How To Understand Data Mining In R And Rattle
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1 http: // togaware. com Copyright 2014, 1/40 Data Analytics and Business Intelligence (8696/8697) Introducing Data Science with R and Rattle [email protected] Chief Data Scientist Australian Taxation Office Adjunct Professor, Australian National University Adjunct Professor, University of Canberra Fellow, Institute of Analytics Professionals of Australia [email protected]
2 An Introduction to Data Mining http: // togaware. com Copyright 2014, 3/40 Overview 1 An Introduction to Data Mining 2 The Rattle Package for Data Mining 3 Moving Into R
3 An Introduction to Data Mining Big Data and Big Business http: // togaware. com Copyright 2014, 4/40 Data Mining A data driven analysis to uncover otherwise unknown but useful patterns in large datasets, to discover new knowledge and to develop predictive models, turning data and information into knowledge and (one day perhaps) wisdom, in a timely manner.
4 An Introduction to Data Mining Big Data and Big Business http: // togaware. com Copyright 2014, 5/40 Data Mining Application of Machine Learning Statistics Software Engineering and Programming with Data Effective Communications and Intuition... to Datasets that vary by Volume, Velocity, Variety, Value, Veracity... to discover new knowledge... to improve business outcomes... to deliver better tailored services
5 An Introduction to Data Mining Big Data and Big Business http: // togaware. com Copyright 2014, 6/40 Data Mining in Research Health Research Adverse reactions using linked Pharmaceutical, General Practitioner, Hospital, Pathology datasets. Astronomy Microlensing events in the Large Magellanic Cloud of several million observed stars (out of 10 billion). Psychology Investigation of age-of-onset for Alzheimer s disease from 75 variables for 800 people. Social Sciences Survey evaluation. Social network analysis - identifying key influencers.
6 An Introduction to Data Mining Big Data and Big Business http: // togaware. com Copyright 2014, 7/40 Data Mining in Government Australian Taxation Office Lodgment ($110M) Tax Havens ($150M) Tax Fraud ($250M) Immigration and Border Control Check passengers before boarding Health and Human Services Doctor shoppers Over servicing
7 An Introduction to Data Mining Big Data and Big Business http: // togaware. com Copyright 2014, 8/40 The Business of Data Mining SAS has annual revenues of $3B (2013) IBM bought SPSS for $1.2B (2009) Analytics is >$100B business and >$320B by 2020 Amazon, ebay/paypal, Google, Facebook, LinkedIn,... Shortage of 180,000 data scientists in US in 2018 (McKinsey)...
8 An Introduction to Data Mining Algorithms http: // togaware. com Copyright 2014, 9/40 Basic Tools: Data Mining Algorithms Cluster Analysis (kmeans, wskm) Association Analysis (arules) 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.
9 The Rattle Package for Data Mining http: // togaware. com Copyright 2014, 10/40 Overview 1 An Introduction to Data Mining 2 The Rattle Package for Data Mining 3 Moving Into R
10 The Rattle Package for Data Mining A GUI for Data Mining http: // togaware. com Copyright 2014, [email protected] 11/40 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?
11 The Rattle Package for Data Mining A GUI for Data Mining http: // togaware. com Copyright 2014, [email protected] 12/40 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 Universities to teach Data Mining Within research projects for basic analyses Consultants and Analytics Teams across business It is and will remain freely available. CRAN and
12 The Rattle Package for Data Mining Setting Things Up http: // togaware. com Copyright 2014, 13/40 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.
13 The Rattle Package for Data Mining http: // togaware. com Copyright 2014, 14/40 Tour A Tour Thru Rattle: Startup
14 The Rattle Package for Data Mining http: // togaware. com Copyright 2014, 15/40 Tour A Tour Thru Rattle: Loading Data
15 The Rattle Package for Data Mining http: // togaware. com Copyright 2014, 16/40 Tour A Tour Thru Rattle: Explore Distribution
16 The Rattle Package for Data Mining http: // togaware. com Copyright 2014, 17/40 Tour A Tour Thru Rattle: Explore Correlations
17 The Rattle Package for Data Mining http: // togaware. com Copyright 2014, 18/40 Tour A Tour Thru Rattle: Hierarchical Cluster
18 The Rattle Package for Data Mining http: // togaware. com Copyright 2014, 19/40 Tour A Tour Thru Rattle: Decision Tree
19 The Rattle Package for Data Mining http: // togaware. com Copyright 2014, 20/40 Tour A Tour Thru Rattle: Decision Tree Plot
20 The Rattle Package for Data Mining http: // togaware. com Copyright 2014, 21/40 Tour A Tour Thru Rattle: Random Forest
21 The Rattle Package for Data Mining http: // togaware. com Copyright 2014, 22/40 Tour A Tour Thru Rattle: Risk Chart Risk Chart Random Forest weather.csv [test] RainTomorrow Risk Scores Lift Performance (%) % 1 RainTomorrow (92%) Rain in MM (97%) 0 Precision Caseload (%)
22 Moving Into R http: // togaware. com Copyright 2014, [email protected] 23/40 Overview 1 An Introduction to Data Mining 2 The Rattle Package for Data Mining 3 Moving Into R
23 Moving Into R Programming with Data http: // togaware. com Copyright 2014, [email protected] 24/40 Data Scientists are Programmers of Data But... Data scientists are programmers of data A GUI can only do so much R is a powerful statistical language Data Scientists Desire... Scripting Transparency Repeatability Sharing
24 Moving Into R Programming with Data http: // togaware. com Copyright 2014, [email protected] 25/40 From GUI to CLI Rattle s Log Tab
25 Moving Into R Programming with Data http: // togaware. com Copyright 2014, [email protected] 26/40 From GUI to CLI Rattle s Log Tab
26 Moving Into R Programming with Data http: // togaware. com Copyright 2014, [email protected] 27/40 Step 1: Load the Dataset dsname <- "weather" ds <- get(dsname) dim(ds) ## [1] names(ds) ## [1] "Date" "Location" "MinTemp" "... ## [5] "Rainfall" "Evaporation" "Sunshine" "... ## [9] "WindGustSpeed" "WindDir9am" "WindDir3pm" "... ## [13] "WindSpeed3pm" "Humidity9am" "Humidity3pm" "......
