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Project</h1>\r\n\r\n<p><strong>Weight Lifting Classe Prediction</strong></p>\r\n\r\n<p><em>Yu Fang, 06/2014</em></p>\r\n\r\n<ul>\r\n<li>Initialization</li>\r\n</ul>\r\n\r\n<pre><code class=\"r\">library(caret)\r\n</code></pre>\r\n\r\n<pre><code>## Loading required package: lattice\r\n## Loading required package: ggplot2\r\n</code></pre>\r\n\r\n<ul>\r\n<li>Read data from csv files</li>\r\n</ul>\r\n\r\n<pre><code class=\"r\">training=data.frame(read.csv(file="/Volumes/WATERMELON/Study/practicalMachinLearning/pml-training.csv",head=TRUE,sep=","))\r\ntesting=data.frame(read.csv(file="/Volumes/WATERMELON/Study/practicalMachinLearning/pml-testing.csv",head=TRUE,sep=","))\r\n</code></pre>\r\n\r\n<ul>\r\n<li>Data cleaning and preprocessing</li>\r\n</ul>\r\n\r\n<pre><code class=\"r\">#Exclude the columns containing blank or NA, this step leaves 60 columns of each data set.\r\ntraining[training==""]=NA\r\ntraining=training[,colSums(is.na(training))==0]\r\ntesting[testing==""]=NA\r\ntesting=testing[,colSums(is.na(testing))==0]\r\n</code></pre>\r\n\r\n<pre><code class=\"r\">#Use "nearZeroVar" function to detect zero covariates. \r\nnsv=nearZeroVar(training,saveMetrics=TRUE)\r\n</code></pre>\r\n\r\n<pre><code class=\"r\">#For variable "new_window", nzv=TRUE. In training set, "new_window" has 19216 "no" and 406 "yes"; in testing set, "new_window" are all "no". So remove samples with new_window=="yes" in training set.\r\nuseTrain=training[ which(training$new_window=="no"),]\r\n</code></pre>\r\n\r\n<pre><code class=\"r\">#Exclude some other variables that obviously not relevant to class prediction:"X","cvtd_timestamp","raw_timestamp_part_1","raw_timestamp_part_2", and also from last section, "new_window"\r\nexcludeVars=names(useTrain) %in% c("X","cvtd_timestamp","raw_timestamp_part_1","raw_timestamp_part_2","new_window")\r\nuseTrain=useTrain[!excludeVars]\r\nuseTest=testing[!excludeVars]\r\n</code></pre>\r\n\r\n<pre><code class=\"r\">#Considering there might be variance between subjects, dummy code the "user_name"\r\ndummies1=dummyVars(classe ~ user_name,data=useTrain)\r\ndummycols1=predict(dummies1,newdata=useTrain)\r\nuseTrain=cbind(dummycols1,useTrain)\r\n\r\ndummies2=dummyVars(problem_id ~ user_name,data=useTest)\r\ndummycols2=predict(dummies2,newdata=useTest)\r\nuseTest=cbind(dummycols2,useTest)\r\n\r\nexcludeVar=names(useTrain) %in% c("user_name")\r\nuseTrain=useTrain[!excludeVar]\r\nuseTest=useTest[!excludeVar]\r\n</code></pre>\r\n\r\n<pre><code class=\"r\">#Standardize the data, and use PCA to further pick the principle components explaining 95% of the variance in the predictors. \r\npreProc=preProcess(useTrain[,-60],method=c("center","scale","pca"),thresh=0.95)\r\ntrainPC=predict(preProc,useTrain[,-60])\r\n# It results in 25 principle components.\r\npreProc\r\n</code></pre>\r\n\r\n<pre><code>## \r\n## Call:\r\n## preProcess.default(x = useTrain[, -60], method = c("center",\r\n## "scale", "pca"), thresh = 0.95)\r\n## \r\n## Created from 19216 samples and 59 variables\r\n## Pre-processing: centered, scaled, principal component signal extraction \r\n## \r\n## PCA needed 25 components to capture 95 percent of the variance\r\n</code></pre>\r\n\r\n<ul>\r\n<li>Fit the model, and do prediction. </li>\r\n</ul>\r\n\r\n<pre><code class=\"r\">#Make 10-fold cross validation\r\ntc=trainControl("cv",10,savePred=T)\r\n</code></pre>\r\n\r\n<pre><code class=\"r\">#As far as I understand, this case is to determine the quality of weight lifting by various measured parameters. So it is likely to be a tree-like selection rather than a linear regression model. Also higher accuracy is desired. Therefore random forest model is selected.\r\nrfModelFit=train(useTrain$classe ~ .,method="rf",data=trainPC,trControl=tc)\r\n</code></pre>\r\n\r\n<pre><code>## Loading required package: randomForest\r\n## randomForest 4.6-7\r\n## Type rfNews() to see new features/changes/bug fixes.\r\n</code></pre>\r\n\r\n<pre><code class=\"r\">#The fit model results summary\r\nrfModelFit\r\n</code></pre>\r\n\r\n<pre><code>## Random Forest \r\n## \r\n## 19216 samples\r\n## 24 predictors\r\n## 5 classes: 'A', 'B', 'C', 'D', 'E' \r\n## \r\n## No pre-processing\r\n## Resampling: Cross-Validated (10 fold) \r\n## \r\n## Summary of sample sizes: 17296, 17293, 17295, 17296, 17293, 17294, ... \r\n## \r\n## Resampling results across tuning parameters:\r\n## \r\n## mtry Accuracy Kappa Accuracy SD Kappa SD\r\n## 2 1 1 0.002 0.002 \r\n## 10 1 1 0.003 0.003 \r\n## 20 1 1 0.004 0.005 \r\n## \r\n## Accuracy was used to select the optimal model using the largest value.\r\n## The final value used for the model was mtry = 2.\r\n</code></pre>\r\n\r\n<ul>\r\n<li>Predict the testing data</li>\r\n</ul>\r\n\r\n<pre><code class=\"r\">testPC=predict(preProc,useTest[,-60])\r\npred=predict(rfModelFit,testPC)\r\n# The predict results on testing data\r\npred\r\n</code></pre>\r\n\r\n<pre><code>## [1] B A C A A E D B A A B C B A E E A B B B\r\n## Levels: A B C D E\r\n</code></pre>\r\n\r\n</body>\r\n\r\n</html>\r\n\r\n","google":"","note":"Don't delete this file! 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