This project focuses on the detection of fake news using machine learning algorithms. Fake news detection is a crucial task in today's information age where misinformation spreads rapidly across various media platforms. We employ logistic regression and ensemble methods such as RandomForestClassifier, GradientBoostingClassifier, and Voting Classifier to predict the authenticity of news articles.
Logistic regression is a statistical modeling technique used to predict the probability of a binary outcome based on input features. It is particularly suitable for problems where the dependent variable is categorical, such as fake news detection (true or false). The logistic regression model applies the logistic function (sigmoid function) to estimate the probability of the binary outcome.
RandomForestClassifier is a machine learning algorithm that belongs to the family of ensemble methods. It is a popular classification algorithm that combines multiple decision trees to create a powerful model. RandomForestClassifier is widely used for various tasks such as classification, regression, and feature selection.
GradientBoostingClassifier is a popular machine learning algorithm that belongs to the family of ensemble methods, specifically gradient boosting. It is primarily used for classification tasks, where it combines multiple weak learners (decision trees) to create a strong predictive model. GradientBoostingClassifier is known for its high predictive accuracy and robustness against overfitting.
Voting Classifier is a machine learning algorithm that combines the predictions of multiple individual classifiers to make a final prediction. It is a type of ensemble learning method that leverages the wisdom of the crowd to improve prediction accuracy and generalization.
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Member 1: Ishan Chaskar
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Email: ishanchaskar@karunya.edu.in
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Member 2: Joel Renny
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Email: joelrenny@karunya.edu.in
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Github: https://github.com/Joel514
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Member 3: Jeswin K Johnson
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Email: jeswink@karunya.edu.in
Feel free to contribute to this project by adding your expertise and ideas! We welcome all contributions to enhance the accuracy and efficiency of fake news detection.