Case Studies in Data Mining with R
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Case Studies in Data Mining with R

Learn to use the "Data Mining with R" (DMwR) package and R software to build and evaluate predictive data mining models.
3.8 (41 ratings)
Instead of using a simple lifetime average, Udemy calculates a course's star rating by considering a number of different factors such as the number of ratings, the age of ratings, and the likelihood of fraudulent ratings.
1,499 students enrolled
Last updated 10/2016
Current price: $12 Original price: $60 Discount: 80% off
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  • 22 hours on-demand video
  • 2 Supplemental Resources
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion

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What Will I 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.
View Curriculum
  • Students will need to install no-cost R software and the no-cost RStudio IDE (instructions are provided).

Case Studies in Data Mining was 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 series data. It also exemplifies the difficulties involved in translating model predictions into decisions and actions in the context of 'real-world' business applications.

Who is the target audience?
  • 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.
Compare to Other R Courses
Curriculum For This Course
136 Lectures
A Brief Introduction to R and RStudio using Scripts
14 Lectures 02:17:29

Introduction to R for Data Mining

A vector is a sequence of data elements of the same basic type. Members in a vector are officially called components. Nevertheless, we will just call them members in this site.

Here is a vector containing three numeric values 2, 3 and 5.

> c(2, 3, 5)
[1] 2 3 5

And here is a vector of logical values.


A vector can contain character strings.

> c("aa", "bb", "cc", "dd", "ee")
[1] "aa" "bb" "cc" "dd" "ee"

Preview 08:41

Data Structures: Vectors (part 2)

The function factor is used to encode a vector as a factor (the terms 'category' and 'enumerated type' are also used for factors). If argument ordered is TRUE, the factor levels are assumed to be ordered. For compatibility with S there is also a function ordered.

is.factor, is.ordered, as.factor and as.ordered are the membership and coercion functions for these classes.

Preview 08:20

Factors (part 2)

seq() is the R function that will produce an enumerated vector.

Generating Sequences

Given a vector of data one common task is to isolate particular entries or censor items that meet some criteria.

Indexing (aka Subscripting or Subsetting)

A matrix is a collection of data elements arranged in a two-dimensional rectangular layout.

Data Structures: Matrices and Arrays (part 1)

An array in R can have one, two or more dimensions. It is simply a vector which is stored with additional attributes giving the dimensions (attribute "dim") and optionally names for those dimensions (attribute "dimnames").

Data Structures: Matrices and Arrays (part 2)

A list is an R structure that may contain object of any other types, including other lists. Lots of the modeling functions (like t.test() for the t test or lm() for linear models) produce lists as their return values, but you can also construct one yourself:

 mylist <- list (a = 1:5, b = "Hi There", c = function(x) x * sin(x)) 
Data Structures: Lists

A data frame is a list of variables of the same number of rows with unique row names, given class "data.frame". If no variables are included, the row names determine the number of rows.

Data Structures: Dataframes (part 1)

Data Structures: Dataframes (part 2)

Creating New Functions
Inputting and Outputting Data and Text
9 Lectures 01:21:31

The scan() function in R reads data into a vector or list from the console or file.

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

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

The readline() function in R reads a line from the terminal (in interactive use).

Preview 12:00

The readLines() function

Using readLines() Function and Text Data

Example Program: powers.r

Example Program: quartiles1.r

Example Program: quad2b.r

Reading and Writing Files (part 1)

Reading and Writing Files (part 2)
Introduction to Predicting Algae Blooms
9 Lectures 01:59:52

This case study introduces you to some basic tasks of data mining: data pre-processing, exploratory data analysis, and predictive model construction. This initial case study studies a relatively small problem by data mining standards. Namely, the case addresses the problem of predicting the frequency occurrence of several harmful algae in water samples.

Predicting Algae Blooms

A histogram is a graphical representation of the distribution of numerical data. It is an estimate of the probability distribution of a continuous variable (quantitative variable) and was first introduced by Karl Pearson.

Data Visualization and Summarization: Histograms

The box plot (a.k.a. box and whisker diagram) is a standardized way of displaying the distribution of data based on the five number summary: minimum, first quartile, median, third quartile, and maximum.

Data Visualization: Boxplot and Identity Plot

Conditioning Plot. Purpose: Check pairwise relationship between two variables conditional on a third variable. A conditional plot, also known as a coplot or subset plot, is a plot of two variables contional on the value of a third variable (called the conditioning variable).

