Data Science with R. Introducing Data Mining with Rattle and R.
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1 http: // togaware. com Copyright 2013, 1/35 Data Science with R Introducing Data Mining with Rattle and R [email protected] Senior Director and Chief Data Miner, Analytics Australian Taxation Office Visiting Professor, SIAT, Chinese Academy of Sciences Adjunct Professor, Australian National University Adjunct Professor, University of Canberra Fellow, Institute of Analytics Professionals of Australia [email protected]
2 http: // togaware. com Copyright 2013, 2/35 Overview 1 An Introduction to Data Mining 2 The Rattle Package for Data Mining 3 Moving Into R 4 Getting Started with Rattle
3 An Introduction to Data Mining http: // togaware. com Copyright 2013, 3/35 Overview 1 An Introduction to Data Mining 2 The Rattle Package for Data Mining 3 Moving Into R 4 Getting Started with Rattle
4 An Introduction to Data Mining http: // togaware. com Copyright 2013, 4/35 Data Mining and Big Data Application of Machine Learning Statistics Software Engineering and Programming with Data Intuition To Big Data Volume, Velocity, Variety... to discover new knowledge... to improve business outcomes... to deliver better tailored services
5 An Introduction to Data Mining http: // togaware. com Copyright 2013, 5/35 The Business of Data Mining Australian Taxation Office Lodgment ($110M) Tax Havens ($150M) Tax Fraud ($250M) IBM Buys SPSS for $1.2B in 2009 SAS has annual revenue approaching $3B Analytics is >$100B business Amazon, ebay/paypal, Google...
6 An Introduction to Data Mining http: // togaware. com Copyright 2013, 6/35 Basic Tools: Data Mining Algorithms Linear Discriminant Analysis (lda) Logistic Regression (glm) Decision Trees (rpart, wsrpart) Random Forests (randomforest, wsrf) Boosted Stumps (ada) Neural Networks (nnet) Support Vector Machines (kernlab)... That s a lot of tools to learn in R! Many with different interfaces and options.
7 The Rattle Package for Data Mining http: // togaware. com Copyright 2013, 7/35 Overview 1 An Introduction to Data Mining 2 The Rattle Package for Data Mining 3 Moving Into R 4 Getting Started with Rattle
8 The Rattle Package for Data Mining http: // togaware. com Copyright 2013, 8/35 Why a GUI? Statistics can be complex and traps await So many tools in R to deliver insights Effective analyses should be scripted Scripting also required for repeatability R is a language for programming with data How to remember how to do all of this in R? How to skill up 150 data analysts with Data Mining?
9 The Rattle Package for Data Mining http: // togaware. com Copyright 2013, 9/35 Users of Rattle Today, Rattle is used world wide in many industries Health analytics Customer segmentation and marketing Fraud detection Government It is used by Consultants and Analytics Teams across business Universities to teach Data Mining It is and will remain freely available. CRAN and
10 The Rattle Package for Data Mining http: // togaware. com Copyright 2013, 10/35 A Tour Thru Rattle: Startup
11 The Rattle Package for Data Mining http: // togaware. com Copyright 2013, 11/35 A Tour Thru Rattle: Loading Data
12 The Rattle Package for Data Mining http: // togaware. com Copyright 2013, 12/35 A Tour Thru Rattle: Explore Distribution
13 The Rattle Package for Data Mining http: // togaware. com Copyright 2013, 13/35 A Tour Thru Rattle: Explore Correlations
14 The Rattle Package for Data Mining http: // togaware. com Copyright 2013, 14/35 A Tour Thru Rattle: Hierarchical Cluster
15 The Rattle Package for Data Mining http: // togaware. com Copyright 2013, 15/35 A Tour Thru Rattle: Decision Tree
16 The Rattle Package for Data Mining http: // togaware. com Copyright 2013, 16/35 A Tour Thru Rattle: Decision Tree Plot
17 The Rattle Package for Data Mining http: // togaware. com Copyright 2013, 17/35 A Tour Thru Rattle: Random Forest
18 The Rattle Package for Data Mining http: // togaware. com Copyright 2013, 18/35 A Tour Thru Rattle: Risk Chart
19 Moving Into R http: // togaware. com Copyright 2013, [email protected] 19/35 Overview 1 An Introduction to Data Mining 2 The Rattle Package for Data Mining 3 Moving Into R 4 Getting Started with Rattle
20 Moving Into R http: // togaware. com Copyright 2013, [email protected] 20/35 Data Miners are Programmers of Data Data miners are programmers of data A GUI can only do so much R is a powerful statistical language Professional data mining Scripting Transparency Repeatability
21 Moving Into R http: // togaware. com Copyright 2013, [email protected] 21/35 From GUI to CLI Rattle s Log Tab
22 Moving Into R http: // togaware. com Copyright 2013, [email protected] 22/35 From GUI to CLI Rattle s Log Tab
23 Moving Into R http: // togaware. com Copyright 2013, [email protected] 23/35 Step 1: Identify the Data dsname <- "weather" target <- "RainTomorrow" risk <- "RISK_MM" form <- formula(paste(target, "~.")) ds <- get(dsname) dim(ds) ## [1] names(ds) ## [1] "Date" "Location" "MinTemp" "... ## [5] "Rainfall" "Evaporation" "Sunshine" "... ## [9] "WindGustSpeed" "WindDir9am" "WindDir3pm" "... ## [13] "WindSpeed3pm" "Humidity9am" "Humidity3pm" "......
