Apply K- Means clustering algorithm on any dataset. import pandas as pd import numpy as np from sklearn.datasets import load_iris from sklearn.metrics import accuracy_score iris_data load_iris() iris_data.feature_names df= pd.DataFrame(iris_data.data,columns iris_data.feature_names) df['Species']=iris_data.target df.head(10) np.unique (df['Species']) df.shape df.info() ax=df [df.Species==0].plot.scatter(x='sepal length (cm)', y='sepal width (cm)", color='red', label='Iris-Setosa') df[df.Species==1].plot.scatter(x='sepal length (cm)', y='sepal width (cm)', color='green', label='Iris-Versicolor', ax=ax) df [df.Species==2].plot.scatter(x='sepal length (cm)', y='sepal width (cm)', color='blue', label='Iris-Virginica', ax=ax) ax.set_title("Scatter Plot") import seaborn as sns correl df.corr() sns.heatmap(correl, annot=True) from sklearn.model_selection import train_test_split X=iris_data.data y=iris data.target X_train, X_test, y_train, y_test train_test_split(X,y, random_state = 50,test_size=0.2 from sklearn.cluster import KMeans kmean=KMeans (n_clusters=3) kmean.fit(X_train,y_train) train_labels=kmean.predict(X_train) test_labels=kmean.predict(X_test) print(accuracy_score(y_train, train_labels)) print(accuracy_score(y_test, test_labels))