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Data Preprocessing
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Kursinnehåll

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

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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

Lösning

Switch to desktopByt till skrivbordet för praktisk övningFortsätt där du är med ett av alternativen nedan
Var allt tydligt?

Hur kan vi förbättra det?

Tack för dina kommentarer!

Avsnitt 6. Kapitel 1
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book
Challenge 1

Uppgift

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

Lösning

Switch to desktopByt till skrivbordet för praktisk övningFortsätt där du är med ett av alternativen nedan
Var allt tydligt?

Hur kan vi förbättra det?

Tack för dina kommentarer!

Avsnitt 6. Kapitel 1
Switch to desktopByt till skrivbordet för praktisk övningFortsätt där du är med ett av alternativen nedan
Vi beklagar att något gick fel. Vad hände?
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