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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

Implementing the rule-based chatbot


In this section, we will understand the implementation of the chatbot. This implementation is divided into two parts. You can find this code by visiting: https://github.com/jalajthanaki/Chatbot_Rule_Based:

  • Implementing the conversation flow

  • Implementing RESTful APIs using flask

Implementing the conversation flow

In order to implement the conversation logic, we are writing a separate Python script, so that whenever we need to add or delete some logic it will be easy for us. Here, we create one Python package in which we put this conversation logic. The name of the file is conversationengine.py and it uses JSON, BSON, and re as Python dependencies.

In this file, we have implemented each conversation in the form of a function. When the user opens the chatbot for the first time, a welcome message should pop up. You can refer to the code given in the following screenshot:

Figure 8.9: Code snippet for the welcome message

Now the users need to type in Hi in order to...

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