Which algorithm is best for categorical variables?
Logistic Regression is a classification algorithm so it is best applied to categorical data.
How do you normalize categorical data in Python?
1 Answer
- Normalization: rescales your data into a range of [0;1]
- Standardization: rescales your data to have a mean of 0 and a standard deviation of 1.
- Back to your question: For your gender column your points are already ranging between 0 and 1. Therefore your data is already “normalized”.
How to handle missing values of categorical variables in Python?
It replaces the NaN values with a specified placeholder.It is implemented by the use of the SimpleImputer () method which takes the following arguments: missing_values : The missing_values placeholder which has to be imputed. By default is NaN. stategy : The data which will replace the NaN values from the dataset.
Why is categorical data a challenge in Python?
Regardless of what the value is used for, the challenge is determining how to use this data in the analysis because of the following constraints: Categorical features may have a very large number of levels, known as high cardinality, (for example, cities or URLs), where most of the levels appear in a relatively small number of instances.
How does onehotencoder handle categorical data in Python?
When we initialized the OneHotEncoder, we defined the column position of the variable that we want to transform via the categorical_features parameter (note that color is the first column in the feature matrix X ).
How to handle categorical features using encoding techniques in Python?
The limitation of label encoding can be overcome by binarizing the categories, i.e. representing those using only 0’s and 1’s. Here we represent each category by a vector of size N, where N is the number of categories in that feature. Each vector has one 1 and rest all values are 0.