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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
course content

Course Content

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

bookChallenge: Rule-based Approach

Task
test

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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.

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Section 2. Chapter 2
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bookChallenge: Rule-based Approach

Task
test

Swipe to show code editor

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.

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Everything was clear?

How can we improve it?

Thanks for your feedback!

Section 2. Chapter 2
toggle bottom row

bookChallenge: Rule-based Approach

Task
test

Swipe to show code editor

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.

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Everything was clear?

How can we improve it?

Thanks for your feedback!

Task
test

Swipe to show code editor

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.

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Section 2. Chapter 2
Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
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