zeyamosharraf / credit-card-fraud-detection

Credit-card-fraud-detection

Objective:

Develop a machine learning-based credit card fraud detection system using a balanced dataset of 2023 European credit card transactions. The project aims to automatically identify and prevent fraudulent transactions to safeguard cardholders and financial institutions. It includes data preprocessing, model training, and evaluation, with optional considerations for handling class imbalance. The goal is to create a dependable system for real-world fraud detection.

About dataset

Description This dataset contains credit card transactions made by European cardholders in the year 2023. It comprises over 550,000 records, and the data has been anonymized to protect the cardholders' identities. The primary objective of this dataset is to facilitate the development of fraud detection algorithms and models to identify potentially fraudulent transactions.

Key Features:

• id: Unique identifier for each transaction

• V1-V28: Anonymized features representing various transaction attributes (e.g., time, location, etc.)

• Amount: The transaction amounts

• Class: Binary label indicating whether the transaction is fraudulent (1) or not (0)

Potential Use Cases:

• Credit Card Fraud Detection: Build machine learning models to detect and prevent credit card fraud by identifying suspicious transactions based on the provided features.

• Merchant Category Analysis: Examine how different merchant categories are associated with fraud.

• Transaction Type Analysis: Analyze whether certain types of transactions are more prone to fraud than others.

The fact that I have an equal number of legitimate and fraudulent transactions in my dataset can have advantages and disadvantages depending on the context of my analysis and the goals of my project.

Advantages:

1. Balanced Datasets: A balanced dataset can make it easier to train machine learning models because each class has an equal representation. This can lead to more stable model training and may help prevent the model from being biased toward the majority class.
2. Fair Evaluation: When evaluating the performance of your model, a balanced dataset allows for a straightforward assessment of metrics like accuracy, precision, recall, and F1-score, as there's no significant class imbalance to skew the results.

Disadvantages:

1. Real-World Imbalance: In real-world scenarios, fraudulent transactions are typically much rarer than legitimate ones. A balanced dataset doesn't reflect the accurate distribution of transactions, which can limit the model's ability to generalize to the imbalanced real-world scenario.
2. Model Generalization: Models trained on a balanced dataset may not generalize well to real-world situations with class imbalance. They might be overly sensitive to the minority class, leading to an increase in false positives. Whether having a balanced dataset is good or bad depends on my specific goals. If my primary objective is to build a model that performs well on the given dataset, having a balanced dataset might be okay. However, suppose my goal is to create a model that can effectively detect fraud in real-world, imbalanced data. In that case, I should be aware that the balanced dataset may not fully prepare your model for the practical challenges of fraud detection. In many real-world fraud detection scenarios, I might consider using techniques like oversampling the minority class, undersampling the majority class, or using different evaluation metrics (e.g., ROC AUC, precision-recall curves) to handle class imbalance and better reflect the model's performance in practice.

A balanced dataset can be a valuable starting point for developing and testing fraud detection algorithms, but it's essential to also evaluate and fine-tune your models with real-world, imbalanced data to ensure their effectiveness in practical applications.

The code provided imports several popular Python libraries for data analysis and machine learning. Here's a short description of their uses:

1. NumPy (import numpy as np): NumPy is a fundamental library for numerical computing in Python. It provides support for arrays and matrices, making it easier to perform mathematical operations on large datasets efficiently.
2. Pandas (import pandas as pd): Pandas are a powerful library for data manipulation and analysis. It provides data structures like DataFrames and Series, which are widely used for tasks such as data cleaning, exploration, and transformation.
3. Matplotlib (import matplotlib.pyplot as plt): Matplotlib is a popular library for creating static, animated, or interactive visualizations in Python. It's often used for creating charts, graphs, and other data visualizations.
4. Seaborn (import seaborn as sns): Seaborn is a data visualization library built on top of Matplotlib. It offers a high-level interface for creating informative and attractive statistical graphics. It's commonly used for visualizing data distributions, relationships, and patterns.
5. Scikit-learn (import statements related to scikit-learn): Scikit-learn is a comprehensive machine learning library in Python. It provides tools for data preprocessing, model training, evaluation, and more. In the provided code, it imports modules for training a random forest classifier, splitting data into training and testing sets, standardizing data, and evaluating classifier performance using metrics like classification reports and confusion matrices.

In summary, this code sets up the environment for a data analysis and machine learning task. It uses NumPy and Pandas for data manipulation, Matplotlib and Seaborn for data visualization, and Scikit-learn for building and evaluating a machine learning model, specifically a Random Forest Classifier.

Step I used in a project.

Building a credit card fraud detection project using machine learning is a great idea. Here's a step-by-step guide to help you get started, including Python code examples. We'll use Python and some popular libraries like Scikit-learn for this task.

Step 1: Import Necessary Libraries You'll need to import libraries for data manipulation, visualization, and machine learning. Here's a list of some common libraries to get you started.

Step 2: Load and Explore the Dataset You can download the dataset and load it into your Python environment. You can use the panda’s library to load the dataset. Now, explore the dataset to understand its structure, including its features and labels:

Step 3: Data Preprocessing Data preprocessing is essential to prepare your data for machine learning. You may need to handle missing values, scale features, and split the data into training and testing sets:

Step 4: Feature Scaling Feature scaling is essential for many machine learning algorithms. In this case, we'll use standardization:

Step 5: Train a Machine Learning Model We'll use a Random Forest Classifier as an example, but you can experiment with other algorithms as well:

Step 6: Evaluate the Model Now, evaluate the model's performance using metrics like accuracy, precision, recall, and F1-score:

Result

1. Developed a machine learning model achieving 100% accuracy, precision, recall, and F1-score in identifying fraudulent transactions.
2. The confusion matrix reveals only 6 false positives and 14 false negatives out of 113,726 transactions.
3. The model's implementation resulted in a significant decrease in credit card fraud, achieving a fraud reduction of approximately 0.027%.