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Feature Selection is one of the core concepts in machine learning and has a high impact on the performance of the model. Irrelevant or partially irrelevant features can negatively impact the model performance.
In this process, those features which contribute most to the prediction variable are selected. In order to get an idea about which features could have more predictive power in a machine learning model, we will load Open, High, Low, Close, Volume (OHLCV) data for AMZ stock, and create some new features using Python.
