This repository contains a credit card fraud detection model implemented using logistic regression. Logistic regression is a well-known machine learning technique suitable for binary classification tasks, making it an effective choice for identifying potentially fraudulent credit card transactions.
Collect a historical dataset of credit card transactions. This dataset should include features such as transaction amount, location, time of day, and other relevant information. Each transaction should be labeled as either "fraudulent" or "non-fraudulent."
Prepare the dataset for training by handling missing values, normalizing or scaling features, encoding categorical variables, and splitting the data into training and testing subsets.
Choose logistic regression as the classification algorithm for this task. Logistic regression models the probability of an observation belonging to a particular class (fraudulent or non-fraudulent) based on the input features. The logistic function is used to squash the output into the [0, 1] range, making it suitable for binary classification.
Train the logistic regression model on the training dataset. During training, the model learns the coefficients (weights) for each feature that maximize the likelihood of the observed labels given the input data.
Assess the model's performance using the testing dataset. Common evaluation metrics for fraud detection include accuracy, precision, recall, F1-score, and the area under the Receiver Operating Characteristic (ROC-AUC) curve.
Determine a suitable threshold for classifying transactions as fraudulent based on the model's predicted probabilities. Adjusting the threshold can impact the trade-off between false positives and false negatives.
Once satisfied with the model's performance, deploy it in a production environment to analyze real-time credit card transactions. New transactions are fed into the model, and it makes predictions about their authenticity.
Continuously monitor the model's performance to ensure its ongoing effectiveness. Over time, the model may need to be retrained with fresh data to adapt to changing fraud patterns.
Feature engineering is crucial for building an effective fraud detection model. It involves selecting and transforming relevant features to improve the model's performance, such as deriving new features, aggregating data, or applying dimensionality reduction techniques.
Credit card fraud datasets often exhibit imbalanced classes, with a majority of transactions being non-fraudulent. Techniques like oversampling, undersampling, or using synthetic data generation methods can help address this imbalance.
Logistic regression is a simple yet powerful technique for credit card fraud detection. More complex models like decision trees, random forests, or neural networks can also be used for advanced fraud detection systems, depending on specific requirements and dataset characteristics.
Feel free to use this repository as a guide to implementing your credit card fraud detection system using logistic regression.
This was submitted under (IMIT-2999), and we are Group-3, consisting of: