Book Image

Data Science Algorithms in a Week - Second Edition

By : David Natingga
Book Image

Data Science Algorithms in a Week - Second Edition

By: David Natingga

Overview of this book

Machine learning applications are highly automated and self-modifying, and continue to improve over time with minimal human intervention, as they learn from the trained data. To address the complex nature of various real-world data problems, specialized machine learning algorithms have been developed. Through algorithmic and statistical analysis, these models can be leveraged to gain new knowledge from existing data as well. Data Science Algorithms in a Week addresses all problems related to accurate and efficient data classification and prediction. Over the course of seven days, you will be introduced to seven algorithms, along with exercises that will help you understand different aspects of machine learning. You will see how to pre-cluster your data to optimize and classify it for large datasets. This book also guides you in predicting data based on existing trends in your dataset. This book covers algorithms such as k-nearest neighbors, Naive Bayes, decision trees, random forest, k-means, regression, and time-series analysis. By the end of this book, you will understand how to choose machine learning algorithms for clustering, classification, and regression and know which is best suited for your problem
Table of Contents (16 chapters)
Title Page
Packt Upsell
Glossary of Algorithms and Methods in Data Science

Chapter 6. Regression

Regression analysis is the process of estimating the relationship between dependent variables. For example, if a variable y is linearly dependent on variable x, then regression analysis tries to estimate the constants a and b in the equation 

, which  expresses the linear relationship between the variables y and x.

In this chapter, you will learn about the following topics:

  • The core idea of regression by performing a simple linear regression on the perfect data from first principles in a Fahrenheit and Celsius conversion example 
  • Performing linear regression analysis by using the least squares method from the  scipy.linalg Python library on perfect and real-world data in the examples of Fahrenheit and Celsius conversion, weight prediction based on height, and flight time duration prediction based on distance
  • The gradient descent algorithm in order to find a regression model with the best fit (using the least mean squares rule) and how to implement it in Python
  • The establishment...