Skip to content

Latest commit

 

History

History
54 lines (39 loc) · 1.86 KB

README.md

File metadata and controls

54 lines (39 loc) · 1.86 KB
Raw

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

image imageimageimageimage

Execute the following command to run the spam detection model:

python spam.py.