This project aims to classify news articles as either fake or real using machine learning techniques. The dataset used for training and evaluation consists of fake and real news articles.
News classification is a common natural language processing (NLP) task that involves training a machine learning model to distinguish between fake and real news articles. This project uses a dataset of labeled news articles to train a Logistic Regression model for classification.
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Clone the repository to your local machine:
https://github.com/swapnilpawar24/News-classification.git cd News-classification
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Download the dataset: Provide the path to your fake and real news datasets in the code where data is loaded.
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Run the Jupyter Notebook: Open the Jupyter Notebook and execute the cells to train the model and evaluate its performance.
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Manual Testing: Use the manual_testing function to test the model with manually entered news articles.
- pandas
- numpy
- seaborn
- matplotlib
- scikit-learn
- jupyter
Swapnil Pawar