The Fake News Detection project aims to develop a system that can distinguish between real and fake news articles using machine learning techniques. This project utilizes a dataset containing labeled examples of both real and fake news to train and evaluate the model.
The project is organized into the following directories and files:
- data: Contains the dataset used for training and testing.
- notebooks: Jupyter notebooks for data exploration, model development, and evaluation.
- src: Source code for the fake news detection model.
- models: Saved machine learning models.
In the notebook data_preprocessing.ipynb, the dataset is loaded, and various preprocessing steps are applied, including:
- Text cleaning (removing stopwords, punctuation, etc.).
- Tokenization.
- Vectorization (using techniques such as TF-IDF).
The model_training.ipynb notebook focuses on developing and training the machine learning model. Various algorithms such as logistic regeression,naive bayes classifier are explored, and the model with the best performance is selected.
The Fake_news_prediction.ipynb notebook assesses the performance of the trained model on a separate test set. Metrics such as accuracy, precision, recall, and F1 score are calculated to evaluate the model's effectiveness in fake news detection.
Summarize the key findings and results from the evaluation.
Discuss potential areas for improvement, such as incorporating more advanced natural language processing techniques, fine-tuning hyperparameters, or exploring ensemble methods.
Note: This is a basic template, and you may need to adjust it based on the specifics of your project, such as the tools and technologies used, the structure of your dataset, and the algorithms applied.