figure - visualizing relationships between wine typesquality and acidity in figure - each violin plot typically depicts the inter-quartile range with the median which is shown with dotted lines in this figure you can also visualize the distribution of data with the density plots where width depicts frequency thus in addition to the information you get from box plotsyou can also visualize the distribution of data with violin plots in fact we have built split-violin plot in this case depicting both types of wine it is quite evident that red wine samples have higher acidity as compared to its white wine counterparts also we can see an overall decrease in acidity with higher quality wine for red wine samples but not so much for white wine samples these code snippets and examples should give you some good frameworks and blueprints to perform effective exploratory data analysis on your datasets in the future predictive modeling we will now focus on our main objectives of building predictive models to predict the wine types and quality ratings based on other features we will be following the standard classification machine learning pipeline in this case there will be two main classification systems we will be building in this section prediction system for wine type (red or white wineprediction system for wine quality rating (lowmediumor highwe will be using the wines data frame from the previous sections the entire code for this section is available in the python file titled predictive_analytics py or you can use the jupyter notebook titled predictive analytics ipynb for more interactive experience to start withlet' load the following necessary dependencies and settings import pandas as pd import numpy as np import matplotlib pyplot as plt import model_evaluation_utils as meu from sklearn model_selection import train_test_split from collections import counter from sklearn preprocessing import standardscaler from sklearn preprocessing import labelencoder %matplotlib inline