rpromoditha

# Getting data
import pandas as pd
from sklearn.datasets import load_breast_cancer

cancer = load_breast_cancer()
df = pd.DataFrame(cancer.data, columns=cancer.feature_names)

X = df
y = cancer.target

rpromoditha

# Getting data
import pandas as pd
df = pd.read_csv('Advertising.csv')

X = df[['TV', 'Radio', 'Newspaper']]
y = df['Sales']

# Importing CooksDistance visualizer
from yellowbrick.regressor import CooksDistance

rpromoditha

# Getting data
import pandas as pd
df = pd.read_csv('Advertising.csv')

X = df[['TV']]
y = df['Sales']

# Create the train and test data
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, 

rpromoditha

# Getting data
import pandas as pd
df = pd.read_csv('Advertising.csv')

X = df[['TV']]
y = df['Sales']

# Create the train and test data
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, 

rpromoditha

# Getting data
from sklearn.datasets import load_breast_cancer

cancer = load_breast_cancer()
classes=cancer.target_names
X = cancer.data
y = cancer.target

# Importing ClassBalance visualizer
from yellowbrick.target import ClassBalance

rpromoditha

# Getting data
from sklearn.datasets import load_iris 

iris = load_iris()
classes=iris.target_names
X = iris.data
y = iris.target

# Importing SilhouetteVisualizer
from yellowbrick.cluster import SilhouetteVisualizer

rpromoditha

# Getting data
from sklearn.datasets import load_iris 

iris = load_iris()
classes=iris.target_names
X = iris.data
y = iris.target

# Importing KElbowVisualizer
from yellowbrick.cluster import KElbowVisualizer

rpromoditha

# Getting data
from sklearn.datasets import load_breast_cancer

cancer = load_breast_cancer()
classes=cancer.target_names
X = cancer.data
y = cancer.target

# Importing learning curve visualizer
from yellowbrick.model_selection import LearningCurve

rpromoditha

# Getting data
from sklearn.datasets import load_breast_cancer

cancer = load_breast_cancer()
classes=cancer.target_names
X = cancer.data
y = cancer.target

# Importing PCA visualizer
from yellowbrick.model_selection import ValidationCurve

rpromoditha

visualizer = PCA(scale=True, projection=3, 
                 classes=classes)

visualizer.fit_transform(X, y)
visualizer.show(outpath="PC_Plot_3D.png")