Case Studies in Data Mining with R

Case Studies in Data Mining with R, available at $39.99, has an average rating of 4.25, with 136 lectures, based on 86 reviews, and has 2255 subscribers.

You will learn about Understand how to implement and evaluate a variety of predictive data mining models in three different domains, each described as extended case studies: (1) harmful plant growth; (2) fraudulent transaction detection; and (3) stock market index changes. Perform sophisticated data mining analyses using the "Data Mining with R" (DMwR) package and R software. Have a greatly expanded understanding of the use of R software as a comprehensive data mining tool and platform. Understand how to implement and evaluate supervised, semi-supervised, and unsupervised learning algorithms. This course is ideal for individuals who are The course is appropriate for anyone seeking to expand their knowledge and analytical skills related to conducting predictive data mining analyses. or The course is appropriate for undergraduate students seeking to acquire additional in-demand job skill sets for business analytics. or The course is appropriate for graduate students seeking to acquire additional data analysis skills. or Knowledge of R software is not required to successfully complete this course. or The course is appropriate for practicing business analytics professionals seeking to acquire additional job skill sets. It is particularly useful for The course is appropriate for anyone seeking to expand their knowledge and analytical skills related to conducting predictive data mining analyses. or The course is appropriate for undergraduate students seeking to acquire additional in-demand job skill sets for business analytics. or The course is appropriate for graduate students seeking to acquire additional data analysis skills. or Knowledge of R software is not required to successfully complete this course. or The course is appropriate for practicing business analytics professionals seeking to acquire additional job skill sets.

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Summary

Title: Case Studies in Data Mining with R

Price: $39.99

Average Rating: 4.25

Number of Lectures: 136

Number of Published Lectures: 136

Number of Curriculum Items: 136

Number of Published Curriculum Objects: 136

Original Price: $159.99

Quality Status: approved

Status: Live

What You Will Learn

Understand how to implement and evaluate a variety of predictive data mining models in three different domains, each described as extended case studies: (1) harmful plant growth; (2) fraudulent transaction detection; and (3) stock market index changes.
Perform sophisticated data mining analyses using the "Data Mining with R" (DMwR) package and R software.
Have a greatly expanded understanding of the use of R software as a comprehensive data mining tool and platform.
Understand how to implement and evaluate supervised, semi-supervised, and unsupervised learning algorithms.

Who Should Attend

The course is appropriate for anyone seeking to expand their knowledge and analytical skills related to conducting predictive data mining analyses.
The course is appropriate for undergraduate students seeking to acquire additional in-demand job skill sets for business analytics.
The course is appropriate for graduate students seeking to acquire additional data analysis skills.
Knowledge of R software is not required to successfully complete this course.
The course is appropriate for practicing business analytics professionals seeking to acquire additional job skill sets.

Target Audiences

The course is appropriate for anyone seeking to expand their knowledge and analytical skills related to conducting predictive data mining analyses.
The course is appropriate for undergraduate students seeking to acquire additional in-demand job skill sets for business analytics.
The course is appropriate for graduate students seeking to acquire additional data analysis skills.
Knowledge of R software is not required to successfully complete this course.
The course is appropriate for practicing business analytics professionals seeking to acquire additional job skill sets.

Case Studies in Data Miningwas originally taught as three separate online data mining courses. We examine three case studies which together present a broad-based tour of the basic and extended tasks of data mining in three different domains: (1) predicting algae blooms; (2) detecting fraudulent sales transactions; and (3) predicting stock market returns. The cumulative “hands-on” 3-course fifteen sessions showcase the use of Luis Torgo’s amazingly useful “Data Mining with R” (DMwR) package and R software. Everything that you see on-screen is included with the course: all of the R scripts; all of the data files and R objects used and/or referenced; as well as all of the R packages’ documentation. You can be new to R software and/or to data mining and be successful in completing the course. The first case study, Predicting Algae Blooms, provides instruction regarding the many useful, unique data mining functions contained in the R software ‘DMwR’ package. For the algae blooms prediction case, we specifically look at the tasks of data pre-processing, exploratory data analysis, and predictive model construction. For individuals completely new to R, the first two sessions of the algae blooms case (almost 4 hours of video and materials) provide an accelerated introduction to the use of R and RStudio and to basic techniques for inputting and outputting data and text. Detecting Fraudulent Transactions is the second extended data mining case study that showcases the DMwR (Data Mining with R) package. The case is specific but may be generalized to a common business problem: How does one sift through mountains of data (401,124 records, in this case) and identify suspicious data entries, or “outliers”? The case problem is very unstructured, and walks through a wide variety of approaches and techniques in the attempt to discriminate the “normal”, or “ok” transactions, from the abnormal, suspicious, or “fraudulent” transactions. This case presents a large number of alternative modeling approaches, some of which are appropriate for supervised, some for unsupervised, and some for semi-supervised data scenarios. The third extended case, Predicting Stock Market Returns is a data mining case study addressing the domain of automatic stock trading systems. These four sessions address the tasks of building an automated stock trading system based on prediction models that utilize daily stock quote data. The goal is to predict future returns for the S&P 500 market index. The resulting predictions are used together with a trading strategy to make decisions about generating market buy and sell orders. The case examines prediction problems that stem from the time ordering among data observations, that is, from the use of time seriesdata. It also exemplifies the difficulties involved in translating model predictions into decisions and actions in the context of ‘real-world’ business applications.

