YB Bikeshare Visualizer

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", "workingday",
    "weather", "temp", "feelslike", "humidity", "windspeed"
]]
y = data["riders"]

visualizer = Rank2D(algorithm="pearson")
visualizer.fit_transform(X)
visualizer.poof()

/Users/rajacsp/anaconda3/envs/py36/lib/python3.6/site-packages/yellowbrick/features/rankd.py:262: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.
  X = X.as_matrix()

Note:

A Pearson correlation of 1.0 means that there is a strong positive, linear relationship between the pairs of variables and a value of -1.0 indicates a strong negative, linear relationship (a value of zero indicates no relationship). Therefore we are looking for dark red and dark blue boxes to identify further.

Dark red = positive (related) - strong correlation Dark blue = negative (inversely related) -