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Guide to Sampling Techniques in Data Science
Sampling techniques in data science that you should know
Sampling is the process of selecting a group of individuals from a target population. It allows us to get information about the total population, based on the statistics from a subset of that target(total) population.
With this sampling process, we can study and analyze the behaviour of the target population, without having to investigate every individual. In machine learning, all the models we build are based on the analysis of the sample data.
The main motive of the sampling process is to collect small and manageable data from the statistical population and then run statistical analysis on that sample data while maintaining the high accuracy of the model.
In this article, we will know why the sampling process is needed, the steps involved in the sampling process, and what are the different types of sampling techniques in Machine Learning. Let’s get started!
Why is sampling needed?
The sampling process is used to collect the sample data which helps us make inferences about the population data. It allows us to draw conclusions about the population from sample data.
