Quantile Transformation maps the data to a uniform distribution and can also map to a normal distribution. It spreads out the most frequent values
and reduces the impact of outliers.
Usage: when you want to normalize the feature distribution to a uniform or normal distribution. This technique is useful when you deal with skewed data features
Load libraries and read data
Separate independent features and dependent variable
Define Quantile transformation function
Transform each feature individually
LIST and join the series in a dataframe
View the transformed data
Boxplot using seaborn and matplot libraries
Logistic Regression after transformation using Quantile
Find coefficients, intercept and R-Square
Confusion Matrix
Confusion Matrix Heat Map
Find ACCURACY and PRECISION
Find RECALL and F1-score
Classification Report
ROC-AUC
Metrics using single program
Comparison of original and transformed data using different techniques
R^2 is highest when we use Robust Scaling Technique. Accuracy is also highest when we use Robust scaling technique for transformation of features
For the given data we have to use Robust Scaling technique for better performance as our data contains more outliers