Simple explanation of Modified Z Score | Modified Z Score to detect outliers with python code

The video begins with an introduction to modified z-score and its application in detecting outliers within datasets. The presenter explains the concepts of mean and median, and how they are used in the context of outlier detection. An example using people's heights is provided to illustrate the impact of outliers on mean and median. The video then transitions into discussing standard deviation and normal distribution, leading to the explanation of z-score. The presenter emphasizes the subjectivity of outliers and the importance of context in deciding whether to treat high values as outliers or not. The segment ends with a practical demonstration of how outliers can skew mean and standard deviation, and introduces the concept of median absolute deviation (MAD) as a more robust measure against the influence of outliers.

This paragraph presents an Excel demonstration on calculating z-scores to detect outliers. The presenter guides the audience through the process of finding the average height and standard deviation, and then computing the z-score for each individual height. The limitations of z-score in detecting outliers when the mean is significantly affected by high values are highlighted. The video then introduces the modified z-score, which uses median and MAD instead of mean and standard deviation, and shows how it can more effectively identify outliers. The Excel demonstration includes step-by-step instructions on calculating MAD and applying the modified z-score formula, and concludes with an example of how a height of 10 feet is correctly identified as an outlier using the modified z-score.

The presenter moves on to using Python for outlier detection in a dataset of movie revenues. After loading and examining a trimmed CSV file from Kaggle, the presenter demonstrates how to convert revenue figures into millions of dollars for easier analysis. The video then shows how to calculate z-scores using Python's pandas library, and how these z-scores can be used to identify outliers. The presenter also explains the potential limitations of z-scores when dealing with skewed data or small sample sizes. The paragraph concludes with the introduction of modified z-score in Python, detailing the process of calculating MAD and using it to compute modified z-scores, which can more accurately detect outliers in the presence of extreme values.

In this section, the presenter compares the effectiveness of z-score and modified z-score in detecting outliers within the movie revenue dataset. The comparison reveals that while z-score identified only one outlier (Avatar), the modified z-score was able to detect additional outliers (Jurassic World and The Bodyguard). The presenter emphasizes that there is no one-size-fits-all approach to outlier detection and that the choice between z-score and modified z-score should be based on the specific characteristics of the dataset and the goals of the analysis. The video ends with a recommendation to experiment with both methods to determine which is more suitable for a given situation, especially when dealing with small datasets or significant outliers that can skew the mean.

The video concludes with a summary of the analysis conducted on movie revenues using both z-score and modified z-score for outlier detection. The presenter provides a link to the code used in the video in the video description and encourages viewers to try out the methods themselves. The presenter also asks viewers to like and share the video if they found it helpful, and thanks them for watching, ending the session on a positive note.