📬 Spam Detection App

A machine learning-powered application that classifies SMS messages as Spam or Ham (Not Spam) using Natural Language Processing (NLP). This project includes:

• ✅ Command-line interface (CLI)
• ✅ Interactive Streamlit web app
• ✅ Trained model using Multinomial Naive Bayes
• ✅ TF-IDF-based text preprocessing
• ✅ K-Fold evaluation metrics

🖼️ Preview

• Live Link

🤖 Model Overview

• Vectorizer: TfidfVectorizer(stop_words='english')
• Classifier: MultinomialNB()
• Training Strategy: 10-Fold Cross Validation
• Evaluation Metrics:
  • Accuracy: ~97.15%
  • Precision: ~99.66%
  • Recall: ~78.98%
  • F1 Score: ~88.10%
  • Geometric Mean: ~88.84%

🧠 Why Naive Bayes?

Multinomial Naive Bayes is efficient and performs well for text classification tasks like spam detection, where input features are word frequencies or TF-IDF scores.

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🚀 How to Run Locally

1. Clone the Repository

git clone https://github.com/mayankraj052/SpamdetectionApp.git cd SpamdetectionApp 

2. Create a Virtual Environment

for window

python -m venv venv
venv\Scripts\activate 

macOS/Linux

source venv/bin/activate  

3. Install Requirements

pip install -r requirements.txt

4. Run Streamlit App

streamlit run app.py

5. Run Command-Line Tool

python spam_check_cli.py

🧪 Example Predictions

"Hey John, I thought you might like this opportunity — earn $500/day working from home!" → Spam

"Are we still meeting at 6 PM today?" → Ham

🗂 Dataset Used

• SMS Spam Collection Dataset (UCI ML Repository)
• Data Set Link
• Format: Label (spam or ham) + Message content

📦 Model Pipeline

The model pipeline is saved in spam_pipeline.pkl contains:

• TF-IDF Vectorizer (preprocessing)
• Multinomial Naive Bayes Classifier

from sklearn.pipeline import Pipeline
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.naive_bayes import MultinomialNB

spam_pipeline = Pipeline([
    ('tfidf', TfidfVectorizer(stop_words='english')),
    ('model', MultinomialNB())
])

📈 Evaluation via K-Fold Cross-Validation

from sklearn.model_selection import KFold
from sklearn.metrics import confusion_matrix

• Used to validate model performance across 10 different splits. Metrics like accuracy, precision, recall, F1, specificity, and geometric mean were averaged.

🛠 Tools & Libraries

• Python 3.11+
• Scikit-learn
• Pandas & NumPy
• Streamlit
• Joblib
• Git for version control

📜 License

This project is open-source and available under the MIT License. 🙋‍♂️ Author

Made with ❤️ by Mayank Raj