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Preprocessing Data
course content

Course Content

Preprocessing Data

Preprocessing Data

1. Data Exploration
Data ExtractingExplore the DatasetData Types
2. Data Cleaning
Missing and Wrong DataWork with NaNsReplace Numerical Missing Data with ValuesDealing with Missing AgesReplace Categorical Missing Data with ValuesReplace Missing Values with InterpolationRemoving DuplicatesRemoving OutliersVisualizing Outliers
3. Data Validation
Types ConversionData Binning
4. Normalization & Standardization
Data NormalizationData Standardization
5. Data Encoding
Label EncodingOne Hot Encoding

bookMissing and Wrong Data

As you already know, it is possible that raw data can contain some dirty data. It can be:

  • NaN: undefined or missing data.
  • empty strings.
  • infinite: very large data.
  • incorrect data: for example, 'Female' in the Price column, that contains numeric data (this value could be stored into the wrong cell accidentally). You may find impossible values of the user's age, for example, if this value should be entered by him manually (like -1, 110, 0, etc.).
  • outliers: critically small or big values(for example, 250 cm in the Height column, or 112 yrs in the Age column), usually in a small amount. They may affect your result of analysis or model weights, so sometimes it makes sense to remove them.

Let's learn how to 'clean' your data and not to lose some useful info.

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Section 2. Chapter 1
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bookMissing and Wrong Data

As you already know, it is possible that raw data can contain some dirty data. It can be:

  • NaN: undefined or missing data.
  • empty strings.
  • infinite: very large data.
  • incorrect data: for example, 'Female' in the Price column, that contains numeric data (this value could be stored into the wrong cell accidentally). You may find impossible values of the user's age, for example, if this value should be entered by him manually (like -1, 110, 0, etc.).
  • outliers: critically small or big values(for example, 250 cm in the Height column, or 112 yrs in the Age column), usually in a small amount. They may affect your result of analysis or model weights, so sometimes it makes sense to remove them.

Let's learn how to 'clean' your data and not to lose some useful info.

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!

Section 2. Chapter 1
toggle bottom row

bookMissing and Wrong Data

As you already know, it is possible that raw data can contain some dirty data. It can be:

  • NaN: undefined or missing data.
  • empty strings.
  • infinite: very large data.
  • incorrect data: for example, 'Female' in the Price column, that contains numeric data (this value could be stored into the wrong cell accidentally). You may find impossible values of the user's age, for example, if this value should be entered by him manually (like -1, 110, 0, etc.).
  • outliers: critically small or big values(for example, 250 cm in the Height column, or 112 yrs in the Age column), usually in a small amount. They may affect your result of analysis or model weights, so sometimes it makes sense to remove them.

Let's learn how to 'clean' your data and not to lose some useful info.

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!

As you already know, it is possible that raw data can contain some dirty data. It can be:

  • NaN: undefined or missing data.
  • empty strings.
  • infinite: very large data.
  • incorrect data: for example, 'Female' in the Price column, that contains numeric data (this value could be stored into the wrong cell accidentally). You may find impossible values of the user's age, for example, if this value should be entered by him manually (like -1, 110, 0, etc.).
  • outliers: critically small or big values(for example, 250 cm in the Height column, or 112 yrs in the Age column), usually in a small amount. They may affect your result of analysis or model weights, so sometimes it makes sense to remove them.

Let's learn how to 'clean' your data and not to lose some useful info.

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Section 2. Chapter 1
Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
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