TfidfVectorizer & PassiveAggressiveClassifier
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Updated
Mar 3, 2022 - Jupyter Notebook
TfidfVectorizer & PassiveAggressiveClassifier
An NLP model to detect fake news and accurately classify a piece of news as REAL or FAKE trained on dataset provided by Kaggle.
A Django webapp that detects fake news with Machine Learning.
Detecting 'FAKE' news using machine learning.
Detect Real or Fake News. To build a model to accurately classify a piece of news as REAL or FAKE. Using sklearn, build a TfidfVectorizer on the provided dataset. Then, initialize a PassiveAggressive Classifier and fit the model. In the end, the accuracy score and the confusion matrix tell us how well our model fares.
CheckThis is a Fake News Detection website developed by Jonathan Lee as part of the Final Year Project (FYP). The aim of this project is to create a simple web application to help ease the process of verifying the validity of a news article online
Fake news classifier model
Penerapan TF-IDF Vectorizer dan Passive Aggressive Classifier dalam pendeteksian berita palsu dengan Python.
A simple Python model that uses TFIDF Vectorizer and Passive Agressive Classifier to detect fake and irrelevant news
This webapp helps to find the inaccurate information around the world through news
A project which examines the prevalence of fake news in light of communication breakthroughs made possible by the rise of social networking sites.
This is a simple model which first vectorizes the training data using TF-IDF and then uses Passive Aggressive Classifier to train on the input data.
Detect FAKE news using sklearn
This is a flask application that detects and identifies the fake or real news.
Fake News Detection using Machine Learning Algorithms and deploying using Flask
A simple end-to-end project on fake v/s real news detection/classification.
This is my Machine Learning model created with PassiveAggressiveClassifier to detect a news as Real or Fake depending on it's contents.
Fake News Detection using Scikit-learn
This advanced python project of detecting fake news deals with fake and real news. Using sklearn, we build a TfidfVectorizer on our dataset. Then, we initialize a PassiveAggressive Classifier and fit the model. In the end, the accuracy score and the confusion matrix tell us how well our model fares.
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