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EXP 07 (ML) - Ashu

The document loads and explores the iris dataset using scikit-learn. It analyzes different attributes of the dataset, performs train-test splitting, and evaluates a k-nearest neighbors classifier with different values of k.

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DSE 04. Chetana Bhoir
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30 views4 pages

EXP 07 (ML) - Ashu

The document loads and explores the iris dataset using scikit-learn. It analyzes different attributes of the dataset, performs train-test splitting, and evaluates a k-nearest neighbors classifier with different values of k.

Uploaded by

DSE 04. Chetana Bhoir
Copyright
© © All Rights Reserved
We take content rights seriously. If you suspect this is your content, claim it here.
Available Formats
Download as PDF, TXT or read online on Scribd
You are on page 1/ 4

8/20/23, 12:18 AM EXP_07(ML).

ipynb - Colaboratory

Name -: Ashutosh Pandey

Roll_No -: 97

%matplotlib inline
import matplotlib.pyplot as plt
from sklearn.datasets import load_iris

iris = load_iris()
type(iris)

sklearn.utils._bunch.Bunch

iris.data

[5.8, 2.6, 4. , 1.2],


[5. , 2.3, 3.3, 1. ],
[5.6, 2.7, 4.2, 1.3],
[5.7, 3. , 4.2, 1.2],
[5.7, 2.9, 4.2, 1.3],
[6.2, 2.9, 4.3, 1.3],
[5.1, 2.5, 3. , 1.1],
[5.7, 2.8, 4.1, 1.3],
[6.3, 3.3, 6. , 2.5],
[5.8, 2.7, 5.1, 1.9],
[7.1, 3. , 5.9, 2.1],
[6.3, 2.9, 5.6, 1.8],
[6.5, 3. , 5.8, 2.2],
[7.6, 3. , 6.6, 2.1],
[4.9, 2.5, 4.5, 1.7],
[7.3, 2.9, 6.3, 1.8],
[6.7, 2.5, 5.8, 1.8],
[7.2, 3.6, 6.1, 2.5],
[6.5, 3.2, 5.1, 2. ],
[6.4, 2.7, 5.3, 1.9],
[6.8, 3. , 5.5, 2.1],
[5.7, 2.5, 5. , 2. ],
[5.8, 2.8, 5.1, 2.4],
[6.4, 3.2, 5.3, 2.3],
[6.5, 3. , 5.5, 1.8],
[7.7, 3.8, 6.7, 2.2],
[7.7, 2.6, 6.9, 2.3],
[6. , 2.2, 5. , 1.5],
[6.9, 3.2, 5.7, 2.3],
[5.6, 2.8, 4.9, 2. ],
[7.7, 2.8, 6.7, 2. ],
[6.3, 2.7, 4.9, 1.8],
[6.7, 3.3, 5.7, 2.1],
[7.2, 3.2, 6. , 1.8],
[6.2, 2.8, 4.8, 1.8],
[6.1, 3. , 4.9, 1.8],
[6.4, 2.8, 5.6, 2.1],
[7.2, 3. , 5.8, 1.6],
[7.4, 2.8, 6.1, 1.9],
[7.9, 3.8, 6.4, 2. ],
[6.4, 2.8, 5.6, 2.2],
[6.3, 2.8, 5.1, 1.5],
[6.1, 2.6, 5.6, 1.4],
[7.7, 3. , 6.1, 2.3],
[6.3, 3.4, 5.6, 2.4],
[6.4, 3.1, 5.5, 1.8],
[6. , 3. , 4.8, 1.8],
[6.9, 3.1, 5.4, 2.1],
[6.7, 3.1, 5.6, 2.4],
[6.9, 3.1, 5.1, 2.3],
[5.8, 2.7, 5.1, 1.9],
[6.8, 3.2, 5.9, 2.3],
[6.7, 3.3, 5.7, 2.5],
[6.7, 3. , 5.2, 2.3],
[6.3, 2.5, 5. , 1.9],
[6.5, 3. , 5.2, 2. ],
[6.2, 3.4, 5.4, 2.3],
[5.9, 3. , 5.1, 1.8]])

print(iris.feature_names)

['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)']

( )
https://colab.research.google.com/drive/1IYOHvE58f1fP_UQDmsVF4QQ0KvQkTCF6#scrollTo=k04__xf-OvBv&printMode=true 1/5
8/20/23, 12:18 AM EXP_07(ML).ipynb - Colaboratory
print(iris.target)

[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2
2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
2 2]

print(iris.target_names)

['setosa' 'versicolor' 'virginica']

plt.scatter(iris.data[:,1],iris.data[:,2],c=iris.target, cmap=plt.cm.Paired)
plt.xlabel(iris.feature_names[1])
plt.ylabel(iris.feature_names[2])
plt.show()

plt.scatter(iris.data[:,0],iris.data[:,3],c=iris.target, cmap=plt.cm.Paired)
plt.xlabel(iris.feature_names[0])
plt.ylabel(iris.feature_names[3])
plt.show()

p = iris.data
q = iris.target

print(p.shape)
print(q.shape)

https://colab.research.google.com/drive/1IYOHvE58f1fP_UQDmsVF4QQ0KvQkTCF6#scrollTo=k04__xf-OvBv&printMode=true 2/5
8/20/23, 12:18 AM EXP_07(ML).ipynb - Colaboratory

