print("keys of iris_dataset\ {}format(iris_dataset keys())out[ ]keys of iris_datasetdict_keys(['target_names''feature_names''descr''data''target']the value of the key descr is short description of the dataset we show the beginning of the description here (feel free to look up the rest yourself)in[ ]print(iris_dataset['descr'][: "\ "out[ ]iris plants database ===================notes ---data set characteristics:number of instances ( in each of three classes:number of attributes numericpredictive att ---the value of the key target_names is an array of stringscontaining the species of flower that we want to predictin[ ]print("target names{}format(iris_dataset['target_names'])out[ ]target names['setosa'versicolor'virginica'the value of feature_names is list of stringsgiving the description of each featurein[ ]print("feature names\ {}format(iris_dataset['feature_names'])out[ ]feature names['sepal length (cm)''sepal width (cm)''petal length (cm)''petal width (cm)'the data itself is contained in the target and data fields data contains the numeric measurements of sepal lengthsepal widthpetal lengthand petal width in numpy arraya first applicationclassifying iris species