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Machine Learning Solutions

You're reading from   Machine Learning Solutions Expert techniques to tackle complex machine learning problems using Python

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Product type Paperback
Published in Apr 2018
Publisher Packt
ISBN-13 9781788390040
Length 566 pages
Edition 1st Edition
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Author (1):
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Jalaj Thanaki Jalaj Thanaki
Author Profile Icon Jalaj Thanaki
Jalaj Thanaki
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Table of Contents (19) Chapters Close

Machine Learning Solutions
Foreword
Contributors
Preface
1. Credit Risk Modeling 2. Stock Market Price Prediction FREE CHAPTER 3. Customer Analytics 4. Recommendation Systems for E-Commerce 5. Sentiment Analysis 6. Job Recommendation Engine 7. Text Summarization 8. Developing Chatbots 9. Building a Real-Time Object Recognition App 10. Face Recognition and Face Emotion Recognition 11. Building Gaming Bot List of Cheat Sheets Strategy for Wining Hackathons Index

Data preprocessing and data analysis


In this section, we will mainly cover data preprocessing and data analysis. As a part of data preprocessing, we are preparing our training dataset. You may be wondering what kind of data preparation I'm talking about, considering we already have the data. Allow me to tell you that we have two different datasets and both datasets are independent. So, we need to merge the DJIA dataset and NYTimes news article dataset in order to get meaningful insights from these datasets. Once we prepare our training dataset, we can train the data using different machine learning (ML) algorithms.

Now let's start the coding to prepare the training dataset. We will be using numpy, csv, JSON, and pandas as our dependency libraries. Here, our code is divided into two parts. First, we will prepare the dataset for the DJIA index dataset and then we will move to the next part, which is preparing the NYTimes news article dataset. During the preparation of the training dataset,...

You have been reading a chapter from
Machine Learning Solutions
Published in: Apr 2018
Publisher: Packt
ISBN-13: 9781788390040
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