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https://github.com/vaibhav-kesarwani/online_payment_fraud_detection

🔒💳 𝗢𝗻𝗹𝗶𝗻𝗲 𝗣𝗮𝘆𝗺𝗲𝗻𝘁 𝗙𝗿𝗮𝘂𝗱 𝗗𝗲𝘁𝗲𝗰𝘁𝗶𝗼𝗻 is a data science project that uses machine learning to identify fraudulent transactions in online payment systems. The project analyzes transaction data to detect suspicious activity, helping to enhance security and reduce financial losses. 🚨📊
https://github.com/vaibhav-kesarwani/online_payment_fraud_detection

juypter-notebook kaggle-dataset numpy pandas plotly python3 sklearn

Last synced: 29 days ago
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🔒💳 𝗢𝗻𝗹𝗶𝗻𝗲 𝗣𝗮𝘆𝗺𝗲𝗻𝘁 𝗙𝗿𝗮𝘂𝗱 𝗗𝗲𝘁𝗲𝗰𝘁𝗶𝗼𝗻 is a data science project that uses machine learning to identify fraudulent transactions in online payment systems. The project analyzes transaction data to detect suspicious activity, helping to enhance security and reduce financial losses. 🚨📊

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README 

        
# Online Payment Fraud Detection



## Project Overview

**Online Payment Fraud Detection** is a data science project aimed at identifying fraudulent transactions in online payment systems. The project leverages machine learning techniques to analyze transaction data and distinguish between legitimate and fraudulent activities. By building and evaluating predictive models, the project aims to enhance security measures and reduce financial losses due to fraud.



## Introduction

With the rise of e-commerce and digital transactions, online payment fraud has become a significant threat. This project focuses on building robust machine learning models to detect fraudulent transactions effectively. The models are trained on historical transaction data and are designed to generalize well to unseen data, ensuring reliable fraud detection in real-world scenarios.



## Dataset

The dataset used for this project contains records of online transactions, each labeled as fraudulent or legitimate. It includes features such as transaction amount, payment method, location, and time of transaction. The dataset is cleaned, preprocessed, and split into training and testing sets to develop and evaluate the models.



- **Dataset link**: [Kaggle link](https://www.kaggle.com/datasets/jainilcoder/online-payment-fraud-detection)



An example of the data structure:

```csv

1,PAYMENT,9839.64,C1231006815,170136.0,160296.36,M1979787155,0.0,0.0,0,0

1,PAYMENT,1864.28,C1666544295,21249.0,19384.72,M2044282225,0.0,0.0,0,0

```



The data file `onlinefraud.csv` should be placed in the root directory of the project.



## Installation



To run this project locally, you'll need to have Python installed. Follow these steps to set up the environment:



1. **Clone the Repository:**

```bash

git clone https://github.com/Vaibhav-kesarwani/Online_Payment_Fraud_Detection.git

cd Online_Payment_Fraud_Detection

```



2. **Create a Virtual Environment (optional but recommended):**

```bash

python -m venv venv

source venv/bin/activate  # On Windows use `venv\Scripts\activate`

```



3. **Install Required Packages:** Install the dependencies by running

```bash

pip install -r requirements.txt

```



## Usage



### Running the Project

To run the Text Emotion Classifier, follow these steps:

1. **Prepare the Dataset:** Ensure that your `onlinefraud.csv` file is in the root directory. This file should contain the text data and corresponding labels, separated by a comma `(,)`.

2. **Run the Script:** Execute the main script to load the data and perform emotion classification

```bash

python main.ipynb

```

3. **Output:** The script will print the first few rows of the dataset to the console, showing the text samples and their associated emotion labels.



## Transaction Type Distribution Visualization



To understand the distribution of different transaction types within the dataset, a pie chart was created using Plotly Express. This chart provides a clear visual representation of the proportion of each transaction type, such as 'fraudulent' and 'legitimate,' in the dataset.



The steps involved include:

- Counting the occurrences of each transaction type.

- Extracting the transaction types and their respective counts.

- Creating a donut-style pie chart to visualize the distribution, with a hole in the center to emphasize the relative sizes of each slice.



The resulting chart helps in quickly identifying which transaction type is more prevalent, offering valuable insights into the dataset's composition.



```python

type = data['type'].value_counts()

transactions = type.index

quantity = type.values



import plotly.express as px # type: ignore

figure = px.pie(data, values = quantity, names = transactions, hole = 0.5, title = "Distribution of Transaction Type")

figure.show()

```





![visualise](https://github.com/user-attachments/assets/342d97d0-d6b3-4d08-abee-993cf87f5855)



## Model Training

The model training is performed within the `main.ipynb` script, which processes the text data and trains a Sequential model using sklearn. You can modify the model architecture, training parameters, or the data processing steps within this script.



```python

# Training the machine learning model

from sklearn.tree import DecisionTreeClassifier # type: ignore

xtrain, xtest, ytrain, ytest = train_test_split(x, y, test_size = 0.20, random_state = 42)

model = DecisionTreeClassifier()

model.fit(xtrain, ytrain)

model.score(xtest, ytest)

```



## Prediction

After training the model, you can use it to predict emotions from new text inputs. Implement the prediction logic in a separate script or extend `main.ipynb` to include a prediction function.



```python

# Prediction

# features = [type, amount, oldbalanceOrg, newbalanceOrig]

features = np.array([[4, 9000.60, 9000.60, 0.0]])

print(model.predict(features))

```



## File Structure

Here is an overview of the project directory structure:



```lua

Online_Payment_Fraud_Detection/

├── Images

│   └── visualise.png

│

├── main.ipynb                # Jupyter notebooks (Source code)

├── onlinefraud.csv           # Kaggle Dataset

├── .gitattributes            # For handle the large files in gihub

├── .gitignore                # Ignore the unwanted file like .venv

├── requirements.txt          # List of dependencies

├── CODE_OF_CONDUCT.md        # Contributing guidelines

├── README.md                 # Project documentation

└── LICENSE                   # License file

```



## Contributing

Contributions are welcome! If you'd like to contribute to this project, please follow these steps:



- Fork the repository & Star the repository

- Create a new branch (git checkout -b feature)

- Make your changes

- Commit your changes (git commit -am 'Add new feature')

- Push to the branch (git push origin feature)

- Create a new Pull Request



## License

This project is licensed under the MIT License - see the [LICENSE](https://github.com/Vaibhav-kesarwani/Online_Payment_Fraud_Detection/blob/main/LICENSE) file for details.



## Acknowledgements

1. [Pandas](https://pandas.pydata.org/)

2. [Sklearn](https://scikit-learn.org/stable/)

3. [Kaggle](https://www.kaggle.com/datasets)

4. [NumPy](https://numpy.org/)

5. [Plotly](https://plotly.com/)



## Contact

If you have any questions or suggestions, feel free to reach out to me at :

1. [GitHub](https://github.com/Vaibhav-kesarwani)

2. [Linkedin](https://www.linkedin.com/in/vaibhav-kesarwani-9b5b35252/)

3. [Twitter](https://twitter.com/Vaibhav_k__)

4. [Portfolio](https://vaibhavkesarwani.vercel.app)



**Happy Coding!** FireStar