Modified Z-score Method for outlier detection and removal in Python

Note: Please Mute (Sound Off) the background Music. This function detects and removes outliers based on Iglewicz and Hoaglin outlier tests. An outlier detection method that does not depend on the number of observations and outliers is replaced with median value. We can calculate the Modified Z-score like: (0.6745*(raw_data - median(raw_data)))/MAD. 0.6745 is the 0.75th quartile of the standard normal distribution, to which the MAD converges to. The threshold is generally taken at 3.5. This means that every point with a score above 3.5 will be considered an outlier. MAD (median absolute deviation) function: https://youtu.be/lHBKIqxxc1Q?t=172 (at 2:50) Please like, share, comment, and SUBSCRIBE for more videos. This Channel helps in teaching how to do different projects in Proteus, Matlab, Python, and Shows different simulations of technical Projects.