Book Image

Python Machine Learning by Example, - Third Edition

By : Yuxi (Hayden) Liu
Book Image

Python Machine Learning by Example, - Third Edition

By: Yuxi (Hayden) Liu

Overview of this book

Python Machine Learning By Example, Third Edition serves as a comprehensive gateway into the world of machine learning (ML). With six new chapters, on topics including movie recommendation engine development with Naïve Bayes, recognizing faces with support vector machine, predicting stock prices with artificial neural networks, categorizing images of clothing with convolutional neural networks, predicting with sequences using recurring neural networks, and leveraging reinforcement learning for making decisions, the book has been considerably updated for the latest enterprise requirements. At the same time, this book provides actionable insights on the key fundamentals of ML with Python programming. Hayden applies his expertise to demonstrate implementations of algorithms in Python, both from scratch and with libraries. Each chapter walks through an industry-adopted application. With the help of realistic examples, you will gain an understanding of the mechanics of ML techniques in areas such as exploratory data analysis, feature engineering, classification, regression, clustering, and NLP. By the end of this ML Python book, you will have gained a broad picture of the ML ecosystem and will be well-versed in the best practices of applying ML techniques to solve problems.
Table of Contents (17 chapters)
15
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16
Index

Predicting ad click-through with a decision tree

After several examples, it is now time to predict ad click-through using the decision tree algorithm you have just thoroughly learned about and practiced. We will use the dataset from a Kaggle machine learning competition, Click-Through Rate Prediction (https://www.kaggle.com/c/avazu-ctr-prediction). The dataset can be downloaded from https://www.kaggle.com/c/avazu-ctr-prediction/data.

Only the train.gz file contains labeled samples, so we only need to download this and unzip it (it will take a while). In this chapter, we will focus on only the first 300,000 samples from the train file unzipped from train.gz.

The fields in the raw file are as follows:

Figure 4.12: Description and example values of the dataset

We take a glance at the head of the file by running the following command:

head train | sed 's/,,/, ,/g;s/,,/, ,/g' | column -s, -t

Rather than...