What is the best imputation method for replacing missing values in a numerical feature?

3- Imputation Using (Most Frequent) or (Zero/Constant) Values: Most Frequent is another statistical strategy to impute missing values and YES!! It works with categorical features (strings or numerical representations) by replacing missing data with the most frequent values within each column.

What is the best way to impute missing value for a data?

Hot-Deck Imputation:-Works by randomly choosing the missing value from a set of related and similar variables. Cold-Deck Imputation:-A systematically chosen value from an individual who has similar values on other variables. This is similar to Hot Deck in most ways, but removes the random variation.

What is generally the best method for imputing missing values of a categorical feature?

One approach to imputing categorical features is to replace missing values with the most common class. You can do with by taking the index of the most common feature given in Pandas’ value_counts function.

Why mean imputation is not good?

Problem #1: Mean imputation does not preserve the relationships among variables. True, imputing the mean preserves the mean of the observed data. So if the data are missing completely at random, the estimate of the mean remains unbiased. That’s a good thing.

Which is better mean or median imputation?

No more than 5\% of the variable contains missing data. Although in theory, the above conditions should be met to minimize the impact of this imputation technique, in practice, mean/median imputation is very commonly used, even in those cases when data is not MCAR and there are a lot of missing values.

How do you impute missing values in Python?

Filling the Missing Values – Imputation The possible ways to do this are: Filling the missing data with the mean or median value if it’s a numerical variable. Filling the missing data with mode if it’s a categorical value. Filling the numerical value with 0 or -999, or some other number that will not occur in the data.

How do you impute missing values in pandas Python?

In order to check missing values in Pandas DataFrame, we use a function isnull() and notnull() . Both function help in checking whether a value is NaN or not. These function can also be used in Pandas Series in order to find null values in a series.

How do you impute missing text values in Python?

How to impute missing values with means in Python?

1. Step 1 – Import the library. import pandas as pd import numpy as np from sklearn.preprocessing import Imputer.
2. Step 2 – Setting up the Data.
3. Step 3 – Using Imputer to fill the nun values with the Mean.

How does Python handle missing values?

The possible ways to do this are:

1. Filling the missing data with the mean or median value if it’s a numerical variable.
2. Filling the missing data with mode if it’s a categorical value.
3. Filling the numerical value with 0 or -999, or some other number that will not occur in the data.