This project aims to predict credit card fraud using Python programming language. The project will use a dataset containing transaction data and labeled instances of fraud to train a machine learning model to predict fraudulent transactions in real-time.
The first step in the project will be to preprocess the data, removing any irrelevant or missing values, and conducting exploratory data analysis to identify trends and patterns in the data. Then, a machine learning model, such as logistic regression, decision trees, or random forests, will be trained using the cleaned data.
The performance of the model will be evaluated using metrics such as accuracy, precision, recall, and F1 score, and the model will be optimized by adjusting its hyperparameters to improve its performance.
Once the model has been trained and optimized, it will be used to predict the likelihood of fraud for new credit card transactions in real-time. The project will also explore the most important features that contribute to fraud detection, such as transaction amount, location, and time of day.
Finally, the project will create an interactive dashboard using Python libraries such as Flask or Django that allows users to input their transaction data to obtain a prediction of the likelihood of fraud. This will provide a valuable tool for financial institutions to detect fraudulent transactions and prevent financial loss.
Overall, this project will leverage the power of machine learning and Python programming to predict credit card fraud and provide a user-friendly tool for financial institutions to protect their customers' financial assets.