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<h1>Practical Machine Learning Project writeup</h1>
<h2>Synopsis</h2>
<p>This reports contains the writeup of the project for the coursera course: <em>practinal machine learning</em>. It consistist on developing a predictive algorithm for the <em>Weight Lifting Exercise Dataset</em>, which has information from seonsors on people while performing barbell lift correctly and incorrectly in 5 different ways. In order to achieve this, several steps are followed:</p>
<ol>
<li>First the data is loaded. </li>
<li>Then some preprocessing is done to the data in order to find the appropiate type for each variable and eliminate variables that don't contain usufull information.</li>
<li>The data is splitted into 10 folds, and the algorithm is tested in each case. This way some conclusions are extracted about the accuracy of the algorithm.</li>
<li>Finally the model is trained on the whole train data and the test outcomes are predicted</li>
</ol>
<h2>Load data</h2>
<pre><code class="r">setwd("C:/Users/miguel.picallo.cruz/Documents/personal/coursera/JH data science/practical ML")
train = read.csv("pml-training.csv")
test = read.csv("pml-testing.csv")
</code></pre>
<h2>Preprocess data</h2>
<h3>Put appropiate types:</h3>
<p>Count different elements in each variable, if too many (10 in this case), then it should be a numeric variable.</p>
<pre><code class="r">countElem = apply(train, 2, function(x) {
length(unique(x))
})
for (i in 1:(ncol(train) - 1)) {
if (countElem[i] > 10) {
train[, i] = as.numeric(train[, i])
test[, i] = as.numeric(test[, i])
}
}
</code></pre>
<h3>Eliminate variables with too much missing information:</h3>
<p>Check which variables have missing data and how much (in %). </p>
<pre><code class="r">countNA = data.frame(train = apply(train, 2, function(x) {
sum(is.na(x))/length(x) * 100
}), test = apply(test, 2, function(x) {
sum(is.na(x))/length(x)
} * 100))
# Either 0% or 100% NAs:
unique(countNA$test)
</code></pre>
<pre><code>## [1] 0 100
</code></pre>
<pre><code class="r"># 0% NAs for train variables where test variables NAs is 0% NAs:
sum(countNA$test == 0 & countNA$train > 0)
</code></pre>
<pre><code>## [1] 0
</code></pre>
<pre><code class="r">plot(countNA$train, col = "blue", main = "% of NAs in each data set (blue for train, red for test)",
xlab = "variable", type = "b", ylab = "%")
lines(countNA$test, col = "red", type = "b")
</code></pre>
