Machine learning

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Inspiration

Internet is one of the important inventions and a large number of persons are its users. These persons use this for different purposes. There are different social media platforms that are accessible to these users. Any user can make a post or spread the news through these online platforms. These platforms do not verify the users or their posts. So some of the users try to spread fake news through these platforms. These fake news can be a propaganda against an individual, society, organization or political party. A human being is unable to detect all these fake news. So there is a need for machine learning classifiers that can detect these fake news automatically. Use of machine learning classifiers for detecting the fake news is described in this systematic literature review.

What it does

Develop a machine learning program to identify when a news source may be producing fake news. We aim to use a corpus of labeled real and fake news articles to build a classifier that can make decisions about information based on the content from the corpus. The model will focus on identifying fake news sources, based on multiple articles originating from a source. Once a source is labeled as a producer of fake news, we can predict with high confidence that any future articles from that source will also be fake news. Focusing on sources widens our article misclassification tolerance because we will have multiple data points coming from each source.

How we built it

Planning: -

Data Collection Model Building Backend work Deployment

Challenges we ran into

We faced some issues while we were working on flask while connecting our dataset for prediction. but later on we figured out and came up with a useful solutions which made our model successful.

What we learned

We learn how we can learn new topics in short span of time. We came to know concepts of Machine Learning, flask, Heroku and also gained confidence inspiration from the event.

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