Spam Detection Using Machine Learning

This project is a Machine Learning-based Spam Detection System that classifies SMS messages as Spam or Ham (Not Spam). The system analyzes message content and predicts whether the message is unwanted or legitimate.

To build a machine learning model that automatically detects spam messages and helps users filter unwanted communications.

Features

• Detects Spam and Ham messages
• Uses Machine Learning for classification
• User-friendly interface
• Fast and accurate predictions
• Real-world text classification application

Technologies Used

• Python
• Pandas
• Scikit-learn
• HTML/CSS
• FastAPI/Flask

Dataset

The project uses a dataset containing SMS messages labeled as:

• Spam
• Ham (Not Spam)

Project Workflow

1. Load Dataset
2. Preprocess Text Data
3. Extract Features
4. Train Machine Learning Model
5. Predict Message Category
6. Display Results

Project Structure

Spam-Detection-Project/ │ ├── static/ ├── main.py ├── spam.csv ├── requirements.txt ├── render.yaml ├── start.sh └── README.md

to Run

Clone Repository

git clone <repository-url>
cd Spam-Detection-Project