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Data Preprocessing
course content

Kursusindhold

Data Preprocessing

Data Preprocessing

1. Brief Introduction
Data TypesData Processing MethodsDataset: Test and TrainingDeleting an "Extra" DataChanging the Data Type
2. Processing Quantitative Data
Data ScalingData Scaling vs Data NormalizationRemoving OutliersRemoving Missing ValuesData Augmentation: Synthetic Data
3. Processing Categorical Data
Methods for Encoding the Categorical DataOne-Hot EncodingOrdinal EncodingLabel Encoding of the Target VariableChallenge
4. Time Series Data Processing
Data Type ConversionData CleaningStationarityDenoisingTrain/Test Split & Cross ValidationChallenge
5. Feature Engineering
Technique IdeaRealizationFeature Extraction from TextFeature Extraction from ImagesFeature Extraction from Time SeriesChallenge
6. Moving on to Tasks
Challenge 1Challenge 2Challenge 3

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Challenge

Opgave

Swipe to start coding

Here is the 'students.csv' dataset, which contains 4 columns, 3 of which are categorical. Your task is to process this dataset and encode all categorical variables.

  1. Determine for which columns to use ordinal encoding, one-hot encoding, and for which label encoding.
  2. Convert columns.
  3. Output the dataset.

Løsning

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Sektion 3. Kapitel 5
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book
Challenge

Opgave

Swipe to start coding

Here is the 'students.csv' dataset, which contains 4 columns, 3 of which are categorical. Your task is to process this dataset and encode all categorical variables.

  1. Determine for which columns to use ordinal encoding, one-hot encoding, and for which label encoding.
  2. Convert columns.
  3. Output the dataset.

Løsning

Switch to desktopSkift til skrivebord for at øve i den virkelige verdenFortsæt der, hvor du er, med en af nedenstående muligheder
Var alt klart?

Hvordan kan vi forbedre det?

Tak for dine kommentarer!

Sektion 3. Kapitel 5
Switch to desktopSkift til skrivebord for at øve i den virkelige verdenFortsæt der, hvor du er, med en af nedenstående muligheder
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