Credit-Card-Fraud-Detection

Overview

This repository contains code and documentation for predicting fraudulent credit card transactions. The project involves data preprocessing, feature engineering, model training, and performance evaluation

Objective

Identify fraudulent transactions in credit card data.

1.Data Preprocessing:

• Load and explore the credit card dataset.
• Handle missing values and encode categorical variables.
• Balance the dataset using upsampling.

2.Feature Engineering:

• Create features from categorical data using LabelEncoder.
• Drop unnecessary columns and split data into features and target variables.

3.Model Training and Evaluation:

• Train a RandomForestClassifier on the training data.
• Evaluate model performance using metrics such as accuracy, precision, recall, F1-score, Matthews correlation coefficient
• Visualize confusion matrix, ROC curve, and precision-recall curve.