What are the reasons for missing values in the data?

Many existing, industrial and research data sets contain Missing Values. They are introduced due to various reasons, such as manual data entry procedures, equipment errors and incorrect measurements. Hence, it is usual to find missing data in most of the information sources used.

Why do we replace missing values with mean?

You can use mean value to replace the missing values in case the data distribution is symmetric. Consider using median or mode with skewed data distribution. Pandas Dataframe method in Python such as fillna can be used to replace the missing values.

What is missing value treatment?

Popular strategies to handle missing values in the dataset The cause of missing values can be data corruption or failure to record data. Deleting Rows with missing values. Impute missing values for continuous variable. Impute missing values for categorical variable. Other Imputation Methods.

Is replacing by the mean the best strategy to handle missing values?

Missing Values in Numerical Columns Replace it with a constant value. This can be a good approach when used in discussion with the domain expert for the data we are dealing with. Replace it with the mean or median. This is a decent approach when the data size is small—but it does add bias.

When should you replace missing data?

In the case of multivariate analysis, if there is a larger number of missing values, then it can be better to drop those cases (rather than do imputation) and replace them. On the other hand, in univariate analysis, imputation can decrease the amount of bias in the data, if the values are missing at random.

What will do you with a missing value in an observation?

In this method, all data for an observation that has one or more missing values are deleted. However, in most cases, the data are not missing completely at random (MCAR). Deleting the instances with missing observations can result in biased parameters and estimates and reduce the statistical power of the analysis.

Should you remove missing values?

Imputation vs Removing Data In the first two cases, it is safe to remove the data with missing values depending upon their occurrences, while in the third case removing observations with missing values can produce a bias in the model. So we have to be really careful before removing observations.

What is a useful strategy to use when you are missing data?

Answer: Multiple imputation is another useful strategy for handling the missing data. In a multiple imputation, instead of substituting a single value for each missing data, the missing values are replaced with a set of plausible values which contain the natural variability and uncertainty of the right values.

What is useful strategy to use when you are missing data?