Regression modeling

The presence of missing data can be used in regression modeling as a dependent variable encoded as 0 and 1.

For demonstration purposes, we will use Titanic dataset. Let’s create a regression model with Age as a dependent variable and Fare, Parch, Pclass, SibSp, Survived as independent variables. Internally, pandas.Series.isna() method is called on Age column, and the resulting boolean values are converted to integers (True and False become 1 and 0). Data preprocessing is totally up to you!

Currently, scikit_na.model() function runs a logistic model using statsmodels package as a backend.

import pandas as pd
import scikit_na as na

# Loading data
data = pd.read_csv("titanic_dataset.csv")

# Selecting columns with numeric data
# Dropping "PassengerId" column
subset = data.loc[:, data.dtypes != object].drop(columns=['PassengerId'])

# Fitting a model
model = na.model(subset, col_na='Age')
model.summary()

Optimization terminated successfully.
Current function value: 0.467801
Iterations 7
                        Logit Regression Results
==============================================================================
Dep. Variable:                    Age   No. Observations:                  891
Model:                          Logit   Df Residuals:                      885
Method:                           MLE   Df Model:                            5
Date:                Sat, 05 Jun 2021   Pseudo R-squ.:                 0.06164
Time:                        17:51:31   Log-Likelihood:                -416.81
converged:                       True   LL-Null:                       -444.19
Covariance Type:            nonrobust   LLR p-value:                 1.463e-10
===============================================================================
                coef    std err          z      P>|z|      [0.025      0.975]
-------------------------------------------------------------------------------
(intercept)    -2.7294      0.429     -6.369      0.000      -3.569      -1.890
Fare            0.0010      0.003      0.376      0.707      -0.004       0.006
Parch          -0.8874      0.223     -3.984      0.000      -1.324      -0.451
Pclass          0.5953      0.147      4.046      0.000       0.307       0.884
SibSp           0.2548      0.095      2.684      0.007       0.069       0.441
Survived       -0.1026      0.198     -0.519      0.604      -0.490       0.285
===============================================================================