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PROJECT


Fake News Detection Using Scikit-learn

In this project, we will use two different data sources of news and combine them as a dataset. After that, we will use the scikit-learn library to create a classifier that will be used to determine if a piece of news is fake.

Fake News Detection Using Scikit-learn

You will learn to:

Create a data frame using data pulled from the News API.

Select the features from the textual data.

Create a classifier to classify the textual data.

Skills

Machine Learning

Natural Language Processing

Prerequisites

Intermediate knowledge of Python

Basic understanding of Scikit-learn

Basic understanding of classification problems

Intermediate knowledge of DataFrames

Technologies

Python

Scikit-learn

Project Description

Let’s start this project with a simple question, do you trust all the news from social media? How can we detect fake news from real news? It’s a tough question. Luckily, we can detect fake news using a supervised machine learning method.

Fake news is a piece of news that is not true and deliberately designed to mislead people. It is usually spread via social media or other online platforms. Fake news is usually politically driven to give advantages or disadvantages to a political party. Such news items may contain false and exaggerated claims and because of certain algorithms, trap users in a filter bubble.

In this project, we’ll use two different datasets:

  • News dataset available on Kaggle.
  • The second dataset we’ll create ourselves using the News API. We will use this API to load some data and then append that data to the other dataset.

In the end, we will use a passive-aggressive classifier to classify and differentiate the fake news from the real ones. The passive-aggressive classifier is a classification algorithm in machine learning that changes the model whenever there is a wrong prediction. If there is no wrong prediction, the model will stay the same.

Project Tasks

1

News API

Task 1: Import the Necessary Modules

Task 2: Create a Get News Method

Task 3: Get News Sources

Task 4: Get News Using Multiple Sources

Task 5: Create a DataFrame of News

2

Scikit Learn

Task 6: Load and Concat the DataFrame

Task 7: Import the scikit-learn Modules

Task 8: Split the Training and Testing Data

Task 9: Feature Selection

Task 10: Initialize and Apply the Classifier

Task 11: Test the Classifier

Task 12: Load the Test Data

Task 13: Select Features and Get Predictions

Task 14: Evaluate the Predictions

Congratulations