This project focuses on detecting fake news using various machine learning techniques. The dataset used in this project is sourced from Kaggle Fake News Challenge. Multiple approaches, such as classical machine learning models, GloVe embeddings, BERT embeddings, and CNN-based models, are used to identify whether a news article is real or fake.
This repository contains code for data preprocessing, model training, and evaluation on a dataset of fake and real news articles. The techniques explored include:
- Data Preprocessing: Text cleaning, tokenization, stop-word removal, and lemmatization.
- Feature Extraction: GloVe word embeddings, BERT embeddings, and classical bag-of-words models.
- Model Training: Naive Bayes, Decision Tree, Random Forest, K-Nearest Neighbors, and CNN models.
- Evaluation: Accuracy, confusion matrix, and classification reports.
The following libraries are required to run the project:
pip install nltk numpy pandas seaborn matplotlib scikit-learn gensim tensorflow torch tqdm kerasThe dataset used in this project is the Fake News dataset, which is available on Kaggle. It contains labeled news articles with the following columns:
id: Unique identifier for each article.title: Title of the article.author: Author of the article.text: The content of the article.label: Target variable (1 for fake news, 0 for real news).
The project has the following directory and file structure:
train.csv,test.csv: Training and test datasets.GloVe Embedding/: Folder containing pre-trained GloVe embeddings and processed data. (not added due to size)BERT Embedding/: Folder containing tokenized data and BERT embeddings. (not added due to size)Models/: Folder containing trained models (e.g., CNN model).Training/: Jupyter notebook containing exploratory data analysis and model training and evaluation.
The models' performances are evaluated using accuracy, precision, recall, and F1-score. The CNN-based model using Glove Embeddings achieved an accuracy of 89.28% on the test set.