Book Image

The Data Science Workshop

By : Anthony So, Thomas V. Joseph, Robert Thas John, Andrew Worsley, Dr. Samuel Asare
Book Image

The Data Science Workshop

By: Anthony So, Thomas V. Joseph, Robert Thas John, Andrew Worsley, Dr. Samuel Asare

Overview of this book

You already know you want to learn data science, and a smarter way to learn data science is to learn by doing. The Data Science Workshop focuses on building up your practical skills so that you can understand how to develop simple machine learning models in Python or even build an advanced model for detecting potential bank frauds with effective modern data science. You'll learn from real examples that lead to real results. Throughout The Data Science Workshop, you'll take an engaging step-by-step approach to understanding data science. You won't have to sit through any unnecessary theory. If you're short on time you can jump into a single exercise each day or spend an entire weekend training a model using sci-kit learn. It's your choice. Learning on your terms, you'll build up and reinforce key skills in a way that feels rewarding. Every physical print copy of The Data Science Workshop unlocks access to the interactive edition. With videos detailing all exercises and activities, you'll always have a guided solution. You can also benchmark yourself against assessments, track progress, and receive content updates. You'll even earn a secure credential that you can share and verify online upon completion. It's a premium learning experience that's included with your printed copy. To redeem, follow the instructions located at the start of your data science book. Fast-paced and direct, The Data Science Workshop is the ideal companion for data science beginners. You'll learn about machine learning algorithms like a data scientist, learning along the way. This process means that you'll find that your new skills stick, embedded as best practice. A solid foundation for the years ahead.
Table of Contents (18 chapters)

Hyperparameter Tuning with GridSearchCV

GridSearchCV will take a model and parameters and train one model for each permutation of the parameters. At the end of the training, it will provide access to the parameters and the model scores. This is called hyperparameter tuning and you will be looking at this in much more depth in Chapter 8, Hyperparameter Tuning.

The usual practice is to make use of a small training set to find the optimal parameters using hyperparameter tuning and then to train a final model with all of the data.

Before the next exercise, let's take a brief look at decision trees, which are a type of model or estimator.

Decision Trees

A decision tree works by generating a separating hyperplane or a threshold for the features in data. It does this by considering every feature and finding the correlation between the spread of the values in that feature and the label that you are trying to predict.

Consider the following data about balloons. The label...