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Preprocessing Data
course content

Conteúdo do Curso

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

bookData Standardization

Another approach to scale the data is to standardize it: transform in such a way that mean is equal to 0 and std is equal to 1. Look at the formula:

Here μ is a mean value of x and σ is a standard deviation. This way, we move each value such that total mean is 0, and normalize it with std, so new std is equal to 1.

Tarefa

Standardize the data in SibSp column by calculating mean and std values and use the formula above. Output some sample to see the modified data and check if the new values of mean and std are equal to 0 and 1 respectively.

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Seção 4. Capítulo 2
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bookData Standardization

Another approach to scale the data is to standardize it: transform in such a way that mean is equal to 0 and std is equal to 1. Look at the formula:

Here μ is a mean value of x and σ is a standard deviation. This way, we move each value such that total mean is 0, and normalize it with std, so new std is equal to 1.

Tarefa

Standardize the data in SibSp column by calculating mean and std values and use the formula above. Output some sample to see the modified data and check if the new values of mean and std are equal to 0 and 1 respectively.

Switch to desktopMude para o desktop para praticar no mundo realContinue de onde você está usando uma das opções abaixo
Tudo estava claro?

Como podemos melhorá-lo?

Obrigado pelo seu feedback!

Seção 4. Capítulo 2
toggle bottom row

bookData Standardization

Another approach to scale the data is to standardize it: transform in such a way that mean is equal to 0 and std is equal to 1. Look at the formula:

Here μ is a mean value of x and σ is a standard deviation. This way, we move each value such that total mean is 0, and normalize it with std, so new std is equal to 1.

Tarefa

Standardize the data in SibSp column by calculating mean and std values and use the formula above. Output some sample to see the modified data and check if the new values of mean and std are equal to 0 and 1 respectively.

Switch to desktopMude para o desktop para praticar no mundo realContinue de onde você está usando uma das opções abaixo
Tudo estava claro?

Como podemos melhorá-lo?

Obrigado pelo seu feedback!

Another approach to scale the data is to standardize it: transform in such a way that mean is equal to 0 and std is equal to 1. Look at the formula:

Here μ is a mean value of x and σ is a standard deviation. This way, we move each value such that total mean is 0, and normalize it with std, so new std is equal to 1.

Tarefa

Standardize the data in SibSp column by calculating mean and std values and use the formula above. Output some sample to see the modified data and check if the new values of mean and std are equal to 0 and 1 respectively.

Switch to desktopMude para o desktop para praticar no mundo realContinue de onde você está usando uma das opções abaixo
Seção 4. Capítulo 2
Switch to desktopMude para o desktop para praticar no mundo realContinue de onde você está usando uma das opções abaixo
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