Stock price prediction has always been a challenging task, requiring careful observation of trends and dynamics of the market because of the volatile and complex nature of financial markets. Various factors affect market behavior all the time. Even some unquantifiable factors like emotions of the masses, social and political dynamics, etc., also play a great role. So perfect prediction of stock prices is next to impossible. But taking other quantifiable factors and their behaviors into consideration is crucial for better prediction of the ups and downs of prices. Various machine learning and deep learning models have been proposed to tackle the challenges by capturing and interpreting complex patterns and relationships in historical price data. Technical features are important for understanding market trends and thus improving the accuracy of stock price predictions. In this paper, we calculate key technical indicators such as Simple Moving Average (SMA), Exponential Moving Average (EMA), Relative Strength Index (RSI), Moving Average Convergence Divergence (MACD), Bollinger Bands, and others. We then focus on selecting the most relevant indicators by employing feature selection methods from these to enhance the extraction of meaningful features reflecting underlying market behavior and increase the probability of more precise prediction. Here, Recursive Feature Elimination (RFE) and Random Forest Regressor-based importance ranking methods have been applied for the feature sel...