K-Nearest Neighbours: Decision Boundaries

import numpy as np
import matplotlib.pyplot as plt
from sklearn import datasets, neighbors
# https://anaconda.org/conda-forge/mlxtend
from mlxtend.plotting import plot_decision_regions
import pandas as pd

---------------------------------------------------------------------------
ModuleNotFoundError                       Traceback (most recent call last)
~\AppData\Local\Temp/ipykernel_13488/2568969536.py in <module>
      3 from sklearn import datasets, neighbors
      4 # https://anaconda.org/conda-forge/mlxtend
----> 5 from mlxtend.plotting import plot_decision_regions
      6 import pandas as pd

ModuleNotFoundError: No module named 'mlxtend'

def knn_comparision(data, k):
    X = data[['x1','x2']].values
    y = data['y'].astype(int).values
    clf = neighbors.KNeighborsClassifier(n_neighbors=k)
    clf.fit(X, y)

    # Plotting decision regions
    plot_decision_regions(X, y, clf=clf, legend=2)

    # Adding axes annotations
    plt.xlabel('X1')
    plt.ylabel('X2')
    plt.title('Knn with K='+ str(k))
    plt.show()

data = pd.read_csv('data/6.overlap.csv', names=['x1', 'x2', 'y'])
for i in [1, 5, 15, 30, 45]:
    knn_comparision(data, i)

data = pd.read_csv('data/1.ushape.csv', names=['x1', 'x2', 'y'])
print(data.head(3))
for i in [1, 5, 15, 30, 45]:
    knn_comparision(data, i)

         x1        x2    y
0  0.031595  0.986988  0.0
1  2.115098 -0.046244  1.0
2  0.882490 -0.075756  0.0

data = pd.read_csv('data/2.concerticcir1.csv', names=['x1', 'x2', 'y'])
print(data.head(3))
for i in [1, 5, 15, 30, 45]:
    knn_comparision(data, i)

         x1        x2    y
0 -0.382891 -0.090840  1.0
1 -0.020962 -0.477874  1.0
2 -0.396116 -1.289427  0.0

data = pd.read_csv('data/3.concertriccir2.csv', names=['x1', 'x2', 'y'])
print(data.head(3))
for i in [1, 5, 15, 30, 45]:
    knn_comparision(data, i)

         x1        x2    y
0  0.700335 -0.247068  0.0
1 -3.950019  2.740080  1.0
2  0.150222 -2.157638  1.0

data = pd.read_csv('data/4.linearsep.csv', names=['x1', 'x2', 'y'])
print(data.head(3))
for i in [1, 5, 15, 30, 45]:
    knn_comparision(data, i)

         x1        x2    y
0 -0.177497  0.930496  1.0
1  1.977424  1.766155  0.0
2  1.800024  1.700343  0.0

data = pd.read_csv('data/5.outlier.csv', names=['x1', 'x2', 'y'])
print(data.head(3))
for i in [1, 5, 15, 30, 45]:
    knn_comparision(data, i)

          x1        x2  y
0 -17.897000  7.662423  0
1 -26.343161 -3.055257  0
2 -19.059771 -8.531838  0

data = pd.read_csv('data/7.xor.csv', names=['x1', 'x2', 'y'])
print(data.head(3))
for i in [1, 5, 15, 30, 45]:
    knn_comparision(data, i)

         x1        x2    y
0  1.764052  0.400157 -1.0
1  0.978738  2.240893 -1.0
2  1.867558 -0.977278  1.0

data = pd.read_csv('data/8.twospirals.csv', names=['x1', 'x2', 'y'])
print(data.head(3))
for i in [1, 5, 15, 30, 45]:
    knn_comparision(data, i)

         x1         x2  y
0 -2.543456 -10.816358  0
1  9.434466  -2.572000  0
2  3.368646 -10.194671  0

data = pd.read_csv('data/9.random.csv', names=['x1', 'x2', 'y'])
print(data.head(3))
for i in [1, 5, 15, 30, 45]:
    knn_comparision(data, i)

      x1    x2    y
0  0.374  1.08  0.0
1  0.445  1.14  1.0
2  0.514  1.13  0.0