Internet Firewall Data Mining Project

Project Overview

This project applies various data mining techniques to analyze internet firewall data collected from a university's traffic records.

Dataset

Internet Firewall Data Set

Techniques Implemented

1. Preprocessing

   • Data cleaning and preparation
   • Principal Component Analysis (PCA) for dimensionality reduction

2. Association Rules Mining

   • Discovering interesting relationships in the data
   • Implemented using the Apriori algorithm in IBM SPSS Modeler

3. Classification

   • Decision Tree algorithm
   • K-Nearest Neighbors (KNN) algorithm

4. Clustering

   • K-means clustering
   • Agglomerative clustering

Project Structure

├── association rules
│ ├── association_rules.str
│ └── preprocessed_cat.csv
├── classification
│ ├── decision_tree.ipynb
│ ├── KNN_cat.ipynb
│ └── KNN.ipynb
├── clustering
│ ├── agglomerative.ipynb
│ └── kmeans.ipynb
├── dataset
│ ├── clustering.csv
│ ├── dataset.csv
│ ├── pca.csv
│ ├── preprocessed_cat.csv
│ └── preprocessed.csv
├── preprocessing
│ ├── PCA.ipynb
│ └── preprocessing.ipynb
└── reports/

Tools and Technologies

• Python
• Jupyter Notebooks
• Scikit-learn for machine learning algorithms
• Pandas and NumPy for data manipulation
• Matplotlib and Seaborn for data visualization

Results and Insights

Findings and insights can be found in the Report.pdf file in the reports directory.