Credit Card Fraud Detection

This project implements a machine learning model to detect fraudulent credit card transactions using logistic regression. The code leverages Python's pandas for data manipulation, numpy for numerical operations, and scikit-learn for building and evaluating the machine learning model.

Dataset

The dataset used is creditcard.csv, which contains transaction details and a target variable indicating whether a transaction is legitimate or fraudulent.

Key Steps in the Code

1. Data Loading and Exploration:

   • The dataset is loaded using pandas and initial exploration is performed using head() to preview the data.
   • The distribution of the target variable (Class) is examined using value_counts() to understand the imbalance between legitimate and fraudulent transactions.

2. Data Balancing:

   • To address class imbalance, a random sample of legitimate transactions is taken to match the number of fraudulent transactions, ensuring a balanced dataset for training.

3. Feature and Target Separation:

   • Features (x) and target (y) variables are separated. The target variable is the "Class" column, indicating transaction legitimacy.

4. Data Splitting:

   • The dataset is split into training and testing sets using train_test_split with stratified sampling to maintain the distribution of the target variable.

5. Model Training:

   • A LogisticRegression model is instantiated and trained on the training data (x_train, y_train) with a maximum iteration of 1000 to ensure convergence.

6. Model Evaluation:

   • The accuracy of the model is 94% on both the training and testing datasets, providing insights into the model's performance.

Output

• The code outputs the accuracy of the model on both the training and testing datasets, demonstrating its effectiveness in detecting fraudulent transactions.

This project serves as a practical example of using logistic regression for binary classification tasks in financial fraud detection.