📩 SMS Spam Prediction Pipeline

This project is a machine learning pipeline that detects spam SMS messages using natural language processing (NLP) techniques and a Naive Bayes classifier. The model is trained on the UCI SMS Spam Collection Dataset.

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🔍 Project Highlights

• Text Preprocessing with NLTK: tokenization, stopword removal, stemming
• Feature Extraction using CountVectorizer with unigrams and bigrams
• Classification Model: Multinomial Naive Bayes
• Hyperparameter Tuning with GridSearchCV
• Spam Prediction with probability scores
• Model Persistence using joblib

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🗃️ Dataset

The dataset contains 5,574 labeled SMS messages, split into spam and ham (not spam). It is publicly available from the UCI Machine Learning Repository.

• Downloaded and extracted using requests and zipfile
• Stored in: sms_spam_collection/SMSSpamCollection

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🛠️ How to Run

1. Clone this repository

git clone https://github.com/YOUR_USERNAME/spam-classification.git
cd spam-classification

2. Create and activate a virtual environment

python -m venv myenv
# Windows
myenv\Scripts\activate
# macOS/Linux
source myenv/bin/activate

3. Install dependencies

pip install -r requirements.txt

4. Run the scripts

📥 Download & extract the dataset

python dataset.py

🤖 Train and evaluate the model

python main.py

📊 Example Predictions

The trained model evaluates custom SMS messages and returns:

• Spam/Not-Spam label

• Probability scores for each class

Example:

Message: Congratulations! You've won a $1000 Walmart gift card.
Prediction: Spam
Spam Probability: 0.98
Not-Spam Probability: 0.02