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Applied Supervised Learning with R

You're reading from   Applied Supervised Learning with R Use machine learning libraries of R to build models that solve business problems and predict future trends

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Product type Paperback
Published in May 2019
Publisher
ISBN-13 9781838556334
Length 502 pages
Edition 1st Edition
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Authors (2):
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Jojo Moolayil Jojo Moolayil
Author Profile Icon Jojo Moolayil
Jojo Moolayil
Karthik Ramasubramanian Karthik Ramasubramanian
Author Profile Icon Karthik Ramasubramanian
Karthik Ramasubramanian
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Table of Contents (12) Chapters Close

Applied Supervised Learning with R
Preface
1. R for Advanced Analytics FREE CHAPTER 2. Exploratory Analysis of Data 3. Introduction to Supervised Learning 4. Regression 5. Classification 6. Feature Selection and Dimensionality Reduction 7. Model Improvements 8. Model Deployment 9. Capstone Project - Based on Research Papers Appendix

Exploratory Data Analysis


We will get started with the dataset available to download from UCI ML Repository at https://archive.ics.uci.edu/ml/datasets/Bank%20Marketing.

Download the ZIP file and extract it to a folder in your workspace and use the file named bank-additional-full.csv. Ask the students to start a new Jupyter notebook or an IDE of their choice and load the data into memory.

Exercise 18: Studying the Data Dimensions

Let's quickly ingest the data using the simple commands we explored in the previous chapter and take a look at a few essential characteristics of the dataset.

We are exploring the length and breadth of the data, that is, the number of rows and columns, the names of each column, the data type of each column, and a high-level view of what is stored in each column.

Perform the following steps to explore the bank dataset:

  1. First, import all the required libraries in RStudio:

    library(dplyr)
    library(ggplot2)
    library(repr)
    library(cowplot)
  2. Now, use the option method to set the width...

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