The 5 most impacting outlier detection methods to become a unicorn data scientist

Outliers detection is a natural preliminary step in exploratory data analysis for getting the most impacting results in most data science projects. This process consists of eliminating exceptionally out-limits values for obtaining the best predictions. Easy to use outlier’s detection methods generally include: Min-max Analysis, Z-Score Extreme Value Analysis, Inter Quartile Range, Extended Isolation forest, Local outliers method. To become a unicorn data-scientist mastering the most efficient outliers detection methods is a must needed skills. In this article, we will review the Kaggle winners’ outliers detection methods which can be implemented in short python codes.

We will analyze our sweet chocolate bar rating dataset you can find here.

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