Final_Project_Fake_News_Detection

Business case :

Nowadays, Fake news becomes to be a significant issue on the internet and social media affecting the mental and physical health of children and adults. The goal of my project is to build a Fake News Detection model using Machine Learning. The model will focus on identifying fake news sources, based on combined articles formed from a source. Once a source is labeled as a producer of fake news, we can predict with high level of confidence that any future articles from that source will also be fake news

Data set :

the Data set was the csv file from Kaggle consisting of news articles and posted between 2015-2018. Two sets of data were used, one consisting of all fake news, and another of all real news. The overall format and columns were the same across both datasets. The colonnes in the data was Id title autho text label

Data Pipline :

Acquiring and loading the data
Cleaning the dataset
   Removing extra symbols 
   Removing punctuations
   Removing the stopwords
   Stemming
   Tokenization
EDA
TF-IDF vectorizer
vectorizer with TF-IDF transformer
Machine learning model training and verification

Results :

EDA :

Logistic Regression :

Multinomial Naive Bayes :

Decision Tree Classifier :