import pandas as pd
from yellowbrick.features import RadViz

def load_yb_data(name = 'occupancy'):
    folder_path = name+'/'+name
    return pd.read_csv('/Users/rajacsp/datasets/yb_data/'+(folder_path)+'.csv')

# Load the classification data set
data = load_yb_data("occupancy")

# Specify the features of interest and the classes of the target
features = ["temperature", "relative humidity", "light", "C02", "humidity"]
classes = ["unoccupied", "occupied"]

# Extract the instances and target
X = data[features]
y = data.occupancy

# Instantiate the visualizer
visualizer = RadViz(classes=classes, features=features)

visualizer.fit(X, y)      # Fit the data to the visualizer
visualizer.transform(X)   # Transform the data
visualizer.poof()         # Draw/show/poof the data

import pandas as pd
from yellowbrick.features import Rank2D

def load_yb_data(name = 'bikeshare'):
    folder_path = name+'/'+name
    return pd.read_csv('/Users/rajacsp/datasets/yb_data/'+(folder_path)+'.csv')

data = load_yb_data('bikeshare')

X = data[[
    "season", "month", "hour", "holiday", "weekday …

import pandas as pd

def load_yb_data(name = 'energy'):
    folder_path = name+'/'+name
    return pd.read_csv('/Users/rajacsp/datasets/yb_data/'+(folder_path)+'.csv')

data = load_yb_data('spam')

print(data.head(2))

   word_freq_make  word_freq_address  word_freq_all  word_freq_3d  \
0            0.21               0 …

import pandas as pd

def load_yb_data(name = 'energy'):
    folder_path = name+'/'+name
    return pd.read_csv('/Users/rajacsp/datasets/yb_data/'+(folder_path)+'.csv')

from yellowbrick.features import ParallelCoordinates
from sklearn.model_selection import train_test_split
from sklearn.datasets import load_breast_cancer

# Load …