Advanced Data Mining projects with R [Video]

Pradeepta Mishra
Monday, February 20, 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 139781787283770

Course Length1 hours 24 minutes

Video Description

Advanced Data Mining Projects with R takes you one step ahead in understanding the most complex data mining algorithms and implementing them in the popular R language. Follow up to our course Data Mining Projects in R, this course will teach you how to build your own recommendation engine. You will also implement dimensionality reduction and use it to build a real-world project. Going ahead, you will be introduced to the concept of neural networks and learn how to apply them for predictions, classifications, and forecasting. Finally, you will implement ggplot2, plotly and aspects of geomapping to create your own data visualization projects.By the end of this course, you will be well-versed with all the advanced data mining techniques and how to implement them using R, in any real-world scenario.

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 use cases. This is your step-by-step guide to developing complex data mining projects.

Clustering with E-commerce Data

The Course Overview

Understanding Customer Segmentation

Clustering Methods – K means and Hierarchical

Clustering Methods – Model Based, Other and Comparison

Building a Retail Recommendation Engine

What Is Recommendation?

Application of Methods and Limitations of Collaborative Filtering

Practical Project

Dimensionality Reduction

Why Dimensionality Reduction?

Practical Project around Dimensionality Reduction

Parametric Approach to Dimension Reduction

Applying Neural Network to Healthcare Data

Introduction to Neural Networks

Understanding the Math Behind the Neural Network

Neural Network Implementation in R

Neural Networks for Prediction

Neural Networks for Classification

Neural Networks for Forecasting

Merits and Demerits of Neural Networks

What You Will Learn

Create predictive models in order to build a recommendation engine
Implement various dimension reduction techniques to handle large datasets
Acquire knowledge about the neural network concept drawn from computer science and its applications in data mining

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.