import pandas as pd import numpy as np import matplotlib.pyplot as plt from sklearn.model_selection import train_test_split from sklearn.neighbors import KNeighborsClassifier from sklearn import metrics from sklearn.preprocessing import MinMaxScaler

glass = pd.read_csv('datasets/glass.csv') glass.head()

glass.shape

X = glass.iloc[:, 0:9] X.head()

y = glass.iloc[:,9] y.head()

X.describe()

y.describe()

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=1)

X_train.shape

X_test.shape

knn = KNeighborsClassifier(n_neighbors = 5) knn.fit(X_train, y_train)

y_pred = knn.predict(X_test)

accuracy = metrics.accuracy_score(y_test, y_pred) accuracy