Fake News Detection

Key Features of the Project:

Fake News Detection: The project focuses on training an LSTM network to detect fake news from a given news corpus.

Practical Application: The project aims to provide a practical solution for media companies to automatically predict whether news articles are fake or not, eliminating the need for manual review.

Dataset: The project utilizes a dataset from Kaggle for training and evaluation.

Data Processing: Libraries are imported and preliminary data processing is performed, including data cleaning and visualization.

Exploratory Data Analysis: Exploratory data analysis is conducted to gain insights and understand the data.

Tokenization and Padding: The data is prepared by performing tokenization and padding, which are common steps in natural language processing tasks.

Recurrent Neural Networks: The project covers the intuition behind recurrent neural networks, which are suitable for sequential data analysis.

Long Short Term Memory (LSTM): The LSTM model is employed for its ability to capture long-term dependencies and effectively model sequential data.