Logistic Regression is a statistical technique of binary classification. In this tutorial, you learned how to train the machine to use logistic regression. Creating machine learning models, the most important requirement is the availability of the data. Without adequate and relevant data, you cannot simply make the machine to learn.
Once you have data, your next major task is cleansing the data, eliminating the unwanted rows, fields, and select the appropriate fields for your model development. After this is done, you need to map the data into a format required by the classifier for its training. Thus, the data preparation is a major task in any machine learning application. Once you are ready with the data, you can select a particular type of classifier.
In this tutorial, you learned how to use a logistic regression classifier provided in the sklearn library. To train the classifier, we use about 70% of the data for training the model. We use the rest of the data for testing. We test the accuracy of the model. If this is not within acceptable limits, we go back to selecting the new set of features.
Once again, follow the entire process of preparing data, train the model, and test it, until you are satisfied with its accuracy. Before taking up any machine learning project, you must learn and have exposure to a wide variety of techniques which have been developed so far and which have been applied successfully in the industry.