I have a dataset including categorical variables(binary) and continuous variables. I’m trying to apply a linear regression model for predicting a continuous variable. Can someone please let me know how to check for correlation among the categorical variables and the continuous target variable.
import pandas as pd df_hosp = pd.read_csv('C:UsersLAPPY-2DesktopLengthOfStay.csv') data = df_hosp[['lengthofstay', 'male', 'female', 'dialysisrenalendstage', 'asthma', 'irondef', 'pneum', 'substancedependence', 'psychologicaldisordermajor', 'depress', 'psychother', 'fibrosisandother', 'malnutrition', 'hemo']] print data.corr()
All of the variables apart from lengthofstay are categorical. Should this work?
Convert your categorical variable into dummy variables here and put your variable in numpy.array. For example:
age,size,color_head 4,50,black 9,100,blonde 12,120,brown 17,160,black 18,180,brown
import numpy as np import pandas as pd df = pd.read_csv('data.csv')
Convert categorical variable
color_head into dummy variables:
df_dummies = pd.get_dummies(df['color_head']) del df_dummies[df_dummies.columns[-1]] df_new = pd.concat([df, df_dummies], axis=1) del df_new['color_head']
Put that in numpy array:
x = df_new.values
Compute the correlation:
correlation_matrix = np.corrcoef(x.T) print(correlation_matrix)
array([[ 1. , 0.99574691, -0.23658011, -0.28975028], [ 0.99574691, 1. , -0.30318496, -0.24026862], [-0.23658011, -0.30318496, 1. , -0.40824829], [-0.28975028, -0.24026862, -0.40824829, 1. ]])
correlation in this scenario is quite misleading as we are comparing categorical variable with continuous variable
There is one more method to compute the correlation between continuous variable and dichotomic (having only 2 classes) variable, since this is also a categorical variable, we can use it for the correlation computation.
The link for point biserial correlation is given below.