Example of K-Means Clustering in Python with GUI

import tkinter as tk
from tkinter import filedialog
import pandas as pd
from pandas import DataFrame
import matplotlib.pyplot as plt
from sklearn.cluster import KMeans
from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg

root= tk.Tk()

canvas1 = tk.Canvas(root, width = 400, height = 300,  relief = 'raised')
canvas1.pack()

label1 = tk.Label(root, text='k-Means Clustering')
label1.config(font=('helvetica', 14))
canvas1.create_window(200, 25, window=label1)

label2 = tk.Label(root, text='Type Number of Clusters:')
label2.config(font=('helvetica', 8))
canvas1.create_window(200, 120, window=label2)

entry1 = tk.Entry (root) 
canvas1.create_window(200, 140, window=entry1)

def getExcel ():
    
    global df
    import_file_path = filedialog.askopenfilename()
    read_file = pd.read_excel (import_file_path)
    df = DataFrame(read_file,columns=['x','y'])  
    
browseButtonExcel = tk.Button(text=" Import Excel File ", command=getExcel, bg='green', fg='white', font=('helvetica', 10, 'bold'))
canvas1.create_window(200, 70, window=browseButtonExcel)

def getKMeans ():
    global df
    global numberOfClusters
    numberOfClusters = int(entry1.get())
    
    kmeans = KMeans(n_clusters=numberOfClusters).fit(df)
    centroids = kmeans.cluster_centers_
    
    label3 = tk.Label(root, text= centroids)
    canvas1.create_window(200, 250, window=label3)
    
    figure1 = plt.Figure(figsize=(4,3), dpi=100)
    ax1 = figure1.add_subplot(111)
    ax1.scatter(df['x'], df['y'], c= kmeans.labels_.astype(float), s=50, alpha=0.5)
    ax1.scatter(centroids[:, 0], centroids[:, 1], c='red', s=50)
    scatter1 = FigureCanvasTkAgg(figure1, root) 
    scatter1.get_tk_widget().pack(side=tk.RIGHT, fill=tk.BOTH)
    
processButton = tk.Button(text=' Process k-Means ', command=getKMeans, bg='brown', fg='white', font=('helvetica', 10, 'bold'))
canvas1.create_window(200, 170, window=processButton)

root.mainloop()

Example:

Dataset: Excel sheet contains the 2 variables X & Y, need to be clustered.... based on the user-provided no of clusters

Program GUI :

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