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<li><strong><a href="./">Machine Learning with R</a></strong></li>
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<li class="chapter" data-level="1" data-path="index.html"><a href="index.html"><i class="fa fa-check"></i><b>1</b> Prerequisites</a><ul>
<li class="chapter" data-level="1.1" data-path="index.html"><a href="index.html#pre-requisite-and-conventions"><i class="fa fa-check"></i><b>1.1</b> Pre-requisite and conventions</a></li>
<li class="chapter" data-level="1.2" data-path="index.html"><a href="index.html#organization"><i class="fa fa-check"></i><b>1.2</b> Organization</a></li>
<li class="chapter" data-level="1.3" data-path="index.html"><a href="index.html#packages"><i class="fa fa-check"></i><b>1.3</b> Packages</a></li>
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<li class="chapter" data-level="2" data-path="testinference.html"><a href="testinference.html"><i class="fa fa-check"></i><b>2</b> Tests and inferences</a><ul>
<li class="chapter" data-level="2.1" data-path="testinference.html"><a href="testinference.html#normality"><i class="fa fa-check"></i><b>2.1</b> Assumption of normality</a><ul>
<li class="chapter" data-level="2.1.1" data-path="testinference.html"><a href="testinference.html#visual-check-of-normality"><i class="fa fa-check"></i><b>2.1.1</b> Visual check of normality</a></li>
<li class="chapter" data-level="2.1.2" data-path="testinference.html"><a href="testinference.html#normality-tests"><i class="fa fa-check"></i><b>2.1.2</b> Normality tests</a></li>
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<li class="chapter" data-level="2.2" data-path="testinference.html"><a href="testinference.html#ttest"><i class="fa fa-check"></i><b>2.2</b> T-tests</a></li>
<li class="chapter" data-level="2.3" data-path="testinference.html"><a href="testinference.html#anova---analyse-of-variance."><i class="fa fa-check"></i><b>2.3</b> ANOVA - Analyse of variance.</a></li>
<li class="chapter" data-level="2.4" data-path="testinference.html"><a href="testinference.html#covariance"><i class="fa fa-check"></i><b>2.4</b> Covariance</a></li>
</ul></li>
<li class="chapter" data-level="3" data-path="mlr.html"><a href="mlr.html"><i class="fa fa-check"></i><b>3</b> Single & Multiple Linear Regression</a><ul>
<li class="chapter" data-level="3.1" data-path="mlr.html"><a href="mlr.html#single-variable-regression"><i class="fa fa-check"></i><b>3.1</b> Single variable regression</a></li>
<li class="chapter" data-level="3.2" data-path="mlr.html"><a href="mlr.html#multi-variables-regression"><i class="fa fa-check"></i><b>3.2</b> Multi-variables regression</a><ul>
<li class="chapter" data-level="3.2.1" data-path="mlr.html"><a href="mlr.html#predicting-wine-price-again"><i class="fa fa-check"></i><b>3.2.1</b> Predicting wine price (again!)</a></li>
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<li class="chapter" data-level="3.3" data-path="mlr.html"><a href="mlr.html#model-diagnostic-and-evaluation"><i class="fa fa-check"></i><b>3.3</b> Model diagnostic and evaluation</a></li>
<li class="chapter" data-level="3.4" data-path="mlr.html"><a href="mlr.html#final-example---boston-dataset---with-backward-elimination"><i class="fa fa-check"></i><b>3.4</b> Final example - Boston dataset - with backward elimination</a><ul>
<li class="chapter" data-level="3.4.1" data-path="mlr.html"><a href="mlr.html#model-diagmostic"><i class="fa fa-check"></i><b>3.4.1</b> Model diagmostic</a></li>
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<li class="chapter" data-level="3.5" data-path="mlr.html"><a href="mlr.html#references"><i class="fa fa-check"></i><b>3.5</b> References</a></li>
</ul></li>
