The Z-Score Method: brushing out outliers from your dataset
Outlier detection is a process used to identify and remove data points from a dataset that deviate from the rest of the data points in the dataset.
It can be used to improve the accuracy of data analysis, to improve the accuracy of predictions made by machine learning models, and to improve the accuracy of decisions made by humans.
In this blog post, we will discuss the different types of outlier detection, how to perform outlier detection in your dataset, and how to use outlier detection using Z-Score to improve the accuracy of your data analysis.
Content
· What is outlier detection?
· Types of outlier detection
· How to find outliers in your dataset
· Let’s remove outliers from your dataset
∘ Identifying Outliers using Z-Score
∘ Removing Outliers using Z-Score
∘ Removing Outliers using IQR
∘ Replacing Outliers with Median
∘ Data Transformation (Log Transformation)
∘ How to report the results of outlier detection
∘ Conclusion
