Spam Comment Identification using Naive Bayes Classifier

Project Overview

This project focuses on training a Naive Bayes classifier to identify spam comments. The classifier is trained on a dataset containing comments from YouTube, specifically focusing on Shakira's videos.

Project Structure

• main.py: Python script containing the code for data preprocessing, model training, and evaluation.
• Youtube05-Shakira.csv: Dataset file with comment data.
• README.md: Project documentation.

Prerequisites

• Python
• Libraries: pandas, numpy, scikit-learn, nltk

How to Run

1. Install the required libraries: pip install pandas numpy scikit-learn nltk.
2. Open and run the spam_classifier.py script in any Python IDE.

Project Details

• Data Loading: The dataset is loaded and explored using pandas to understand its structure.
• Text Preprocessing: Comments are tokenized, stop words are removed, and stemming is applied.
• Vectorization: CountVectorizer is used to convert text data into a numerical format.
• Model Training: A Multinomial Naive Bayes classifier is trained on the transformed data.
• Cross-validation: The model is evaluated using 5-fold cross-validation.
• Testing: The trained model is tested on a set of new comments, and accuracy is reported.

Feel free to explore and modify the code to suit your needs!