Book Image

Data Science with Python

By : Rohan Chopra, Aaron England, Mohamed Noordeen Alaudeen
Book Image

Data Science with Python

By: Rohan Chopra, Aaron England, Mohamed Noordeen Alaudeen

Overview of this book

Data Science with Python begins by introducing you to data science and teaches you to install the packages you need to create a data science coding environment. You will learn three major techniques in machine learning: unsupervised learning, supervised learning, and reinforcement learning. You will also explore basic classification and regression techniques, such as support vector machines, decision trees, and logistic regression. As you make your way through the book, you will understand the basic functions, data structures, and syntax of the Python language that are used to handle large datasets with ease. You will learn about NumPy and pandas libraries for matrix calculations and data manipulation, discover how to use Matplotlib to create highly customizable visualizations, and apply the boosting algorithm XGBoost to make predictions. In the concluding chapters, you will explore convolutional neural networks (CNNs), deep learning algorithms used to predict what is in an image. You will also understand how to feed human sentences to a neural network, make the model process contextual information, and create human language processing systems to predict the outcome. By the end of this book, you will be able to understand and implement any new data science algorithm and have the confidence to experiment with tools or libraries other than those covered in the book.
Table of Contents (10 chapters)

Saving and Loading a Model

The last piece in mastering structured data is the ability to save and load the models that you have trained and fine-tuned. Training a new model every time we need a prediction will waste a lot of time, so being able to save a trained model is imperative for data scientists. The saved model allows us to replicate the results and to create apps and services that make use of the machine learning model. The steps are as follows:

  1. To save an XGBoost model, you need to call the save_model function.


  2. To load a previously saved model, you have to call load_model on an initialized XGBoost variable.

    loaded_model = xgb.Booster({'nthread': 2})



    If you give XGBoost access to all the threads it can get, your computer might become slow while training or predicting.

You are now ready to get started on modeling your structured dataset using the XGBoost...