• Ms.Deeksha K
• Dr. Shashidhar Kini K

This study undertakes the development of a Stock Market Forecasting System focused on predicting stock prices using machine learning regression models. To forecast price movements, key features derived from historical market data are utilized. These features include stock prices, trading volume, technical indicators, and market sentiment. For this purpose, recent time-series data was collected from financial data sources and APIs such as Yahoo Finance and Alpha Vantage. The analytical framework involves data collection, preprocessing, feature engineering, and the training and evaluation of predictive models like Linear Regression and XGBoost. The goal is to support informed investment decisions through accurate and timely market forecasts

Stock Price Prediction, Machine Learning, Regression Analysis, XGBoost, Linear Regression, Time-Series Forecasting, Financial Analytics, Market Data Analysis

"Stock Market Forecasting System", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 7, page no.1142-1145, July-2025, Available :http://www.jetir.org/papers/JETIRGX06219.pdf

"Stock Market Forecasting System", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 7, page no. pp1142-1145, July-2025, Available at : http://www.jetir.org/papers/JETIRGX06219.pdf