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Jupyter for Data Science

You're reading from   Jupyter for Data Science Exploratory analysis, statistical modeling, machine learning, and data visualization with Jupyter

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Product type Paperback
Published in Oct 2017
Publisher Packt
ISBN-13 9781785880070
Length 242 pages
Edition 1st Edition
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Author (1):
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Dan Toomey Dan Toomey
Author Profile Icon Dan Toomey
Dan Toomey
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Table of Contents (11) Chapters Close

Preface 1. Jupyter and Data Science FREE CHAPTER 2. Working with Analytical Data on Jupyter 3. Data Visualization and Prediction 4. Data Mining and SQL Queries 5. R with Jupyter 6. Data Wrangling 7. Jupyter Dashboards 8. Statistical Modeling 9. Machine Learning Using Jupyter 10. Optimizing Jupyter Notebooks

Random forests

The random forests algorithm attempts a number of random decision trees and provides the tree that works best within the parameters used to drive the model.

Random forests in R

With R we include the packages we are going to use:

install.packages("randomForest", repos="http://cran.r-project.org") 
library(randomForest) 

Load the data:

filename = "http://archive.ics.uci.edu/ml/machine-learning-databases/housing/housing.data" 
housing <- read.table(filename) 
colnames(housing) <- c("CRIM", "ZN", "INDUS", "CHAS", "NOX",  
                       "RM", "AGE", "DIS", "RAD", "TAX"...
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