FNP made by training classification models using dataset from Kaggle. The models employed include Logistic Regression, Decision Trees, and Random Forest, and the text preprocessing involves PorterStemmer and TF-IDF Vectorizer.

1. Text Preprocessing: Utilizes PorterStemmer for stemming and TF-IDF Vectorizer for feature extraction.
2. Machine Learning Models: Implements Logistic Regression, Decision Trees, and Random Forest classifiers.
3. Model Evaluation: Provides metrics to evaluate the performance of each model.

1. Clone the Repository git clone https://github.com/TaruunMalik/Fake-News-Predictor cd fake-news-predictor

2. Install Libraries pip install pandas numpy scikit-learn nltk

The project provides a comparative analysis of the performance of different models, allowing you to choose the best model for your use case.