STOCK PRICE PREDICTION

Abstract: Predicting stock prices is a difficult task due to the volatility and non-linearity of the market, as well as the influence of many factors such as past price trends, trading volume, and investor sentiment. In this project I try to compare machine learning algorithms to see which algorithm predicts the stock prices better from historical data. Historical data (Open, High, Low, Close and Volume) is downloaded from Yahoo Finance (yfinance) and preprocessed by dealing with missing values, scaling and keeping the chronological order. Technical indicators like Moving Average (MA), Relative Strength Index (RSI and MACD) are engineered as additional features. First, three models, Linear Regression, Decision Tree and Random Forest, are trained and tested one after another. These are then combined into an ensemble to see if pooling their predictions improves accuracy. The predictions are compared with the real historical prices and evaluated with RMSE, MAE and R2 score. The predicted prices are translated into BUY/SELL signals to measure practical profitability. The goal of the study is to find the best performing approach, individually or in an ensemble, and to make conclusions to guide investment decisions based on data. A survey of 15 recent literatures shows that tree-based and ensemble methods are strong motivating this comparison.

Keywords: Stock price prediction, Machine learning, Linear Regression, Decision Tree, Random Forest, Ensemble learning