Welcome to the BBC News Text Classification with TF-IDF project! This Python project demonstrates how to classify BBC news articles into various categories using the Term Frequency-Inverse Document Frequency (TF-IDF) technique. This project aims to provide a clear example of text classification and natural language processing (NLP) using open-source datasets.
TF-IDF Computation: Transform text documents into TF-IDF feature vectors.
Text Classification: Classify news articles into categories using machine learning models.
Document Similarity: Calculate the similarity between documents based on TF-IDF features.
Keyword Extraction: Extract significant keywords from text documents.
Dataset
The project uses the BBC News Dataset, which includes news articles classified into categories such as sports, business, politics, and technology. The dataset is available in CSV format with columns for text and category.