<p><img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAfgAAAH4CAMAAACR9g9NAAAAolBMVEX9/v0AAAAAADkAAGUAAP8AOTkAOWUAOY8AZo8AZrU5AAA5ADk5AGU5OWU5OY85j485j9plAABlADllAGVlOQBlOY9lZgBlZjllZmVltbVltf2POQCPOTmPOWWPtY+P27WP29qP2/21ZgC1Zjm1jzm1tWW124+1/rW1/tq1/v3ajznatWXa24/a/rXa/tra/v39tWX924/9/rX9/tr9/v3/AAB4o7uLAAAANnRSTlP/////////////////////////////////////////////////////////////////////AP8fqVzOAAAACXBIWXMAAAsSAAALEgHS3X78AAATCUlEQVR4nO2dCXvqutVG65z0S0nvd0rO7ZicDqFjaHsDwfz/v1ZLtmQGa2sjJoe93tuG4D3olRaezhPgZzUyqZ9d2wC6jgBvVIA3KsAbFeCNCvBGBXijArxRAd6oAG9UgDcqwBsV4I0K8EYFeKMCvFEB3qgAb1SANyrAGxXgjQrwRgV4owK8UQHeqM4BfvlYTet69jyw/e61efx4qprY6uXLm/tZTXbSPp5cQKO2xXbtw3v/bN/CrmLG/O41dhMMhCmkG/VOlLOIlvW9w9bl41Q1xJDOAX728Lcvb8sf9uY9q9oZNOCbubbr3MxWi3lfGfBhPMFpyPh4mtQa8KmW+aGSipYP6+23zsrX7jzg/333Ou9fjPOqag4Bzb5dVX6ODXj/3LmeV7/382peANXGujfBv1f9S8J1cFmL5tHtFa5XE2yy/tG/cNzGP7hVbLP8eJNYUm8O0vbrMtoB3FM3ZlPfGXjrmITB6ziFdkLN07vfRTvVpHvaDTgwi74itIyWc717F+0c4uSKd/lz7fH//BaPWrOqatelB//lt93SenST9rXQRbsla4s2O0xjVhvdyeqePLx3WT/5tdlu3P3e9evBN8v8utlgE3wYvI5wugn1L+UA3j3979Nmk81Z7DaY9iNme+8uQTu5nTPbQTrXOX4+6Z/513/zoz/UN7SnfmmXj5PusZ9Bt2TT5vXcbvTBcCB20eVj83vTzWeFUr9xvvHi2RzvLfbZ6tdn+C1+Ye9et8BvDe4L4oQ2rk9m7QFk0s9gbxZObUpsuWVZ7L1Rsnli2D/XqXWmq/rVy/Osm9DCz8AdSzdBzO7+8uJn/ez/71/IW4d6vwChg5c78Lm8L2/hCLeTNQknzDarG6970v3mfu37BUcbdJtNW+A3Bq/b5Y4T8sPXcXt42g64N4touB9/w3KmdyyJC9WBH74cVOhM4BcP//nln5/8qg6D/3j6xcvmsdCduqqwcFun2I2F8kkefDi6b2V1qxiyul2l6l9S3SBnBR8G3JtFa/hY8HGhWuuzsYGfTRd3f37y++Xgod7t65U7cFUbXLr43pLFw9vcH9njIXK6k9UdN0OW7xefRGfVc39e2QG/daj3xd2hfrN683A8AD4MKICPLcVD/VbvfRfj3OObe7ll2OPDxV29Db5Z2S9vc3/M9iz6F8D+ks26a6BFtXlRt5MVr5RCliubLDZeWRuHgv4qb9IO2YLvr8u2csIFWLiJihdgG3D8VX3cnXfAh6uMriK03Ly4k3vHks05TMZ3jp97zOFF2t2h7FxsLaov/3rxW+bNFNyEuqkO7Cuzbnmax+m8Wx9/Hb6Z5ZbxV911uM9qevrL5LakrvtBQj+f4Qdou/W3c/5M3d5pzXo23RT6W64AxzX6KVJ1A0rgY8toOdf7fdNy1Z1SXN9RXdV/Qi3K/wFGpeXXUkCiRnYf/wnl/uXunO2/lZ6KZY3sX+4+o+bFV0lX1Nj+rR59AgHeqABvVIA3KsAbFeCNCvBGBXijArxRAd6oAG9UgDcqwBsV4I0K8EYFeKMCvFHlwW//CTS6EWXBr17aP0NcFP89JxqjsuA/fnzbekS3oWP2+AqNWUeC796YO3SOz7RGV9XR4Itbo6sK8EZ1NPj07ZwW/PoYlfYud3JA7qHjH2ajqHfUseClizuVgbqdgp9G9yP1+0CwlmcZeg90UWanKrvRM+YOG39oQVJD1MlgnVmTTseCH7idU143BgFeWpDRgmePNwr+BLdz62NU2rvcyQG5t3yOL2+NrirAG9X4wN87dQ/34aF73JemWUGdUCykbrs9enxxQY7tPTrw9+1/fqbuWfi1nXJ86H6v5Vm267VdFCrVThSVQ27r+yPHFxekTpjrgnkdfTv31N277V/dqcFvXY0cBH6dBb8eXPhU3aCTROV6K3UA/Prw8dUL0vYeBJ9bk05H7/Grl9TnbQD+psGnP7mnDPxB5/h19hy/PqhuwEmycr2fuu12ffj46gUp7x00hnO87sazrDJxw36Cftnc9eHjH2ajqHcQ4Ev7Ab6wdS/Al9kA/MEZgB8j+O4mt7shDfelg5csZwY/MGYK/J7b+9OBH1gQwB/YO1MH+CDAJzMAX9i6F+BzNgAvV6ozAA942YmQAfjC1r0An7