Fake-News-Detection-System-

Fake News Detection System (Python) Overview The Fake News Detection System is a Python-based application that uses natural language processing and machine learning techniques to identify and classify news articles as either genuine or fake. The system utilizes a combination of text preprocessing, feature extraction, and logistic regression for accurate classification.

Features

1. Text Preprocessing Remove stop words and perform stemming to clean and simplify the text data. Use regular expressions to clean the text and handle special characters.
2. Feature Extraction with TF-IDF Utilize the Term Frequency-Inverse Document Frequency (TF-IDF) vectorizer to convert text data into numerical features. TF-IDF weighting helps capture the importance of words in the context of the entire dataset.
3. Data Splitting Split the dataset into training and testing sets using the train_test_split function from scikit-learn. Ensure a balanced distribution of genuine and fake news in both sets.
4. Logistic Regression Model Implement a logistic regression model using scikit-learn's LogisticRegression. Train the model on the training dataset to learn patterns and relationships between features and labels.
5. Model Evaluation Assess the accuracy of the trained model using the accuracy_score metric. Evaluate the model's performance on the testing dataset to ensure generalization. Dataset The Fake News Detection System uses the (submit.csv). You can download the dataset from https://www.kaggle.com/c/fake-news/data. Technologies Used

Data Processing: NumPy, Pandas, NLTK (Natural Language Toolkit) Machine Learning: scikit-learn Version Control: Git

How to Contribute Fork the repository. Clone the forked repository to your local machine. Create a new branch for your feature or bug fix. Make your changes and commit them. Push your changes to your fork on GitHub. Submit a pull request to the original repository. Installation and Setup Clone the repository to your local machine. Install dependencies using pip install -r requirements.txt. Run the application or script to train and test the fake news detection model.

License This project is licensed under the MIT License.