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

Sentiment Analysis with Spark Streaming

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

Machine Learning - Binary Classification

Fake News Detection

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 …

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.

"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.

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

In this project, we will be creating a Fake news classifier model that will classify the news based on the 'title' and 'text', whether it is 'Real' or 'Fake'. The dataset that we are using here is taken from www.kaggle.com.

In this laboratory, PAC and XGBoost models have been developed that predict positive or negative comments about various movies.

News Classification and Sentiment Analysis

a model that accurately classify a piece of news as REAL or FAKE.

Model Comparison for Fake News Classification