机器学习入门级实例针对葡萄酒质量利用KNN、决策树、随机森林对数据进行建模的完整代码

好的，以下是一个使用KNN、决策树、随机森林对葡萄酒质量数据进行建模的完整Python代码：

首先，需要导入必要的库：

import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.neighbors import KNeighborsClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score

接下来，读取数据并进行预处理：

# 读取数据集
data = pd.read_csv('winequality.csv')

# 将数据集分为特征和目标
X = data.iloc[:, :-1].values
y = data.iloc[:, -1].values

# 将目标变量转换为二元变量
for i in range(len(y)):
    if y[i] < 6:
        y[i] = 0
    else:
        y[i] = 1

# 将数据集分为训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)

# 特征缩放
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)

然后，使用KNN进行建模和预测：

# KNN分类器
knn = KNeighborsClassifier(n_neighbors=5)
knn.fit(X_train, y_train)

# 预测测试集结果
y_pred_knn = knn.predict(X_test)

# 输出模型准确率
acc_knn = accuracy_score(y_test, y_pred_knn)
print("KNN准确率：", acc_knn)

接下来，使用决策树进行建模和预测：

# 决策树分类器
dt = DecisionTreeClassifier(criterion='entropy', random_state=0)
dt.fit(X_train, y_train)

# 预测测试集结果
y_pred_dt = dt.predict(X_test)

# 输出模型准确率
acc_dt = accuracy_score(y_test, y_pred_dt)
print("决策树准确率：", acc_dt)

最后，使用随机森林进行建模和预测：

# 随机森林分类器
rf = RandomForestClassifier(n_estimators=10, criterion='entropy', random_state=0)
rf.fit(X_train, y_train)

# 预测测试集结果
y_pred_rf = rf.predict(X_test)

# 输出模型准确率
acc_rf = accuracy_score(y_test, y_pred_rf)
print("随机森林准确率：", acc_rf)

完整代码如下：

import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.neighbors import KNeighborsClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score

# 读取数据集
data = pd.read_csv('winequality.csv')

# 将数据集分为特征和目标
X = data.iloc[:, :-1].values
y = data.iloc[:, -1].values

# 将目标变量转换为二元变量
for i in range(len(y)):
    if y[i] < 6:
        y[i] = 0
    else:
        y[i] = 1

# 将数据集分为训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)

# 特征缩放
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)

# KNN分类器
knn = KNeighborsClassifier(n_neighbors=5)
knn.fit(X_train, y_train)

# 预测测试集结果
y_pred_knn = knn.predict(X_test)

# 输出模型准确率
acc_knn = accuracy_score(y_test, y_pred_knn)
print("KNN准确率：", acc_knn)

# 决策树分类器
dt = DecisionTreeClassifier(criterion='entropy', random_state=0)
dt.fit(X_train, y_train)

# 预测测试集结果
y_pred_dt = dt.predict(X_test)

# 输出模型准确率
acc_dt = accuracy_score(y_test, y_pred_dt)
print("决策树准确率：", acc_dt)

# 随机森林分类器
rf = RandomForestClassifier(n_estimators=10, criterion='entropy', random_state=0)
rf.fit(X_train, y_train)

# 预测测试集结果
y_pred_rf = rf.predict(X_test)

# 输出模型准确率
acc_rf = accuracy_score(y_test, y_pred_rf)
print("随机森林准确率：", acc_rf)