import pandas as pd
import numpy as np

Load data

df = pd.DataFrame(data=[
    ['Jay', 35, 35, 3, 'No'],
    ['Rina', 22, 50, 2, 'Yes'],
    ['Emma', 63, 200, 1, 'No'],
    ['Tommy', 59, 170, 1, 'No'],
    ['Neil', 25, 40, 4, 'Yes']
],
columns=['Name', 'Age', 'Income', 'Credit cards', 'Response'])

df

	Name	Age	Income	Credit cards	Response
0	Jay	35	35	3	No
1	Rina	22	50	2	Yes
2	Emma	63	200	1	No
3	Tommy	59	170	1	No
4	Neil	25	40	4	Yes

# Valeur à prédire
x = pd.Series(['David', 37, 50, 2], index=df.columns[:-1])
x

Name            David
Age                37
Income             50
Credit cards        2
dtype: object

From scratch

# Normalize data
df2 = df.copy()
x2  = x.copy()

for col in df.select_dtypes(exclude=['object']).columns:
    _max      = df2[col].max()
    df2[col]  = df2[col]/_max
    x2[col]   = x2[col]/_max
df2

	Name	Age	Income	Credit cards	Response
0	Jay	0.555556	0.175	0.75	No
1	Rina	0.349206	0.250	0.50	Yes
2	Emma	1.000000	1.000	0.25	No
3	Tommy	0.936508	0.850	0.25	No
4	Neil	0.396825	0.200	1.00	Yes

x2

Name               David
Age             0.587302
Income              0.25
Credit cards         0.5
dtype: object

# Compute the euclidean distance
distances = pd.Series(dtype='float')

for idx, row in df2.iterrows():
    d  = (row['Age'] - x2['Age'])**2
    d += (row['Income'] - x2['Income'])**2
    d += (row['Credit cards'] - x2['Credit cards'])**2
    d  = np.sqrt(d)

    distances.loc[idx] = d

distances = distances.sort_values(ascending=True)
distances

1    0.238095
0    0.262931
4    0.537384
3    0.737865
2    0.891807
dtype: float64

# 3 closest records
df.loc[distances[:3].index]

	Name	Age	Income	Credit cards	Response
1	Rina	22	50	2	Yes
0	Jay	35	35	3	No
4	Neil	25	40	4	Yes

df.loc[distances[:3].index]['Response'].mode()

0    Yes
dtype: object

Rina, Jay & Neil sont les plus proches de David.
On prédit la réponse la plus commune: Yes.

Using sklearn

from sklearn import neighbors

X = df.drop(['Name', 'Response'], axis=1)
Y = df['Response']

knn = neighbors.KNeighborsClassifier(n_neighbors=3)
knn.fit(X, Y)

KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',
                     metric_params=None, n_jobs=None, n_neighbors=3, p=2,
                     weights='uniform')

knn.predict([x.drop('Name')])

array(['Yes'], dtype=object)