Preview 14:48

In statistics, imputation is the process of replacing missing data with substituted values. When substituting for a data point, it is known as "unit imputation"; when substituting for a component of a data point, it is known as "item imputation". Because missing data can create problems for analyzing data, imputation is seen as a way to avoid pitfalls involved with listwise deletion of cases that have missing values. That is to say, when one or more values are missing for a case, most statistical packages default to discarding any case that has a missing value, which may introduce bias or affect the representativeness of the results. Imputation preserves all cases by replacing missing data with a probable value based on other available information. Once all missing values have been imputed, the data set can then be analysed using standard techniques for complete data

Imputation: Dealing with Unknown or Missing Values

Imputation: Removing Rows with Missing Values

Imputation: Replace Missing Values with Central Measures

Imputation: Replace Missing Values through Correlation

The lattice package, written by Deepayan Sarkar, attempts to improve on base R graphics by providing better defaults and the ability to easily display multivariate relationships. In particular, the package supports the creation of trellis graphs - graphs that display a variable or the relationship between variables, conditioned on one or more other variables.

Visualizing other Imputations with Lattice Plots
Obtaining Prediction Models
5 Lectures 01:14:38
Read in Data Files

Creating Prediction Models

In statistics, regression analysis is a statistical process for estimating the relationships among variables. It includes many techniques for modeling and analyzing several variables, when the focus is on the relationship between a dependent variable and one or more independent variables (or 'predictors').

Examine Alternative Regression Models

Regression trees are for dependent variables that take continuous or. ordered discrete values, with prediction error typically measured by the squared. difference between the observed and predicted values.

Preview 15:42

Strategy for Pruning Trees
Evaluating and Selecting Models
9 Lectures 01:38:25
Alternative Model Evaluation Criteria

Cross-validation, sometimes called rotation estimation, is a model validation technique for assessing how the results of a statistical analysis will generalize to an independent data set. It is mainly used in settings where the goal is prediction, and one wants to estimate how accurately a predictive model will perform in practice. In a prediction problem, a model is usually given a dataset of known data on which training is run (training dataset), and a dataset of unknown data (or first seen data) against which the model is tested (testing dataset). The goal of cross validation is to define a dataset to "test" the model in the training phase (i.e., the validation dataset), in order to limit problems like overfitting, give an insight on how the model will generalize to an independent dataset (i.e., an unknown dataset, for instance from a real problem), etc.

Introduction to K-Fold Cross-Validation

In k-fold cross-validation, the original sample is randomly partitioned into k equal sized subsamples. Of the k subsamples, a single subsample is retained as the validation data for testing the model, and the remaining k − 1 subsamples are used as training data. The cross-validation process is then repeated k times (the folds), with each of the k subsamples used exactly once as the validation data. The k results from the folds can then be averaged (or otherwise combined) to produce a single estimation. The advantage of this method over repeated random sub-sampling (see below) is that all observations are used for both training and validation, and each observation is used for validation exactly once. 10-fold cross-validation is commonly used

Preview 10:30

Setting up K-Fold Evaluation (part 2)

Best Model (part 1)

Best Model (part 2)

Predicting from the Models

Comparing the Predictions
Examine the Data in the Fraudulent Transactions Case Study
10 Lectures 01:31:52
Exercise Solution from Evaluating and Selecting Models

This case study addresses an instantiation of the general problem of detecting unusual observations of a phenomena, that is, finding rare and quite different observations. The driving application has to do with transactions of a set of products that are reported by the salespeople of some company. The goal is to find "strange" transaction reports that may indicate fraud attempts by some of the salespeople.

Fraudulent Case Study Introduction

Prelude to Exploring the Data

Data visualization is the presentation of data in a pictorial or graphical format. For centuries, people have depended on visual representations such as charts and maps to understand information more easily and quickly.

Preview 11:21

Exploring the Data Continued (part 1)

Exploring the Data Continued (part 2)

Exploring the Data Continued (part 3)

Dealing with Missing Data (part 1)

Dealing with Missing Data (part 2)

Dealing with Missing Data (part 3)
Pre-Processing the Data to Apply Methodology
8 Lectures 01:24:52
Review the Data and the Focus of the Fraudulent Transactions Case

Pre-Processing the Data (part 1)

Here we explain the whys and hows of creating a list structure containing the unit prices by product.

Preview 10:39

Pre-Processing the Data (part 3)

In supervised learning the categories, data is assigned to are known before computation. So they are being used in order to 'learn' the parameters that are really significant for those Clusters. In unsupervised learning Datasets are assigned to segments, without the clusters being known.

Defining Data Mining Tasks

Semi-supervised learning is a class of supervised learning tasks and techniques that also make use of unlabeled data for training - typically a small amount of labeled data with a large amount of unlabeled data.

