This Python script uses machine learning models to detect fraudulent credit card transactions in a dataset. The dataset is loaded using the pandas library and preprocessed for machine learning by removing irrelevant features and rescaling the data.
numpy pandas matplotlib seaborn scipy scikit-learn
To use this script, ensure you have all the dependencies installed. Then, follow these steps: Clone the repository to your local machine. Open a terminal window and navigate to the directory where the script is saved. Run the script by typing the following command: python credit_card_fraud_detection.py. The script will load the dataset, preprocess the data, and apply two outlier detection models: Isolation Forest and Local Outlier Factor. The script will print the number of errors for each model and display the classification report and accuracy score for each model.
The dataset used in this script is the Credit Card Fraud Detection dataset from Kaggle. ( Dataset Link : https://www.kaggle.com/datasets/mlg-ulb/creditcardfraud ) The dataset contains credit card transactions made by European cardholders over a two-day period in September 2013. The dataset has 31 columns, with the Class column indicating whether the transaction was fraudulent (1) or valid (0).
Suppose you want to detect fraudulent credit card transactions in a dataset. You can use this script to preprocess the data and apply two outlier detection models to identify potential fraudulent transactions.