main.py

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")
print(data.head())

le = preprocessing.LabelEncoder()
buying = le.fit_transform((list(data["buying"])))
maint = le.fit_transform((list(data["maint"])))
doors = le.fit_transform((list(data["doors"])))
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"

X = list(zip(buying, maint, doors, persons, lug_boot, safety))
y = list(cls)

x_train, x_test, y_train, y_test = sklearn.model_selection.train_test_split(X, y, test_size=0.1)

model = KNeighborsClassifier(n_neighbors=7)

model.fit(x_train, y_train)
acc = model.score(x_test,y_test)
print(acc)

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

for x in range(len(x_test)):
    print("\nPredicted:", names[predicted[x]], "\nData: ", x_test[x], "\nActual: ", names[y_test[x]])
    n = model.kneighbors([x_test[x]], 7, True)
    print("N: ", n)