STOCKPRICEPREDICTIONUSING MACHINELEARNINGMODELS

Stock price prediction is an important application of machine learning in the financial domain, as it helps investors and analysts understand possible market trends based on historical data. In this project, a stock price prediction system was developed using the Linear Regression machine learning algorithm. The system utilized historical stock market data containing features such as opening price, highest price, lowest price, closing price, and trading volume. Through proper data preprocessing and model training, the system was able to learn the relationships between different financial indicators andstock prices. The results demonstrated that machine learning techniques can effectively analyze historical market patterns and generate useful predictions. The experimental evaluation of the proposed model showed promising performance in predicting stock price trends. The prediction results were assessed using standard regression evaluation metrics such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and the R² score. The relatively low error values and high R² score indicate that the Linear Regression model was able to capture meaningful relationships within the dataset. Visualization of the predicted and actual stock prices further confirmed that the model successfully followed the overall market trend, demonstrating its ability to provide reliable predictions.   Overall, the developed stock price prediction system highlights the potential of machine learning techniques for financial data analysis and forecasting. Although financial markets are inherently unpredictable and influenced by many external factors, machine learning models can still provide valuable insights by analyzing historical data. The proposed approach offers a simple, efficient, and interpretable solution for stock price prediction. Such systems can support investors and financial analysts in making data-driven decisions and understanding potential market movements.

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