🛡️ Online Payment Fraud Detection

🔍 Overview

This repository focuses on detecting online payment fraud in transactions involving credit cards, UPI, and other modes. Fraud detection is essential for securing financial transactions and protecting users from cyber threats. In this project, we perform an Exploratory Data Analysis (EDA) followed by building a Machine Learning Model to predict potential fraudulent activities.

The project is divided into the following main sections:

• Exploratory Data Analysis (EDA): Gaining insights into the transaction patterns and identifying potential fraud indicators.
• Fraud Detection Model: Training a machine learning model to detect fraud in real-time transactions.

🏗️ Project Features

• 📊 Comprehensive EDA to uncover important patterns, correlations, and outliers in the transaction data.
• 🔄 Data Preprocessing including handling missing values, encoding categorical features, and feature engineering.
• 🤖 Machine Learning Models such as Logistic Regression, Decision Trees, and Random Forest to identify fraudulent transactions.
• 📈 Evaluation Metrics including accuracy, precision, recall, F1-score, and ROC-AUC to measure model performance.
• 🎨 Custom Visualizations to interpret model results and explore data insights.

📂 Project Structure

online_fraud_detection/
│
├── data/
│   └── transactions.csv                # Transaction data
│
├── notebooks/
│   ├── Online_fraud_detection_EDA.ipynb        # EDA Notebook
│   └── Online_fraud_detection_model.ipynb      # Model Notebook
│
├── images/                             # Visualizations used in the analysis
│
├── README.md                           # This README file
├── requirements.txt                    # Required dependencies
└── LICENSE                             # License file

🛠️ Installation

Prerequisites

Ensure the following are installed on your system:

• 🐍 Python 3.8+
• 📒 Jupyter Notebook
• 🐼 Pandas
• 🧮 NumPy
• 📊 Matplotlib
• 📉 Scikit-learn
• 🎨 Seaborn

Installation Steps

1. Clone the repository to your local machine:

   git clone https://github.com/yourusername/online_fraud_detection.git

2. Navigate to the project directory:

   cd online_fraud_detection

3. Install dependencies listed in requirements.txt:

   pip install -r requirements.txt

🚀 Usage

Running the Exploratory Data Analysis (EDA)

1. Open the Online_fraud_detection_EDA.ipynb in Jupyter Notebook.
2. Execute the cells step by step to explore transaction behavior, identify potential fraud indicators, and gain insights through visualizations.

Running the Fraud Detection Model

1. Open the Online_fraud_detection_model.ipynb in Jupyter Notebook.
2. Follow the steps for data preprocessing and training machine learning models.
3. Evaluate the model's performance using metrics like precision, recall, and F1-score.
4. Tune model hyperparameters to optimize fraud detection performance.

🧠 Key Insights from EDA

During the EDA, some key insights were uncovered:

• 💡 Correlation Analysis: Certain features show a high correlation with fraudulent transactions.
• 🔍 Anomalies: Several high-value transactions were identified as potential fraud cases.
• 📊 Visualization: Fraud patterns were visualized using scatter plots, histograms, and correlation heatmaps.

🔧 Model Pipeline

1. Data Preprocessing:
   • Handle missing data.
   • Encode categorical variables.
   • Standardize/normalize continuous features.
2. Model Training:
   • Logistic Regression, Decision Trees, Random Forest.
   • Compare performance using cross-validation.
3. Evaluation:
   • Precision, recall, F1-score.
   • ROC-AUC curve analysis.

🧪 Model Performance

The models were evaluated based on the following metrics:

• Accuracy: 📈 High accuracy indicates that the model correctly identifies the majority of transactions as either fraudulent or legitimate.
• Precision: 🎯 Precision reflects the percentage of correctly identified fraudulent transactions among all predicted frauds.
• Recall: 🔄 Recall indicates the percentage of actual frauds correctly identified by the model.
• F1-Score: ⚖️ F1-Score is the harmonic mean of precision and recall, providing a balance between both.

📊 Visualizations

Some important visualizations generated during the project include:

• 📉 Correlation Heatmap: Shows relationships between transaction features and fraud occurrence.
• 🔍 Scatter Plots: Visualizes the distribution of transaction amounts and fraud probability.
• 📈 ROC Curve: Evaluates the model's classification threshold trade-offs.