Spam Detection Model

A spam detection model built using Python and Logistic Regression to identify spam emails or messages with high accuracy.

Overview

This project leverages logistic regression, a powerful binary classification algorithm, to distinguish between spam and non-spam messages. The model uses a dataset of email messages to train and evaluate its performance.

Key Features

• Algorithm: Logistic Regression
• Activation Function: Sigmoid Rule
• Dataset:
  • Total Records: 2100
  • Training Records: 2000
  • Testing Records: 100
• Epochs: 8
• Accuracy: 100%

Project Structure

• spam.py: Core implementation of the spam detection model.
• SpamDetectionData.txt: Dataset used for training and testing the model.
• Neural_Net Project Doc.docx: Detailed project documentation.

Getting Started

Prerequisites

• Python 3.x
• Required libraries (specified in requirements.txt)

Installation

1. Clone the repository:

   git clone https://github.com/ahmedrafat-SW/Spam-Detection-Model.git

2. Navigate to the project directory:

   cd Spam-Detection-Model

3. Install dependencies:

   pip install -r requirements.txt

Running the Model

Execute the following command to run the spam detection model:

python spam.py.