A Practical Approach to Timeseries Forecasting Using Python - Data Manipulation for Deep Learning

The video tutorial covers the use of LSTM models and the handling of date data in dataframes. It addresses common errors such as key errors when dates are not in the correct format and demonstrates how to reset the index and use 'inplace=True' to fix these issues. The tutorial also explains how to prepare volume data for training by converting it to float and plotting it. Finally, it discusses the importance of normalization and scalarization using the StandardScaler from sklearn to ensure data is in the correct magnitude for LSTM models.

What is the final step mentioned regarding the data frame values?

What is the purpose of the standard scalar in the context of the text?

What should be done after fitting the scalar to the data frame?