This project focuses on detecting fraudulent payment transactions using machine learning techniques. The goal is to empower financial security by identifying and preventing fraudulent activities in payment systems.

The dataset used for this project is available on Kaggle: Payment Fraud: Empowering Financial Security

- Data loading and initial exploration
- Data cleaning (handling missing values)
- Feature encoding (Label Encoding for categorical features)
- Feature scaling (Standardization)
- Train/test split
- Model training and evaluation using:
	- Logistic Regression
	- Decision Tree Classifier
	- Random Forest Classifier
- Performance reporting for each model

1. Clone this repository.
2. Download the dataset from the Kaggle link above and place it in your project directory.
3. Open payment-fraud-analysis.ipynb and follow the steps for data exploration, preprocessing, and model building.

• Python 3.x
• Jupyter Notebook
• pandas
• numpy
• scikit-learn
• matplotlib
• seaborn

You can install the required packages using:

pip install pandas numpy scikit-learn matplotlib seaborn

• Run the notebook step by step to explore the data, preprocess it, train machine learning models, and evaluate their performance.