Fake News Classification

Objective

In this project, we are going to classify whether a given news is reliable or unreliable using the given data set

Data

Data Source : https://www.kaggle.com/c/fake-news
Train DataSet

• id: unique id for a news article

• title: the title of a news article

• author: author of the news article

• text: the text of the article; could be incomplete

• label: a label that marks the article as potentially unreliable

  -   1: unreliable
  -   0: reliable
  

Test Dataset

• id: unique id for a news article
• title: the title of a news article
• author: author of the news article
• text: the text of the article; could be incomplete

Methods Used

• Classifications
• Logistic regression
• Decision tree
• KNeighbours
• Linear Discriminant

Project Description

Given a title, author and text determine whether the news is reliable or unreliable (0 for unreliable and 1 for reliable).
With the help of given attributes we are going to combine all of the three important attributes i.e title, author and text into a single column so as to preserve all the important data and to make our data cleaning and preprocessing task much easier

Quick Go Through Our Project

1. Merged all three attributes title, author and text into one new column total
2. Filled Null Values
3. Removed duplicate entries
4. Text Cleaning - Removed Url, special characters, stopwords, and performed Snow Ball Steaming.
5. Cleaned both test and train data
6. Used Tf-Idf to convert text into vector and also used bigram
7. Used different Machine Learning Models to find the one with the best accuracy
8. Finally used Logistic Regression to predict our test data
9. Saved the output in a CSV file
10. Submitted on Kaggle with accuracy of 97%

Conclusion

No model can be perfect, so with a score of 97 % accuracy, I am pretty much surprised. But we also use some more turning to see if we can further increase our model accuracy.