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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

Challenge

Task

Now you can solve a fairly simple task - read a synthetic dataset with profiles on a social network and create new features.

  1. Create a new feature Age Binning (like before) that bins the users' ages into age groups (e.g. 20-30, 30-40, 40-50, etc.). Like, 35 (int) -> 30-40 (str)
  2. Create a second feature Average Hours that counts the average number of hours per week spent on social media by individual users

Task

Now you can solve a fairly simple task - read a synthetic dataset with profiles on a social network and create new features.

  1. Create a new feature Age Binning (like before) that bins the users' ages into age groups (e.g. 20-30, 30-40, 40-50, etc.). Like, 35 (int) -> 30-40 (str)
  2. Create a second feature Average Hours that counts the average number of hours per week spent on social media by individual users

Switch to desktop for real-world practiceContinue from where you are using one of the options below

Everything was clear?

Section 5. Chapter 6
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Challenge

Task

Now you can solve a fairly simple task - read a synthetic dataset with profiles on a social network and create new features.

  1. Create a new feature Age Binning (like before) that bins the users' ages into age groups (e.g. 20-30, 30-40, 40-50, etc.). Like, 35 (int) -> 30-40 (str)
  2. Create a second feature Average Hours that counts the average number of hours per week spent on social media by individual users

Task

Now you can solve a fairly simple task - read a synthetic dataset with profiles on a social network and create new features.

  1. Create a new feature Age Binning (like before) that bins the users' ages into age groups (e.g. 20-30, 30-40, 40-50, etc.). Like, 35 (int) -> 30-40 (str)
  2. Create a second feature Average Hours that counts the average number of hours per week spent on social media by individual users

Switch to desktop for real-world practiceContinue from where you are using one of the options below

Everything was clear?

Section 5. Chapter 6
toggle bottom row

Challenge

Task

Now you can solve a fairly simple task - read a synthetic dataset with profiles on a social network and create new features.

  1. Create a new feature Age Binning (like before) that bins the users' ages into age groups (e.g. 20-30, 30-40, 40-50, etc.). Like, 35 (int) -> 30-40 (str)
  2. Create a second feature Average Hours that counts the average number of hours per week spent on social media by individual users

Task

Now you can solve a fairly simple task - read a synthetic dataset with profiles on a social network and create new features.

  1. Create a new feature Age Binning (like before) that bins the users' ages into age groups (e.g. 20-30, 30-40, 40-50, etc.). Like, 35 (int) -> 30-40 (str)
  2. Create a second feature Average Hours that counts the average number of hours per week spent on social media by individual users

Switch to desktop for real-world practiceContinue from where you are using one of the options below

Everything was clear?

Task

Now you can solve a fairly simple task - read a synthetic dataset with profiles on a social network and create new features.

  1. Create a new feature Age Binning (like before) that bins the users' ages into age groups (e.g. 20-30, 30-40, 40-50, etc.). Like, 35 (int) -> 30-40 (str)
  2. Create a second feature Average Hours that counts the average number of hours per week spent on social media by individual users

Switch to desktop for real-world practiceContinue from where you are using one of the options below
Section 5. Chapter 6
Switch to desktop for real-world practiceContinue from where you are using one of the options below
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