How This Workflow Works
This workflow analyzes a sample dataset, applies two established methods to detect outliers, and then removes those outliers to produce a refined dataset. It first uses an interquartile range approach to flag potential outliers, then standardizes the data and identifies extreme values using z-scores, filtering out rows that fall outside typical ranges.
Key Features:
- Detects outliers using both interquartile range and z-score methods
- Standardizes numeric data for consistent analysis
- Removes anomalous data points to improve data quality
- Prepares a clean dataset for further, more reliable analysis
Step-by-step:
1. Identify Outliers Using Interquartile Range:
The workflow examines numeric columns to find values that fall outside the typical spread, using the interquartile range as a benchmark. This step flags data points that are unusually high or low compared to the majority of the dataset.
2. Standardize Data with Z-Score Normalization:
Next, the workflow transforms numeric values so that each column has a mean of zero and a standard deviation of one. This standardization makes it easier to compare values across different features and to spot extreme deviations.
3. Detect and Mark Extreme Values:
After normalization, the workflow checks each row for values whose absolute z-score exceeds a threshold (in this case, 2). Rows meeting this criterion are marked as containing outliers, highlighting data points that are statistically unusual.
4. Filter Out Outlier Rows:
Finally, the workflow removes all rows identified as containing extreme values. This leaves a dataset composed only of typical data points, ready for more accurate analysis or modeling.