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https://github.com/antarmukhopadhyaya/fraud-warden

Fraudulent Credit Transaction detection system using SMOTE, Random Forest Classifier and Streamlit
https://github.com/antarmukhopadhyaya/fraud-warden

data-science imblearn machine-learning pandas python sklearn streamlit

Last synced: 7 days ago
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Fraudulent Credit Transaction detection system using SMOTE, Random Forest Classifier and Streamlit

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README 

        
# Fraud Warden

## Overview

Fraud Warden is a next-generation credit card fraud detection system that uses machine learning to predict whether a transaction is fraudulent or not. The system leverages a Random Forest Classifier to make predictions based on various features of the transaction.  

Technology Stack

Programming Language: Python

### Libraries:

- **streamlit** for building the web application

- **pandas** for data manipulation

- **plotly.express** and seaborn for data visualization

- **scikit-learn** for machine learning

- **pickle** for model serialization

### Installation Instructions

- Clone the Repository:  

- git clone https://github.com/yourusername/fraud-warden.git

- cd fraud-warden

- Create a Virtual Environment:

   - python -m venv venv

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

- Install Dependencies:  

  - pip install -r requirements.txt

- Run the Application:  

   - streamlit run app.py

## How It Works

- Data Preprocessing:  

  - The application preprocesses the uploaded CSV file by removing unnecessary columns and converting date columns to datetime objects.

Additional features such as time_of_day and age are derived from existing columns.

- Feature Engineering:  

  - Categorical features are encoded into numerical values.

The data is reindexed to ensure all required columns are present.

- Oversampling:  

  - The application uses Synthetic Minority Over-sampling Technique (SMOTE) to balance the dataset.

- Model Prediction:  

  - The preprocessed data is fed into a pre-trained Random Forest Classifier model.

The model predicts whether a transaction is fraudulent based on the input features.

- Visualization:  

  - The application provides various visualizations such as histograms, bar charts, and correlation heatmaps to help users understand the data.

## Features

- **Upload CSV**: Users can upload a CSV file containing transaction data.

- **Data Preview**: Displays a preview of the uploaded data.

- **Basic Statistics**: Shows basic statistics of the dataset.

- **Data Types**: Displays the data types of each column.

- **Missing Values**: Shows the count of missing values in each column.

- **Distribution of Numerical Columns**: Visualizes the distribution of numerical columns.

- **Counts of Categorical Columns**: Visualizes the counts of categorical columns.

- **Correlation Heatmap**: Displays a heatmap of the correlation between numerical features.

- **SMOTE Sampling**: Balances the dataset using SMOTE sampling.

- **Fraud Prediction**: Predicts whether a transaction is fraudulent based on user input.

## Resources Used

- Dataset: [Credit Card Fraud Detection Dataset (Kaggle)](https://www.kaggle.com/datasets/kartik2112/fraud-detection)

- Sklearn Documentation: [Random Forest Classifier](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html)

- Streamlit Documentation: [Streamlit](https://docs.streamlit.io/library)

- Plotly Documentation: [Plotly Express](https://plotly.com/python/plotly-express/)

- Seaborn Documentation: [Seaborn](https://seaborn.pydata.org/)

- Pandas Documentation: [Pandas](https://pandas.pydata.org/docs/)

- Python Documentation: [Python](https://docs.python.org/3/)

- SMOTE Documentation: [SMOTE](https://imbalanced-learn.org/stable/references/generated/imblearn.over_sampling.SMOTE.html)