The goal:
The creation and evaluation of machine learning models capable of precisely detecting and predicting credit card transactions is the primary objective of this project. The emphasis is on using algorithms like as Random Forest, Decision Tree, LSTM Neural Network, and Logistic Regression to categorize transactions as either fraudulent or non-fraudulent, thereby guaranteeing the security and dependability of transactions.
Main Objectives and Approach:
Model Building:
Using transaction data, create models for Random Forest, Decision Trees, Logistic Regression, and LSTM Neural Networks. The main goal is to use patterns to categorize transactions.
Model Assessment:
Metrics like accuracy, precision, recall, and F1 score can be used to compare the models. In order to choose the best model for fraud detection, this step is essential.
Confusion Matrix Analysis:
To learn more about true positives, false positives, true negatives, and false negatives, look over the confusion matrix for each model. Preparing Data and Addressing Class Imbalance:
Normalize attributes such as 'Amount' and 'Time'. Use strategies like as oversampling or undersampling to correct the dataset's class imbalance. Examination of Features:
Analyze how important a feature is for fraud prediction. To comprehend how particular features affect fraud prediction, perform analysis and create data visualizations.
Testing for Model Robustness:
Determine the models' durability and adaptability by evaluating them in a range of circumstances. Test their effectiveness in various operational settings and update them often to stay up to speed with the evolving scope of fraudulent activity.
Testing the models' performance and adaptability in diverse operating situations is essential to ensuring their continued efficacy in the face of changing fraud tactics and transaction patterns. The dynamic nature of fraudulent activity necessitates ongoing assessment and modification. Strengthening credit card transaction fraud detection systems would reduce fraud incidents and improve the security of financial transactions. This is the main goal. This research combines advanced machine learning methods with thorough statistical analysis and rigorous model evaluation to provide a reliable, dynamic, and efficient system for detecting and stopping credit card fraud.