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This project is a machine learning model that detects whether a news article is real or fake based on its content. It uses Natural Language Processing (NLP) techniques to process the text and classify the news using a Logistic Regression classifier.

"Real-Time Detection and Analysis of Fake News on Social Media", This repository hosts a real-time fake news detection system utilizing Natural Language Inference (NLI) and advanced machine learning techniques, including BERT and SBERT, Multi-Classifier aimed at identifying and categorizing misinformation across social media platforms.

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A fine-tuned LLM-based model that classifies tweets as real or fake disaster news using advanced preprocessing and a neural network classifier

Develop a mechanism that uses a classifier to differentiate between a Real and a Fake news. it must be able to read a link and perform analysis to suggest the probability of it being either legit or made-up. A visual interface may be added in the future for interactivity

This was the mini project to predict the News is Real or Fake Using Decision Tree Classifier algorithm

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.

A machine learning web app that classifies news articles as Fake or Real using NLP and a Decision Tree classifier. Built with Python, scikit-learn, and Streamlit, it features real-time predictions, custom text preprocessing, and TF-IDF-based feature extraction on a dataset of 40,000+ news articles.

News Classification (real and fake news dataset from kaggle)

This project analyzes fake and real news articles using NLP techniques and an SVM classifier. By performing sentiment analysis, word cloud, and bar chart visualizations, we identified common themes and linguistic patterns in both types of news. The resulting model achieved high accuracy in predicting whether an article is fake or real.

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.

A machine learning model that detects whether a news article is real or fake using Natural Language Processing (TF-IDF) and a Passive Aggressive Classifier. Built with Python and Scikit-learn, it helps combat misinformation by analyzing textual content of news.

Fake news is disseminated to intentionally persuade readers to accept biased or untrue beliefs by changing the way people interpret and respond to real news. Detecting fake news manually is relatively tedious especially with the rate at which information is been dispersed on Twitter, hence the need to leverage Machine Learning classifiers for this task. Search for a generally accepted COVID-19 dataset for fake news detection is still on, largely due to its novelty. A novel – more recent, and robust FND Dataset – was curated by scraping tweets from Twitter handles of some health organisations using Twitter API and socialscrapr. The dataset was preprocessed using Python libraries and Microsoft Excel after which it was split into train (80%), validation (10%) and test (10%) datasets and used on SVM, LR, DT baseline Machine Learning algorithms with SVM classifier obtaining the best result with 93.17% for both accuracy and F1 – score performance metrics.

In this project, I have cleaned, analyzed, visualized and classified news data using Logistic Regression.

This project is to generate a fake and real news classifier based on Kaggle dataset: https://www.kaggle.com/clmentbisaillon/fake-and-real-news-dataset

This project was done as a part of the Advanced Data Science Capstone project which is a part of a specialization on coursera by IBM. The data has been taken from 4 sources and saved in to dataframes. These are then processed and cleaned as per the requirement. Various models have then been applied to get the best result for classification of news. A deep learning approach using neural networks has also been attempted as part of my learning experience.

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Fake News Classifier using a variety of classifiers (report)

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.

Classifier that distinguishes fake and real news.

Identify and classify fake news articles.

Project - Create a classifier to separate fake news from real news

Detecting Fake or Real News using PassiveAggressive Classifier Algorithm

This is a Fake News Classifier Project that can Predict the news into fake news or real news

Used a Naive Bayes Classifier for text classification on real and fake news

The Fake News Classifier is a machine learning model that classifies news articles as real or fake based on their content.

Applying Topic Modeling and DNN Classifier to judge Fake News and classify real news according the contents()

ML classifier models for deciding whether a piece of news is "real" or "fake"

Using the scikit-learn decision tree classifier to classify real vs. fake news headlines