Study of Impact of Deneovetization in India

Demonetization in India, initiated in November 2016, marked a significant disruption in the economic and financial landscape of the country. The sudden invalidation of ₹500 and ₹1,000 currency notes aimed to curb black money, counterfeit currency, and promote a digital economy. While the short-term impacts included liquidity crunch, reduced consumption, and economic slowdown, the long-term effects on digital transactions, banking activity, and financial inclusion are still being debated. Traditional economic analyses have assessed these outcomes using surveys and statistical tools; however, the emergence of Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) now offers advanced techniques to study such disruptions through real-time data and predictive modeling.This study leverages AI and ML algorithms to analyze large-scale datasets from digital payment systems, ATM withdrawal patterns, tax return filings, and GDP indicators before and after demonetization. Clustering techniques such as K-means are used to segment economic activity across regions, while supervised ML models like Random Forest and SVM evaluate behavioral shifts in banking and spending. Furthermore, Deep Learning models, particularly LSTM networks, are employed for time-series forecasting of economic indicators, capturing temporal trends and anomalies induced by the policy. Sentiment analysis using NLP tools is also performed on social media and news content to assess public perception during the period.The findings demonstrate that AI-driven approaches not only provide a more granular and real-time