ML_KNN/KNN_working_file.py at main · CodingTheUnknown/ML_KNN · GitHub

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import sklearn
from sklearn.utils import shuffle
from sklearn.neighbors import KNeighborsClassifier
import pandas as pd
import numpy as np
from sklearn import linear_model, preprocessing

data = pd.read_csv("Car.data")

le = preprocessing.LabelEncoder()

buying = le.fit_transform(list(data["buying"]))
maint = le.fit_transform(list(data["maint"]))
doors = le.fit_transform(list(data["door"]))
persons = le.fit_transform(list(data["persons"]))
lug_boot = le.fit_transform(list(data["lug_boot"]))
safety = le.fit_transform(list(data["safety"]))
cls = le.fit_transform(list(data["class"]))

predict = "class"

print(buying)

#Features
x = list(zip(buying, maint, doors, persons, lug_boot, safety))
#Label
y = list(cls)

x_train, x_test, y_train, y_test = sklearn.model_selection.train_test_split(x, y, test_size=0.02)

model = KNeighborsClassifier(n_neighbors=9)

model.fit(x_train, y_train)

acc = model.score(x_test, y_test)
print("ACC: ", acc)

predicted = model.predict(x_test)
names = ["unacc", "acc", "good", "Vgood"]


for i in range(len(predicted)):
    print("Predicted: ", names[predicted[i]], " Data: ", x_test[i], " Actual: ", names[y_test[i]])