Text Classification with Naive Bayes

This project demonstrates text classification using the Twenty Newsgroups dataset with both a pre-built Multinomial Naive Bayes model from Scikit-learn and a custom Naive Bayes implementation from scratch. The aim is to compare the performance of the custom implementation with the Scikit-learn model.

Overview

The project is divided into the following steps:

1. Load and Preprocess Data:

   • Download the Twenty Newsgroups dataset using fetch_20newsgroups from Scikit-learn.
   • Preprocess the text data into numerical features using TfidfVectorizer.

2. Multinomial Naive Bayes with Scikit-learn:

   • Train a Multinomial Naive Bayes model using Scikit-learn's MultinomialNB.
   • Evaluate the model on the test data using accuracy, precision, recall, and F1-score.

3. Custom Naive Bayes Implementation:

   • Implement a Naive Bayes classifier from scratch.
   • Train the custom Naive Bayes model on the training data.
   • Evaluate the custom model on the test data using the same metrics as the Scikit-learn model.

4. Comparison:

   • Compare the performance of the Scikit-learn model with the custom implementation.

Installation

1. Clone the repository:

   git clone https://github.com/dhruvpal102005/naive-bayes-text-classification.git
   cd naive-bayes-text-classification
   

2. Install the required dependencies:

   pip install -r requirements.txt
   

Results

The results section in the notebook provides a detailed comparison of the accuracy, precision, recall, and F1-score of both the Scikit-learn model and the custom Naive Bayes implementation. This comparison helps in understanding the differences and performance characteristics of both approaches.