What to do if target column has missing values?

What to do if target column has missing values?

Conclusion:

1. If missing values in target column, drop the record by dropna.
2. If more than 70 % missing values in a column, drop the record by drop.
3. Fill the missing by using fillna.
4. fillna : take mean for integer column.
5. fillna : take mode for categorical column.

What is the best way to impute missing values?

The following are common methods:

1. Mean imputation. Simply calculate the mean of the observed values for that variable for all individuals who are non-missing.
2. Substitution.
3. Hot deck imputation.
4. Cold deck imputation.
5. Regression imputation.
6. Stochastic regression imputation.
7. Interpolation and extrapolation.

What happens when you impute a missing value of a feature?

Imputation preserves all cases by replacing missing data with an estimated value based on other available information. Once all missing values have been imputed, the data set can then be analysed using standard techniques for complete data.

What is the best imputation method you would consider for replacing missing values in a numeric feature?

Mean imputation. Perhaps the easiest way to impute is to replace each missing value with the mean of the observed values for that variable.

Why is it important to understand how do you manage missing values?

The concept of missing values is important to understand in order to successfully manage data. If the missing values are not handled properly by the researcher, then he/she may end up drawing an inaccurate inference about the data.

When to impute values in a specific column?

If you have a dataframe with missing data in multiple columns, and you want to impute a specific column based on the others, you can impute everything and take that specific column that you want:

How to impute missing values in statistics package?

Start by installing and loading the package. Then, impute missing values with the following code. After the missing value imputation, we can simply store our imputed data in a new and fully completed data set. If you check the structure of our imputed data, you will see that there are no missings left. The imputation process is finished.

How are missing values replaced in imputation method?

Imputed values, i.e. values that replace missing data, are created by the applied imputation method. Researchers developed many different imputation methods during the last decades, including very simple imputation methods (e.g. mean imputation) and more sophisticated approaches (e.g. multiple imputation).

Are there any columns that have no missing data?

As you said some of columns are have no missing data that means when you use any of imputation methods such as mean, KNN, or other will just imputes missing values in column C. only you have to do pass your data with missing to any of imputation method then you will get full data with no missing. Thanks for contributing an answer to Stack Overflow!