Fake-News-Detector

Fake news Detection using TfidfVectorizer

• The fake news detector was made using TfidfVectorizer and intializing the Passive Agressive classifier.It helps in detection of fake news.
• TF (Term Frequency) - The number of times a word appears in a document is its Term Frequency.
• IDF - IDF is a measure of how significant a term is in the entire corpus.
• TfidfVectorizer - Transforms text to feature vectors that can be used as input to estimator.
• Passive Agressive classifier-The passive-aggressive algorithms are a family of algorithms for large-scale learning.
• The libraries used are :
  • Numpy
  • Pandas
  • Scikit Learn
  • Itertools
  • Matplotlib and Seaborn(for plotting the confusion matrix)
• The accuracy was predicted taking different values of max_iter and confusion matrix was plotted. Accuracy is the number of correct predictions made divided by the total number of predictions made, multiplied by 100 to turn it into a percentage.The confusion matrix summarizes the performance of a classification algorithm.
• The combination of a TF-IDF Vectorizer and a Passive Aggressive Classifier gives accuracy of about 93 %.