Email Spam Detection Machine Learning Project

This is a machine learning project that aims to detect email spam using various classification algorithms. The project utilizes a dataset of labeled emails to train and evaluate the performance of different models for spam detection.

Table of Contents

• Introduction
• Features
• Getting Started
  • Prerequisites
  • Installation
• Usage
• Dataset
• Model Training
• Evaluation
• Contributing
• License

Introduction

Spam emails are unwanted and potentially harmful messages that clutter inboxes and may contain phishing attempts or malicious content. Email spam detection is an essential task in email filtering systems to keep users safe from such content. This project demonstrates the implementation of machine learning algorithms for identifying spam emails based on their content and features.

Features

• Preprocessing: Text preprocessing techniques are applied to convert raw email text into a format suitable for machine learning models.
• Feature Extraction: Various features such as word frequency, TF-IDF scores, and N-grams are used to represent the emails.
• Model Training: Different classification algorithms, including Naive Bayes, Logistic Regression, and Support Vector Machines, are trained on the dataset.
• Model Evaluation: The performance of each model is evaluated using metrics like accuracy, precision, recall, and F1-score.

Getting Started

To run this project locally on your machine, follow the instructions below.

Prerequisites

Make sure you have the following installed on your system:

• Python (3.6 or higher)
• Jupyter Notebook (for running the project notebook)

Installation

1. Clone this repository to your local machine using the following command:

   git clone https://github.com/your-username/email-spam-detection.git
   

2. Navigate to the project directory:

   cd email-spam-detection
   

3. Create a virtual environment to isolate the project's dependencies:

   python -m venv venv
   

4. Activate the virtual environment:

   • For Windows:

     venv\Scripts\activate
     

   • For macOS and Linux:

     source venv/bin/activate
     

5. Install the required packages:

   pip install -r requirements.txt
   

Usage

The main project file is provided as a Jupyter Notebook: email_spam_detection.ipynb. Open the notebook in Jupyter and run each cell to execute the project step-by-step.

Dataset

The dataset used for this project consists of labeled emails, where each email is classified as either "spam" or "ham" (non-spam). The dataset is included in the repository (data/spam.csv).

Model Training

The notebook guides you through the process of:

• Data preprocessing and cleaning.
• Feature extraction and vectorization.
• Splitting the dataset into training and testing sets.
• Training various classification models.

Evaluation

After training the models, the notebook provides code to evaluate their performance on the test dataset. The results are presented using accuracy, precision, recall, and F1-score metrics.

Contributing

We welcome contributions to this project. If you find any issues or have suggestions for improvements, please create a pull request. Before submitting a pull request, make sure to discuss major changes with the project maintainers.

License

This project is licensed under the MIT License. Feel free to use, modify, and distribute it as per the terms of the license.

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Thank you for using our Email Spam Detection Machine Learning Project! If you have any questions or need further assistance, please don't hesitate to contact us or open an issue on GitHub.