In this project, with XGBoost and PAC models, it has been tried to guess whether the news is fake or real based on title and content.

NLP program to detect passive aggressive statements

Detecting 'FAKE' news using machine learning.

Machine Learning - Binary Classification

To build a model to accurately classify a piece of news as REAL or FAKE.

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.

Classification of news to fake or real using the Support Vector Machine (SVC) achieving an accuracy of 92.8% and PAssive Aggressive Classifier (PAC) achieving 92.5% accuracy

YouTube Spam

A machine learning model for detecting fake news

Fake news detection system built using TF-IDF vectorization and passive-aggressive classifier, implemented in Python 3.

An NLP model to detect fake news and accurately classify a piece of news as REAL or FAKE trained on dataset provided by Kaggle.

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.

"DressMeUp" project utilizes fashion images and color combinations to achieve image classification for clothing combinations. Algorithms include SGD (SVM), Passive Aggressive Classifier, ResNet50 CNN, and EfficientNetV2-S CNN with K-Means for color analysis. Achieved accuracy exceeds 90%. Built with Python, Scikit-Learn, TensorFlow, and Streamlit.

What is Fake News? A type of yellow journalism, fake news encapsulates pieces of news that may be hoaxes and is generally spread through social media and other online media. This is often done to further or impose certain ideas and is often achieved with political agendas. Such news items may contain false and/or exaggerated claims, and may end …

Detect FAKE news using sklearn

Text classification model trained on the song lyrics of two similar artists, with the corpus built from web scraping and HTML parsing

With PAC and XGBoost classifiers, two models have been developed to predict toxic or non-toxic tweets.

Fake News Detection using Machine Learning is a comprehensive project that utilizes machine learning and natural language processing techniques to identify and classify fake news articles. The project includes data analysis, model training, and a real-time web application for detecting fake news.