KNN.py

import numpy as np
import pandas as pd
from sklearn import neighbors, metrics
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder

data = pd.read_csv('car.data')
print(data.head())
X = data[[
    "buying",
    "maint",
    "safety"
]].values
Y = data[['class']]
# converting the data
# converting X
Le = LabelEncoder()
for i in range(len(X[0])):
    X[:, i] = Le.fit_transform(X[:, i])

label_mapping = {
    'unacc':0,
    'acc':1,
    'good':2,
    'vgood':3
}
Y['class'] = Y['class'].map(label_mapping)
Y = np.array(Y)

# Create a KNN object for the algorithm
knn = neighbors.KNeighborsClassifier(n_neighbors=25, weights='uniform')
# We need testing data and testing data
X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2)
knn.fit(X_train, Y_train)
prediction = knn.predict(X_test)
accuracy = metrics.accuracy_score(Y_test, prediction)
print("predictions:", prediction)
print("Accuracy:", accuracy)