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https://github.com/2003harsh/sms-spam-classifier
ML model for spam detection using Naive Bayes & TF-IDF. Achieved 0.98 accuracy. Utilized Scikit-learn, Numpy, nltk. Implements NLP concepts. Explore precise spam classification effortlessly. #MachineLearning #SpamDetection 🚀✉️📱
https://github.com/2003harsh/sms-spam-classifier
naive-bayes-classifier natural-language-processing tf-idf-vectorizer
Last synced: about 2 months ago
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ML model for spam detection using Naive Bayes & TF-IDF. Achieved 0.98 accuracy. Utilized Scikit-learn, Numpy, nltk. Implements NLP concepts. Explore precise spam classification effortlessly. #MachineLearning #SpamDetection 🚀✉️📱
- Host: GitHub
- URL: https://github.com/2003harsh/sms-spam-classifier
- Owner: 2003HARSH
- License: mit
- Created: 2023-07-23T14:00:29.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-07-06T06:07:38.000Z (6 months ago)
- Last Synced: 2024-07-06T07:25:12.418Z (6 months ago)
- Topics: naive-bayes-classifier, natural-language-processing, tf-idf-vectorizer
- Language: Python
- Homepage: https://sms-spam-classifier-nlp.streamlit.app/
- Size: 117 KB
- Stars: 0
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# SMS/Email Spam Classifier 📧🚫
Developed a powerful ML model for classifying SMS/emails as spam or legitimate using advanced Vectorization and Natural Language Processing (NLP) techniques. The application is built using Streamlit for a user-friendly experience.
## Key Achievements
- **High Accuracy:** Achieved an outstanding accuracy score of 0.98.
- **Precision:** Boasted a precision score of 0.991, showcasing the model's reliability.
- **Technology Stack:** Utilized Scikit-learn, Pandas, Numpy, NLTK, Matplotlib, Seaborn, WordCloud, Streamlit, and more.
- **NLP Expertise:** Gained proficiency in NLP concepts like Tokenization, stopword removal, stemming, term frequency-inverse document frequency, etc.
## How to Use
1. **Clone Repository:**
```
git clone https://github.com/your-username/spam-classifier.git
cd spam-classifier
```
2. **Install Dependencies:**
```
pip install -r requirements.txt
```
3. **Run the Streamlit App:**
```
streamlit run app.py
```
4. **Access the App:**
Open your browser and go to `http://localhost:8501`.
5. **Input:**
Provide the text you want to classify.
6. **Output:**
The app will predict whether the input is spam or legitimate.
This project demonstrates excellence in spam detection, leveraging Streamlit for an interactive and seamless user experience. #MachineLearning #NLP #SpamClassification #Streamlit 🤖📤📥