Data Science and Machine Learning (Theory and Projects) A to Z - Project II_ Stock Price Prediction: Data Preparation

The video tutorial covers the preparation of a dataset for a regression task, focusing on predicting the opening price of a stock. It discusses the importance of maintaining sequential data, the challenges of setting up training and testing datasets, and the process of building a model using sequential data. The tutorial also includes a practical implementation in Jupyter Notebook, demonstrating data preprocessing, sequence generation, and model building using LSTM networks.

How are the input and target values defined in the context of the regression problem discussed?

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What role does scaling play in preparing the data for modeling?

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What are the key packages mentioned for building a recurrent neural network?

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What is the next step after preparing the data as discussed in the video?

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