<li class="chapter" data-level="4" data-path="logistic.html"><a href="logistic.html"><i class="fa fa-check"></i><b>4</b> Logistic Regression</a><ul>
<li class="chapter" data-level="4.1" data-path="logistic.html"><a href="logistic.html#introduction"><i class="fa fa-check"></i><b>4.1</b> Introduction</a></li>
<li class="chapter" data-level="4.2" data-path="logistic.html"><a href="logistic.html#the-logistic-equation."><i class="fa fa-check"></i><b>4.2</b> The logistic equation.</a></li>
<li class="chapter" data-level="4.3" data-path="logistic.html"><a href="logistic.html#performance-of-logistic-regression-model"><i class="fa fa-check"></i><b>4.3</b> Performance of Logistic Regression Model</a></li>
<li class="chapter" data-level="4.4" data-path="logistic.html"><a href="logistic.html#setting-up"><i class="fa fa-check"></i><b>4.4</b> Setting up</a></li>
<li class="chapter" data-level="4.5" data-path="logistic.html"><a href="logistic.html#example-1---graduate-admission"><i class="fa fa-check"></i><b>4.5</b> Example 1 - Graduate Admission</a></li>
<li class="chapter" data-level="4.6" data-path="logistic.html"><a href="logistic.html#example-2---diabetes"><i class="fa fa-check"></i><b>4.6</b> Example 2 - Diabetes</a><ul>
<li class="chapter" data-level="4.6.1" data-path="logistic.html"><a href="logistic.html#accounting-for-missing-values"><i class="fa fa-check"></i><b>4.6.1</b> Accounting for missing values</a></li>
<li class="chapter" data-level="4.6.2" data-path="logistic.html"><a href="logistic.html#imputting-missing-values"><i class="fa fa-check"></i><b>4.6.2</b> Imputting Missing Values</a></li>
<li class="chapter" data-level="4.6.3" data-path="logistic.html"><a href="logistic.html#roc-and-auc"><i class="fa fa-check"></i><b>4.6.3</b> ROC and AUC</a></li>
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<li class="chapter" data-level="4.7" data-path="logistic.html"><a href="logistic.html#references-1"><i class="fa fa-check"></i><b>4.7</b> References</a></li>
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<li class="chapter" data-level="5" data-path="softmax-and-multinomial-regressions.html"><a href="softmax-and-multinomial-regressions.html"><i class="fa fa-check"></i><b>5</b> Softmax and multinomial regressions</a><ul>
<li class="chapter" data-level="5.1" data-path="softmax-and-multinomial-regressions.html"><a href="softmax-and-multinomial-regressions.html#multinomial-logistic-regression"><i class="fa fa-check"></i><b>5.1</b> Multinomial Logistic Regression</a></li>
<li class="chapter" data-level="5.2" data-path="softmax-and-multinomial-regressions.html"><a href="softmax-and-multinomial-regressions.html#references-2"><i class="fa fa-check"></i><b>5.2</b> References</a></li>
</ul></li>
<li class="chapter" data-level="6" data-path="gradient-descent.html"><a href="gradient-descent.html"><i class="fa fa-check"></i><b>6</b> Gradient Descent</a><ul>
<li class="chapter" data-level="6.1" data-path="gradient-descent.html"><a href="gradient-descent.html#example-on-functions"><i class="fa fa-check"></i><b>6.1</b> Example on functions</a></li>
<li class="chapter" data-level="6.2" data-path="gradient-descent.html"><a href="gradient-descent.html#example-on-regressions"><i class="fa fa-check"></i><b>6.2</b> Example on regressions</a></li>
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<li class="chapter" data-level="7" data-path="knnchapter.html"><a href="knnchapter.html"><i class="fa fa-check"></i><b>7</b> KNN - K Nearest Neighbour</a><ul>
<li class="chapter" data-level="7.1" data-path="knnchapter.html"><a href="knnchapter.html#example-1.-prostate-cancer-dataset"><i class="fa fa-check"></i><b>7.1</b> Example 1. Prostate Cancer dataset</a></li>
<li class="chapter" data-level="7.2" data-path="knnchapter.html"><a href="knnchapter.html#example-2.-wine-dataset"><i class="fa fa-check"></i><b>7.2</b> Example 2. Wine dataset</a><ul>
