Question

When data are missing in a systematic way, you can simply extrapolate the data or impute the missing data by filling in the average of the values around the missing data. is it true or false

Answer 1

Explanation:

Many real-world datasets may contain missing values for various reasons. They are often encoded as NaNs, blanks or any other placeholders. Training a model with a dataset that has a lot of missing values can drastically impact the machine learning model’s quality. Some algorithms such as scikit-learn estimators assume that all values are numerical and have and hold meaningful value.

One way to handle this problem is to get rid of the observations that have missing data. However, you will risk losing data points with valuable information. A better strategy would be to impute the missing values. In other words, we need to infer those missing values from the existing part of the data. There are three main types of missing data:

Missing completely at random (MCAR)

Missing at random (MAR)

Not missing at random (NMAR)

However, in this article, I will focus on 6 popular ways for data imputation for cross-sectional datasets ( Time-series dataset is a different story ).

Answer 2

Answer:

False

Explanation: