KmeansClustering

import pandas as pd
import numpy as np
from sklearn.cluster import KMeans
import matplotlib.pyplot as plt
from matplotlib import style
style.use("ggplot")
from mpl_toolkits.mplot3d import Axes3D

path = "C:/Users/ashis/Downloads/Project2_dataset(1).csv"
DataFrame = pd.read_csv(path, header=None, delimiter=r",")

noramlized_data = (DataFrame - DataFrame.min()) / (DataFrame.max() - DataFrame.min())

k = 3

# Create a k-means model on our data, using k clusters.
# Random_state helps ensure that the algorithm returns the
# same results each time.
kmeans_model = KMeans(n_clusters=k, random_state=1)
kmeans_model.fit(noramlized_data)

# These are our fitted labels for clusters --
# The first cluster has label 0, and the second has label 1.
labels = kmeans_model.labels_

centroids = kmeans_model.cluster_centers_
print("The value of centroids : ")
print(centroids)

fig = plt.figure()
ax = fig.gca(projection='3d')
X = np.array(DataFrame)

color = (DataFrame - np.nanmin(DataFrame))/(np.nanmax(DataFrame) - np.nanmin(DataFrame))

ax.scatter(noramlized_data.iloc[:, 0], noramlized_data.iloc[:, 1], noramlized_data.iloc[:, 2], alpha=0.3, cmap='hot', c=color)

ax.scatter(centroids[:, 0], centroids[:, 1], centroids[:, 2], marker="x", alpha=1, s=150, linewidths=5, zorder=100, c="black")
ax.set_xlabel('X')
ax.set_ylabel('Y')
ax.set_zlabel('Z')
plt.title('3-D projection of clustering obtained from K-means Clustering Algorithm')
plt.show()