Assessing Machine Learning's Accuracy in Stock Price Prediction

Abstract

This research examines how well machine learning models can predict the closing price of traded stocks. The financial industry has seen an increase, in the use of these models due to the availability of datasets and technological advancements. The study compares machine learning models such as Linear Regression, Random Forest and K Nearest Neighbor (KNN) to determine which ones are the accurate predictors and what factors contribute to their effectiveness. To gain insights into model performance a diverse dataset consisting of five stocks from sectors is used. Data analysis and modeling are conducted using Python programming language with libraries, like Pandas, NumPy, Matplotlib and Scikit learn. The performance evaluation metric utilized is Mean Squared Error (MSE). The research findings have the potential to assist investors and traders in making decisions while also contributing to the growth of the financial industry.