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Please enable JavaScript in your browser settings or update your browser.Challenge: Rule-based Approach | Statistical Methods in Anomaly Detection
Data Anomaly Detection
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Contenido del Curso

Data Anomaly Detection

Data Anomaly Detection

1. What is Anomaly Detection?
General InformationTypes of AnomaliesHow Outliers Influence On Prediction ResultsWhat Should We Do With Detected Outliers
2. Statistical Methods in Anomaly Detection
Rule-based ApproachChallenge: Rule-based Approach1.5 IQR Rule3-Sigma RuleMedian Absolute DeviationChallenge: Outlier Detection Using MAD Rule
3. Machine Learning Techniques
ClusteringChallenge: Using DBSCAN Clustering to Detect OutliersRegularisationChallenge: Solving Task Using RegularisationAutoencoders

Challenge: Rule-based Approach

Tarea

Your task is to create a function that identifies outliers based on the Euclidean distance between each data point and the mean value of the dataset:

  1. Calculate the Euclidean distance for each data point in the dataset.
  2. If the calculated distance of a data point falls outside a predefined range, classify it as an outlier.
  3. Create a list to store the identified outliers and print the list.

Once you've completed this task, click the button below the code to check your solution.

Tarea

Your task is to create a function that identifies outliers based on the Euclidean distance between each data point and the mean value of the dataset:

  1. Calculate the Euclidean distance for each data point in the dataset.
  2. If the calculated distance of a data point falls outside a predefined range, classify it as an outlier.
  3. Create a list to store the identified outliers and print the list.

Once you've completed this task, click the button below the code to check your solution.

Cambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones

¿Todo estuvo claro?

Sección 2. Capítulo 2
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Challenge: Rule-based Approach

Tarea

Your task is to create a function that identifies outliers based on the Euclidean distance between each data point and the mean value of the dataset:

  1. Calculate the Euclidean distance for each data point in the dataset.
  2. If the calculated distance of a data point falls outside a predefined range, classify it as an outlier.
  3. Create a list to store the identified outliers and print the list.

Once you've completed this task, click the button below the code to check your solution.

Tarea

Your task is to create a function that identifies outliers based on the Euclidean distance between each data point and the mean value of the dataset:

  1. Calculate the Euclidean distance for each data point in the dataset.
  2. If the calculated distance of a data point falls outside a predefined range, classify it as an outlier.
  3. Create a list to store the identified outliers and print the list.

Once you've completed this task, click the button below the code to check your solution.

Cambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones

¿Todo estuvo claro?

Sección 2. Capítulo 2
toggle bottom row

Challenge: Rule-based Approach

Tarea

Your task is to create a function that identifies outliers based on the Euclidean distance between each data point and the mean value of the dataset:

  1. Calculate the Euclidean distance for each data point in the dataset.
  2. If the calculated distance of a data point falls outside a predefined range, classify it as an outlier.
  3. Create a list to store the identified outliers and print the list.

Once you've completed this task, click the button below the code to check your solution.

Tarea

Your task is to create a function that identifies outliers based on the Euclidean distance between each data point and the mean value of the dataset:

  1. Calculate the Euclidean distance for each data point in the dataset.
  2. If the calculated distance of a data point falls outside a predefined range, classify it as an outlier.
  3. Create a list to store the identified outliers and print the list.

Once you've completed this task, click the button below the code to check your solution.

Cambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones

¿Todo estuvo claro?

Tarea

Your task is to create a function that identifies outliers based on the Euclidean distance between each data point and the mean value of the dataset:

  1. Calculate the Euclidean distance for each data point in the dataset.
  2. If the calculated distance of a data point falls outside a predefined range, classify it as an outlier.
  3. Create a list to store the identified outliers and print the list.

Once you've completed this task, click the button below the code to check your solution.

Cambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones
Sección 2. Capítulo 2
Cambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones
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