(150, 4)
(150,)

!pip install matplotlib-venn


# https://pypi.python.org/pypi/libarchive

Requirement already satisfied: matplotlib-venn in /usr/local/lib/python3.10/dist-packages (0.11.9)


Requirement already satisfied: matplotlib in /usr/local/lib/python3.10/dist-packages (from matplotlib-venn) (3.7.1)
Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from matplotlib-venn) (1.23.5)
Requirement already satisfied: scipy in /usr/local/lib/python3.10/dist-packages (from matplotlib-venn) (1.10.1)
Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->matplotlib-venn) (1.1.0)
Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib->matplotlib-venn) (0.11.0)
Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->matplotlib-venn) (4.42.0)
Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->matplotlib-venn) (1.4.4)
Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->matplotlib-venn) (23.1)
Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->matplotlib-venn) (9.4.0)
Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->matplotlib-venn) (3.1.1)
Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.10/dist-packages (from matplotlib->matplotlib-venn) (2.8.2
Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.7->matplotlib->matplotlib-ve

!pip install matplotlib-venn


!apt-get -qq install -y libfluidsynth1

Requirement already satisfied: matplotlib-venn in /usr/local/lib/python3.10/dist-packages (0.11.9)


Requirement already satisfied: matplotlib in /usr/local/lib/python3.10/dist-packages (from matplotlib-venn) (3.7.1)
Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from matplotlib-venn) (1.23.5)
Requirement already satisfied: scipy in /usr/local/lib/python3.10/dist-packages (from matplotlib-venn) (1.10.1)
Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->matplotlib-venn) (1.1.0)
Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib->matplotlib-venn) (0.11.0)
Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->matplotlib-venn) (4.42.0)
Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->matplotlib-venn) (1.4.4)
Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->matplotlib-venn) (23.1)
Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->matplotlib-venn) (9.4.0)
Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->matplotlib-venn) (3.1.1)
Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.10/dist-packages (from matplotlib->matplotlib-venn) (2.8.2
Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.7->matplotlib->matplotlib-ve
E: Package 'libfluidsynth1' has no installation candidate

from sklearn.model_selection import train_test_split


p_train,p_test,q_train,q_test = train_test_split(p,q,test_size=0.2,random_state=4)

print(p_train.shape)
print(p_test.shape)

(120, 4)
(30, 4)

print(q_train.shape)
print(q_test.shape)

(120,)
(30,)

from sklearn.neighbors import KNeighborsClassifier

from sklearn import metrics

k_range = range(1,26)
scores = {}
scores_list = []
for k in k_range:
knn = KNeighborsClassifier(n_neighbors=k)
knn.fit(p_train,q_train)
q_pred=knn.predict(p_test)
scores[k] = metrics.accuracy_score(q_test,q_pred)
scores_list.append(metrics.accuracy_score(q_test,q_pred))

scores

{1: 0.9333333333333333,
2: 0.9333333333333333,

https://colab.research.google.com/drive/1IYOHvE58f1fP_UQDmsVF4QQ0KvQkTCF6#scrollTo=k04__xf-OvBv&printMode=true 3/5
8/20/23, 12:18 AM EXP_07(ML).ipynb - Colaboratory
3: 0.9666666666666667,
4: 0.9666666666666667,
5: 0.9666666666666667,
6: 0.9666666666666667,
7: 0.9666666666666667,
8: 0.9666666666666667,
9: 0.9666666666666667,
10: 0.9666666666666667,
11: 0.9666666666666667,
12: 0.9666666666666667,
13: 0.9666666666666667,
14: 0.9666666666666667,
15: 0.9666666666666667,
16: 0.9666666666666667,
17: 0.9666666666666667,
18: 0.9666666666666667,
19: 0.9666666666666667,
20: 0.9333333333333333,
21: 0.9666666666666667,
22: 0.9333333333333333,
23: 0.9666666666666667,
24: 0.9666666666666667,
25: 0.9666666666666667}

#plot the relationship between K and the testing accuracy


plt.plot(k_range,scores_list)
plt.xlabel('Value of K for KNN')
plt.ylabel('Testing Accuracy')

Text(0, 0.5, 'Testing Accuracy')

knn = KNeighborsClassifier(n_neighbors=5)
knn.fit(p,q)

▾ KNeighborsClassifier
KNeighborsClassifier()

#0 = setosa, 1=versicolor, 2=virginica


classes = {0:'setosa',1:'versicolor',2:'virginica'}

x_new = [[3,4,5,2],
[5,4,2,2]]
y_predict = knn.predict(x_new)

print(classes[y_predict[0]])
print(classes[y_predict[1]])

versicolor
setosa

https://colab.research.google.com/drive/1IYOHvE58f1fP_UQDmsVF4QQ0KvQkTCF6#scrollTo=k04__xf-OvBv&printMode=true 4/5

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