analyzing wine types and quality in the last we looked at specific case studies leveraging unsupervised machine learning techniques like clustering and rule-mining frameworks in this we focus on some more case studies relevant to supervised machine learning algorithms and predictive analytics we have looked at classification based problems in where we built sentiment classifiers based on text reviews to predict the sentiment of movie reviews in this the problem at hand is to analyzemodeland predict the type and quality of wine using physicochemical attributes wine is pleasant tasting alcoholic beverageloved by millions across the globe indeed many of us love to celebrate our achievements or even unwind at the end of tough day with glass of winethe following quote from francis bacon should whet your appetite about wine and its significance "age appears best in four thingsold wood to burnold wine to drinkold friends to trustand old authors to read --francis bacon regardless of whether you like and consume wine or notit will definitely be interesting to analyze the physicochemical attributes of wine and understand their relationships and significance with wine quality and types since we will be trying to predict wine types and qualitythe supervised machine learning task involved here is classification in this we look at various ways to analyze and visualize wine data attributes and features we focus on univariate as well as multivariate analyses for predicting wine types and qualitywe will be building classifiers based on state-of-the-art supervised machine learning techniquesincluding logistic regressiondeep neural networksdecision treesand ensemble models like random forests and gradient boosting to name few special emphasis is on analyzingvisualizingand modeling data such that you can emulate similar principles on your own classification based real-world problems in the future we would like to thank the uc irvine ml repository for the dataset also special mention goes to datacamp and karlijn willemsnotable data science journalistwho has done some excellent work in analyzing the wine quality dataset and has written an article on her findings at more details we have taken couple of analyses and explanations from this article as an inspiration for our and karlijn has been more than helpful in sharing the same with us problem statement "given datasetor in this case two datasets that deal with physicochemical properties of winecan you guess the wine type and quality?this is the main objective of this of course this doesn' mean the entire focus will be only on leveraging machine learning to build predictive models we will processanalyzevisualizeand model our dataset based on standard machine learning and data mining workflow models like the crisp-dm model (cdipanjan sarkarraghav bali and tushar sharma sarkar et al practical machine learning with python