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<h1>Human Activity Recognition Analysis – Using R</h1>
<h1></h1>
<h2>Brief summary</h2>
<p>In this analysis, we try to apply some machine learning algorithms on the Human Activity Recognition dataset provided by Groupware. Out of 4 ML algorithms we apply, which is bagging with classification trees, neural network, logistic regression, and support vector machines, bagging with classification trees yields the highest accuracy rate, over 95%. All the model except neural network are ensembled to give a better prediction.</p>
<p>The final outcome is pretty amazing, with 95% accuracy rate on the 20 testing data point.</p>
<h2>Data preprocessing</h2>
<p>Firstly we load the data into memory.</p>
<pre><code class="r">train <- read.csv("pml-training.csv", stringsAsFactors = FALSE)
validation <- read.csv("pml-testing.csv")
</code></pre>
<p>Secondly we should preprocess the data, there's some blank cells, NA, NaN, and Inf. These data point actually should be 0, thus enabling us to analyze the data.</p>
<pre><code class="r">temp <- train[, c(1:7, 160)]
temp.a <- train[, -c(1:7, 160)]
for (i in 1:ncol(temp.a)) {
if (class(temp.a[, i]) == "character") {
suppressWarnings(temp.a[, i] <- as.numeric(temp.a[, i]))
}
}
train <- cbind(temp, temp.a)
train[is.na(train)] <- 0
train[is.nan(unlist(train))] <- 0
train[is.infinite(unlist(train))] <- 0
</code></pre>
<p>Not only train data, but we have to preprocess the testing data in order to have correct output</p>
<pre><code class="r">temp <- validation[, c(1:7, 160)]
temp.a <- validation[, -c(1:7, 160)]
for (i in 1:ncol(temp.a)) {
if (class(temp.a[, i]) == "character") {
temp.a[, i] <- as.numeric(temp.a[, i])
}
}
validation <- cbind(temp, temp.a)
validation[is.na(validation)] <- 0
validation[is.nan(unlist(validation))] <- 0
validation[is.infinite(unlist(validation))] <- 0
</code></pre>
<p>Now we can start to analyze our data.</p>
<h2>Data Analysis</h2>
<h3>Step1: Split the data into training & testing data</h3>
<p>Notice that the ratio of training data to testing data is 0.2, not typical 0.75, that's because my old-fashioned Mac cannot train data on large scale.</p>
<pre><code class="r">library(caret)
</code></pre>
<pre><code>## Loading required package: lattice
## Loading required package: ggplot2
</code></pre>
<pre><code class="r">set.seed(32323)
inTrain <- createDataPartition(y = train$classe, p = 0.2, list = FALSE)
training <- train[inTrain, ]
testing <- train[-inTrain, ]
p <- training[, -1:-7]
p[, 1] <- as.factor(p[, 1])
</code></pre>
<p>Before applying machine learning algorithms to train our model, let us first tune the cross-validation parameters. We expect our out-of-sample error to be low because 5-fold CV should take its effect and avoid overfitting.</p>
<pre><code class="r">fitControl <- trainControl(method = "cv", number = 5, returnResamp = "all")
</code></pre>
<h3>Step2: Train models with the training data</h3>
<p>Now we can train our model by applying some machine learning techniques, first we use “Bagging with trees”.</p>
<pre><code class="r">set.seed(32323)
model.treebag <- train(classe ~ ., method = "treebag", data = p, trControl = fitControl)
</code></pre>
<pre><code>## Loading required package: ipred
## Loading required package: plyr
</code></pre>
<pre><code class="r">result2 <- predict(model.treebag, newdata = testing)
confusionMatrix(result2, testing$classe)
</code></pre>
<pre><code>## Confusion Matrix and Statistics
##
## Reference
## Prediction A B C D E
## A 4375 113 6 3 6
## B 52 2846 57 9 22
## C 31 63 2641 61 25
## D 1 3 28 2498 25
## E 5 12 5 1 2807
##
## Overall Statistics
##
## Accuracy : 0.966
## 95% CI : (0.963, 0.969)
## No Information Rate : 0.284
## P-Value [Acc > NIR] : < 2e-16
##
## Kappa : 0.957
## Mcnemar's Test P-Value : 6.84e-16
##
## Statistics by Class:
##
## Class: A Class: B Class: C Class: D Class: E
## Sensitivity 0.980 0.937 0.965 0.971 0.973
## Specificity 0.989 0.989 0.986 0.996 0.998
## Pos Pred Value 0.972 0.953 0.936 0.978 0.992
## Neg Pred Value 0.992 0.985 0.993 0.994 0.994
## Prevalence 0.284 0.194 0.174 0.164 0.184
## Detection Rate 0.279 0.181 0.168 0.159 0.179
## Detection Prevalence 0.287 0.190 0.180 0.163 0.180
## Balanced Accuracy 0.984 0.963 0.976 0.983 0.986
</code></pre>
