import pandas as pd
from sklearn.neighbors import KNeighborsClassifier
from sklearn.preprocessing import MinMaxScaler

array = [
    ['SKU1', 7, 1000],
    ['SKU2', 16, 2000],  
    ['SKU3', 10, 3000],
    ['SKU4', 20, 4000],
    #...
]

columns = ['SKU', 'Признак1', 'Признак2']

df = pd.DataFrame(array, columns = columns)

min_max_scaler = MinMaxScaler(feature_range  = (0.0, 1.0)) 
min_max_scaler.fit(df.iloc[:,1:])

df[df.iloc[:,1:].columns] = min_max_scaler.transform(df.iloc[:,1:])

X = df.iloc[:, 1:].values
Y = df.iloc[:, 0].values

point = [[15.0, 2000.0]]
point = min_max_scaler.transform(point)

#Задаем значимость (важность) каждого признака
weights = [0.9, 0.1]

knn = KNeighborsClassifier(n_neighbors=1,
                           metric='euclidean',                           
                           #weights = weights,
                           n_jobs=-1).fit(X, Y)
print(knn.predict(point))