Stock Price Prediction Research—Machine Learning Model Evaluation

Stock investment prices are never still; they are always changing. It is important to stay informed on the upward or downward trends of the market to make future investments. This paper aims to examine the question: which of the python models used in this study are the most accurate at predicting the price of the stock market, x days into the future? To accustom the machine learning (ML) predictor to the multitude of possibilities that could categorize stock patterns, 7 different ML models were trained on 1250 pieces of open stock market data dating to the last 5 years by assigning weight values to all the models based on their accuracy. Results showed that two of the ML models, specifically the Linear Regression and the Random Sample Consensus (RANSAC) Regressor models consistently outperformed the other 5 models, both ending up with the highest weight values of around 0.5 when predicting for Amazon, Apple, and Tesla. Therefore, the RANSAC and Linear Regression models are the best models to rely on when predicting open stock market prices using ML.