Full project on GitHub click link https://bit.ly/Credit_card_fraud
In today's digital age, online transactions have become an integral part of our daily lives. Whether we're shopping online, paying bills, or booking flights, we rely heavily on credit and debit cards for these transactions. While the convenience of cashless payments is undeniable, it also brings with it the risk of credit card fraud.
Credit card fraud is a prevalent issue that affects both consumers and businesses. According to recent statistics, billions of dollars are lost annually due to fraudulent activities involving credit cards. Detecting and preventing such fraudulent activities is a challenging task for financial institutions and individuals alike. However, with advancements in technology and machine learning, we now have powerful tools at our disposal to combat this problem effectively.
In this article, we will explore a real-world project focused on detecting credit card fraud using machine learning techniques. We'll walk you through the entire process, from data acquisition to model deployment, and discuss the various steps involved in building a robust fraud detection system.
Project Overview
Our journey begins with a dataset obtained from Kaggle, a popular platform for data science competitions and datasets. The dataset in question contains credit card transaction data, and our goal is to build a machine learning model that can accurately identify fraudulent transactions. Let's dive into the project's key steps.
Data Acquisition and Preprocessing
The first step in any machine learning project is data acquisition. We used the Kaggle API to download the credit card fraud dataset. This dataset contains 284,807 transactions, each with 31 features, including time, transaction amount, and 28 anonymized features derived from the credit card's details. The 'Class' column indicates whether a transaction is fraudulent (1) or legitimate (0).
To prepare the data for modeling, we performed several preprocessing steps:
Scaling Amount: We used a robust scaler to normalize the 'Amount' column since it had a wide range of values. This ensures that all features are on a similar scale, preventing any particular feature from dominating the model.
Scaling Time: We scaled the 'Time' column to have values between 0 and 1, making it easier for the model to capture patterns in the data.
Data Splitting: We divided the dataset into training, testing, and validation sets. Approximately 80% of the data was used for training, 10% for testing, and 10% for validation.
Exploratory Data Analysis (EDA)
Before diving into modeling, we performed exploratory data analysis to gain insights into the dataset. We visualized the distribution of features and the class imbalance between legitimate and fraudulent transactions. The class distribution revealed a severe class imbalance, with a vast majority of transactions being legitimate. This imbalance needed to be addressed to prevent model bias.
Model Selection and Training
For this project, we experimented with two machine learning models: Logistic Regression and a shallow Neural Network. These models offer a good balance between performance and interpretability.
Logistic Regression: Logistic Regression is a well-established algorithm for binary classification tasks. We trained the model on the preprocessed data and achieved an impressive accuracy of over 99% on the training set. However, its recall on the validation set for fraudulent transactions was relatively low.
Shallow Neural Network: We also implemented a shallow neural network with one hidden layer. The network was trained using the Adam optimizer and binary cross-entropy loss. Batch normalization was applied to improve convergence. The neural network outperformed Logistic Regression in terms of recall for fraudulent transactions while maintaining high accuracy.
Model Evaluation and Validation
To assess the model's performance, we used metrics such as precision, recall, F1-score, and accuracy. Given the class imbalance in the dataset, precision and recall are particularly important. Precision measures the ratio of true positive predictions to all positive predictions, while recall measures the ratio of true positives to all actual positive cases. Achieving high recall is crucial in credit card fraud detection because it minimizes false negatives, ensuring that fraudulent transactions are not overlooked.
Results and Conclusion
Our machine learning models achieved impressive results in detecting credit card fraud. The shallow neural network outperformed Logistic Regression, offering a good balance between precision and recall. In fraud detection, it is crucial to minimize false negatives to prevent financial losses for both consumers and businesses. Our model's high recall indicates its effectiveness in identifying fraudulent transactions.
In conclusion, machine learning plays a vital role in mitigating credit card fraud. By leveraging data and advanced algorithms, we can build robust fraud detection systems that protect consumers and businesses from financial losses. However, it's essential to continuously update and improve these models to stay ahead of evolving fraud techniques.
As technology continues to advance, so do the tools available for fraud detection. With ongoing research and development in the field of machine learning and artificial intelligence, we can look forward to even more effective and efficient fraud detection solutions in the future.
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