手把手教你用python开始第一个机器学习项目

Posted on 2022-04-08 17:09 work hard work smart 阅读(109) 评论(0) 编辑收藏举报

1、安装Python

安装 python -m pip install --user numpy scipy matplotlib ipython jupyter pandas sympy nose

pip install -U scikit-learn

效果图：

运行结果：

完整代码：

from pandas import read_csv
from pandas.plotting import scatter_matrix
from matplotlib import pyplot
from sklearn.model_selection import train_test_split
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import StratifiedKFold
from sklearn.metrics import classification_report
from sklearn.metrics import confusion_matrix
from sklearn.metrics import accuracy_score
from sklearn.linear_model import LogisticRegression
from sklearn.tree import DecisionTreeClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearn.naive_bayes import GaussianNB
from sklearn.svm import SVC
 
print("------------------------------------------------")
 
# Load dataset
#url = "https://raw.githubusercontent.com/jbrownlee/Datasets/master/iris.csv"
names = ['sepal-length', 'sepal-width', 'petal-length', 'petal-width', 'class']
dataset = read_csv("C:\\Users\\Administrator\\Downloads\\iris.data", names=names)
 
# shape
print("------------------------------------------------")
print(dataset.shape)
 
#print(dataset.head(20))
 
# descriptions
print("------------------------------------------------")
print(dataset.describe())
 
 
# classdistribution
print("------------------------------------------------")
print(dataset.groupby('class').size())
 
# boxand whisker plots
print("------------------------------------------------")
#dataset.plot(kind='box', subplots=True, layout=(2,2), sharex=False, sharey=False)
#pyplot.show()
 
print("------------------------------------------------")
# histograms
#dataset.hist()
#pyplot.show()
 
print("------------------------------------------------")
# scatter plot matrix
#scatter_matrix(dataset)
#pyplot.show()
 
print("------------------------------------------------")
# Split-out validation dataset
array = dataset.values
X = array[:,0:4]
y = array[:,4]
X_train, X_validation, Y_train, Y_validation = train_test_split(X, y, test_size=0.20, random_state=1)
 
 
models = []
models.append(('LR', LogisticRegression(solver='liblinear', multi_class='ovr')))
models.append(('LDA', LinearDiscriminantAnalysis()))
models.append(('KNN', KNeighborsClassifier()))
models.append(('CART', DecisionTreeClassifier()))
models.append(('NB', GaussianNB()))
models.append(('SVM', SVC(gamma='auto')))
# evaluate each model in turn
results = []
names = []
for name, model in models:
    kfold = StratifiedKFold(n_splits=10, random_state=1, shuffle=True)
    cv_results = cross_val_score(model, X_train, Y_train, cv=kfold, scoring='accuracy')
    results.append(cv_results)
    names.append(name)
    print('%s: %f (%f)' % (name, cv_results.mean(), cv_results.std()))
     
     
# Compare Algorithms
pyplot.boxplot(results, labels=names)
pyplot.title('Algorithm Comparison')
pyplot.show()
 
 
# Make predictions on validation dataset
model = SVC(gamma='auto')
model.fit(X_train, Y_train)
predictions = model.predict(X_validation)
 
# Evaluate predictions
print(accuracy_score(Y_validation, predictions))
print(confusion_matrix(Y_validation, predictions))
print(classification_report(Y_validation, predictions))