Summary

In this chapter, we have covered how to prepare data and construct machine learning models. We have achieved this utilizing Python and libraries such as pandas and scikit-learn. We have also used the algorithms in scikit-learn to build our machine learning models.

In this chapter, we learned how to load data into Python, and how to manipulate data so that a machine learning model can be trained on the data. This involved converting all columns to numerical data types. We also learned to create a basic logistic regression classification model using scikit-learn algorithms. We divided the dataset into training and test datasets and fit the model to the training dataset. We evaluated the performance of the model on the test dataset using the model evaluation metrics: accuracy, precision, recall, and f1 score.

Finally, we iterated on this basic model by creating two models with different types of regularization to the model. We utilized cross-validation to determine the optimal parameter to use for the regularization parameter.

In the next chapter, we will use the same concept learned in this chapter; however, we will create the model using the Keras library. We will use the same dataset, and attempt to predict the same target value, for the same classification task. We will cover how to use regularization, cross-validation, and model evaluation metrics when fitting our neural network to the data.