有大佬帮帮我吗？用python完成以下题目

Question

ML之kNN：基于kNN算法对莺尾(Iris)数据集进行多分类预测

 

输出结果



输出数据说明：

Iris Plants Database

====================

Notes

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Data Set Characteristics:

:Number of Instances: 150 (50 in each of three classes)

:Number of Attributes: 4 numeric, predictive attributes and the class

:Attribute Information:

- sepal length in cm

- sepal width in cm

- petal length in cm

- petal width in cm

- class:

- Iris-Setosa

- Iris-Versicolour

- Iris-Virginica

:Summary Statistics:

============== ==== ==== ======= ===== ====================

Min Max Mean SD Class Correlation

============== ==== ==== ======= ===== ====================

sepal length: 4.3 7.9 5.84 0.83 0.7826

sepal width: 2.0 4.4 3.05 0.43 -0.4194

petal length: 1.0 6.9 3.76 1.76 0.9490 (high!)

petal width: 0.1 2.5 1.20 0.76 0.9565 (high!)

============== ==== ==== ======= ===== ====================

:Missing Attribute Values: None

:Class Distribution: 33.3% for each of 3 classes.

:Creator: R.A. Fisher

:Donor: Michael Marshall (MARSHALL%[email protected])

:Date: July, 1988

This is a copy of UCI ML iris datasets.

http://archive.ics.uci.edu/ml/datasets/Iris

The famous Iris database, first used by Sir R.A Fisher

This is perhaps the best known database to be found in the

pattern recognition literature. Fisher's paper is a classic in the field and

is referenced frequently to this day. (See Duda & Hart, for example.) The

data set contains 3 classes of 50 instances each, where each class refers to a

type of iris plant. On

Answer 1

直接用sklearn吧，有现成的iris数据集和KNN算法给你用