Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/samp1012/spam_email_detector
https://github.com/samp1012/spam_email_detector
Last synced: 9 days ago
JSON representation
- Host: GitHub
- URL: https://github.com/samp1012/spam_email_detector
- Owner: samp1012
- Created: 2024-12-20T07:01:11.000Z (16 days ago)
- Default Branch: main
- Last Pushed: 2024-12-20T07:57:20.000Z (16 days ago)
- Last Synced: 2024-12-20T08:24:55.212Z (16 days ago)
- Language: Jupyter Notebook
- Homepage:
- Size: 991 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Spam Email and SMS Detector
This project is a machine learning-based application designed to classify email and SMS messages as spam or not spam. It utilizes natural language processing (NLP) techniques and various classification algorithms to achieve accurate predictions.
## Table of Contents
- [Features](#features)
- [Project Structure](#project-structure)
- [Installation](#installation)
- [Usage](#usage)
- [Model Training](#model-training)
- [Saved Models](#saved-models)
- [Technologies Used](#technologies-used)
- [Contact](#contact)
## Features
- **Spam Detection**: Classifies messages as spam or not spam with high accuracy.
- **Text Preprocessing**: Performs text cleaning, tokenization, and vectorization to prepare data for modeling.
- **Machine Learning Models**: Employs algorithms such as Naive Bayes and Logistic Regression.
- **Web Application**: Provides a user-friendly interface built with Flask for message classification.
- **Pre-trained Models**: Includes saved models and preprocessors for immediate use.
## Project Structure
```
Spam_Email_Spam_Detector/
├── spam.csv # Dataset containing labeled SMS data
├── main.ipynb # Jupyter Notebook for model training and testing
├── app.py # Flask web application for spam detection
├── preprocessor.pkl # Saved preprocessor for text data
├── model.pkl # Saved machine learning model
├── templates/ # HTML templates for the web application
├── requirements.txt # Python dependencies
└── README.md # Project documentation
```
## Installation
1. **Clone the repository**:
```bash
git clone https://github.com/samp1012/Spam_Email_Detector.git
cd Spam_Email_Detector
```
2. **Create and activate a virtual environment** (optional but recommended):
```bash
python -m venv env
source env/bin/activate # On Windows: env\Scripts\activate
```
3. **Install the required packages**:
```bash
pip install -r requirements.txt
```
## Usage
1. **Run the web application**:
```bash
python app.py
```
2. **Access the application**:
Open your web browser and navigate to `http://127.0.0.1:5000/`.
3. **Classify a message**:
- Enter the email or SMS text into the input field.
- Click the "Predict" button to determine if the message is spam.
## Model Training
To train the model on new data or adjust parameters:
1. **Open the Jupyter Notebook**:
```bash
jupyter notebook main.ipynb
```
2. **Follow the steps in the notebook** to preprocess data, train the model, and evaluate performance.
3. **Save the trained model and preprocessor**:
- Update the `model.pkl` and `preprocessor.pkl` files with the new models.
## Saved Models
The project includes pre-trained files for quick predictions:
- **preprocessor.pkl**: Text preprocessing pipeline.
- **model.pkl**: Machine learning model for spam detection.
These files are used by the `app.py` script to process input data and generate predictions.
## Technologies Used
- **Python**
- **Flask**: Web framework for deployment
- **Pandas**: Data manipulation
- **Scikit-learn**: Machine learning models
- **Numpy**: Numerical operations
- **Matplotlib/Seaborn**: Data visualization
## Contact
For queries or suggestions:
- **GitHub**: [samp1012](https://github.com/samp1012)
- **Email**: [email protected]
- **Linked In**: [Samparka Das](https://www.linkedin.com/in/samparka-das-b4317726b/)