This document aims to predict the price of shares for Tata Consultancy Services (TCS) using automatic learning techniques. Take advantage of the data of historical actions that include open price, closing price, volume and technical indicators to build predictive models such as XGBOOST, linear regression, random forest, SVM (support vector machine) and LSTM (long -term memory). Characteristics engineering methods, such as mobile averages and feelings analysis, are used to improve precision. The data set is obtained from the NSE (National Stock Exchange) and other financial platforms, and the model performance is evaluated using MSE (Middle square error), RMSE (square error of root) and R² metric. The findings help investors make decisions based on data, with scope for future improvements through deep learning and alternative data sources.