/Handling-Missing-Data

Know about all the different techniques to handle missing data to make better ML models.

Primary LanguageJupyter Notebook

Handling-Missing-Data

Know about all the different techniques to handle missing data to make better ML models.

Complete Case Analysis

For Detailed Explanation Go to CompleteCaseAnalysis folder

It is a technique in which the null value data is deleted considering some constraints.

new_data = data[col].dropna()

dropna() function deletes all the null values

Multivariate Imputations

It is a technique to fill in the missing data with appropriate values with the help of different column/features which is mostly similar to that of missing data.
Famous techniques are:
  1. KNN Imputer
  2. MICE