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Category: Feature Engineering

Feature Scaling

January 28, 2022January 28, 2022
Sammy Ongaya
Feature Engineering

Feature scaling is a technique of normalizing or standardizing data into a certain range suitable for fitting a machine learning algorithm. If we don’t scale our data for example we have a variable called age with values in range 12

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Feature Encoding

January 27, 2022March 8, 2022
Sammy Ongaya
Feature Engineering, Uncategorized

One key challenge with most of the machine learning algorithm is the inability to work with categorical variables. They need the features to be converted to numeric form. The processing of transforming categorical features to numerical form is referred to

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Feature Selection

January 24, 2022January 27, 2022
Sammy Ongaya
Feature Engineering

Feature selection is a feature engineering process (sometimes considered as data pre-processing method) of choosing the variables with high predictive power from the dataset. It can be conducted for both supervised and unsupervised learning. For supervised approach we statistically evaluate

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Dimensionality Reduction

January 20, 2022March 15, 2022
Sammy Ongaya
Feature Engineering

Working with data with high dimension often possess a challenge commonly referred to as the “curse of dimensionality”. It’s difficult to analyse, visualize and model high-dimensional data hence the need to transform the data from high-dimension to low-dimension. The process

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Feature Engineering

January 18, 2022March 22, 2022
Sammy Ongaya
Feature Engineering

Feature engineering is the process of using domain knowledge and scientific techniques to select and transform the most important features/variables that will yield an optimum machine learning or statistical model. The main goal of feature engineering is to increase the

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