• Megha C. Thute
• Dr. Pankaj Agarkar

This research aims to develop a robust predictive model for financial market forecasting by leveraging machine learning techniques. The proposed approach integrates Long Short-Term Memory (LSTM) networks to capture complex time dependencies in historical financial data. Additionally, sentiment analysis using the VADER tool is incorporated to extract insights from news articles and social media, enhancing the prediction accuracy of market trends. Traditional machine learning models such as Random Forest and XGBoost are used for comparison to evaluate their effectiveness against LSTM networks. Furthermore, the study employs SHAP (SHapley Additive exPlanations) to interpret model outputs and identify the key features influencing predictions. This comprehensive framework aims to improve predictive accuracy and provide greater transparency and explainability in financial market forecasting.

"Financial Market Prediction Using Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 4, page no.m357-m362, April-2025, Available :http://www.jetir.org/papers/JETIR2504C48.pdf

"Financial Market Prediction Using Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 4, page no. ppm357-m362, April-2025, Available at : http://www.jetir.org/papers/JETIR2504C48.pdf