KNN car models

I used the K nearest neighbors machine learning method to predict conditions of used cars, and after some trial and error, I assigned 7 to the K value. I first found data online that display cars with conditions, acceptable, unacceptable, good, and very good. I then turned those values into integer values that I used for predictions.

Code:

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"])) #turn values in list into integer values

maint = le.fit_transform(list(data["maint"]))

door = 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"

x = list(zip(buying, maint, door, 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) #predict the model

names = ["unacc", "acc", "good", "very good"]

for x in range(len(predicted)):

print("Predicted: ", names[predicted[x]], "Data: ", x_test[x], "Actual: ", y_test[x]) #print out data

n= model.kneighbors([x_test[x]], 7, True)

print("N: ", n)