<p>We can see this model gives very high accuracy rate, 96.64%. In fact this one is sufficient for predicting our testing data, but in order to improve our prediction ability, we should try some more models and mix them up.</p>
<p>Let us apply “logistic regression with boosting”. Boosting is required in order to make our model better at prediction and lower the variance.</p>
<pre><code class="r">set.seed(32323)
model.logit <- train(classe ~ ., method = "LogitBoost", data = p, trControl = fitControl)
</code></pre>
<pre><code>## Loading required package: caTools
</code></pre>
<pre><code class="r">result3 <- predict(model.logit, newdata = testing)
confusionMatrix(result3, testing$classe)
</code></pre>
<pre><code>## Confusion Matrix and Statistics
##
## Reference
## Prediction A B C D E
## A 3958 283 62 100 18
## B 69 1916 130 35 43
## C 50 159 1809 121 53
## D 25 32 98 1743 58
## E 17 62 44 101 2396
##
## Overall Statistics
##
## Accuracy : 0.883
## 95% CI : (0.878, 0.889)
## No Information Rate : 0.308
## P-Value [Acc > NIR] : <2e-16
##
## Kappa : 0.851
## Mcnemar's Test P-Value : <2e-16
##
## Statistics by Class:
##
## Class: A Class: B Class: C Class: D Class: E
## Sensitivity 0.961 0.781 0.844 0.830 0.933
## Specificity 0.950 0.975 0.966 0.981 0.979
## Pos Pred Value 0.895 0.874 0.825 0.891 0.915
## Neg Pred Value 0.982 0.952 0.970 0.969 0.984
## Prevalence 0.308 0.183 0.160 0.157 0.192
## Detection Rate 0.296 0.143 0.135 0.130 0.179
## Detection Prevalence 0.330 0.164 0.164 0.146 0.196
## Balanced Accuracy 0.955 0.878 0.905 0.906 0.956
</code></pre>
<p>This model gives 88.34% accuracy rate, a very efficient model as well.</p>
<p>Now let's try support vector machines with boosting.</p>
<pre><code class="r">library(kernlab)
set.seed(32323)
model.svm <- suppressWarnings(train(classe ~ ., data = p, method = "svmRadialCost",
trControl = fitControl))
result4 <- predict(model.svm, newdata = testing)
</code></pre>
<pre><code class="r">confusionMatrix(result4, testing$classe)
</code></pre>
<pre><code>## Confusion Matrix and Statistics
##
## Reference
## Prediction A B C D E
## A 3951 380 155 117 125
## B 100 2153 254 111 339
## C 174 274 2003 337 271
## D 211 91 251 1920 196
## E 28 139 74 87 1954
##
## Overall Statistics
##
## Accuracy : 0.763
## 95% CI : (0.757, 0.77)
## No Information Rate : 0.284
## P-Value [Acc > NIR] : <2e-16
##
## Kappa : 0.7
## Mcnemar's Test P-Value : <2e-16
##
## Statistics by Class:
##
## Class: A Class: B Class: C Class: D Class: E
## Sensitivity 0.885 0.709 0.732 0.747 0.677
## Specificity 0.931 0.936 0.919 0.943 0.974
## Pos Pred Value 0.836 0.728 0.655 0.719 0.856
## Neg Pred Value 0.953 0.931 0.942 0.950 0.931
## Prevalence 0.284 0.194 0.174 0.164 0.184
## Detection Rate 0.252 0.137 0.128 0.122 0.124
## Detection Prevalence 0.301 0.188 0.195 0.170 0.145
## Balanced Accuracy 0.908 0.823 0.825 0.845 0.826
</code></pre>
<p>This model gives 76.34% accuracy rate.
All models are evalued, now we turn to ensemble them.</p>
<h3>Step3: Ensemble learning algorithm & predict</h3>
<pre><code class="r">pred1 <- predict(model.treebag, validation)
pred2 <- predict(model.logit, validation)
pred3 <- predict(model.svm, validation)
ensemble <- data.frame(pred1, pred2, pred3)
ensemble
</code></pre>
<pre><code>## pred1 pred2 pred3
## 1 B D C
## 2 A A A
## 3 B <NA> C
## 4 A <NA> A
## 5 A A A
## 6 E E C
## 7 D D D
## 8 B D B
## 9 A A A
## 10 A A A
## 11 B <NA> A
## 12 C C C
## 13 B B E
## 14 A A A
## 15 E E E
## 16 E E E
## 17 A A A
## 18 B B B
## 19 B A B
## 20 B B B
</code></pre>
<p>Here we can see the final outcome, remember pred1 has much higher accuracy than any other predictors.</p>
<p>This table gives the rules to pick out that final answer:
1. If all 3 models give the same answer, that's definitely the correct answer.
2. If not, compare the results, if two of them gives the same answer, pick that one.
3. If all three give different answers, follow the one pred1 gives.</p>
<h2>Results</h2>
<p>So here we get the final answers, which reportedly 95% accuracy rate on the final outcome.</p>
<pre><code class="r">answers = c("B", "A", "B", "A", "A", "E", "D", "B", "A", "A", "A", "C", "B",
"A", "E", "E", "A", "B", "B", "B")
pml_write_files = function(x) {
n = length(x)
for (i in 1:n) {
filename = paste0("problem_id_", i, ".txt")
write.table(x[i], file = filename, quote = FALSE, row.names = FALSE,
col.names = FALSE)
}
}
setwd("answer")
pml_write_files(answers)
</code></pre>
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