Fake News Detection with SVM

• Overview :

This repository contains a project for detecting fake news using a Support Vector Machine (SVM) model. The dataset used is from a Kaggle competition, and the model achieved an impressive accuracy of 98.9% on the training dataset.

• Preprocessing Steps :

Handling Missing Values Converted NaN values to empty strings to ensure consistency in text processing.

•Feature Engineering :

Created a new Content column by merging the title and author columns. This combined text was used as the feature for the model instead of the entire text column.

•Text Vectorization:

Used TF-IDF vectorization to convert the text data into numerical form, making it suitable for training the SVM model.

•Stemming :

Applied stemming to normalize words, reducing them to their root form.

•Model Training and Evaluation :

An SVM model was trained on the preprocessed data. Achieved an accuracy of 98.9% on the training dataset.

•Dataset

The dataset used in this project is from a Kaggle competition. It contains various news articles labeled as fake(1) or real(0).

•Contribution

Contributions are welcome! If you have any improvements or suggestions, please feel free to create a pull request or open an issue. For any questions, you can reach out via the issue tracker.

•Contact :

If you have any questions or need further information, please feel free to contact me. If you have any questions or need further information, please feel free to contact me.