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anomaly-detection

The given original data had cities of a country and KPIs of a company. These were obstructed for privacy. The original data values were randomized by adding noise.

This change in input data does not affect the functionalities of the notebook.

The data has 5 headers:

  1. DATE: Date of the recorded value

  2. KIND: The determines how the KPI is NOT supposed to act.

    • "LOW" means the KPI can have anomalies if the value decreases too much suddenly.
    • "HIGH" means the KPI can have anomalies if the value increases too much suddenly.
  3. CITY: The city in which the value was measured

  4. KPI: The key performance indicator that was evaluated to get the value.

  5. VALUE: The result of the KPI evaluation at the city on the specified date.

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