Email Spam Classification Project

Overview

This project aims to classify emails as spam or not-spam using two machine learning methods: k-Nearest Neighbors (k-NN) and Multi-Layer Perceptron (MLP). The dataset used contains 4601 examples with 57 attributes each.

Methods

1. k-NN Classifier:

   • k: 3
   • Distance Metric: Euclidean distance

2. MLP Classifier:

   • Hidden Layers: Two layers (first layer with 10 neurons, second layer with 5 neurons)
   • Activation Function: Logistic (sigmoid)

How to Run

1. Ensure you have Python and scikit-learn installed.
2. Run the main.py script to execute the classifiers and generate results.

Results

• Accuracy, Precision, Recall, and F1-Score for both classifiers will be reported in the output.
• Confusion matrices will be generated and included in the report.

Code Structure

• NN Class: Implements the k-NN classifier.
• load_data: Loads data from a CSV file.
• preprocess: Normalizes the features of the dataset.
• train_mlp_model: Trains the MLP classifier using scikit-learn.
• evaluate: Computes evaluation metrics.
• main: Main function to run the classifiers and print results.