Book Image

Machine Learning Solutions

Book Image

Machine Learning Solutions

Overview of this book

Machine learning (ML) helps you find hidden insights from your data without the need for explicit programming. This book is your key to solving any kind of ML problem you might come across in your job. You’ll encounter a set of simple to complex problems while building ML models, and you'll not only resolve these problems, but you’ll also learn how to build projects based on each problem, with a practical approach and easy-to-follow examples. The book includes a wide range of applications: from analytics and NLP, to computer vision domains. Some of the applications you will be working on include stock price prediction, a recommendation engine, building a chat-bot, a facial expression recognition system, and many more. The problem examples we cover include identifying the right algorithm for your dataset and use cases, creating and labeling datasets, getting enough clean data to carry out processing, identifying outliers, overftting datasets, hyperparameter tuning, and more. Here, you'll also learn to make more timely and accurate predictions. In addition, you'll deal with more advanced use cases, such as building a gaming bot, building an extractive summarization tool for medical documents, and you'll also tackle the problems faced while building an ML model. By the end of this book, you'll be able to fine-tune your models as per your needs to deliver maximum productivity.
Table of Contents (19 chapters)
Machine Learning Solutions
Foreword
Contributors
Preface
Index

Collecting the dataset


In order to build the model, first we need to collect the data. We will use the following two data points:

  • Dow Jones Industrial Average (DJIA) index prices

  • News articles

DJIA index prices give us an overall idea about the stock market's movements on a particular day, whereas news articles help us find out how news affects the stock prices. We will build our model using these two data points. Now let's collect the data.

Collecting DJIA index prices

In order to collect the DJIA index prices, we will use Yahoo Finance. You can visit this link: https://finance.yahoo.com/quote/%5EDJI/history?period1=1196706600&period2=1512325800&interval=1d&filter=history&frequency=1d. Once you click on this link, you can see that the price data shows up. You can change the time period and click on the Download Data link and that's it; you can have all the data in .csv file format. Refer to the following screenshot of the Yahoo finance DJIA index price page:

Figure 2.1: Yahoo...