fake news detection using passive Aggresive classifier

Here, we will implement these steps to build a language model in NLP. NLP focuses on the interaction between computers and human language, enabling machines to understand, interpret, and generate human language in a way that is both meaningful and useful.

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

"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.

Fake news detection in English and Vietnamese 📰❌

TARP Project

Fake News Classification using Naive Bayes, Passive-Aggressive (PA) Classifier and Artificia Neural Network (ANN)

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.

NLP program to detect passive aggressive statements

Analyzing Instagram Reach

Fake News Detection using Machine Learning Algorithms and deploying using Flask

TfidfVectorizer is a technique used to transform text data into numerical feature vectors.

This webapp helps to find the inaccurate information around the world through news

Detect FAKE news using sklearn

Fake News Detection with Multinomial NB Classifier and Passive Aggressive Classifier

YouTube Spam

Fake News Detection using Scikit-learn

A project which examines the prevalence of fake news in light of communication breakthroughs made possible by the rise of social networking sites.