Summary

Introduction

Credit card fraud is a significant problem that affects both individuals and financial institutions. It involves unauthorized transactions and can lead to financial loss for both cardholders and businesses. Detecting fraudulent credit card transactions is crucial to prevent such incidents and mitigate their impact. In this project, we aim to develop a fraud detection system using machine learning techniques to identify fraudulent credit card transactions accurately.

Dataset

We will be using the "creditcard.csv" dataset, which contains a large number of credit card transactions. The dataset includes various features such as time, transaction amount, and anonymized numerical features (V1, V2, V3, etc.) obtained through principal component analysis (PCA). The last column, "Class," indicates whether a transaction is fraudulent (1) or not (0).

Exploratory Data Analysis (EDA)

To gain insights into the dataset, we performed exploratory data analysis (EDA) using various visualizations and statistical measures. Some key observations from the EDA are as follows:

• The dataset contains 284,807 transactions, with a very low proportion of fraudulent transactions (492 out of 284,807).
• The distribution of transaction amounts shows a wide range, with a mean of 88.35 and a maximum value of 25,691.16.
• The "Class" distribution plot indicates a severe class imbalance, with a vast majority of non-fraudulent transactions and only a small number of fraudulent transactions.
• We identified several features that have a strong positive or negative correlation with the "Class" (fraudulent vs. non-fraudulent) column, such as V2, V3, V4, and V5.

Data Preprocessing

To prepare the data for training the machine learning models, we performed the following preprocessing steps:

• Split the dataset into fraud and non-fraud dataframes to balance the classes.
• Detected outliers in the V2, V3, V4, and V5 features using the interquartile range (IQR) method and replaced them with the respective feature's median value.
• Standardized the numerical features using the StandardScaler to ensure all features have similar scales.

Model Training and Evaluation

For fraud detection, we experimented with multiple machine learning models, including Decision Tree Classifier, Support Vector Classifier (SVC), and XGBoost Classifier. We employed cross-validation and hyperparameter tuning techniques to optimize the models' performance. The evaluation metrics used for model assessment are as follows:

• F1-score: a measure of the balance between precision and recall, which is important for imbalanced datasets.
• Accuracy: the overall accuracy of the model in predicting fraudulent and non-fraudulent transactions.

Results and Performance

After training and evaluating the models, we obtained the following results:

• Decision Tree Classifier: F1-score = 0.89, Accuracy = 0.90
• Support Vector Classifier (SVC): F1-score = 0.90, Accuracy = 0.92

Conclusion

In this project, we developed a fraud detection system, "Fraud Buster," that leverages machine learning algorithms to identify fraudulent credit card transactions. The system achieved promising results