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fakeNews.ipynb

# Fake News Detection System The Fake News Detection System is a software application designed to classify news articles as either real or fake based on their content and features. This README file provides an overview of the system, including its purpose, functionality, and usage. ## Dataset The system utilizes two datasets: - **true.csv**: A dataset containing news articles that are verified to be true. - **fake.csv**: A dataset containing news articles that are identified as fake or false. These datasets serve as the training and testing data for building and evaluating the fake news detection model. ## Functionality The Fake News Detection System performs the following key functions: 1. **Data Loading**: Loads the true and fake news datasets (`true.csv` and `fake.csv`, respectively) into memory. 2. **Data Preprocessing**: Preprocesses the loaded datasets, including text cleaning, tokenization, and feature extraction. 3. **Model Training**: Trains a machine learning model (e.g., classification algorithm) using the preprocessed data to classify news articles as real or fake. 4. **Model Evaluation**: Evaluates the performance of the trained model using appropriate metrics such as accuracy, precision, recall, and F1-score. 5. **Prediction**: Accepts input text or news articles and uses the trained model to predict whether they are real or fake. ## Usage To use the Fake News Detection System, follow these steps: 1. **Download Datasets**: Obtain the `true.csv` and `fake.csv` datasets containing true and fake news articles, respectively. 2. **Preprocess Data**: Preprocess the datasets, including cleaning, tokenization, and feature extraction. This step prepares the data for training the model. 3. **Train Model**: Train a machine learning model using the preprocessed data. Experiment with different algorithms (e.g., logistic regression, random forest, support vector machines) to find the best-performing model. 4. **Evaluate Model**: Evaluate the performance of the trained model using evaluation metrics such as accuracy, precision, recall, and F1-score. Ensure that the model generalizes well to unseen data. 5. **Prediction**: Deploy the trained model as part of the Fake News Detection System. Users can input news articles, and the system will predict whether they are real or fake based on the trained model. ## Dependencies The Fake News Detection System may require the following dependencies: - Python 3.x - Machine learning libraries such as scikit-learn, TensorFlow, or PyTorch for model training and prediction. - Data preprocessing libraries such as Pandas and NLTK.

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