Fake News Classifier

Summary 1. Fake news can be very harmful and can affect people’s daily life and have adverse effects on it. 2. Social media has become a daily part of people’s lives and is a major source for spreading of rumors and news which can be fake. Fake News Classification using NLP 1. Setup: Importing Libraries 2. Loading the data set and exploratory Data Analysis 3. Text - Pre Processing 4. Extraction of vectors from text( TF-IDF Vectorization) 5. Running ML Classification Algorithms 6. Generating classification reports and classification matrix 3 -> Tokenization, Stemming, Stop-word Removal 4 -> Logistic Regression, Criterion Boost, Random Forest Real Chart 1. Data Collection 2. Text Processing 3. TF-IDF vectorization 4. Training - Validation 5. Classification Report and Confusion Matrix Tech Stack: Python, AI-ML Based classification algorithms