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 availability. Without sufficient relevant data, you simply cannot get the machine to learn.

Once you have the data, your next major task is to clean it, eliminating unnecessary rows and fields and selecting appropriate fields for your model development. After completing this, you need to map the data into the format required for classifier training. Therefore, data preparation is a major task in any machine learning application. Once you have prepared the data, you can choose a specific type of classifier.

In this tutorial, you learned how to use the logistic regression classifier provided by the sklearn library. To train this classifier, we used approximately 70% of the data to train the model. We used the remaining data for testing. We tested the model’s accuracy. If it was not within acceptable limits, we selected a new feature set.

Again, follow the entire process of preparing data, training the model, and testing until you are satisfied with its accuracy. Before embarking on any machine learning project, it is essential to learn and be exposed to the various techniques that have been developed to date and successfully used in the industry.