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bdb650_example's Introduction

Fake News Detection

In this project, we aim at creating a basic Fake News detection system.

Our system can take, as an input, text copied from social media, such as Twitter, Facebook, WhatsApp, etc. After processing, a label will be attached to the text deeming it either REAL or FALSE.

We will use a machine learning model, written in Python using scikit-learn. Other libraries such as pandas and NumPy will also be used for data processing. As for data sources, a News.csv file, posted at Kaggle will be used to train our system.

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bdb650_example's Issues

As a data scientist, I need datasets, so that my ML algorithm can be trained

Description

A dataset, called News.csv will be used in this project. This dataset contains data in a csv format with four columns: # (number), a title, a text, and a label (FALSE or TRUE). This dataset will be downloaded from Kaggle. We will also run queries on Kaggle and other similar databases in order to find other possible sources of training data. Any other useful datasets will be also added to our system. Also, the list of databases that were searched, as well as the list of queries that were performed in these databases will also be documented.

Acceptance Criteria

  • News.csv and other datasets are downloaded and available as csv files
  • A list of databases that were searched will be made available in text format
  • A list of queries performed in the databases described above will be made available in text format

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