This project implements a spam email detection system using the Perceptron learning algorithm. The goal is to classify emails as spam or non-spam based on their content.

A Perceptron is a type of artificial neuron used in machine learning for binary classification tasks. It operates as follows:

1. Input: Receives features from the dataset.
2. Weights: Each input is multiplied by a weight.
3. Activation Function: Applies a step function to determine the output (spam or non-spam).
4. Learning: Adjusts weights based on the error between the predicted and actual labels using a learning rule.

The Perceptron algorithm updates weights iteratively to minimize classification errors and improve model accuracy.

The project uses the SMS Spam Collection Dataset which includes labeled text messages for training and testing the model.

1. Clone the repository and set up the environment.
2. Load the dataset and preprocess the text data.
3. Train the Perceptron model on the dataset.
4. Evaluate the model's performance on test data.

This project showcases the application of the Perceptron learning algorithm for detecting spam emails, demonstrating its effectiveness in text classification tasks.