https://github.com/shanmukhsrisaivedullapalli/smsspamclassification

SMSSpamClassification is a machine learning project aimed at accurately classifying SMS messages as either spam or ham (non-spam). It employs natural language processing techniques to extract relevant features from the text data and utilizes various classification algorithms to build a robust spam detection model.
https://github.com/shanmukhsrisaivedullapalli/smsspamclassification

jupyter-notebook numpy pandas pickle python3 sklearn spam-classification spam-detection

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SMSSpamClassification is a machine learning project aimed at accurately classifying SMS messages as either spam or ham (non-spam). It employs natural language processing techniques to extract relevant features from the text data and utilizes various classification algorithms to build a robust spam detection model.

• Host: GitHub
• URL: https://github.com/shanmukhsrisaivedullapalli/smsspamclassification
• Owner: shanmukhsrisaivedullapalli
• Created: 2024-07-22T05:58:37.000Z (4 months ago)
• Default Branch: main
• Last Pushed: 2024-07-22T06:17:26.000Z (4 months ago)
• Last Synced: 2024-07-22T07:27:40.226Z (4 months ago)
• Topics: jupyter-notebook, numpy, pandas, pickle, python3, sklearn, spam-classification, spam-detection
• Language: Jupyter Notebook
• Homepage:
• Size: 389 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
## GitHub Repository: SMSSpamClassification

**SMSSpamClassification** is a machine learning project focused on categorizing SMS messages as spam or ham (non-spam). Leveraging the SMSSpamCollection dataset, the project employs Natural Language Processing (NLP) techniques, specifically TF-IDF vectorization, to extract meaningful features from the text data. A Logistic Regression model is trained on these features to build a robust spam detection classifier.

This repository houses the code for an SMS spam classification project. It encompasses data preprocessing, feature engineering using TF-IDF, model training with Logistic Regression, and model evaluation.

**Project Structure**

```

SMSSpamClassification/

├── data/

│   └── SMSSpamCollection.csv

├── models/

│   ├── feature_extraction.pkl

│   └── spam_detection_model.pkl

├── notebooks/

│   └── SMSSpamClassification.ipynb

├── requirements.txt

└── README.md

```

**Data**

* The dataset utilized for this project is the publicly accessible SMS Spam Collection dataset.

* Raw data is stored in the `data` directory.

**Notebooks**

* **SMSSpamClassification.ipynb**: Contains the entire workflow, including data exploration, preprocessing, feature extraction using TF-IDF, model training with Logistic Regression, and model evaluation.

**Models**

* **feature_extraction.pkl**: Saved TF-IDF vectorizer for future use.

* **spam_detection_model.pkl**: Trained Logistic Regression model for spam classification.

**requirements.txt**: Lists necessary Python libraries for project execution.

**Installation**

1. Clone the repository:

   ```bash

   git clone https://github.com/shanmukhsrisaivedullapalli/SMSSpamClassification.git

   ```

2. Create a virtual environment:

   ```bash

   python -m venv venv

   source venv/bin/activate  # or venv\Scripts\activate on Windows

   ```

3. Install dependencies:

   ```bash

   pip install -r requirements.txt

   ```

**Usage**

1. Run the `SMSSpamClassification.ipynb` notebook to execute the entire project workflow.

2. The trained model and feature extractor are saved for potential future use.

**Contributing**

Contributions are welcome! You can enhance the project by:

* Implementing different NLP techniques or feature engineering methods.

* Experimenting with various classification algorithms.

* Improving model performance through hyperparameter tuning.

* Enhancing the project's documentation.