Fraudulent Transaction Identification System

Overview

This project aims to develop a system to identify fraudulent transactions in financial data using Python and Machine Learning techniques, specifically Logistic Regression. The system analyzes a dataset of transaction data to identify patterns associated with fraudulent activities, trains a logistic regression model, and visualizes the results to provide insights into fraud detection.

Libraries Used

• NumPy
• pandas
• seaborn
• matplotlib
• scikit-learn
• imbalanced-learn

Dataset

The dataset used for this project is a transaction dataset sourced from Kaggle, containing various features related to financial transactions. The target variable indicates whether a transaction is fraudulent (1) or not (0).

Data Exploration

• Checking for missing values.
• Summary statistics to understand data distribution.
• Visualization of class distribution to assess the balance of the dataset.
• Correlation heatmap to identify relationships between features.

Model Development

1. Data Preprocessing:

   • Separating features and the target variable.
   • Applying SMOTE to handle class imbalance.
   • Splitting the data into training and testing sets.

2. Model Training:

   • Training a Logistic Regression model on the training data.
   • Making predictions on the test data.

3. Model Evaluation:

   • Calculating accuracy and generating a classification report.
   • Visualizing the confusion matrix to assess model performance.

Visualization

• Feature importance analysis to identify key features affecting fraudulent transactions.
• Confusion matrix visualization for performance assessment.