葡萄酒分类

import numpy as np
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split
from sklearn.neural_network import MLPClassifier
from sklearn.metrics import classification_report,confusion_matrix
import pandas as pd

#读取数据进行处理
data = pd.read_csv('wine_data.csv',names = ['a','b','c','d','e','f','g','h','i','j','k','l','m','n'])  #读取数据指定列名
x_data = data[['b','c','d','e','f','g','h','i','j','k','l','m','n']]   #获取数据集
y_data = data['a'] #获取数据集的真实值
print(x_data.shape)
print(y_data.shape)

#数据拆分  %30的测试集，70%的训练集
x_train,x_test,y_train,y_test = train_test_split(x_data, y_data,test_size = 0.3)

#数据标准化  特征缩放
scaler = StandardScaler()
x_train = scaler.fit_transform(x_train)
x_test = scaler.fit_transform(x_test)

# 构建模型，1个隐藏层，隐藏层100个神经元.训练500周期
mlp = MLPClassifier(hidden_layer_sizes=(100), max_iter=500)
mlp.fit(x_train, y_train)

predictions = mlp.predict(x_test)
print(classification_report(y_test, predictions))

print(confusion_matrix(y_test,predictions))