This project implements a fake news detection model using the Passive Aggressive Classifier (PAC) algorithm. The Passive Aggressive Classifier is a simple and efficient machine learning algorithm commonly used for binary classification tasks.
The goal of this project is to build a model that can distinguish between fake and real news articles. By training the model on a labeled dataset of news articles, it learns to make predictions based on the text features extracted from the articles.
To train the model, you will need a labeled dataset of news articles where each article is classified as either fake or real. You can either find an existing dataset online or create your own by manually labeling articles. Ensure that the dataset is balanced and representative of the types of articles you want to detect.
Text Preprocessing: Clean and preprocess the text data by removing noise and irrelevant information. This involves steps such as removing stop words, punctuation, converting text to lowercase, and tokenizing the text into individual words.
Feature Extraction: Convert the preprocessed text into numerical features that the classifier can understand. Common techniques include using the Bag-of-Words (BoW) representation or TF-IDF (Term Frequency-Inverse Document Frequency) vectors.
Model Training: Initialize the Passive Aggressive Classifier and train it using the labeled dataset. During training, the classifier updates its model based on the input instances and their corresponding labels. The algorithm learns to make accurate predictions by minimizing the loss function.
Model Evaluation: Evaluate the trained model's performance using a testing dataset. Compute evaluation metrics such as accuracy, precision, recall, and F1 score to assess how well the model distinguishes between fake and real news.
Model Tuning: Experiment with different hyperparameters of the Passive Aggressive Classifier to optimize the model's performance. Techniques like cross-validation or grid search can be used to find the best hyperparameter values.
Prediction: Once the model is trained and evaluated, you can use it to predict the authenticity of new, unseen news articles. Preprocess the new articles in the same way as the training data, extract the features, and feed them into the trained classifier for prediction.
This project provides a comprehensive implementation of a fake news detection model using the Passive Aggressive Classifier. By following the steps outlined in this readme, you can train the model, evaluate its performance, and use it to predict the authenticity of new news articles.