Scribd
Upload
43 views4 pages

Data Entry

The document loads packages, reads in a CSV file containing student marks data, scales the numeric data using standardization, performs k-means clustering on the scaled data, and plots the results of the clustering to visualize the different clusters. Specifically, it loads data on student marks, scales the science, accounting, and maths columns, performs k-means clustering with k=3 clusters, and plots the clustered data with each cluster in a different color.

Uploaded by

2147033
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
43 views4 pages

Data Entry

The document loads packages, reads in a CSV file containing student marks data, scales the numeric data using standardization, performs k-means clustering on the scaled data, and plots the results of the clustering to visualize the different clusters. Specifically, it loads data on student marks, scales the science, accounting, and maths columns, performs k-means clustering with k=3 clusters, and plots the clustered data with each cluster in a different color.

Uploaded by

2147033
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

Assignment 7

In [1]: # Load pacakages


import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
import seaborn as sn

In [2]: #Load the csv file


marks = pd.read_csv('Marks.csv')

In [3]: # Understanding the data


marks.info()
<class 'pandas.core.frame.DataFrame'>
Rangeindex: 10 entries, 0 to 9
Data columns (total 4 columns):
# Column Non-Null Count Dtype
--------------
0 Roll no 10 non-null int64
1 Science 10 non-null int64
2 Accounting 10 non-null int64
3 Maths 10 non-null int64
dtypes: int64(4)
memory usage: 448.0 bytes

In [4]: # Scaling the data


from sklearn import preprocessing
from sklearn.preprocessing import StandardScaler
scale=StandardScaler()
scaled_marks =scale.fit_transform(marks[["Science","Accounting","Maths"]])
scaled_marks

Out[4]: array([[ 0.84964017, -0.14463921, -0.44396005],


[-0.20508556, 0.43391764, 0.89824474],
[ 0.08789381, 0.72319607, -0.75369961],
[ 0.79104429, -2.31422742, -0.96019266],
[ 0.3222773 , -0.43391764, -1.99265788],
[-1.6699824 , 0.28927843, 1.10473779],
[-1.37700303, -0.86783528, 1.41447736],
[-1.08402366, 1.59103135, 0.48525865],
[ 1.25981128, 0.57855685, -0.03097396],
[ 1.02542779, 0.14463921, 0.27876561]])

In [5]: # Creating a dataframe of the scaled data


scaledmarks = pd.DataFrame(scaled_marks, index =['1', '2', '3', '4', '5', '6', '7',
columns =['Science','Accounting','Ma1
scaledmarks

localhost:8889/nbconvert/html/Downloads/lDS/Chp 7 Clustering Marks dataset.ipynb?download=false 1/4


3/14/24, 2:13 PM Chp 7 Clustering Marks dataset
kmeans=kmeansclusters.fit_predict(scaledmarks)
kmeans

Out[10]: array([2, 0, 2, 1, 1, 0, 0, 0, 2, 2])

In [11]: # Scatter plot of the clusters


#Create seperate lists of each cluster
kmeans0 = scaledmarks[kmeans -- 0]
kmeansl = scaledmarks[kmeans -- 1]
kmeans2 = scaledmarks[kmeans -- 2]

#Scatter plot of each cluster


plt.scatter(kmeans0.iloc[:,0] , kmeans0.iloc[:,1], color='blue')
plt.scatter(kmeansl.iloc[:,0] , kmeansl.iloc[:,1], color='red')
plt.scatter(kmeans2.iloc[:,0] , kmeans2.iloc[:,1], color='green')
plt.show()

l'S •
• •
lO

• •
••
0.'5

0.0
•
•
-0.5

-1.0

-1.5

-2.0

-2.5 '-----.----,-------.----.-------.----.-----'
-1.5 -1.0 -0.5 0.0 0.5 HI

In [ ]:

4/4

You might also like