Fake News Detection

Overview

This project uses NLP techniques to classify news articles as real or fake. It employs text preprocessing, TF-IDF feature extraction, and Logistic Regression for classification.

Folder Structure

• data/raw/: Place downloaded CSVs here (fake.csv, true.csv).
• data/processed/: Generated cleaned data.
• notebooks/: Jupyter notebooks for exploration, preprocessing, training, and evaluation.
• src/: Python scripts for preprocessing, feature extraction, training, evaluation, and prediction.
• models/: Saved models and vectorizers.
• tests/: Unit tests.
• reports/: Figures and final report.

Setup

1. Download dataset from Kaggle and place in data/raw/.
2. Install dependencies: pip install -r requirements.txt
3. Run notebooks in order: 01 -> 02 -> 03 -> 04.
4. Or use scripts: python src/train.py to train and save model.

Usage

• Train: python src/train.py
• Evaluate: python src/evaluate.py
• Predict: python src/predict.py "Your news text here"
• Tests: pytest

Model

• Preprocessing: Cleaning, stopword removal, lemmatization.
• Features: TF-IDF.
• Classifier: Logistic Regression (accurate for this task).