Logistic Regression in Python – Case Study Consider a bank that asks you to develop a machine learning application to help them identify potential customers who will open a term deposit (some banks also call it a time deposit) with…
Logistic Regression in Python – Splitting the Data We have approximately 41,010 records. If we use the entire data set to build the model, we won’t have any data left for testing. So, generally, we split the dataset into two…
Logistic Regression in Python – Restructuring Data Whenever any organization conducts a survey, they attempt to gather as much information as possible from their customers, with the idea that this information will be useful to the organization at some later…
Logistic Regression in Python – Testing Before putting the classifier we created above into production, we need to test it. If testing reveals that the model does not achieve the expected accuracy, we will have to go back to the…
Logistic Regression in Python – Preparing Data In order to create a classifier, we must prepare the data in the format required by the classifier building block. We prepare the data by doing one hot encoding. Encoding the Data We…
Logistic Regression in Python – An Introduction Logistic regression is a statistical method for classifying objects. This chapter introduces logistic regression with some examples. Classification To understand logistic regression, you should know what classification means. Let’s consider the following examples…
Logistic Regression in Python – Summary Logistic regression is a statistical technique for binary classification. In this tutorial, you learned how to train a machine using logistic regression. The most important requirement for creating a machine learning model is data…
Logistic Regression in Python – Setting Up a Project In this chapter, we’ll take a detailed look at the process of setting up a project in Python to perform logistic regression. Installing Jupyter We will be using Jupyter, one of…
Logistic Regression in Python – Building a Classifier You don’t necessarily need to build a classifier from scratch. Building a classifier is complex and requires knowledge of fields such as statistics, probability theory, and optimization techniques. Several pre-built libraries are…
Logistic Regression in Python – Limitations As you can see from the examples above, applying logistic regression to machine learning is not difficult. However, it does have its limitations. Logistic regression cannot handle a large number of categorical features. In…