https://github.com/niladri-1/Spam-Mail-Classification
The Spam Mail Classification project is a web-based application that uses machine learning to classify emails as spam or ham. It features a Flask backend, a frontend created with HTML, CSS, and JavaScript, and a MySQL database for storing user data and email classifications.
https://github.com/niladri-1/Spam-Mail-Classification
spam-classification spam-detection spam-filtering spam-mail spam-mail-classifier spam-protection
Last synced: about 1 month ago
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The Spam Mail Classification project is a web-based application that uses machine learning to classify emails as spam or ham. It features a Flask backend, a frontend created with HTML, CSS, and JavaScript, and a MySQL database for storing user data and email classifications.
- Host: GitHub
- URL: https://github.com/niladri-1/Spam-Mail-Classification
- Owner: niladri-1
- Created: 2023-10-16T07:21:17.000Z (almost 3 years ago)
- Default Branch: main
- Last Pushed: 2024-05-19T14:32:52.000Z (about 2 years ago)
- Last Synced: 2026-04-21T05:44:32.094Z (3 months ago)
- Topics: spam-classification, spam-detection, spam-filtering, spam-mail, spam-mail-classifier, spam-protection
- Language: HTML
- Homepage:
- Size: 2.58 MB
- Stars: 10
- Watchers: 1
- Forks: 12
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Spam Mail Classification
## Description
The **Spam Mail Classification** project is a web-based application that uses machine learning to classify emails as spam or ham. It features a Flask backend, a frontend created with HTML, CSS, and JavaScript, and a MySQL database for storing user data and email classifications.
### Features
- **Email Classification**: Categorizes incoming emails as spam or ham.
- **User Registration and Login**: Secure account creation and authentication.
- **Real-Time Email Classification**: Classifies emails in real time.
- **User Dashboard**: Users can view their email history and classifications.
- **Machine Learning Model**: Employs a trained model to classify emails.
- **Customization**: Users can configure spam filter settings.
## Technologies Used
- **Flask** (Python Web Framework): For the backend server.
- **HTML, CSS, and JavaScript** (Frontend): For the user interface.
- **MySQL** (Database): For storing user data and email classifications.
- **Machine Learning Libraries** (e.g., Scikit-Learn): Used to build and deploy the email classification model.
## Getting Started
To use the Spam Mail Classification app, follow these steps:
1. **Clone this Repository**: Get the project source code by cloning this repository to your local machine.
2. **Set Up the Flask Backend and MySQL Database**:
- Refer to the documentation or instructions provided in the code for setting up the Flask backend and MySQL database.
3. **Install Required Python Packages**:
- You'll need to install a few Python packages using pip. Open your terminal and run:
```bash
pip install Flask
pip install nltk
pip install mysql-connector-python
```
5. **Create a MySQL Database and Table**:
- Set up the MySQL database and table by running the following SQL commands in your MySQL server:
```sql
CREATE DATABASE smc;
```
```sql
USE smc;
```
```sql
CREATE TABLE users (
id INT AUTO_INCREMENT PRIMARY KEY,
full_name VARCHAR(255) NOT NULL,
username VARCHAR(255) UNIQUE NOT NULL,
email VARCHAR(255) UNIQUE NOT NULL,
phone VARCHAR(15) NOT NULL,
password VARCHAR(255) NOT NULL
);
```
6. **Run the Flask App**:
- Start the Flask app by running the following command in your terminal:
```bash
python app.py
```
- Goto browser to open this website in Localhost:
```bash
http://127.0.0.1:5000/
```
## Author
- **Niladri Chatterjee**
### Contributors
- Niladri Chatterjee - If others have contributed to this project, consider adding their names here.
## License
You can specify the license under which you want to distribute your project. If it's open source, you can use a popular license like MIT or Apache 2.0.
## Acknowledgments
Mention any libraries, tools, or resources that you used or were inspired by in your project here.
Feel free to adapt this template to your project's specific needs.