Custom Project¶
Dataset¶
I used clinic_data_yourname.csv as the origin data, it's renamed to clinic_data_alex.py in implementation.
Signals¶
Z Scores were calculated from a dataset of Age and Height. Age versus Height was output as a scatterplot.
Experiments¶
After adjusting the treshhold a few times and viewing the scatterplot. I decided to use Z scores to detect anomalies.
Results¶
Choosing a Z score threshhold of 2 reduced the number of flagged anomalies. This resulted in one patient anomaly, age 118 was flagged as an anomaly.
Interpretation¶
For this type of dataset anomaly detection could help flag outdated patient records or clerical errors.
Continuous Intelligence¶
This site provides documentation for this project. Use the navigation to explore module-specific materials.
How-To Guide¶
Many instructions are common to all our projects.
See ⭐ Workflow: Apply Example to get these projects running on your machine.
Project Documentation Pages (docs/)¶
- Home - this documentation landing page
- Project Instructions - instructions specific to this module
- Your Files - how to copy the example and create your version
- Glossary - project terms and concepts