Paper Title
INTELLIGENT LEARNING FROM COUNTERFACTUAL STATEMENTS FOR PREDICTIVE ANALYSIS OF STOCKS

Abstract
Abstract - Counterfactual explanations are an arising procedure under the umbrella of interpretability of AI (ML) models. They give ''consider the possibility that'' criticism of the structure ''on the off chance that an information data point were x′ rather than x, a ML model's result would be y′ rather than y.'' Counterfactual reasonableness for ML models still can't seem to see far and wide reception in industry. This paper talks about making use of counterfactuals in predictive analysis, specifically in the prediction of stock prices. The paper has used ADANI dataset of stock process with news sentiments to find the prediction and counterfactual effect. It concludes that counterfactual machine learning can give accurate results in predictive analysis and can even increase its efficiency .Further research is required to test its efficiency in different sectors . Keywords - Counterfactual Machine Learning, Predictive Analysis, News Sentiments