MBeLlSnQF4wMtOhAzAF7buBficDcDLleoMwANediJkAL6wdS/A52wAXq5UZwAe8LITIQPwha17AT5nA/BypToD8ICXnQgZgC9s3QvwORuAlyvVGYAHvOxEyAB8YetegM/ZALxcqc4APOBlJ0IG4Atb9wJ8zgbg5Up1BuABLzsRMgBf2LoX4HM2AC9XqjMAD3jZiZAB+MLWvQCfswF4uVKdAXjAy06EDMAXtu4F+JwNwMuV6gzAA152ImQAvrB1L8DnbABerlRnAB7wshMhA/CFrXsBPmcD8HKlOgPwgJedCBmAL2zdC/A5G4CXK9UZgAe87ETIAHxh616Az9kAvFypzgA84GUnQgbgC1v3AnzOBuDlSnUG4I8Hv3w89tukjwB/n53l2cDvp+6C9ylnAl/eO+hY8Md/fzzg8zZGCP7jx7etR33rXoDP2RghePb4mGoLfPhWYc7x1sAXt+6lAT/I/czgh8bUgT8SzuANw/jBV0HaAsUaJsDnZ3kb4E/YO+gUt3OT+YkP9YDftTE+8Kvvr/V80vD/esKLO8Dv2hgfeHcbN5+e+HYO8Ls2xgeePT7m2gLvzvFTzvEGwRe37gX4rA3Ay6XaBMADXtgO+DIB/uDxkzYAL5dqEwAPeGE74MsE+IPHT9oAvFyqTQA84IXtgC8T4A8eP2kD8HKpNgHwgBe2A75MgD94/KQNwMul2gTAA17YDvgyAf7g8ZM2bhL8rtNTgl8P1yX+LHsfZrpSB/6w8dM2TtY7CvDpboAvE+ABLwrweRuAz9TuxQGf0PjAb8yx3n4bXb5WHS8GXw+DH3DrP7bisPHTNvYWpLR3FOBlJ8kcwJe2jgJ83gbg5Vp1HPCAl50kcwBf2joK8HkbgJdr1XHAA152kswBfGnrKMDnbQBerlXHAQ942UkyB/ClraMAn7cBeLlWHQc84GUnyRzAl7aOAnzeBuDlWnUc8ICXnSRzAF/aOgrweRuAl2vVccADXnaSzAF8aesowOdtAF6uVccBD3jZSTIH8KWtowCftwF4uVYdBzzgZSfJHMCXto4CfN4G4OVadRzwgJedJHMAX9o6CvB5G4CXa9VxwANedpLMAXxp6yjA520AXq5VxwEPeNlJMgfwpa2jAJ+3AXi5Vh0HPOBlJ8kc8+CXj5XTEd8tC/i8jfGBX708+8fFQ/G3SQM+b2N84MP3xh/x/fGAz9sYH3j2+D7VFPj644lzvEnwxa2jAJ+38SnAV0HKfMDnbYwQ/PLxefVSVfunePb4mwbvLu5mzfXd8isXd6bAN7dxq++v3M61PwyBd7v7Ytrczk0ObR0F+LyNEYKvZ/5Cbp874G8cfGnrKMDnbQBerlXHAQ942UkyB/ClraMAn7cBeLlWHQc84GUnyRzAl7aOAnzeBuDlWnUc8ICXnSRzAF/aOgrweRuAl2vVccADXnaSzAF8aesowOdtAF6uVccBD3jZSTIH8KWtowCftwF4uVYdBzzgZSfJHMCXto4CfN4G4OVadRzwgJedJHMAX9o6CvB5G4CXa9VxwANedpLMAXxp6yjA520AXq5VxwEPeNlJMgfwpa2jAJ+3cT3w7qNunlUdta2jAJ+3cT3w8+d69cf9Dzg6onXUMeDvM7M8G/ih1F3wTdLZwJf2jlKA//j2CvjdHAvg69VLNR3poR7wB/aOUh7q50OffHBc6yjNPBPczwp+eEwN+CPhDF4+XAP8wn1+4WLog62OaR2lWcMU+NwsbwX8yXpHac7xv3n3H3Oz/OFi53jA79q4JvhDBfhPDr491Ku6HdI6CvAKG1cBXyjAA14U4BU2AC8Xa8OAB3xyK+ALBXjAiwK8wgbg5WJtGPCAT24FfKEAD3hRgFfYALxcrA0DHvDJrYAvFOABLwrwChuAl4u1YcADPrkV8IUCPOBFAV5hA/BysTYMeMAntwK+UIC/afDLR/8VowN/hgv4WwbvvkbcabH/PhvA3zL48PXhp/0accDv2hgdePb4PtcUeP9RGZzjDYIvbh0FeIWNzwC+ClLmA15hY6TgB99BDfhbBt+d4odO8oC/ZfAN+QY5e3z7wxJ4h/7hP4Cv7YF3/2w79MEJgL958EWtowCvsAF4uVgbBjzgk1sBXyjAA14U4BU2AC8Xa8OAB3xyK+ALBXjAiwK8wgbg5WJtGPCAT24FfKEAD3hRgFfYALxcrA0DHvDJrYAvFOABLwrwChuAl4u1YcADPrkV8IUCPOBFAV5hA/BysTYMeMAntwK+UIAHvCjAK2wAXi7WhgEP+ORWwBcK8IAXBXiFDcDLxdow4AGf3Ar4QgEe8KIAr7ABeLlYGwY84JNbAV8owANeFOAVNgAvF2vDgAd8civgCwV4wIsCvMIG4OVibRjwgE9uBXyhlOB3fZ4S/DpVdz9UNwQzVakDf9j4ko0T9e4F+GQvwBeqDHw3z/vNZ/FptlodLQZfD4EfdHvfwClwpluQwt69AJ9xksgCfHHrIMBrbABerlZHAS+HAZ/IAnxx6yDAa2wAXq5WRwEvhwGfyAJ8cesgwGtsAF6uVkcBL4cBn8gCfHHrIMBrbABerlZHAS+H8+CXj4kvkwb8TYNfvTz7x8XD+4GtgwCvsTE68B8/vm096lsHAV5jY3Tg2eM3Ui2Brz+eOMebBF/cOgjwGhufAXwVpEsHvMbG+MA3t3N3r1zcbT1YAO8u7lYvU8BvPlgA3wKfTQBf2wLf3c7Nf/4D4E2Bb27npu5hvn8/B/ibBl/cOgjwGhuA35H8fqGzgR9O3QV/4PiSjT3wZb17fRLwyerzgU+NqQAfH04Afuelf+vghx8U5drgZwN/ot69AJ/aBvhSAR7wkgCvsgF4uVwbBLwcBvxwFuDLWwcBXmUD8HK5Ngh4OQz44SzAl7cOArzKBuDlcm0Q8HIY8MNZgC9vHQR4lQ3Ay+XaIODlMOCHswBf3joI8CobgJfLtUHAy2HAD2cBvrx1EOBVNgAvl2uDgJfDgB/OAnx56yDAq2wAXi7XBgEvhwE/nAX48tZBgFfZALxcrg0CXg4DfjgL8OWtgwCvsgF4uVwbBLwcBvxwFuDLWwcBXmUD8HK5Ngh4OQz44SzAl7cOArzKBuDlcm0Q8HIY8MNZgC9vHQR4lQ3Ay+XaIODlMOCHswBf3joI8CobgJfLtUHAy2HAD2cBvrx1EOBVNgAvl2uDgJfDgB/OAnx56yDAq2wAXi7XBgEvhwE/nAX48tZBgFfZALxcrg0CXg4DfjgL8OWtgwCvsgF4uVwbBLwcBvxwFuDLWwcBXmXj9sCjqwjwRgV4owK8UR0NfvlYOe1/i/hO63Va99ty2YMPQxLaeh1Wtm8kWakxffD4aRun6L2VdSz41cuzf1w8vO+Gtlqv3X9u8PbH9u9uOvfdD3/F6ma39zA4Samtm+rw6qz98uwW3XdOOi+DY4bK+3WfmDK9Tl57D48/tCCdjYLembU4FvzHj29bj65l0LYdwOfG/1Tg2eONgq8/njjHWzzHC+KqfswCvFEB3qgAb1SANyrAGxXgjQrwRgV4owK8UQHeqM4JHo1Z5wO/9SI4UR8GvtDAgDc6MOCNDgx4owMD3ujAgDc6MOCNDnwq8OiTCfBGBXijArxRAd6oAG9UgDcqwBsV4I0K8EZ1EvAfT9X+m6jPrnnl38N76cGXP7zFGV90bD/wySZ9CvDuLfTzyQkaHabZ8zUGX7h17wa96Nh+4NNN+hTg3Ydl+JfjRbX6/nqFwWd3f2oG6wa95NjtwKeb9CnAL7++1x/fXk/Q6RD5D2x4vvzgbsG7QS87thv4dJM+BXj3KSmXB7/85avbAS4+uFv/btDLju1fcSeb9Ofd471mz7b2eK+TTPrznuO9Zs8XH3x5nXP8FviRnONXL9MrXNW7w93qj28XH9wteDfoZccO55jTTPpz38ffvV5+8Kvfx59m0vzLnVEB3qgAb1SANyrAGxXgjQrwRgV4owK8UQHeqABvVIA3KsAbFeCNCvBGBXijArxRAd6oAG9UgDcqwNf15l8qt+9Nu9Ifi19SgN8W4G9Y7n0oq++vy0f/NrT///WXf7g3x/hn9ez3fmP7NrWBL1K+GVkE795yuvz632+vDvDy0XP+aJ/Vs4d3t9H9do13iVxOFsHX86n7X+3f/OXfCtce2d0bk9yhfvbcvRLchluVSfDLrz+595nP3IdLRPD+Wf3X1wDevSH57irvBL2ITIJfff/T1/ePJ3+M78B3z9wev3p57t4QecsyCb6eV9PwdvMOfHjz+WxS9+f4xRXeEXgp2QTvELv3H/7fr5/Dob595q7q717DVf0NH+mNgkeAtyrAGxXgjQrwRgV4owK8UQHeqABvVIA3KsAbFeCNCvBGBXijArxRAd6oAG9UgDeq/wFkxp+7WhEJ6AAAAABJRU5ErkJggg==" alt="plot of chunk unnamed-chunk-3"/> </p>
<p>It can be observed that test has some of its variable with all variables NAs, those variables can be eliminated. The rest of the variables have for test and train 0% of missing data.</p>
<pre><code class="r">NAs.test = which(countNA$test != 0)
out = NAs.test
train = train[, -out]
test = test[, -out]
</code></pre>
<h2>Data splitted using k-fold and model's performance is tested</h2>
<h3>Split Data:</h3>
<p>Split the data for k-fols cross-validation and initialize predictions:</p>
<pre><code class="r">set.seed(1)
library(caret)
</code></pre>
