This repository contains a machine learning model for detecting fake news, developed in a Jupyter notebook using Google Colab. The model uses Logistic Regression to classify news articles as real or fake based on various textual features.
- Binary Classification: Classifies news articles as either fake or real.
- Logistic Regression: Utilizes Logistic Regression, a commonly used linear model, for binary classification.
- High Accuracy: Achieves a competitive accuracy in detecting fake news.
- Text Preprocessing: Includes techniques such as tokenization, stopword removal, and TF-IDF vectorization to prepare the text data for modeling.
- Dataset: Trained and validated on a labeled dataset of fake and real news articles.
- Performance Evaluation: Implements various performance metrics, including accuracy, precision, recall, F1-score, and confusion matrix, to evaluate the model.
- Visualization: Provides visualizations of model performance through graphs and charts, aiding in the understanding of its efficacy.
The dataset for this file can be found here: https://www.kaggle.com/c/fake-news/data