A Comparative Study of Machine Learning Models for Stock Price Prediction

The stock market has always been evolutionary. It has been an area of interest for researchers to forecasting the stock prices too guide investment decisions and avoiding financial risk since many years. This paper highlights the significance of precise stock price prediction and investigates the use of several machine learning techniques to strengthen the predictive accuracy. The proposed system makes uses two different approaches: Regression, which predicts the company’s stock closing price, and Classification predicts whether the closing price of the stocks will increase or decrease the next day. Historical stock data is examined carefully and preprocessed to ensure high quality of data. Using feature engineering appropriate features are extracted to identify the intrinsic patterns and trends within the stock market. Algorithms like Linear Regression, ARIMA, LSTM and Random Forest are implemented and thoroughly evaluated for their efficiency in stock price prediction. After conducting a comparative analysis of the two algorithms, it becomes evident that while LSTM performs exceptionally in regression tasks, identifying the temporal associations and patterns that guide the stock market data in its development, Random Forest provides more firm predictions of the stock price movement. This study provides the extensive analysis of the real-life application of machine learning for discussions on the stock market, thus allowing investors to derive a more reliable resource to refer to when dealing with the challenges of the stock market.