27 Moving Into R Programming with Data http: // togaware. com Copyright 2014, [email protected] 28/40 Step 2: Observe the Data Observations head(ds) ## Date Location MinTemp MaxTemp Rainfall Evapora... ## Canberra ## Canberra ## Canberra tail(ds) ## Date Location MinTemp MaxTemp Rainfall Evapo... ## Canberra ## Canberra ## Canberra
28 Moving Into R Programming with Data http: // togaware. com Copyright 2014, [email protected] 29/40 Step 2: Observe the Data Structure str(ds) ## 'data.frame': 366 obs. of 24 variables: ## $ Date : Date, format: " " " ## $ Location : Factor w/ 49 levels "Adelaide","Alba... ## $ MinTemp : num ## $ MaxTemp : num ## $ Rainfall : num ## $ Evaporation : num ## $ Sunshine : num ## $ WindGustDir : Ord.factor w/ 16 levels "N"<"NNE"<"N... ## $ WindGustSpeed: num ## $ WindDir9am : Ord.factor w/ 16 levels "N"<"NNE"<"N... ## $ WindDir3pm : Ord.factor w/ 16 levels "N"<"NNE"<"N......
29 Moving Into R Programming with Data http: // togaware. com Copyright 2014, [email protected] 30/40 Step 2: Observe the Data Summary 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.: ## Max. : BadgerysCreek: 0 Max. : ## (Other) : 0... ## Rainfall Evaporation Sunshine Wind... ## Min. : 0.00 Min. : 0.20 Min. : 0.00 NW... ## 1st Qu.: st Qu.: st Qu.: 5.95 NNW... ## Median : 0.00 Median : 4.20 Median : 8.60 E......
30 Moving Into R Programming with Data http: // togaware. com Copyright 2014, [email protected] 31/40 Step 2: Observe the Data Variables id <- c("date", "Location") target <- "RainTomorrow" risk <- "RISK_MM" (ignore <- union(id, 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" "......
31 Moving Into R Programming with Data http: // togaware. com Copyright 2014, [email protected] 32/40 Step 3: Clean the Data Remove Missing dim(ds) ## [1] sum(is.na(ds[vars])) ## [1] 47 ds <- ds[-attr(na.omit(ds[vars]), "na.action"),]
32 Moving Into R Programming with Data http: // togaware. com Copyright 2014, [email protected] 33/40 Step 3: Clean the Data Remove Missing dim(ds) ## [1] sum(is.na(ds[vars])) ## [1] 0
33 Moving Into R Programming with Data http: // togaware. com Copyright 2014, [email protected] 34/40 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")
34 Moving Into R Programming with Data http: // togaware. com Copyright 2014, [email protected] 35/40 Step 3: Clean the Data Target as Categoric summary(ds[target]) ## RainTomorrow ## 0:268 ## 1: count RainTomorrow
35 Moving Into R Programming with Data http: // togaware. com Copyright 2014, [email protected] 36/40 Step 4: Prepare for Modelling (form <- formula(paste(target, "~."))) ## RainTomorrow ~. (nobs <- nrow(ds)) ## [1] 328 train <- sample(nobs, 0.70*nobs) length(train) ## [1] 229 test <- setdiff(1:nobs, train) length(test) ## [1] 99
36 Moving Into R Programming with Data http: // togaware. com Copyright 2014, [email protected] 37/40 Step 5: Build the Model Random Forest library(randomforest) model <- randomforest(form, ds[train, vars], na.action=na.omit) model ## ## Call: ## randomforest(formula=form, data=ds[train, vars],... ## Type of random forest: classification ## Number of trees: 500 ## No. of variables tried at each split: 4...
37 Moving Into R Programming with Data http: // togaware. com Copyright 2014, [email protected] 38/40 Step 6: Evaluate the Model Risk Chart pr <- predict(model, ds[test,], type="prob")[,2] riskchart(pr, ds[test, target], ds[test, risk], title="random Forest - Risk Chart", risk=risk, recall=target, thresholds=c(0.35, 0.15)) Random Forest Risk Chart 100 Risk Scores Lift 5 Performance (%) RainTomorrow (98%) RISK_MM (97%) Precision Caseload (%) 19%
38 Moving Into R Resources http: // togaware. com Copyright 2014, [email protected] 39/40 Resources and References OnePageR: Tutorial Notes Rattle: 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)
39 Summary Starting Data Science with Rattle and R http: // togaware. com Copyright 2014, [email protected] 40/40 Lecture Summary Data Science Analytics Data Mining; Rattle as a GUI for Quickly Analysing Data; The Power is with R. This document, sourced from StartL.Rnw revision 430, was processed by KnitR version 1.6 of and took 3.6 seconds to process. It was generated by gjw on nyx running Ubuntu LTS with Intel(R) Xeon(R) CPU 2.67GHz having 4 cores and 12.3GB of RAM. It completed the processing :16:29.
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