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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

Building the training and testing datasets for the baseline model


In this section, we will be generating the training dataset as well as the testing dataset. We will iterate over the files of our dataset and consider all files whose names start with the digit 12 as our test dataset. So, roughly 90% of our dataset is considered the training dataset and 10 % of our dataset is considered the testing dataset. You can refer to the code for this in the following figure:

Figure 5.6: Code snippet for building the training and testing dataset

As you can see, if the filename starts with 12 then we consider the content of those files as the testing dataset. All files apart from these are considered the training dataset. You can find the code at this GitHub link: https://github.com/jalajthanaki/Sentiment_Analysis/blob/master/Baseline_approach.ipynb.

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