The stock market is complex - volatile, multifactorial, and often lacking actionable data. Our mission was to address this challenge through rigorous analysis, innovative feature engineering, and robust forecasting techniques. Our study is centered on technical indicators rather than developing an algorithmic trading strategy. Our objective is to evaluate whether machine learning models can predict future stock price movements. While our approach does not directly aim to maximize trading profits, it offers valuable insights that traders and analysts can incorporate into broader strategic decisions. Our project is the first step to explore and detect many more interesting factors that affect the volatility of the market data.