figure - two-way partial dependence plots for our random forest model predictor based on alcohol and volatile acidity the plots in figure - bear some resemblance with the plots in figure - for predicting high quality winedue to the lack of training datawhile some dependency is there for high wine quality class prediction with the increase in alcohol and corresponding decrease in volatile acidity is it quite weakas we can see in the left most plot there also seems to be strong dependency on low wine quality class prediction with the corresponding decrease in alcohol and the increase in volatile acidity levels this is clearly visible in the rightmost plot the plot in the middle talks about medium wine quality class predictions we can observe predictions having strong dependency with corresponding increase in alcohol and with decrease in volatile acidity levels this should give you good foundation on leveraging partial dependence plots to dive deeper into model interpretation ummary in this case study oriented we processedanalyzedand modeled on dataset pertaining to wine samples with focus on type and quality ratings special emphasis was on exploratory data analysiswhich is often overlooked by data scientists in hurry to build and deploy models some background in the domain was explored with regard to various features in our wine datasetwhich were explained in detail we recommend always exploring the domain in which you are solving problems and taking the help of subject matter experts whenever neededbesides focusing on mathmachine learningand analysis we looked at multiple ways to analyze and visualize our data and its featuresincluding descriptive and inferential statistics and univariate and multivariate analysis special techniques for visualizing categorical and multi-dimensional data were explained in detail the intent is for you to build on these principles and re-use similar principles and code to visualize attributes and relationships on your own datasets in the future two main objectives of this were to build predictive models for predicting wine types and quality based on various physicochemical wine attributes we covered variety of predictive modelsincluding linear models like logistic regression and complex models including deep neural networks besides thiswe also covered tree based models like decision trees and ensemble models like random forests and the very popular extreme gradient boosting model various aspects of model trainingpredictionevaluationtuningand interpretation were covered in detail we recommend you to not only build models but evaluate them thoroughly with validation metricsuse hyperparameter tuning where necessaryand leverage ensemble modeling to build robustgeneralizedand superior models special focus has also been given to concepts and techniques for interpreting modelsincluding analyzing feature importancesvisualizing model roc curves and decision surfacesexplaining model predictionsand visualizing partial dependence plots