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Data Preprocessing
course content

Course Content

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

Task

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Now, you know how to work with time-series data, and you are faced with the task of processing it:

  1. Convert column data type from str 'Month' to datetime.
  2. Fill NaN values using the interpolation method.
  3. Use the moving average method to remove noise from the data.

Solution

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SectionΒ 4. ChapterΒ 6
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book
Challenge

Task

Swipe to start coding

Now, you know how to work with time-series data, and you are faced with the task of processing it:

  1. Convert column data type from str 'Month' to datetime.
  2. Fill NaN values using the interpolation method.
  3. Use the moving average method to remove noise from the data.

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!

SectionΒ 4. ChapterΒ 6
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