Spam Filtration with UCI Spambase Dataset

This project aims to develop a spam filter using the UCI Spambase dataset (https://archive.ics.uci.edu/dataset/94/spambase) within the scope of a machine learning course. The project is implemented using the Python programming language and various libraries in a Jupyter Notebook.

Project Content

• spambase.csv: UCI Spambase dataset.
• spambase.names: Contains the features of the UCI Spambase dataset.
• spambase.DOCUMENTATION: Documentation of the UCI Spambase dataset.
• SpamFilterML.ipynb: Jupyter Notebook file. It includes steps such as exploring the dataset, splitting into training/test sets, and creating models using K-NN, SVM, Decision Trees, Random Forest, and Artificial Neural Networks algorithms.

Project Installation and Usage

• Download the project files to your computer.
• Start your Jupyter Notebook environment.
• Open the SpamFilterML.ipynb file.
• Evaluate the performance of the models and make any desired improvements.

Used Libraries

• Pandas: For data manipulation and analysis.
• Matplotlib: For data visualization.
• Scikit-learn: For the usage of machine learning algorithms.
• Numpy