Therefore in today’s article, we are going to discuss some of the most effective and indeed easy-to-use data imputation techniques which can be used to deal with missing data. So without any further delay, let’s get started.
Most machine learning algorithms expect complete and clean noise-free datasets, unfortunately, real-world datasets are messy and have multiples missing cells, in such cases handling missing data becomes quite complex.
Therefore in today’s article, we are going to discuss some of the most effective and indeed easy-to-use data imputation techniques which can be used to deal with missing data.
So without any further delay, let’s get started.
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Data Imputation is a method in which the missing values in any variable or data frame(in Machine learning) is filled with some numeric values for performing the task. Using this method the sample size remains the same, only the blanks which were missing are now filled with some values. This method is easy to use but the variance of the dataset is reduced.
There can be various reasons for imputing data, many real-world datasets(not talking about CIFAR or MNIST) containing missing values which can be in any form such as** blanks, NaN, 0s, any integers or any categorical symbol*. Instead of just dropping the Rows or Columns containing the missing values which come at the **price of losing data *which may be valuable, a better strategy is to impute the missing values.
Having a good theoretical knowledge is amazing but implementing them in code in a real-time machine learning project is a completely different thing. You might get different and unexpected results based on different problems and datasets. So as a Bonus,I am also adding the links to the various courses which has helped me a lot in my journey to learn Data science and ML, experiment and compare different data imputations strategies which led me to write this article on comparisons between different data imputations methods.
Most popular Data Science and Machine Learning courses — August 2020. This list was last updated in August 2020 — and will be updated regularly so as to keep it relevant
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