Marcellinus123/spam_detector
This is a Python-based spam detection system that uses machine learning to classify messages as spam or not spam (ham). The system connects to a MySQL database for training data, uses TF-IDF vectorization for text processing, and employs logistic regression for classification.
Spam Detector Project
This is a Python-based spam detection system that uses machine learning to classify messages as spam or not spam (ham).
The system connects to a MySQL database for training data, uses TF-IDF vectorization for text processing, and employs logistic regression for classification.
Table of Contents
Features
Requirements
Installation
Database Setup
Usage
API Documentation
Training Data
Testing
Contributing
License
Features
MySQL database integration for storing and retrieving training data
TF-IDF vectorization for text feature extraction
Logistic Regression classifier for spam detection
Simple prediction function for classifying new messages
Example test cases included
Requirements
Python 3.6+
MySQL Server
Python packages:
mysql-connector-python
pandas
scikit-learn
Installation
- Clone the repository:
git clone https://github.com/marcellinus123/spam-detector.git
cd spam-detector
- Install the required Python packages:
pip install mysql-connector-python pandas scikit-learn
- Set up your MySQL database ( Link: see Database Setup )
Database Setup
-
Create a MySQL database named spam_detector:
CREATE DATABASE spam_detector; -
Create a table for training data:
USE spam_detector;
CREATE TABLE spam_training_data (
id INT AUTO_INCREMENT PRIMARY KEY,
message TEXT NOT NULL,
label INT NOT NULL COMMENT '0 for ham, 1 for spam'
);
- Insert some sample training data:
INSERT INTO spam_training_data (message, label) VALUES
('Free vacation to Bahamas! Click here to book.', 1),
('Hi there, I''m following up on our last conversation.', 0),
('Win cash instantly, limited time!', 1),
('Meeting reminder: Tomorrow at 2pm in conference room', 0),
('You''ve won a free iPhone! Claim now!', 1),
('Hi John, just checking in about the project', 0);
Usage
-
Configure the database connection in the script by modifying these lines:
conn = mysql.connector.connect(
host='localhost',
user='root', # your MySQL username
password='', # your MySQL password
database='spam_detector'
) -
Run the script:
python spam_detector.py -
The script will:
Connect to the MySQL database
Fetch training data
Train the model
Run test predictions on sample messages
API Documentation
predict_message(msg)
Classifies a given message as spam or not spam.
Parameters:
msg (str): The message to classify
Returns:
str: 'Spam' or 'Not Spam'
Example:
prediction = predict_message("Free vacation offer!")
print(prediction) # Output: 'Spam'
Training Data
Training Data
The model expects training data in the following format stored in a MySQL table called spam_training_data:
| Column | Type | Description |
|---|---|---|
| message | TEXT | The text message to learn |
| label | INT | 0 for ham, 1 for spam |
To improve accuracy, add more diverse examples of both spam and ham messages to your training data.
Testing
The script includes some test cases that run when executed directly:
test_messages = [ "Free vacation to Bahamas! Click here to book.",
"Hi there, I'm following up on our last conversation.",
"Win cash instantly, limited time!"
]
To add your own tests, modify this section or create a separate test file.
Contributing
Contributions are welcome! Here's how:
Fork the project
Create your feature branch (git checkout -b feature/AmazingFeature)
Commit your changes (git commit -m 'Add some amazing feature')
Push to the branch (git push origin feature/AmazingFeature)
Open a Pull Request
License
Distributed under the MIT License. See LICENSE for more information.
The model expects training data in the following format stored in a MySQL table called spam_training_data: