This project, Anti-Scamming Predictor, aims to detect credit card fraud using custom machine learning models. It provides a Streamlit-based GUI for easy interaction with the predictive models.

Before setting up the project, ensure you have Anaconda installed on your system. You can download it from Anaconda's website. (https://www.anaconda.com/download)

The project uses a dataset named creditcard.csv which is essential for the fraud detection models. This dataset can be obtained in two ways:

• Using Provided Zip File:
  • Locate the creditcard.csv.zip file in the project directory.
  • Unzip this file to extract the creditcard.csv file.
  • Ensure that the extracted CSV file is in the same directory as your project files for easy access by the application.
• Downloading from Kaggle:
  • Alternatively, you can download the dataset directly from Kaggle at this link (https://www.kaggle.com/datasets/mlg-ulb/creditcardfraud).
  • After downloading, unzip the file and place creditcard.csv in the project's working directory.

Follow these steps to set up the conda environment:

• conda create --name myenv
• conda activate myenv
• conda install pip
• pip install -r requirements.txt
• streamlit run Group4_GUI.py

After installation, you can verify the setup by running 'conda list' in your environment to check if all required packages are installed.

Once the environment is set up and the application is running, navigate to the local URL provided by Streamlit in your browser to interact with the application.

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