Question-13: You are working as a data scientist in a company which analyze the transaction detail to make an offer to the higher credit limit and you are receiving the transaction data in real time using AWS Kinesis Data Stream. However, in all the transactions you are not getting some required information like description about the product purchased online store, and to apply the machine learning you have to deal with this missing data, what you can do?
- You would be removing all the transaction from your analysis data which has missing information, so that accurate prediction can be done.
- You would be using Mean/Median average to impute the missing values.
- You would be using Mode Average to insert the missing values.
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Exp: Removing the transaction is not at all good solution, we want to analyze " All AWS Certification & Training Material Can be accessed from this link as well " all the transactions. Mean/Median are the averages which can only be applied on the numerical data and not on the categorical data. Using mode can introduce the bias in the data. So only option remain is the Deep Learning. Deep Learning method works very well with categorical and non-numerical features. It is a library that learns Machine Learning models using Deep Neural Networks to impute missing values