Spam Message Classifier

Overview

This project is a Machine Learning-based Spam Message Classifier that predicts whether a given message is spam or not spam (ham).

Objective

To build a model that can automatically detect spam messages using Natural Language Processing (NLP).

Technologies Used

• Python
• Pandas
• Scikit-learn
• NLTK

Dataset

The dataset used is the SMS Spam Collection dataset containing labeled messages as spam or ham.

Methodology

1. Data preprocessing
2. Text vectorization using CountVectorizer
3. Model training using Multinomial Naive Bayes
4. Model evaluation using accuracy score

Results

The model achieved an accuracy of approximately 98%.

🚀 How to Run

1. Install dependencies: pip install pandas scikit-learn nltk

2. Run the model: python model.py

Example

Input: "You have won ₹5000!" Output: Spam

Input: "Let's meet tomorrow" Output: Not Spam

Conclusion

The project successfully classifies spam messages with high accuracy and demonstrates the use of NLP in real-world applications.

📸 Output Examples

Spam Example

Not Spam Example