This is the CodSoft task 5.

This repository hosts a data science and machine learning project focused on the detection of credit card fraud. Credit card fraud is a significant problem, with financial institutions and cardholders suffering from fraudulent transactions. The main goals of this project are to:

Data Exploration: Analyze the provided dataset to understand the distribution of legitimate and fraudulent transactions, as well as the features associated with these transactions.

Data Preprocessing: Clean and prepare the data for machine learning, which includes handling missing values, scaling features, and addressing class imbalance issues.

Feature Engineering: Create new features and modify existing ones to improve the model's ability to detect fraud.

Model Building: Develop and train machine learning models to predict fraudulent transactions using algorithms such as Logistic Regression.

Model Evaluation: Assess the performance of the models using metrics like precision, recall, F1-score, and ROC AUC. Employ techniques like cross-validation to ensure model generalization.

Visualization: Visualize the results and insights from the analysis using matplotlib.

Contribute: This project is open to contributions aimed at enhancing the fraud detection models, exploring additional features, or improving the project's documentation.

Get Dataset from this link:https://www.kaggle.com/datasets/mlg-ulb/creditcardfraud