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Data Preprocessing

Data Scaling vs Data NormalizationData Scaling vs Data Normalization

Data scaling and normalization are two terms that are often used interchangeably, but they actually refer to slightly different concepts.

Data scaling refers to transforming a dataset's values so that they fall within a specific range. This can involve rescaling the data to a specific minimum and maximum value, or standardizing the data so that it has a mean of zero and a standard deviation of one. The goal of data scaling is to ensure that all the dataset's features are on the same scale so that no feature dominates the others.

Normalization, on the other hand, refers to the process of transforming the values of a dataset so that they conform to a specific distribution. This can involve transforming the data so that it has a normal (Gaussian) distribution or some other distribution. Normalization aims to make the data more interpretable or to meet the assumptions of a particular statistical test or machine learning algorithm.

Data scaling is a more common preprocessing step in machine learning, as it is often necessary to ensure that all features are on the same scale to avoid bias and improve accuracy. Normalization is less commonly used but can be important in certain situations, such as when working with data with a skewed distribution or when using certain statistical tests.

Everything was clear?

Section 2. Chapter 2
course content

Course Content

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

Data Preprocessing

Data Scaling vs Data NormalizationData Scaling vs Data Normalization

Data scaling and normalization are two terms that are often used interchangeably, but they actually refer to slightly different concepts.

Data scaling refers to transforming a dataset's values so that they fall within a specific range. This can involve rescaling the data to a specific minimum and maximum value, or standardizing the data so that it has a mean of zero and a standard deviation of one. The goal of data scaling is to ensure that all the dataset's features are on the same scale so that no feature dominates the others.

Normalization, on the other hand, refers to the process of transforming the values of a dataset so that they conform to a specific distribution. This can involve transforming the data so that it has a normal (Gaussian) distribution or some other distribution. Normalization aims to make the data more interpretable or to meet the assumptions of a particular statistical test or machine learning algorithm.

Data scaling is a more common preprocessing step in machine learning, as it is often necessary to ensure that all features are on the same scale to avoid bias and improve accuracy. Normalization is less commonly used but can be important in certain situations, such as when working with data with a skewed distribution or when using certain statistical tests.

Everything was clear?

Section 2. Chapter 2
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