Welcome to our video on feature selection using variance threshold! In this tutorial, we'll be exploring a technique for identifying and removing features with low variance. This method can be particularly useful when working with datasets that contain many redundant or highly correlated features.
We'll be demonstrating how to apply this technique in Python using scikit-learn. We'll start by loading and preparing the dataset, then we'll use scikit-learn's VarianceThreshold method to identify the features with low variance. Next, we'll use the .fit_transform() method to transform the dataset and remove the low variance features. Finally, we'll evaluate the impact of feature selection on the model's performance.
By the end of this video, you'll have a solid understanding of how to use variance threshold to select relevant features and improve the performance of your machine learning models.
#featureselection #variancethreshold #datapreprocessing #machinelearning #python #scikitlearn #modelperformance #correlatedfeatures #datacleansing #datawrangling
