R Data Mining Projects [Video]

Pradeepta Mishra
Tuesday, January 31, 2017

Discover the versatility of R for data mining with this collection of real-world dataset analysis techniques

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Video Details

ISBN 139781787283954

Course Length3 hours 19 minutes

Video Description

The R language is a powerful open source functional programming language. At its core, R is a statistical programming language that provides impressive tools for data mining and analysis. It enables you to create high-level graphics and offers an interface to other languages. This means R is best suited to producing data and visual analytics through customization scripts and commands, instead of the typical statistical tools that provide tick boxes and drop-down menus for users.

This video course explores data mining techniques, showing you how to apply different mining concepts to various statistical and data applications in a wide range of fields. We will teach you about R and its application to data mining, and give you relevant and useful information you can use to develop and improve your applications. It will help you complete complex data mining cases and guide you through handling issues you might encounter during projects.

Style and Approach

This fast-paced video tutorial will help you solve predictive modeling problems using the most popular data mining algorithms through simple, practical cases. It’s your step-by-step helping hand to developing complex data mining projects.

Data Manipulation Using In-Built R Data

The Course Overview

What Is Data Mining?

Introduction to the R Programming Language

Data Type Conversion

Sorting, Merging, Indexing, and Subsetting Dataframes

Date and Time Formatting

Types of Functions

Loop Concepts

Applying Concepts

String Manipulation

NA and Missing Value Management and Imputation Techniques

Exploratory Data Analysis with Automobile Data

Univariate Data Analysis

Bivariate Analysis

Multivariate Analysis

Understanding Distributions and Transformation

Interpreting Distributions and Variable Binning

Contingency Tables, Bivariate Statistics, and Checking for Data Normality

Hypothesis Testing

Non-Parametric Methods

Visualizing Diamond Dataset

Introduction to Data Visualization

Visualizing Charts, and Geo Mapping

Visualizing Scatterplot, Word Cloud and More

Using plotly

Creating Geo Mapping

Regression with Automobile Data

Introduction about Regression

Linear Regression

Stepwise Regression Method for Variable Selection

Logistic Regression

Cubic Regression

Market Basket Analysis with Groceries Data

Introduction to Market Basket Analysis

Practical project

What You Will Learn

Make use of statistics and programming to understand data mining concepts and their application
Use R programming to apply statistical models to data
Use various libraries available in R CRAN (comprehensive R archives network) in data mining
Apply data management steps to handle large datasets
Get to know various data visualization libraries available in R to represent data

Authors

Pradeepta Mishra

Pradeepta Mishra is a data scientist, predictive modeling expert, deep learning and machine learning practitioner, and an econometrician. He is currently leading the data science and machine learning practice for Ma Foi Analytics, Bangalore, India. Ma Foi Analytics is an advanced analytics provider for Tomorrow's Cognitive Insights Ecology, using a combination of cutting-edge artificial intelligence, proprietary big data platform, and data science expertise.

He holds a patent for enhancing planogram design for the retail industry. Pradeepta has published and presented research papers at IIM Ahmedabad, India. He is a visiting faculty at various leading B-schools and regularly gives talks on data science and machine learning.

Pradeepta has spent more than 10 years in his domain and has solved various projects relating to classification, regression, pattern recognition, time series forecasting, and unstructured data analysis using text mining procedures, spanning across domains such as healthcare, insurance, retail and e-commerce, manufacturing, and so on.

If you have any questions, don't hesitate to look me up on Twitter via @mishra1_PK, I will be more than glad to help a fellow web professional wherever, whenever.