Thread by @Sachintukumar

Day 44 of #100dayswithmachinelearning

Topic -- Outlier Detection using Percentile Method

A Thread 🧵

Outliers are a very important and crucial aspect of Data Analysis.

It can be treated in different ways, such as trimming, capping, discretization, or by treating them as missing values.

Percentile Method -

This technique works by setting a particular threshold value, which is decided based on our problem statement.

While we remove the outliers using capping, then that particular method is known as Winsorization.

Here, we always maintain symmetry on both sides, meaning if we remove 1% from the right, the left will also drop by 1%.

as outliers can significantly impact the accuracy of a model if they are not handled properly.

Benefit of removing outliers is to enhance the accuracy and stability of statistical models and ML algorithms by reducing their impact on results. Outliers can distort statistical analyses and skew results as they are extreme values that differ from the rest of the data.

@github NoteBook --

github.com/sachinkumar160…

Removing outliers makes the results more robust and accurate by eliminating their influence. It reduces overfitting in ML algorithms by avoiding fitting to extreme values instead of the underlying data pattern. @AnalyticsVidhya

analyticsvidhya.com/blog/2021/05/f…

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