Credit-Card-Fraud-Detection-Using-Transactions-Data

This project focuses on detecting fraudulent credit card transactions using advanced machine-learning techniques. By leveraging various classification models, including Logistic Regression, Linear Discriminant Analysis (LDA), and Gaussian Naive Bayes (GNB), this project aims to identify potentially fraudulent activities with high accuracy.

Key Components:

Data Preparation:

Data Loading:

Imported and explored the dataset to understand its structure and characteristics.

Preprocessing:

Applied normalization techniques to preprocess the data for effective model training.

Resampling:

Utilized SMOTE for balancing the dataset to address the class imbalance.

Model Building:

Train-Test Split:

Divided the dataset into training and testing sets (80% training, 20% testing) to ensure robust model evaluation.

Logistic Regression:

Implemented and evaluated the Logistic Regression model using 10-fold cross-validation, achieving a mean score of 0.9536.

Linear Discriminant Analysis (LDA):

Applied LDA for classification and assessed its performance with a mean score of 0.9259.

Gaussian Naive Bayes (GNB):

Evaluated the GNB model, which achieved a mean score of 0.9035.

Model Evaluation:

Accuracy and Metrics:

Evaluated the best-performing model (Logistic Regression) using accuracy score, confusion matrix, and classification report, resulting in an accuracy of 0.95.

Final Prediction:

Tested the model on sample data to classify transactions as either fraudulent or normal.

Technologies Used:

Python, Scikit-learn, Pandas, NumPy