Handling Missing Values in Machine Learning using Python in 2021 (Code Along)

Publicado em: 09 Julho 2021
no canal de: Imaks Insights

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Handling Missing Values in Machine Learning using Python in 2021 (Code Along)
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#MissingValues #MachineLearning #Python
Learning, inference, and prediction in the presence of missing data are pervasive problems in machine learning and statistical data analysis. Missing data can reduce the accuracy and performance of a predictive model and can produce biased estimates, leading to invalid conclusions or predictions.
How to address missing data is an issue most researchers and model developers face. How to handle missing data therefore depends on the kind of data you are dealing with and the expected model output.

In this tutorial we have handled the missing values in two ways:
1. By first grouping the data into classes, finding the means of each class and replacing the nulls with the mean of each class.
2. Dropping the columns with significant missing values and which are not required in our machine learning model.

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