08. Dealing with Missing Data in Scikit-Learn - sklearn.preprocessing | Scikit-learn Tutorial

Publicado el: 30 enero 2023
en el canal de: CodersArts

1,370

#machinelearning #datascience #scikitlearn #codersarts #mltutorial #ml #mlalgorithms #missingdata #datacleaning #dataanalysis #dataprocessing

In this video we discuss the sklearn.preprocessing package and focus on how missing values are handled using the Imputator class. We also talk about how missing values are represented, types of missing values and the ways in which they can be handled.

Timestamps for Important Topics:
00:00:00 Getting started and Pre-requisites
00:01:15 Introduction
00:04:13 Types of Missing Values
00:06:58 How to handle missing values
00:09:45 sklearn Documentation for Imputing Missing Values
00:11:48 Sample Code

Dealing with missing data is a common problem in the field of machine learning. Data can be missing for various reasons, such as incomplete data collection, errors during data entry, or data that was not recorded. Regardless of the reason, missing data can significantly impact the performance of machine learning models.

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