Simple Fruit Classifier

fruit_classifier.py

import pandas as pd
from sklearn.model_selection import train_test_split

data = pd.read_csv('fruit_data.csv')
types = pd.read_csv('fruit_types.csv', index_col=0)

y = data['Label']
X = data[['Mass', 'Width', 'Height']]

X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)

from sklearn.preprocessing import MinMaxScaler
scaler = MinMaxScaler()

X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)

from sklearn.neighbors import KNeighborsClassifier
knn = KNeighborsClassifier()
knn.fit(X_train, y_train)

print("%d%% accuracy" % (knn.score(X_test, y_test) * 100))