COVID-19 OUTREAK IMPACT ON THE INDIAN ECONOMY

Abstract

In the rapidly evolving industrial and energy sectors, data-driven decision-making is becoming increasingly vital for assessing and forecasting corporate financial performance. This study focuses on analyzing the financial performance of Solar Industries Limited using advanced Machine Learning (ML) and Deep Learning (DL) models. By leveraging historical financial data such as revenue, profit margins, return on equity, debt ratios, and stock prices, the study applies predictive algorithms including Random Forest, Support Vector Machines (SVM), and Long Short-Term Memory (LSTM) networks to uncover patterns and forecast future trends. The primary objective is to evaluate the company's financial health, identify potential risks, and predict future outcomes with high accuracy. The use of ML and DL techniques enhances the understanding of complex financial variables and their interdependencies, providing deeper insights compared to traditional financial analysis methods. The results demonstrate that AI-driven models can serve as effective tools for investors, financial analysts, and corporate decision-makers to monitor performance, optimize strategy, and support long-term planning. This research highlights the practical application of artificial intelligence in financial analytics and its transformative potential in the energy and manufacturing industries