# Analyzing Test Data Using K-Means Clustering in Python

| | |

matplot -lib
Let’s first render the test data with Multiple Features using the matplot-lib tool.

` `

` # import of required tools import numpy as np from matplotlib import pyplot as plt   # create two test data X = np.random.randint ( 10 , 35 , ( 25 , 2 )) Y = np.random.randint ( 55 , 70 , ( 25 , 2 )) Z = np.vstack ((X, Y)) Z = Z.reshape (( 50 , 2 ))    # convert to np.float32 Z = np.float32 (Z)   plt .xlabel ( ’Test Data’ ) plt.ylabel ( ’Z samples’ )    plt.hist (Z, 256 , [ 0 , 256 ])   plt.show () `

Here & # 39; Z & # 39; — it is an array of size 100 and values ​​in the range 0 to 255. Now the shape of & # 39; z & # 39; per column vector. It will be more useful when more than one function is present. Then change the data to type np.float32.

Output: Now apply the k-Means clustering algorithm to the same example as in the test data above and see its behavior.
Steps included:
1) First, we need to install the test data.
2) Define the criteria and apply kmeans ().
3) Now split the data.
4) Finally, fill in the data.

 ` import ` ` numpy as np ` ` import ` ` cv2 ` ` from ` ` matplotlib ` ` import ` ` pyplot as plt `   ` X ` ` = ` ` np.random.randint (` ` 10 ` `, ` ` 45 ` `, (` ` 25 ` `, ` ` 2 ` `)) ` ` Y ` ` = ` ` np.random.randint (` ` 55 ` `, ` ` 70 ` `, (` ` 25 ` `, ` ` 2 ` `)) ` ` Z ` ` = ` ` np.vstack ((X, Y)) `   ` # convert to np.float32 ` ` Z ` ` = ` ` np.float32 (Z) `   ` # define criteria and apply kmeans () ` ` criteria ` ` = ` ` (cv2.TERM_CRITERIA_EPS ` ` + ` ` cv2.TERM_CRITERIA_MAX_ITER, ` ` 10 ` `, ` ` 1.0 ` `) ` ` ret, lab el, center ` ` = ` ` cv2.kmeans (Z, ` ` 2 ` `, ` ` None ` `, criteria, ` ` 10 ` `, cv2.KMEANS_RANDOM_CENTERS) `   ` # Now strip the data ` ` A ` ` = ` ` Z [label.ravel () ` ` = ` ` = ` ` 0 ` `] ` ` B ` ` = ` ` Z [label.ravel () ` ` = ` ` = ` ` 1 ` `] ` ` `  ` # Data plot ` ` plt.scatter (A [:, ` ` 0 ` `], A [:, ` ` 1 ` `]) ` ` plt.scatter (B [:, ` ` 0 ` `], B [:, ` ` 1 ` `], c ` ` = ` ` ’r’ ` `) ` ` plt.scatter (center [:, ` ` 0 ` `], center [:, ` ` 1 ` `], s ` ` = ` ` 80 ` `, c ` ` = ` ` ’y’ ` `, marker ` ` = ` ` ’s’ ` `) ` ` plt.xlabel (` ` ’Test Data’ ` `), plt.ylabel (` ` ’Z sample s’ ` `) ` ` plt.show () `

Output: This example is intended to illustrate where k-means creates intuitively possible clusters.

Applications :
1) Identification of cancer data.
2) Predicting student progress.
3) Prediction of drug activity.

## Shop Learn programming in R: courses

\$ Best Python online courses for 2022

\$ Best laptop for Fortnite

\$ Best laptop for Excel

\$ Best laptop for Solidworks

\$ Best laptop for Roblox

\$ Best computer for crypto mining

\$ Best laptop for Sims 4

\$

Latest questions

NUMPYNUMPY

psycopg2: insert multiple rows with one query

NUMPYNUMPY

How to convert Nonetype to int or string?

NUMPYNUMPY

How to specify multiple return types using type-hints

NUMPYNUMPY

Javascript Error: IPython is not defined in JupyterLab

## Wiki

Python OpenCV | cv2.putText () method

numpy.arctan2 () in Python

Python | os.path.realpath () method

Python OpenCV | cv2.circle () method

Python OpenCV cv2.cvtColor () method

Python - Move item to the end of the list

time.perf_counter () function in Python

Check if one list is a subset of another in Python

Python os.path.join () method