the datasets used in this are available in the very popular uci machine learning repository under the name of wine quality data set you can access more details at ml/datasets/wine+qualitywhich gives you access to the raw datasets as well as details about the various features in the datasets there are two datasetsone for red wines and the other for white wines to be more specificthe wine datasets are related to red and white vinho verde wine samplesfrom the north of portugal another file in the same web page talks about the details for the datasets including attribute information credits for the datasets go out to corteza cerdeiraf almeidat matosand reis and you can get more details in their paper"modeling wine preferences by data mining from physicochemical propertiesin decision support systemselsevier ( ) - to summarize our main objectiveswe will be trying to solve the following major problems by leveraging machine learning and data analysis on our wine quality dataset predict if each wine sample is red or white wine predict the quality of each wine samplewhich can be lowmediumor high let' get started by setting up the necessary dependencies before moving on to accessing and analyzing our datasetting up dependencies we will be using several python libraries and frameworks specific to machine learning and deep learning just like in our previous you need to make sure you have pandasnumpyscipyand scikit-learn installedwhich will be used for data processing and machine learning we will also use matplotlib and seaborn extensively for exploratory data analysis and visualizations deep learning frameworks used in this include keras with the tensorflow backendbut you can also use theano as the backend if you choose to do so we also use the xgboost library for the gradient boosting ensemble model utilities related to supervised model fittingpredictionand evaluation are present in model_evaluation_utils pyso make sure you have these modules in the same directory and the other python files and jupyter notebooks for this which you can obtain from the relevant directory for this on github at com/dipanjans/practical-machine-learning-with-python getting the data the datasets will be available along with the code files for this in the github repository for this book at folder for the following files refer to the datasets of interest the file named winequality-red csv contains the dataset pertaining to records of red wine samples the file named winequality-white csv contains the dataset pertaining to records of white wine samples the file named winequality names consists of detailed information and the data dictionary pertaining to the datasets you can also download the same data from wine+quality if needed once you have the csv fileyou can easily load it in python using the read_ csvutility function from pandas