This project focuses on building a Machine Learning model that can identify whether an email/message is:

✔ Spam
✔ Ham (Not Spam)

Spam classification is one of the most common real-world applications of:
• Machine Learning
• Natural Language Processing (NLP)
• Text Classification

🚀 What This Project Covers

In this project, you will learn:

✔ Data preprocessing
✔ Text cleaning
✔ NLP techniques
✔ Feature extraction using TF-IDF
✔ Model training & prediction
✔ Spam vs Ham classification

🛠️ Technologies Used

• Python
• Pandas
• Scikit-learn
• NLP
• TF-IDF Vectorizer

📊 Dataset

The dataset contains labeled messages used to train the model for spam detection.

💡 Why This Project is Important

Spam filtering is widely used in:

✔ Email systems
✔ SMS filtering
✔ Cybersecurity
✔ Fraud detection systems

This project is excellent for:
• Beginners in Machine Learning
• NLP practice
• Portfolio building
• Interview preparation

🔗 Explore the Project Code

To understand the complete implementation, preprocessing steps, and model training process, go through the GitHub project links below 👇

📂 Dataset

https://github.com/santhulak/NLP_Projects/blob/main/Spam%20Classification-NLP/spam.csv

💻 Project Code

https://github.com/santhulak/NLP_Projects/blob/main/Spam%20Classification-NLP/Spam%20Classification%20-%20NLP.ipynb

🎯 Final Thought

The best way to learn Machine Learning is by building real projects.

Start with beginner-friendly NLP projects like Spam Classification and gradually move toward advanced AI applications.