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https://github.com/tushard48/sms-spam-detection
This repository contains code and models for identifying spam SMS messages. It utilizes machine learning techniques to classify messages as spam or ham (non-spam).
https://github.com/tushard48/sms-spam-detection
machine-learning spam-detection streamlit tfidf-vectorizer
Last synced: 11 days ago
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This repository contains code and models for identifying spam SMS messages. It utilizes machine learning techniques to classify messages as spam or ham (non-spam).
- Host: GitHub
- URL: https://github.com/tushard48/sms-spam-detection
- Owner: TusharD48
- Created: 2024-08-01T10:33:45.000Z (5 months ago)
- Default Branch: main
- Last Pushed: 2024-08-01T11:53:48.000Z (5 months ago)
- Last Synced: 2024-11-08T11:43:03.621Z (2 months ago)
- Topics: machine-learning, spam-detection, streamlit, tfidf-vectorizer
- Language: Jupyter Notebook
- Homepage:
- Size: 1.01 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# SMS-Spam-Detection
[](https://ww38.rishavchanda.io/)
# Table of Content
- Introduction
- Project Structure
- Installation
- Usage
- Dataset
- Model
- Results
- Contributing
- License
- Contact
# Introduction
This project aims to build a machine learning model to detect spam messages in SMS. Spam detection is crucial for filtering out unwanted and potentially harmful messages, enhancing user experience, and improving communication security.
# Project Structure
The project directory is structured as follows:
```
sms-spam-detection/
├── data/
│ ├── raw/
│ ├── processed/
├── notebooks/
│ ├── sms-spam-detection.ipynb
├── src/
│ ├── data_preprocessing.py
│ ├── model_training.py
│ ├── spam_detection.py
├── models/
├── results/
├── README.md
```
## Installation
```
python -m venv venv
source venv/bin/activate # On Windows, use `venv\Scripts\activate`
pip install pandas numpy scikit-learn nltk xgboost
```
# Model and Technique
This project utilizes several machine learning techniques and models to perform spam detection:
- Logistic Regression
- Support Vector Classifier (SVC)
- Multinomial Naive Bayes (MultinomialNB)
- Decision Tree Classifier
- K-Neighbors Classifier (KNeighborsClassifier)
- Random Forest Classifier
- AdaBoost Classifier
- Bagging Classifier
- Extra Trees Classifier
- Gradient Boosting Classifier
- XGBoost Classifier
Results
The performance of the spam detection model is evaluated using metrics such as accuracy, precision, recall, and F1-score. The results are stored in the results/ directory. Here is a summary of the model's performance:
| Algorithm | Accuracy | Precision |
|-----------|----------|-----------|
| KN | 0.904255 | 1.000000 |
| NB | 0.972921 | 0.991597 |
| ETC | 0.977756 | 0.984127 |
| RF | 0.971954 | 0.975410 |
| GBDT | 0.946809 | 0.968750 |
| SVC | 0.976789 | 0.954887 |
| xgb | 0.968085 | 0.937500 |
| BgC | 0.962282 | 0.884058 |
| LR | 0.946809 | 0.868852 |
| AdaBoost | 0.950677 | 0.867188 |
| DT | 0.933269 | 0.827586 |