Date of Publication :2nd September 2024

Abstract:A nation's stock market plays a vital role in providing the necessary financial framework for its economy by facilitating the efficient utilisation of both financial and social capital. While marketing models enable market participants to select companies with high dividend payments while minimising investment risk, reliable equity market models provide investors with reasonable options for making decisions. Share market prediction has improved with technological advancements. Using statistical techniques like exploratory data analysis and machine learning performance measures like RMSE, MSE, MAE, R2 score regression, Random Forests, MGD, and MPD, this paper investigates strategies for predicting the closing prices of Tesla stock. For future projections, neural networks with gated recurrent units (GRUs) are employed. As machine learning techniques can prevent overfitting, jamming and are better at predicting stock prices, for high-volatility stocks like Tesla, this makes them appropriate.

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