Fraudulent-Transactions-Prediction

Overview

This repository contains a Fraud Detection project that focuses on predicting whether a financial transaction is fraudulent or not. The project utilizes machine learning models, specifically the Random Forest Classifier. The trained model is saved for future use, and a Streamlit app is provided for easy interaction with the model.

Data

The dataset used for training and analysis is named Fraud.csv. It has about 6.5 million rows and 10 columns, containing various features related to financial transactions.

Project Structure

The project folder includes the following files:

1. model.sav: The saved Random Forest Classifier model.
2. scaler.sav: The saved StandardScaler object used for feature scaling.
3. Requirements.txt: A file containing the required dependencies for the project.
4. app.py: A Streamlit app that allows users to input transaction details and receive predictions.
5. fraud-detection.ipynb: A Jupyter Notebook file containing the exploratory data analysis (EDA), data preprocessing, model training, and evaluation.

How to Use

Installation

Make sure you have the following installed:

• Python (version 3.6 or higher)
• Pip (package installer for Python)

1. Clone the repository to your local machine:

   git clone https://github.com/hardikjp7/Fraudulent-Transactions-Prediction.git

2. Navigate to the project directory:

   cd Fraudulent-Transactions-Prediction

3. Install the required dependencies:

   pip install -r requirements.txt

Usage

Streamlit App

1. Ensure you have the required dependencies installed.

2. Run the Streamlit app:

   streamlit run app.py

3. Open your web browser to use the Fraud Detection App.

Model Deployment

If you want to use the trained model programmatically:

import pickle
import numpy as np

# Load the saved model and scaler
model = pickle.load(open('model.sav', 'rb'))
scaler = pickle.load(open('scaler.sav', 'rb'))

# Create an input array with transaction details
input_array = np.array([[228, 5, 117563.11, 0.0, 208908.41, 0]])

# Scale the input array
input_array_scaled = scaler.transform(input_array)

# Make a prediction
prediction = model.predict(input_array_scaled)

print("Prediction:", prediction)

Remember to adjust the input array according to the specific details of the transaction you want to predict.

Demo