24 Moving Into R http: // togaware. com Copyright 2013, [email protected] 24/35 Step 2: Observe the Data head(ds) ## Date Location MinTemp MaxTemp Rainfall Evapora... ## Canberra ## Canberra ## Canberra ## Canberra ## Canberra tail(ds) ## Date Location MinTemp MaxTemp Rainfall Evapo... ## Canberra ## Canberra ## Canberra ## Canberra ## Canberra
25 Moving Into R http: // togaware. com Copyright 2013, [email protected] 25/35 Step 2: Observe the Data str(ds) ## 'data.frame': 366 obs. of 24 variables: ## $ Date : Date, format: " " " ## $ Location : Factor w/ 46 levels "Adelaide","Alba... ## $ MinTemp : num ## $ MaxTemp : num ## $ Rainfall : num summary(ds) ## Date Location MinTemp... ## Min. : Canberra :366 Min. : ## 1st Qu.: Adelaide : 0 1st Qu.: ## Median : Albany : 0 Median : ## Mean : Albury : 0 Mean : ## 3rd Qu.: AliceSprings : 0 3rd Qu.:
26 Moving Into R http: // togaware. com Copyright 2013, [email protected] 26/35 Step 3: Clean the Data Identify Variables (ignore <- c(names(ds)[c(1,2)], risk)) ## [1] "Date" "Location" "RISK_MM" (vars <- setdiff(names(ds), ignore)) ## [1] "MinTemp" "MaxTemp" "Rainfall" "... ## [5] "Sunshine" "WindGustDir" "WindGustSpeed" "... ## [9] "WindDir3pm" "WindSpeed9am" "WindSpeed3pm" "... ## [13] "Humidity3pm" "Pressure9am" "Pressure3pm" " dim(ds[vars]) ## [1]
27 Moving Into R http: // togaware. com Copyright 2013, [email protected] 27/35 Step 3: Clean the Data Remove Missing dim(ds[vars]) ## [1] sum(is.na(ds[vars])) ## [1] 47 ds <- na.omit(ds[vars]) sum(is.na(ds)) ## [1] 0 dim(ds) ## [1]
28 Moving Into R http: // togaware. com Copyright 2013, [email protected] 28/35 Step 3: Clean the Data Target as Categoric summary(ds[target]) ## RainTomorrow ## Min. :0.000 ## 1st Qu.:0.000 ## Median :0.000 ## Mean :0.183 ## 3rd Qu.:0.000 ## Max. : ds[target] <- as.factor(ds[[target]]) levels(ds[target]) <- c("no", "Yes") summary(ds[target]) ## RainTomorrow ## 0:268 ## 1: 60
29 Moving Into R http: // togaware. com Copyright 2013, [email protected] 29/35 Step 4: Build the Model Train/Test (n <- nrow(ds)) ## [1] 328 train <- sample(1:n, 0.70*n) length(train) ## [1] 229 test <- setdiff(1:n, train) length(test) ## [1] 99
30 Moving Into R http: // togaware. com Copyright 2013, [email protected] 30/35 Step 4: Build the Model Random Forest library(randomforest) ## randomforest ## Type rfnews() to see new features/changes/bug fixes. m <- randomforest(form, ds[train,]) m ## ## Call: ## randomforest(formula=form, data=ds[train, ]) ## Type of random forest: classification ## Number of trees: 500 ## No. of variables tried at each split: 4 ## ## OOB estimate of error rate: 12.23% ## Confusion matrix:...
31 Moving Into R http: // togaware. com Copyright 2013, [email protected] 31/35 Step 5: Evaluate the Model Risk Chart pr <- predict(m, ds[test,], type="prob")[,2] ev <- evaluaterisk(pr, ds[test, target], ds[test, risk]) riskchart(ev) 100 Score Lift Performance (%) % 1 Recall (70%) 0 Precision Caseload (%)
32 Getting Started with Rattle http: // togaware. com Copyright 2013, 32/35 Overview 1 An Introduction to Data Mining 2 The Rattle Package for Data Mining 3 Moving Into R 4 Getting Started with Rattle
33 Getting Started with Rattle http: // togaware. com Copyright 2013, 33/35 Installation Rattle is built using R Need to download and install R from cran.r-project.org Recommend also install RStudio from Then start up RStudio and install Rattle: install.packages("rattle") Then we can start up Rattle: rattle() Required packages are loaded as needed.
34 Getting Started with Rattle http: // togaware. com Copyright 2013, 34/35 Resources and References Rattle: OnePageR: Guides: Practise: Book: Data Mining using Rattle/R Chapter: Rattle and Other Tales Paper: A Data Mining GUI for R R Journal, Volume 1(2)
35 That Is All Folks Time For Questions http: // togaware. com Copyright 2013, 35/35 Thank You
How To Understand Data Mining In R And Rattle
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