使用pandas、sklearn等外部库进行iris数据的分类和绘图，并计算正确率

from sklearn.model_selection import train_test_split
from sklearn.datasets import load_iris
from sklearn.neighbors import KNeighborsClassifier
import pandas as pd
import numpy as np
from pandas.plotting import scatter_matrix
import matplotlib.pyplot as plt
data = load_iris()
X_train, X_test, Y_train, Y_test = train_test_split(
    data.data, data.target, random_state=0)
cheng = pd.DataFrame(data.data, columns=data.feature_names)
scatter_matrix(
    cheng,
    figsize=(
        10,
        10),
    c=data.target,
    alpha=0.8,
    s=20,
    hist_kwds={
        ‘bins‘: 30})
knn = KNeighborsClassifier(n_neighbors=5)
knn.fit(X_train, Y_train)
prelist = knn.predict(X_test)
true_values = np.mean(prelist == Y_test)
print(true_values)
plt.show()