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Data Preprocessing
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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 1

Oppgave

Swipe to start coding

In this challenge, you will need to work with the 'adult-census.csv' dataset. It contains both categorical and numerical data. Your task will be to prepare the data for processing.

  1. Read the dataset 'adult-census.csv'
  2. Explore the dataset. Carefully check which character indicates the missed data in the dataset and replace it with the np.nan object
  3. Remove rows with missing values
  4. Let's start with processing categorical data - columns 'workclass', 'sex' Use a one-hot encoding method to encode them
  5. For numeric data ('age', 'hours-per-week'), you will need to scale the data
  6. Print processed data

Løsning

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Alt var klart?

Hvordan kan vi forbedre det?

Takk for tilbakemeldingene dine!

Seksjon 6. Kapittel 1
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book
Challenge 1

Oppgave

Swipe to start coding

In this challenge, you will need to work with the 'adult-census.csv' dataset. It contains both categorical and numerical data. Your task will be to prepare the data for processing.

  1. Read the dataset 'adult-census.csv'
  2. Explore the dataset. Carefully check which character indicates the missed data in the dataset and replace it with the np.nan object
  3. Remove rows with missing values
  4. Let's start with processing categorical data - columns 'workclass', 'sex' Use a one-hot encoding method to encode them
  5. For numeric data ('age', 'hours-per-week'), you will need to scale the data
  6. Print processed data

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 6. Kapittel 1
Switch to desktopBytt til skrivebordet for virkelighetspraksisFortsett der du er med et av alternativene nedenfor
Vi beklager at noe gikk galt. Hva skjedde?
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