Ensemble methods for classifying Fake News

[Rishabhc711]

In accordance to Issue #12

• I was tasked with building classifiers using ensemble methods.

Fixes: #12

Describe the changes you've made

1. Added KNN , Support Vector and Random Forest classifiers to the file Final.ipynb which previously included only Logistic Regression, Decision Tree, Passive-Agressive and Naive Bayes Classifiers.

2. Added 4 ensemble method classifiers based on the relative accuracies of the individual models on the test sets.

3. Computed accuracies of the models (in %) -
   i. KNN : 82.51262626262627
   ii. SUPPORT VECTOR CLASSIFIER : 93.813
   iii. RANDOM FOREST CLASSIFIER : 90.593
   iv. ENSEMBLE METHOD 1 (All) : 93.434
   v. ENSEMBLE METHOD 2 (models with accuracy > 90% ) : 93.497
   vi. ENSEMBLE METHOD 3 (Top 3 models) : 93.813
   vii. ENSEMBLE METHOD 4 (Top 2 models) : 93.876

Type of change

• My code follows the code style of this project.
  -->
• Bug fix (non-breaking change which fixes an issue)
• New feature (non-breaking change which adds functionality)
• Code style update (formatting, local variables)
• Breaking change (fix or feature that would cause existing functionality to not work as expected)
• This change requires a documentation update

How Has This Been Tested?

The tests were conducted in google colab after having cloned the entire repo to google drive. The Final.ipynb reflects all the chnges made and contains all the saved checkpoints.

Describe if there is any unusual behaviour of your code(Write NA if there isn't)

NA

Checklist:

• My code follows the style guidelines of this project.
• I have performed a self-review of my own code.
• I have commented my code, particularly in hard-to-understand areas.
• I have made corresponding changes to the documentation.
• My changes generate no new warnings.
• I have added tests that prove my fix is effective or that my feature works.
• New and existing unit tests pass locally with my changes.
• Any dependent changes have been merged and published in downstream modules.

[Rishabhc711]

This PR inadvertently adds few more models to the original work, thereby resolving issue #27

[Femme-js]

@Rishabhc711 Make a separate 'Final.ipynb' file with your initials, for all the work you did.