We have seen an introduction of logistic regression with a simple example how to predict a student admission to university based on past exam results.

This was done using Python, from scratch defining the sigmoid function and the gradient descent, and we have seen also the same example using the *statsmodels* library.

Now we are going to see how to solve a logistic regression problem using the popular *SciKitLearn* library, specifically the *LogisticRegression* module.

The example this time is to predict survival on the Titanic ship (that sank against an iceberg).

It’s a basic learning competition on the ML platform Kaggle, a simple introduction to machine learning concepts, specifically binary classification (survived / not survived).

Here we are looking into how to apply Logistic Regression to the Titanic dataset.

You can follow along the Python notebook on GitHub or the Python kernel on Kaggle. Continue reading “Logistic regression using SKlearn”