Notice: Undefined index: alg in /home/u681245571/domains/studyglance.in/public_html/labprograms/mldisplay.php on line 81

Notice: Undefined index: url2 in /home/u681245571/domains/studyglance.in/public_html/labprograms/mldisplay.php on line 83

Notice: Undefined index: url3 in /home/u681245571/domains/studyglance.in/public_html/labprograms/mldisplay.php on line 84

Notice: Undefined index: url4 in /home/u681245571/domains/studyglance.in/public_html/labprograms/mldisplay.php on line 85

Notice: Undefined index: title in /home/u681245571/domains/studyglance.in/public_html/labprograms/mldisplay.php on line 86

Notice: Undefined index: opurl2 in /home/u681245571/domains/studyglance.in/public_html/labprograms/mldisplay.php on line 88

Notice: Undefined index: opurl3 in /home/u681245571/domains/studyglance.in/public_html/labprograms/mldisplay.php on line 89

Notice: Undefined index: opurl4 in /home/u681245571/domains/studyglance.in/public_html/labprograms/mldisplay.php on line 90

Machine Learning - Lab Programs

Aim:

Source Code:

Week4.py

'''Aim: Given the following data, which specify classifications for nine ombinationsof VAR1 and VAR2 predict a classification for a case where VAR1=0.906and VAR2=0.606, using the result of k-means clustering with 3 means (i.e., 3centroids)

=================================
Explanation:
=================================
===> To run this program you need to install the sklearn Module

===> Open Command propmt and then execute the following command to install sklearn Module

---> pip install scikit-learn

In this program, we are going to use the following data

VAR1 VAR2 CLASS
1.713 1.586 0
0.180 1.786 1
0.353 1.240 1
0.940 1.566 0
1.486 0.759 1
1.266 1.106 0
1.540 0.419 1
0.459 1.799 1
0.773 0.186 1

And, we need apply k-means clustering with 3 means (i.e., 3 centroids)


Finally, you need to predict the class for the VAR1=0.906 and VAR2=0.606

===============================
Source Code :
===============================
'''
from sklearn.cluster import KMeans
import numpy as np
X = np.array([[1.713,1.586], [0.180,1.786], [0.353,1.240],
[0.940,1.566], [1.486,0.759], [1.266,1.106],[1.540,0.419],[0.459,1.799],[0.773,0.186]])
y=np.array([0,1,1,0,1,0,1,1,1])
kmeans = KMeans(n_clusters=3, random_state=0).fit(X,y)
print("The input data is ")
print("VAR1 \t VAR2 \t CLASS")
i=0
for val in X:
	print(val[0],"\t",val[1],"\t",y[i])
	i+=1
print("="*20)
# To get test data from the user
print("The Test data to predict ")
test_data = []
VAR1 = float(input("Enter Value for VAR1 :"))
VAR2 = float(input("Enter Value for VAR2 :"))
test_data.append(VAR1)
test_data.append(VAR2)
print("="*20)
print("The predicted Class is : ",kmeans.predict([test_data]))

Output:

Machine Learning Lab Programs

1) The probability that it is Friday and that a student is absent is 3%. Since there are 5 school days in a week, the probability that it is Friday is 20%. What is theprobability that a student is absent given that today is Friday? Apply Baye’s rule in python to get the result.(Ans: 15%) View Solution

2) Extract the data from database using python View Solution

3) Implement k-nearest neighbours classification using python View Solution

4) Given the following data, which specify classifications for nine ombinations of VAR1 and VAR2 predict a classification for a case where VAR1=0.906 and VAR2=0.606, using the result of k-means clustering with 3 means (i.e., 3 centroids) View Solution

5) The following training examples map descriptions of individuals onto high, medium and low credit-worthiness.Input attributes are (from left to right) income, recreation, job, status, age-group, home-owner. Find the unconditional probability of 'golf' and the conditional probability of 'single' given 'medRisk' in the dataset View Solution

6) Implement linear regression using python View Solution

7) Implement naive baye's theorem to classify the English text View Solution

8) Implement an algorithm to demonstrate the significance of genetic algorithm View Solution

9) Implement the finite words classification system using Back-propagation algorithm View Solution

Menu