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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

Understanding the revised approach


In this section, we will be looking at the key concepts and approaches for alignment and smoothing. It is not that difficult to implement the Logistic Regression algorithm; we will be using the scikit-learn API. So, we will start with understanding the concepts and approaches for implementation.

Understanding concepts and approaches

Here, we will discuss how alignment and smoothing will work. Once we understand the technicality behind alignment and smoothing, we will focus on the Logistic Regression-based approach.

Alignment-based approach

Using this approach, we will be increasing the prices using a constant value so that our predicted price and actual price in testing the dataset will be aligned. Suppose we take 10 days into consideration. We will generate the average of the value of the prices. After that, we generate the average value for the prices that have been predicted by the first ML model. Once we generate both average values, we need to subtract...

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