#BHOJESHWAR JAMRE
***Fraud Detection App***
-Overview:
This project is a web application for detecting fraudulent transactions using an XGBoost machine learning model. Users can upload their dataset, and the app will preprocess the data, make predictions, and display detailed results including fraud detection statistics and visualizations.
-Features:
Upload transaction data for fraud detection
Preprocess data automatically
Use an XGBoost model for making predictions
Display fraud detection results and statistics
Visualize transaction distributions
-Technologies Used:
Python
Streamlit
XGBoost
pandas
joblib
***STEPS FOR IMPLEMENTATION OF THE PROJECT:
1.To install the necessary dependencies for this project, use the following commands:
-Create a virtual environment (optional but recommended): python -m venv venv
-Activate the virtual environment: venv\Scripts\activate
-Install the required packages: pip install flask streamlit xgboost pandas numpy scikit-learn matplotlib joblib
2.Clone the repository(execute the below commands in CMD):
git clone https://github.com/yourusername/fraud_detection_app.git
cd fraud_detection_app
3.Run the streamlit application(execute the below commands in CMD):
streamlit run streamlit_app.py
4.Streamlit Interface:
Open your web browser and go to the URL provided by Streamlit (usually http://localhost:8501).
Upload your transaction dataset(provided in the repository).
The app will preprocess the data, make predictions, and display the results.
#BHOJESHWAR JAMRE