Fake-News-Predictor

Overview

Fake-News-Predictor is a machine learning project that detects whether news articles are real or fake. It uses a dataset of news articles with author, title, and text fields and trains a logistic regression model on text features.

Project structure

• Fake_News.ipynb: Jupyter notebook containing the data loading, preprocessing, model training, evaluation, and prediction workflow.
• train.csv: Dataset used for training and testing the fake news classifier.

Dataset

The dataset includes the following fields:

• id: unique identifier for the news article
• title: title of the article
• author: author of the article
• text: main text content of the article
• label: target label where 0 indicates real news and 1 indicates fake news

Key steps in the notebook

1. Load the dataset into a pandas DataFrame.
2. Fill missing values and concatenate author and title into a content field.
3. Clean text using regular expressions, lowercase conversion, tokenization, stopword removal, and stemming.
4. Convert text to numerical features with TfidfVectorizer.
5. Split data into training and test sets.
6. Train a LogisticRegression model.
7. Evaluate accuracy on training and test sets.
8. Test the predictive system using a sample input from the test set.

Dependencies

The notebook uses the following Python libraries:

• numpy
• pandas
• nltk
• scikit-learn

Getting started

1. Install Python and create a virtual environment if desired.
2. Install the required packages:

pip install numpy pandas nltk scikit-learn

3. Download the train.csv file into the project folder if it is not already present.
4. Open Fake_News.ipynb in Jupyter Notebook or JupyterLab.
5. Run the notebook cells in order.

Notes

• The notebook downloads the NLTK stopwords dataset with nltk.download('stopwords').
• The final interactive prediction step selects a sample by index from the test split.
• The model is a prototype and can be improved by using more features, deeper preprocessing, or a different classification algorithm.