outlier-detective

You can detect outliers using statistical methods like Z-score and IQR or machine learning algorithms like Isolation Forest and LOF to identify anomalous data points in numerical datasets. This skill applies these techniques directly to pandas dataframes for data cleaning and quality control.

The best way to clean a sales dataset is to apply the IQR or Modified Z-score method to flag transactions with unusually high or low values. This isolates anomalous revenue records before you perform trend analysis or generate distribution plots.

Use Z-score or IQR for basic statistical anomaly detection on normally distributed numerical data, and use Isolation Forest or LOF for complex machine learning outlier detection. This skill supports both approaches to handle varying data quality and fraud detection needs.

Yes, you can generate box plots and distribution plots to visually inspect data anomalies. This skill uses matplotlib to create visualizations that help you assess outliers and understand the distribution of your numerical data during quality control analysis.

Yes, this anomaly detection method works with pandas and scikit-learn, along with numpy, scipy, and matplotlib. It integrates these dependencies to run statistical and machine learning outlier detection algorithms on your numerical datasets.