最近學習python在網上找到乙個很好的小入門級的專案但是發現由於版本的問題，不能直接執行，於是自己修改了一下
**實現：
import sys
from pandas.plotting import scatter_matrix
print('python: {}'.format(sys.version))
# scipy
import scipy
print('scipy: {}'.format(scipy.__version__))
# numpy
import numpy
print('numpy: {}'.format(numpy.__version__))
# matplotlib
import matplotlib
import matplotlib.pyplot as plt
print('matplotlib: {}'.format(matplotlib.__version__))
# pandas
import pandas
print('pandas: {}'.format(pandas.__version__))
# scikit-learn
import sklearn
from sklearn.model_selection import kfold
from sklearn.linear_model import logisticregression
from sklearn.discriminant_analysis import lineardiscriminantanalysis
print('sklearn: {}'.format(sklearn.__version__))
# load dataset
url = ""
names = ['sepal-length', 'sepal-width', 'petal-length', 'petal-width', 'class']
dataset = pandas.read_csv(url, names=names)
# shape
print(dataset.shape)
# descriptions
print(dataset.describe())
# box and whisker plots
dataset.plot(kind='box', subplots=true, layout=(2, 2), sharex=false, sharey=false)
plt.show()
# histograms
dataset.hist()
plt.show()
# scatter plot matrix
scatter_matrix(dataset)
plt.show()
# split-out validation dataset
array = dataset.values
x = array[:, 0:4]
y = array[:, 4]
validation_size = 0.20
seed = 7
x_train, x_validation, y_train, y_validation = sklearn.model_selection.train_test_split(x, y, test_size=validation_size, random_state=seed)
# test options and evaluation metric
seed = 7
scoring = 'accuracy'
#spot check algorithms
models =
# evaluate each model in turn
results =
names =
for name, model in models:
kfold = kfold(n_splits=10, random_state=seed)
cv_results = sklearn.model_selection.cross_val_score(model, x_train, y_train, cv=kfold, scoring=scoring)
msg = "%s: %f (%f)" % (name, cv_results.mean(), cv_results.std())
print(msg)
# compare algorithms
fig = plt.figure()
fig.suptitle('algorithm comparison')
ax = fig.add_subplot(111)
plt.boxplot(results)
ax.set_xticklabels(names)
plt.show()
# make predictions on validation dataset
knn = sklearn.neighbors.kneighborsclassifier()
knn.fit(x_train, y_train)
predictions = knn.predict(x_validation)
print(sklearn.metrics.accuracy_score(y_validation, predictions))
print(sklearn.metrics.confusion_matrix(y_validation, predictions))
print(sklearn.metrics.classification_report(y_validation, predictions))
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