https://github.com/azaz9026/email-spam-detection
Welcome to the Email Spam Detection project! This repository provides a machine learning model for detecting spam emails using a Naive Bayes classifier and a simple web interface built with Streamlit.
https://github.com/azaz9026/email-spam-detection
data-analysis data-cleaning data-structures data-visualization deep-learning machine-learning python sql streamlit
Last synced: 7 months ago
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Welcome to the Email Spam Detection project! This repository provides a machine learning model for detecting spam emails using a Naive Bayes classifier and a simple web interface built with Streamlit.
- Host: GitHub
- URL: https://github.com/azaz9026/email-spam-detection
- Owner: azaz9026
- Created: 2024-08-11T15:40:47.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2024-08-27T18:13:04.000Z (about 1 year ago)
- Last Synced: 2025-01-19T07:15:30.169Z (9 months ago)
- Topics: data-analysis, data-cleaning, data-structures, data-visualization, deep-learning, machine-learning, python, sql, streamlit
- Language: Python
- Homepage: https://email-spam-detection-b6tq.onrender.com
- Size: 278 KB
- Stars: 0
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Email Spam Detection
Welcome to the Email Spam Detection project! This repository provides a machine learning model for detecting spam
emails using a Naive Bayes classifier and a simple web interface built with Streamlit.
***Overview***
The goal of this project is to classify emails as spam or not spam based on their content. The model is trained using the SMS Spam Collection Dataset, which contains labeled examples of spam and non-spam messages. The project includes:
Data Processing: Handling and cleaning the dataset to prepare it for model training.
Text Vectorization: Using CountVectorizer to convert text data into numerical features.
Model Training: Training a Multinomial Naive Bayes classifier on the processed data.
Web Interface: A user-friendly web application built with Streamlit to classify new messages in real-time.
***Features***
Real-time Classification: Input any email or message and receive an instant classification as either spam or not spam.
User-Friendly Interface: A clean and simple interface built with Streamlit for easy interaction.
Data Visualization: Display sample data and relevant metrics.
# Getting Started
***Prerequisites***'
To run this project, you need to have Python 3.7 or higher installed. You also need to install the following packages:
pandas
scikit-learn
streamlit
You can install these packages using pip:
***pip install pandas scikit-learn streamlit***
***Installation***
Clone the Repository
git clone https://github.com/yourusername/email-spam-detection.git
cd email-spam-detection
***Download the Dataset***
Download the SMS Spam Collection dataset from here and place it in the project directory.
***Run the Streamlit App***
streamlit run app.py
This command will start the Streamlit server and open the application in your default web browser.
***Contributing***
Feel free to fork this repository and submit pull requests if you have improvements or additional features in mind. Please make sure to follow the coding standards and include appropriate tests for any new functionality.
***License***
This project is licensed under the MIT License. See the LICENSE file for more details.
***Acknowledgements***
Dataset: SMS Spam Collection Dataset from UCI Machine Learning Repository.
Libraries: scikit-learn for machine learning, Streamlit for the web interface.