In [4]:

from krmining.classification import KNearestNeighborsClassifier
from krmining.datasets import make_dummy_data_classification

from krmining.classification import KNearestNeighborsClassifier from krmining.datasets import make_dummy_data_classification

Using dummy data¶

In [5]:

df = make_dummy_data_classification()

df.head()

df = make_dummy_data_classification() df.head()

Out[5]:

	0	1
0	22	1
1	23	1
2	21	1
3	18	1
4	19	1

In [6]:

df.info()

df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 10 entries, 0 to 9
Data columns (total 2 columns):
 #   Column  Non-Null Count  Dtype
---  ------  --------------  -----
 0   0       10 non-null     int64
 1   1       10 non-null     int64
dtypes: int64(2)
memory usage: 288.0 bytes

In [15]:

X = df[[0]]
y = df[1]

knn = KNearestNeighborsClassifier(3)
knn.fit(X, y)

X = df[[0]] y = df[1] knn = KNearestNeighborsClassifier(3) knn.fit(X, y)

C:\Users\Bina Umat\anaconda3\lib\site-packages\krmining\classification\_knn.py:15: UserWarning: The model still in maintaining in slow or extended memory
  warnings.warn(

Out[15]:

<krmining.classification._knn.KNearestNeighborsClassifier at 0x2107c3ead60>

In [17]:

knn.predict(X)

knn.predict(X)

Out[17]:

[1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0]

In [18]:

knn.evaluate(X, y)

knn.evaluate(X, y)

{1.0: [5, 0], 0.0: [5, 0]}

Out[18]:

{1.0: 1.0, 0.0: 1.0, 'accuracy': 1.0}