#K nearest neighbours
from sklearn.neighbors import KNeighborsClassifier
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from sklearn.tree import DecisionTreeClassifier
X = [[30], [40], [50], [60], [20], [10], [70]]
Y = [0, 1, 1, 1, 0, 0, 1]
# Here 0 means fail and 1 means pass
classifier = KNeighborsClassifier(n_neighbors=5, metric='minkowski', p=2)
classifier.fit(X, Y)
a = int(input("Enter marks "))
X_marks = [[a]]
print(classifier.predict(X_marks))
