👉 The project begins with setting up a foundational framework for a machine learning application designed to predict stock prices. This involves collecting historical financial data, preprocessing it to handle missing values and outliers, and selecting appropriate features that influence stock movements. We then choose a suitable machine learning model, such as a Long Short-Term Memory (LSTM) network, which is effective for time series forecasting. The initial phase includes training the model on a labeled dataset of past stock prices and corresponding future prices, followed by rigorous validation to ensure the model's accuracy and robustness.
