[K Nearest Neighbor Algorithm KNN] Use Case: Wine Classification

Use the K nearest neighbor algorithm KNN to predict the type of wine. The data comes from the sklearn dataset.

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import dataset

from sklearn.datasets import load_wine
wine_data = load_wine()
wine_data.keys()

 Each dataset in datasets in sklearn contains the following information: data: feature data, target: target variable, target_names: name of target variable, DESCR: data description, feature_names: feature name.

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explore data

wine_data['data'].shape

 It can be found that this data set has a total of 178 samples and 13 feature variables.

print(wine_data['DESCR'])

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Divide the dataset

from sklearn.model_selection import train_test_split
X_train,X_test,y_train,y_test = train_test_split(wine_data['data'],wine_data['target'],random_state=0)

wine_data['data'] and wine_data['target'] in train_test_split() represent feature variables and target variables;

You can also assign X=wine_data['data'], y=wine_data['target'], train_test_split(X,y,random_state=0);

random_state is a random seed, you can write a number arbitrarily.

 Using the shape method, you can see that the training set accounts for 75% and the test set accounts for 25%.

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Modeling & Calculating Scoring

# KNN分类模型
from sklearn.neighbors import KNeighborsClassifier  
model_knn = KNeighborsClassifier()
model_knn.fit(X_train,y_train)

format(model_knn.score(X_test,y_test))

 The accuracy of the model is about 73%, that is, the probability of making a prediction about new wine is correct is 73%.