This repository contains a Fake News Detection AI model built using Logistic Regression. The model aims to classify news articles as either "Fake" or "Real" based on their content. Fake news has become a significant issue in the digital age, and this model provides a machine learning-based solution to help mitigate the spread of misinformation.
- Binary Classification: The model classifies news articles into two categories: Fake and Real.
- Text Preprocessing: Includes steps like tokenization, stop-word removal, and TF-IDF vectorization.
- Model Training: Utilizes Logistic Regression for training the classification model.
- Evaluation Metrics: Accuracy, Precision, Recall, and F1-Score are used to evaluate the model's performance.
The dataset used for training and testing the model is sourced from Kaggle. It consists of two CSV files:
Test.csvTrain.csv