Day: August 12, 2019

Applied Data Science Coding in Python: Feature Extraction with Univariate Statistics

Applied Data Science Coding in Python: Feature Extraction with Univariate Statistics “Feature Extraction with Univariate Statistics” is a technique used in Python to select the most informative features from a dataset. It is based on the statistical tests for the presence of a relationship between each feature and the target variable. The feature selection is …

Applied Data Science Coding in Python: Feature Extraction with RFE

Applied Data Science Coding in Python: Feature Extraction with RFE “Feature Extraction with RFE” is a technique used in Python to select the most informative features from a dataset. RFE stands for “Recursive Feature Elimination” and it is a method used to select the most relevant features from a dataset by recursively eliminating less important …

Applied Data Science Coding in Python: How to normalise data

Applied Data Science Coding in Python: How to normalise data Normalizing data is a way of scaling the data so that it falls within a specific range. The most commonly used range is between 0 and 1. Normalizing data is useful in situations where the scale of the data can affect the performance of a …

Applied Data Science Coding in Python: How to standardize Data

Applied Data Science Coding in Python: How to standardize Data Standardizing data in Python using scikit-learn is a way to bring all of the features in your dataset to the same scale. This is useful because some machine learning models can be sensitive to the scale of the input features. The process of standardizing involves …