This project showcases a Spam Classification System built using Natural Language Processing (NLP) techniques. The classification was performed using Logistic Regression and Multinomial Naive Bayes, comparing their effectiveness on a dataset of spam and ham (non-spam) messages.

• Preprocessing & Feature Engineering: Tokenization, stopword removal, lemmatization, and TF-IDF vectorization.
• Supervised Learning Models: Logistic Regression & Naive Bayes.
• Performance Evaluation: Classification reports, confusion matrices, ROC & Precision-Recall curves.
• Decision Boundary Visualization: Graphical representation of model performance.

A visual representation of how Logistic Regression classifies spam vs. non-spam messages.

• Python
• Scikit-Learn
• NLTK
• Matplotlib & Seaborn

# Clone the repository
$ git clone https://github.com/SYEDFAIZAN1987/Spam-Classification-using-Natural-Language-Processing.git

# Navigate to the directory
$ cd Spam-Classification-using-Natural-Language-Processing

# Install dependencies
$ pip install -r requirements.txt

# Run the Jupyter Notebook
$ jupyter notebook

Contributions, issues, and feature requests are welcome! Feel free to fork the repository and submit a pull request.

This project is licensed under the MIT License.

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