A Practical Approach to Timeseries Forecasting Using Python - Scaling

The video tutorial explains how to use the standard scaler for data normalization. It covers importing the standard scaler, fitting the data, transforming it, and checking the results. The tutorial also discusses entering and verifying train X and train Y values, emphasizing the importance of data shape in the process.

What is the primary purpose of using the StandardScaler in data preprocessing?

• To convert categorical data into numerical data

• To remove missing values from the dataset

• To increase the size of the dataset

• To normalize the data to have a mean of 0 and a standard deviation of 1

What is the first step in using the StandardScaler for data normalization?

• Transforming the data

• Fitting the data

• Importing the StandardScaler from sklearn.preprocessing

• Checking the shape of the data

After fitting the StandardScaler to the training data, what is the next step?

• Splitting the data into train and test sets

• Visualizing the data

• Transforming the data to obtain scaled results

• Checking the data for missing values

What change is observed in the data values after transformation using StandardScaler?

• The values increase significantly

• The values remain unchanged

• The values are scaled to be around 0

• The values are converted to binary

What is the next step after checking the shape of the transformed data?

• Exporting the data to a file

• Entering train X and train Y values

• Visualizing the transformed data

• Repeating the transformation process