search

Shahidaakhtar1 / Fake_News_Detection

The project is a Fake News Detection and Classification web application that uses a combination of Long Short-Term Memory (LSTM) and Dense neural networks to classify text as either fake or real news. Key Features: Text Preprocessing LSTM Model Dense Neural Network Web Application
0 stars 0 forks source link

readme

Fake_News_Detection

The web application uses a pre-trained LSTM model for feature extraction. The extracted features are concatenated with the input data and passed through a Dense neural network for classification.

Technologies Used

Python

Flask

TensorFlow

Transformers

Pandas

NumPy

scikit-learn

Dataset

Dataset used in this project was downloaded from kaggle

https://www.kaggle.com/code/therealsampat/fake-news-detection/input

Difference from Existing

As you can see in this kaggle repository they used different machine learning models for this work but in our work we used LSTM for features extraction and those features then fed to a dense neural network for detection.

Architecture Diagram

Arch