Spam Classification

Introduction

The Spam Email Classification App is a web application built using Streamlit that identifies spam emails based on various input parameters. The app leverages a pre-trained machine learning model to provide accurate classification. Users can input details such as email subject, sender address, email body, and other metadata to determine whether an email is likely to be spam or not. This tool helps in efficiently filtering out unwanted emails and enhancing inbox security

Features

• User-Friendly Interface: Simple and intuitive interface for inputting email details.
• Real-Time Classifications: Provides instant spam detections based on user inputs.
• Data-Driven Insights: Utilizes a pre-trained machine learning model for accurate spam detections.

Installation

Prerequisites

• Python 3.6 or higher
• Streamlit
• Pandas
• NumPy
• Scikit-learn
• Joblib

Setup

1. Create a Virtual Environment
   python -m venv venv
   source venv/bin/activate  # On Windows use `venv\Scripts\activate`
   

2. Install Dependencies
   pip install -r requirements.txt
   

3. Ensure Dataset and Model are Available
   • Train the model in the colab file given below and place model.pkl & spam_vectorizer.pkl in the Model directory.

Usage

1. Run the Streamlit App
   streamlit run streamlit_app.py
   

2. Open the App in Your Browser
   • The app will automatically open in your default web browser. If not, navigate to http://localhost:8501 in your browser.
3. Input Email Text
   • Use the provided input field to enter details such as the email subject, sender address, email body, and other relevant metadata to determine whether an email is likely to be spam. This helps ensure your inbox remains free of unwanted and potentially harmful emails.
4. Get Spam Classification
   • Click the "Classify" button to check whether the email is spam or not.

Project Structure

spam-email-classification
│
├── Dataset
│   └── email.csv
│
├── Model
│   └── model.pkl
|   └── spam_vectorizer.pkl
│
├── venv
│   └── ... (virtual environment files)
│
├── streamlit_app.py
├── requirements.txt
└── README.md

Google Colab Notebook

You can also run the Spam Email Classification model in a Google Colab notebook. NOTEBOOK LINK

This notebook allows you to experiment with the code and datasets without setting up a local environment.

Acknowledgements

• The app uses data from the email.csv file for classifications.
• The machine learning model was trained using scikit-learn.

Contact

Visit codebase here