<pre><code>## Loading required package: lattice
## Loading required package: ggplot2
</code></pre>
<pre><code class="r">folds = createFolds(y = train$classe, k = 10, list = T, returnTrain = T)
pred = factor(rep("A", nrow(train)), levels = levels(train$classe))
</code></pre>
<h3>k-folds predictions:</h3>
<p>Use predictive algorithm random forest to train model and predict outcome for the k-folds:</p>
<pre><code class="r">library(randomForest)
</code></pre>
<pre><code>## randomForest 4.6-7
## Type rfNews() to see new features/changes/bug fixes.
</code></pre>
<pre><code class="r">for (i in 1:length(folds)) {
print(i) # print in which k-fols is the for loop
model = randomForest(classe ~ ., data = train[folds[[i]], -1])
pred[-folds[[i]]] = predict(model, train[-folds[[i]], -1])
}
</code></pre>
<pre><code>## [1] 1
## [1] 2
## [1] 3
## [1] 4
## [1] 5
## [1] 6
## [1] 7
## [1] 8
## [1] 9
## [1] 10
</code></pre>
<h3>Estimate out of sample missclassification error:</h3>
<p>Out of sample missclassification error of whole data set:</p>
<pre><code class="r">missClass = sum(pred != train$classe)/nrow(train) * 100
missClass
</code></pre>
<pre><code>## [1] 0.07644
</code></pre>
<p>Compute mean and standard deviation of each missclassification error of the k-fold:</p>
<pre><code class="r">missClassi = c()
for (i in 1:length(folds)) {
missClassi = c(missClassi, sum(pred[-folds[[i]]] != train$classe[-folds[[i]]])/(nrow(train) -
length(folds[[i]])) * 100)
}
mu = mean(missClassi)
sdev = sd(missClassi)
# mean:
mu
</code></pre>
<pre><code>## [1] 0.07644
</code></pre>
<pre><code class="r"># standard deviation:
sdev
</code></pre>
<pre><code>## [1] 0.02686
</code></pre>
<pre><code class="r"># 95% confident internal for missClassification error:
mu - 2 * sdev
</code></pre>
<pre><code>## [1] 0.02272
</code></pre>
<pre><code class="r">mu + 2 * sdev
</code></pre>
<pre><code>## [1] 0.1302
</code></pre>
<p>It can be observed that at most missclassification rate is expected to be less than 0.15% with 95% probability.</p>
<h2>Final training and testing</h2>
<h3>Train with whole data and test:</h3>
<pre><code class="r">test$new_window = factor(test$new_window, levels = c("no", "yes"))
model = randomForest(classe ~ ., data = train[, -1])
answers = predict(model, test[, -c(1, ncol(test))])
</code></pre>
<h3>Write down answers for submission:</h3>
<pre><code class="r">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)
}
}
pml_write_files(answers)
</code></pre>
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