Course Curriculum

Chapter 1: A Brief Introduction to R and RStudio using Scripts

Lecture 1: Course Overview

Lecture 2: Introduction to R for Data Mining

Lecture 3: Data Structures: Vectors (part 1)

Lecture 4: Data Structures: Vectors (part 2)

Lecture 5: Factors (part 1)

Lecture 6: Factors (part 2)

Lecture 7: Generating Sequences

Lecture 8: Indexing (aka Subscripting or Subsetting)

Lecture 9: Data Structures: Matrices and Arrays (part 1)

Lecture 10: Data Structures: Matrices and Arrays (part 2)

Lecture 11: Data Structures: Lists

Lecture 12: Data Structures: Dataframes (part 1)

Lecture 13: Data Structures: Dataframes (part 2)

Lecture 14: Creating New Functions

Chapter 2: Inputting and Outputting Data and Text

Lecture 1: Using the scan() Function for Input (part 1)

Lecture 2: Using the scan() Function for Input (part 2)

Lecture 3: Using readline(), cat() and print() Functions

Lecture 4: Using readLines() Function and Text Data

Lecture 5: Example Program: powers.r

Lecture 6: Example Program: quartiles1.r

Lecture 7: Example Program: quad2b.r

Lecture 8: Reading and Writing Files (part 1)

Lecture 9: Reading and Writing Files (part 2)

Chapter 3: Introduction to Predicting Algae Blooms

Lecture 1: Predicting Algae Blooms

Lecture 2: Data Visualization and Summarization: Histograms

Lecture 3: Data Visualization: Boxplot and Identity Plot

Lecture 4: Data Visualization: Conditioning Plots

Lecture 5: Imputation: Dealing with Unknown or Missing Values

Lecture 6: Imputation: Removing Rows with Missing Values

Lecture 7: Imputation: Replace Missing Values with Central Measures

Lecture 8: Imputation: Replace Missing Values through Correlation

Lecture 9: Visualizing other Imputations with Lattice Plots

Chapter 4: Obtaining Prediction Models

Lecture 1: Read in Data Files

Lecture 2: Creating Prediction Models

Lecture 3: Examine Alternative Regression Models

Lecture 4: Regression Trees

Lecture 5: Strategy for Pruning Trees

Chapter 5: Evaluating and Selecting Models

Lecture 1: Alternative Model Evaluation Criteria

Lecture 2: Introduction to K-Fold Cross-Validation

Lecture 3: Setting up K-Fold Evaluation (part 1)

Lecture 4: Setting up K-Fold Evaluation (part 2)

Lecture 5: Best Model (part 1)

Lecture 6: Best Model (part 2)

Lecture 7: Finish Evaluating Models

Lecture 8: Predicting from the Models

Lecture 9: Comparing the Predictions

Chapter 6: Examine the Data in the Fraudulent Transactions Case Study

Lecture 1: Exercise Solution from Evaluating and Selecting Models

Lecture 2: Fraudulent Case Study Introduction

Lecture 3: Prelude to Exploring the Data

Lecture 4: Exploring the Data

Lecture 5: Exploring the Data Continued (part 1)

Lecture 6: Exploring the Data Continued (part 2)

Lecture 7: Exploring the Data Continued (part 3)

Lecture 8: Dealing with Missing Data (part 1)

Lecture 9: Dealing with Missing Data (part 2)

Lecture 10: Dealing with Missing Data (part 3)

Chapter 7: Pre-Processing the Data to Apply Methodology

Lecture 1: Review the Data and the Focus of the Fraudulent Transactions Case

Lecture 2: Pre-Processing the Data (part 1)

Lecture 3: Pre-Processing the Data (part 2)

Lecture 4: Pre-Processing the Data (part 3)

Lecture 5: Defining Data Mining Tasks

Lecture 6: Semi-Supervised Techniques

Lecture 7: Precision and Recall

Lecture 8: Lift Charts and Precision Recall Curves

Chapter 8: Methodology to Find Outliers (Fraudulent Transactions)

Lecture 1: Exercise from Previous Session

Lecture 2: Review Precision and Recall

Lecture 3: Review Lift Charts and Precision Recall Curves

Lecture 4: Cumulative Recall Chart

Lecture 5: Creating More Functions for the Experimental Methodology

Lecture 6: Experimental Methodology to find Outliers (part 1)

Lecture 7: Experimental Methodology to find Outliers (part 2)

Lecture 8: Experimental Methodology to find Outliers (part 3)

Lecture 9: Experimental Methodology to find Outliers (part 4)

Lecture 10: Experimental Methodology to find Outliers (part 5)

Chapter 9: The Data Mining Tasks to Find the Fraudulent Transactions

Lecture 1: Review of Fraud Case (part 1)

Lecture 2: Review of Fraud Case (part 2)

Lecture 3: Review of Fraud Case (part 3)

Lecture 4: Baseline Boxplot Rule

Lecture 5: Local Outlier Factors

Lecture 6: Plotting Everything

Lecture 7: Supervised and Unsupervised Approaches

Lecture 8: SMOTE and Naive Bayes (part 1)

Lecture 9: SMOTE and Naive Bayes (part 2)

Chapter 10: Sidebar on Boosting

Lecture 1: Introduction to Boosting (from Rattle course)

Lecture 2: Boosting Demo Basics using R

Lecture 3: Replicating Adaboost using Rpart (Recursive Partitioning) Package

Lecture 4: Replicating Adaboost using Rpart (part 2)

Lecture 5: Boosting Extensions and Variants

Lecture 6: Boosting Exercise

Chapter 11: Introduction to Stock Market Prediction Case Study

Instructors

Geoffrey Hubona, Ph.D.
Associate Professor of MIS and Data Analytics

Rating Distribution

1 stars: 3 votes
2 stars: 6 votes
3 stars: 16 votes
4 stars: 31 votes
5 stars: 30 votes

Frequently Asked Questions

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