Semi-Supervised Techniques

In pattern recognition and information retrieval with binary classification, precision (also called positive predictive value) is the fraction of retrieved instances that are relevant, while recall (also known as sensitivity) is the fraction of relevant instances that are retrieved.

Precision and Recall

Lift is a measure of the effectiveness of a predictive model calculated as the ratio between the results obtained with and without the predictive model. Cumulative gains and lift charts are visual aids for measuring model performance. Both charts consist of a lift curve and a baseline.

Lift Charts and Precision Recall Curves
Methodology to Find Outliers (Fraudulent Transactions)
10 Lectures 01:25:36
Exercise from Previous Session

Review Precision and Recall

Review Lift Charts and Precision Recall Curves

Creating More Functions for the Experimental Methodology

Experimental Methodology to find Outliers (part 1)

An outlier is an observation that lies outside the overall pattern of a distribution (Moore and McCabe 1999). Usually, the presence of an outlier indicates some sort of problem. This can be a case which does not fit the model under study, or an error in measurement. Outliers are often easy to spot in histograms.

Experimental Methodology to find Outliers (part 2)

Experimental Methodology to find Outliers (part 3)

Experimental Methodology to find Outliers (part 4)

Experimental Methodology to find Outliers (part 5)
The Data Mining Tasks to Find the Fraudulent Transactions
9 Lectures 01:33:10
Review of Fraud Case (part 1)

Review of Fraud Case (part 2)

Review of Fraud Case (part 3)

Baseline Boxplot Rule

state-of-the-art outlier ranking method. The main idea of this system is to

try to obtain an outlyingness score for each case by estimating its degree of

isolation with respect to its local neighborhood. The method is based on the

notion of the local density of the observations. Cases in regions with very low

density are considered outliers. The estimates of the density are obtained using

the distances between cases.

Preview 11:38

Plotting Everything

From a theoretical point of view, supervised and unsupervised learning differ only in the causal structure of the model. In supervised learning, the model defines the effect one set of observations, called inputs, has on another set of observations, called outputs. In other words, the inputs are assumed to be at the beginning and outputs at the end of the causal chain. The models can include mediating variables between the inputs and outputs. In unsupervised learning, all the observations are assumed to be caused by latent variables, that is, the observations are assumed to be at the end of the causal chain. In practice, models for supervised learning often leave the probability for inputs undefined. This model is not needed as long as the inputs are available, but if some of the input values are missing, it is not possible to infer anything about the outputs. If the inputs are also modelled, then missing inputs cause no problem since they can be considered latent variables as in unsupervised learning.

Supervised and Unsupervised Approaches

SMOTE and Naive Bayes (part 1)

SMOTE and Naive Bayes (part 2)
Sidebar on Boosting
6 Lectures 01:00:03
Introduction to Boosting (from Rattle course)

Boosting is a machine learning ensemble meta-algorithm for reducing bias primarily and also variance in supervised learning, and a family of machine learning algorithms which convert weak learners to strong ones.

Preview 09:01

Recursive partitioning is a statistical method for multivariable analysis. Recursive partitioning creates a decision tree that strives to correctly classify members of the population by splitting it into sub-populations based on several dichotomous independent variables.

Replicating Adaboost using Rpart (Recursive Partitioning) Package

AdaBoost, short for "Adaptive Boosting", is a machine learning meta-algorithm formulated by Yoav Freund and Robert Schapire who won the prestigious "Gödel Prize" in 2003 for their work.

Replicating Adaboost using Rpart (part 2)

Boosting Extensions and Variants

Boosting Exercise
5 More Sections
About the Instructor
Geoffrey Hubona, Ph.D.
4.0 Average rating
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12,722 Students
28 Courses
Associate Professor of Information Systems

Dr. Geoffrey Hubona held full-time tenure-track, and tenured, assistant and associate professor faculty positions at 3 major state universities in the Eastern United States from 1993-2010. Currently, he is a visiting associate professor of MIS at Texas A&M International University. In these positions, he taught dozens of various statistics, business information systems, and computer science courses to undergraduate, master's and Ph.D. students. He earned a Ph.D. in Business Administration (Information Systems and Computer Science) from the University of South Florida (USF) in Tampa, FL; an MA in Economics, also from USF; an MBA in Finance from George Mason University in Fairfax, VA; and a BA in Psychology from the University of Virginia in Charlottesville, VA. He is the founder of the Georgia R School (2010-2014) and of R-Courseware (2014-Present), online educational organizations that teach research methods and quantitative analysis techniques. These research methods techniques include linear and non-linear modeling, multivariate methods, data mining, programming and simulation, and structural equation modeling and partial least squares (PLS) path modeling.