This repository contains a Jupyter Notebook file that implements a machine learning model for predicting fake news. The model is built using logistic regression and Python.
- The model is trained on the Fake News competition dataset.
- The dataset consists of various textual features and labels indicating whether the news is fake or not.
- The model is based on a supervised learning algorithm called logistic regression.
- It uses a linear classifier to predict the probability of a news article being fake or not based on its features.
- Download the Fake News competition dataset.
- Place the dataset file in the same directory as the Jupyter Notebook file.
- Open the Jupyter Notebook file in this repository.
- Run the notebook to train the model and make predictions on new data.
- Python (version 3.6 or higher)
- scikit-learn (version 0.24 or higher)
- Jupyter Notebook
- The results can be viewed and analyzed within the Jupyter Notebook.
This project is licensed under the MIT License. See the LICENSE file for more details.
- The Fake News competition dataset is provided by Kaggle.
- The model implementation and techniques are inspired by various resources and tutorials in the field of machine learning.
For more details and a step-by-step guide, refer to the Jupyter Notebook file in this repository.