Decoding Market Dynamics

(link to the public repository in GitHub)

Embarking on a captivating journey through the realm of financial markets, I am excited to share a remarkable endeavor that delves into the heart of stock value prediction. The focus of this pursuit? None other than the stocks of industry giants – AAPL, META, AMZN, GOOG, and MSFT.

At the core of this exploration lie the intricacies of Machine Learning Engineering algorithms that cast a predictive light on the future trajectories of these influential stocks. Through a meticulously curated blend of methodologies – including Linear Regression, Random Forest, XGBoost, and the dynamic Long Short-term Memory (LSTM) – we’re decoding the complex dance of market dynamics.

Linear Regression, with its elegance and simplicity, sets the foundation for modeling relationships between variables. Random Forest and XGBoost, armed with their ensemble power, harness the collective wisdom of decision trees to uncover hidden patterns in the stock market chaos. And then there’s LSTM, a marvel of deep learning, which unravels time series data to reveal trends and fluctuations that might escape the human eye.

AAPL, META, AMZN, GOOG, and MSFT – these stock symbols resonate as the titans of our modern economy. And with the insights gleaned from our machine learning journey, we’re inching closer to unlocking the enigma of their value evolution.