📊 Stratified Sampling: Improve Representativeness
📊 Today we're talking about stratified sampling, an unknown yet super useful function in data science.
But what is it? Stratified sampling is a method of sampling that involves dividing the population into smaller subgroups called strata, and then selecting a representative sample from each stratum.
This is useful when we want to ensure that our sample is representative of the overall population, especially when certain subgroups within the population are underrepresented.
Check out the video to see how to do it simply with Python and Sklearn.
So next time you're sampling data, give stratified sampling a try and see if it helps improve the representativeness of your sample.
Hopefully you liked this video 💚
🔥 Subscribe to Bitswired
👍🏽 Leave us a like/comment to support us
💬HASHTAGS:
#stratifiedsampling #datascience #samplingmethods #representativedata #pythoncoding #sklearn #statistics #datamanipulation #dataanalysis #machinelearning #bigdata #datavisualization #datainsights #statisticalanalysis #dataengineering #pythonprogramming #programming #coding #shorts #short
