The project focuses on Text Classification using Naive Bayes, where the goal is to categorize text documents into predefined classes or categories.
Objective: To develop and compare the performance of Multinomial Naive Bayes implemented using sklearn with a custom implementation of Naive Bayes for text classification. Dataset - http://archive.ics.uci.edu/ml/datasets/Twenty+Newsgroups
Key Components:
Text Data: The dataset comprises textual documents that are labeled into distinct categories.
Multinomial Naive Bayes (Sklearn Implementation):
Training Phase: Utilize the sklearn library's Multinomial Naive Bayes algorithm to train a model on a portion of the dataset. Testing Phase: Evaluate the model's performance on a separate test set using metrics like accuracy, precision, recall, and F1-score. Custom Naive Bayes Implementation:
Design: Develop a Naive Bayes algorithm specifically tailored for text classification. Implementation: Create necessary functions to calculate probabilities, handle text data, build vocabulary, compute likelihoods, and determine class priors. Comparison of Results:
Performance Metrics: Evaluate the self-implemented Naive Bayes model's performance against the sklearn implementation. Metrics Comparison: Analyze and compare metrics like accuracy, precision, recall, and F1-score to understand the strengths and weaknesses of each implementation. Workflow:
Data Preparation: Preprocess the dataset, including text tokenization, cleaning, and organizing into a suitable format for classification.
Model Training: Train the Multinomial Naive Bayes model using sklearn on a training subset of the data.
Custom Model Development: Implement the Naive Bayes algorithm from scratch, tailored for text classification.
Model Evaluation: Evaluate the performance of both models using test data and standard classification evaluation metrics.
Comparison and Analysis: Compare the results obtained from both implementations, highlighting differences in accuracy, precision, recall, and F1-score.
Outcome:
The project aims to showcase proficiency in: Understanding and implementing the Naive Bayes algorithm for text classification. Utilizing sklearn for standard algorithm implementation. Customizing and developing an algorithm from scratch tailored to specific requirements. Evaluating and comparing the performance of different implementations for text classification tasks. Overall, this project serves as a practical exercise to reinforce understanding and implementation skills in text classification using Naive Bayes while encouraging critical analysis and comparison between a standard library implementation and a custom-built solution.