KNN算法项目实战之酒的分类_基于knn模型的葡萄酒种类预测

加载数据集

from sklearn.datasets import load_wine

wine_dataset = load_wine()
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数据集有什么？

data：数据

target：目标分类

target_names：目标分类名称

DESCR：数据描述

features_names：特征变量名称

查看数据集大小：

数据准备

from sklearn.datasets import load_wine
from sklearn.model_selection import train_test_split

wine_dataset = load_wine()
X_train, X_test, y_train, y_test = train_test_split(wine_dataset["data"], wine_dataset["target"], random_state=0)
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KNN 训练模型

from sklearn.datasets import load_wine
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier

wine_dataset = load_wine()
X_train, X_test, y_train, y_test = train_test_split(wine_dataset["data"], wine_dataset["target"], random_state=0)

knn = KNeighborsClassifier(n_neighbors=1)
knn.fit(X_train, y_train)
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使用模型对测试数据集进行预测

from sklearn.datasets import load_wine
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier

wine_dataset = load_wine()
X_train, X_test, y_train, y_test = train_test_split(wine_dataset["data"], wine_dataset["target"], random_state=0)

knn = KNeighborsClassifier(n_neighbors=1)
knn.fit(X_train, y_train)

print(knn.score(X_test, y_test))
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分数只有0.75，比较低。

邻居数量调整成4的时候，分数会变成0.77，稍微高一点点：

对新酒进行预测

特征变量表如下：

编写代码，进行预测：

from sklearn.datasets import load_wine
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
import numpy as np

wine_dataset = load_wine()
X_train, X_test, y_train, y_test = train_test_split(wine_dataset["data"], wine_dataset["target"], random_state=0)

knn = KNeighborsClassifier(n_neighbors=4)
knn.fit(X_train, y_train)

print(knn.score(X_test, y_test))

new_point = [[13.2,2.77,2.51,18.5,96.6,1.04,2.55,0.57,1.47,6.2,1.05,3.33,820]]
X_new = np.array(new_point)
prediction = knn.predict(X_new)
print(prediction)
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查看分类名称：

from sklearn.datasets import load_wine
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
import numpy as np

wine_dataset = load_wine()
X_train, X_test, y_train, y_test = train_test_split(wine_dataset["data"], wine_dataset["target"], random_state=0)

knn = KNeighborsClassifier(n_neighbors=4)
knn.fit(X_train, y_train)

print(knn.score(X_test, y_test))

new_point = [[13.2,2.77,2.51,18.5,96.6,1.04,2.55,0.57,1.47,6.2,1.05,3.33,820]]
X_new = np.array(new_point)
prediction = knn.predict(X_new)
print(prediction)

print(wine_dataset["target_names"][prediction])
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