactions.
*Data Preprocessing: Load, inspect, and clean the dataset. *Data Analysis: Analyze and visualize transaction distributions and amounts. *Under-Sampling: Balance the dataset by under-sampling legitimate transactions. *Model Training: Train a Logistic Regression model. *Model Evaluation: Evaluate the model's accuracy on training and test data.