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Data Preprocessing
course content

Kursinnhold

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

Oppgave

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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Alt var klart?

Hvordan kan vi forbedre det?

Takk for tilbakemeldingene dine!

Seksjon 3. Kapittel 5
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book
Challenge

Oppgave

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 desktopBytt til skrivebordet for virkelighetspraksisFortsett der du er med et av alternativene nedenfor
Alt var klart?

Hvordan kan vi forbedre det?

Takk for tilbakemeldingene dine!

Seksjon 3. Kapittel 5
Switch to desktopBytt til skrivebordet for virkelighetspraksisFortsett der du er med et av alternativene nedenfor
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