UNSW Categorical Analysis – Final Project

This repository contains a Jupyter Notebook for analyzing and testing categorical features of the UNSW-NB15 dataset, a benchmark dataset widely used in network security and intrusion detection research.

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📌 Project Overview

• Focused on categorical preprocessing and evaluation for the UNSW dataset.
• Explores encoding techniques, feature separation, and classification performance.
• Provides a reproducible framework for testing categorical handling methods on security-related data.

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📂 Repository Structure

unsw-categorical-final-separate-test.ipynb   # Main notebook with preprocessing and experiments
data/                                        # (Optional) Folder for dataset storage
results/                                     # (Optional) Folder for experiment outputs
README.md                                    # Project documentation

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⚙️ Requirements

Make sure you have the following installed before running the notebook:

• Python 3.8+
• Jupyter Notebook or JupyterLab
• Required libraries:
  • pandas
  • numpy
  • scikit-learn
  • matplotlib
  • seaborn

Install dependencies with:

pip install -r requirements.txt

(If no requirements.txt is provided, install the libraries manually.)

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🚀 How to Run

1. Clone the repository:

   git clone https://github.com/YOUR_USERNAME/unsw-categorical-analysis.git
   cd unsw-categorical-analysis

2. Launch Jupyter Notebook:

   jupyter notebook

3. Open unsw-categorical-final-separate-test.ipynb and run the cells in order.

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📊 Key Features

• Data Cleaning – handling missing values, categorical separation.
• Encoding Methods – one-hot encoding, label encoding, frequency encoding.
• Model Training & Testing – supervised learning models on processed categorical features.
• Evaluation – accuracy, precision, recall, and F1-score metrics.

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📈 Results

• Insights into the effect of different categorical encoding techniques.
• Model performance benchmarks on the UNSW-NB15 dataset.
• Framework extendable to other cybersecurity datasets.

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🧠 Inspiration

This project was inspired by the need to better understand how categorical data preprocessing impacts intrusion detection and cybersecurity analytics.

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🤝 Contributing

Contributions are welcome! If you’d like to extend the project (e.g., new encoding techniques, model architectures, or visualizations), please fork the repo and submit a PR.

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📜 License

This project is licensed under the MIT License. Feel free to use, modify, and share with attribution.