This repository contains a text classification project focused on Wikipedia articles. Using Natural Language Processing (NLP) techniques, the project classifies articles into distinct categories, emphasizing geographical and non-geographical content. The classification is powered by a Naive Bayes model trained on a diverse set of Wikipedia articles. Explore, contribute, and enhance the capabilities of this NLP-driven classification system. Key Features:
Text classification using NLP Distinct categories: geographical and non-geographical Naive Bayes machine learning model Wikipedia article dataset. Operational Structure:
Data Collection:
Fetch Wikipedia articles using the Wikipedia API. Pre-processing:
Tokenization, lowercasing, and stopwords removal. Feature Extraction:
Utilize TF-IDF vectorizer for feature extraction. Model Training:
Train a Naive Bayes machine learning model. Classification:
Classify new Wikipedia articles into geographical or non-geographical categories. Technology Stack:
Python:
The project is implemented using Python, leveraging its rich ecosystem of libraries for NLP and machine learning. Scikit-learn:
Scikit-learn is utilized for machine learning tasks, providing efficient tools for data processing and model training. NLTK:
The Natural Language Toolkit (NLTK) aids in tokenization and stopwords removal during pre-processing.