Fake news detection using XGboost, SVM, and Random Forest models.

# Project description:

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 up being viralized by algorithms, and users may end up in a filter bubble.

In this project, I trained various machine learning models to predict Fake news from Twitter data (and if the title didn’t already give it away, the Passive-Aggressive Classifier came out on top). The project itself is not new, as there have been numerous machine learning analyses performed on the same data relaying the same conclusions

> Further infos regarding this project can be found [here](https://data-flair.traini...).

## Dataset description:

The data that will be used was gathered from Twitter around the time of the 2016 US General Election. As such, most of the tweet data is political, however, the analysis itself is not political. The analysis does not aim to make any sweeping political conclusions or generalizations, thus nothing of the sort should be inferred from it. This is simply a fun exercise in machine learning!

The data contains three main columns:

1. Title- the title of the news tweet.

2. Text- the actual text of the news tweet.

3. Label- the label of the news tweet, either Fake or Real.

For this analysis, I will only be focusing on the actual text of the tweet.