DETECTING ANOMALIES IN FINANCIAL TRANSACTIONS:

This project aims to detect anomalies in financial transactions using machine learning algorithms.

Goal

Enhance fraud detection through improved feature engineering, incorporating transaction frequency and user behavior.
Implement real-time monitoring with sub-second latency, providing prompt insights into transaction patterns.
Utilize behavioral analytics to identify evolving patterns, contributing to the reduction in false positives.

Methodology

Three machine learning models are employed: Logistic Regression, Decision Tree, and XGBoost.
Each model predicts anomalies independently based on the features engineered.
The predictions of the three models are combined using a Majority Voting rule.
The final prediction is determined by the majority vote of the three models, ensuring a more robust result.

How to Run the Project

Clone the project repository by executing the following command in the terminal:
```
git clone https://github.com/yukii1004/Daft
```
Navigate to the project directory.
Install the required dependencies by running:
```
pip install -r Requirements.txt
```
Finally, run the web application by executing:
```
streamlit run App.py
```

Team Members: