Credit-card-Fraud-detection-analysis

Data link : [https://www.kaggle.com/datasets/neharoychoudhury/credit-card-fraud-data]

To visualise the interactive plots using plotly, refer:

Code link : [https://www.kaggle.com/code/neharoychoudhury/credit-card-fraud-detection-analysis]

Overview

The Credit Card Fraud Prediction project aims to develop a machine learning-based system to detect fraudulent transactions. By analyzing various features of credit card transactions, the goal is to create a model that accurately identifies fraudulent activities, helping to reduce financial losses and enhance transaction security.

Dataset

The dataset used in this project includes credit card transactions from the western United States. It contains 14,446 entries with the following columns:

• trans_date_trans_time: The exact date and time of the transaction.
• amt: The transaction amount.
• city: Customer's city.
• state: Customer's state.
• lat: Latitude of the customer's location.
• long: Longitude of the customer's location.
• dob: Customer's date of birth (used to derive age).
• is_fraud: Binary indicator (0 or 1) of whether the transaction was fraudulent.

Libraries

This project utilizes various Python libraries:

• pandas: For data manipulation and analysis.
• numpy: For numerical operations.
• seaborn: For statistical data visualization.
• matplotlib.pyplot: For static plotting.
• plotly.express and plotly.graph_objects: For interactive visualizations.
• scikit-learn: For machine learning, including:
• train_test_split and GridSearchCV for model evaluation and hyperparameter tuning.
• accuracy_score, classification_report, and confusion_matrix for performance metrics.
• LogisticRegression, SVC, and RandomForestClassifier for classification algorithms.
• LabelEncoder and StandardScaler for data preprocessing.