<li class="chapter" data-level="7.2.1" data-path="knnchapter.html"><a href="knnchapter.html#understand-the-data"><i class="fa fa-check"></i><b>7.2.1</b> Understand the data</a></li>
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<li class="chapter" data-level="7.3" data-path="knnchapter.html"><a href="knnchapter.html#references-3"><i class="fa fa-check"></i><b>7.3</b> References</a></li>
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<li class="chapter" data-level="8" data-path="kmeans.html"><a href="kmeans.html"><i class="fa fa-check"></i><b>8</b> Kmeans clustering</a><ul>
<li class="chapter" data-level="8.1" data-path="kmeans.html"><a href="kmeans.html#multinomial-logistic-regression-1"><i class="fa fa-check"></i><b>8.1</b> Multinomial Logistic Regression</a></li>
<li class="chapter" data-level="8.2" data-path="kmeans.html"><a href="kmeans.html#references-4"><i class="fa fa-check"></i><b>8.2</b> References</a></li>
</ul></li>
<li class="chapter" data-level="9" data-path="hierclust.html"><a href="hierclust.html"><i class="fa fa-check"></i><b>9</b> Hierarichal Clustering</a><ul>
<li class="chapter" data-level="9.1" data-path="hierclust.html"><a href="hierclust.html#example-on-the-pokemon-dataset"><i class="fa fa-check"></i><b>9.1</b> Example on the Pokemon dataset</a></li>
<li class="chapter" data-level="9.2" data-path="hierclust.html"><a href="hierclust.html#example-on-regressions-1"><i class="fa fa-check"></i><b>9.2</b> Example on regressions</a></li>
<li class="chapter" data-level="9.3" data-path="hierclust.html"><a href="hierclust.html#references-5"><i class="fa fa-check"></i><b>9.3</b> References</a></li>
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<li class="chapter" data-level="10" data-path="pca.html"><a href="pca.html"><i class="fa fa-check"></i><b>10</b> Principal Component Analysis</a><ul>
<li class="chapter" data-level="10.1" data-path="pca.html"><a href="pca.html#pca-on-an-easy-example."><i class="fa fa-check"></i><b>10.1</b> PCA on an easy example.</a></li>
<li class="chapter" data-level="10.2" data-path="pca.html"><a href="pca.html#references."><i class="fa fa-check"></i><b>10.2</b> References.</a></li>
</ul></li>
<li class="chapter" data-level="11" data-path="trees-and-classification.html"><a href="trees-and-classification.html"><i class="fa fa-check"></i><b>11</b> Trees and Classification</a><ul>
<li class="chapter" data-level="11.1" data-path="trees-and-classification.html"><a href="trees-and-classification.html#introduction-1"><i class="fa fa-check"></i><b>11.1</b> Introduction</a></li>
<li class="chapter" data-level="11.2" data-path="trees-and-classification.html"><a href="trees-and-classification.html#first-example."><i class="fa fa-check"></i><b>11.2</b> First example.</a></li>
<li class="chapter" data-level="11.3" data-path="trees-and-classification.html"><a href="trees-and-classification.html#second-example."><i class="fa fa-check"></i><b>11.3</b> Second Example.</a></li>
<li class="chapter" data-level="11.4" data-path="trees-and-classification.html"><a href="trees-and-classification.html#how-does-a-tree-decide-where-to-split"><i class="fa fa-check"></i><b>11.4</b> How does a tree decide where to split?</a></li>
<li class="chapter" data-level="11.5" data-path="trees-and-classification.html"><a href="trees-and-classification.html#third-example."><i class="fa fa-check"></i><b>11.5</b> Third example.</a></li>
<li class="chapter" data-level="11.6" data-path="trees-and-classification.html"><a href="trees-and-classification.html#references-6"><i class="fa fa-check"></i><b>11.6</b> References</a></li>
</ul></li>
<li class="chapter" data-level="12" data-path="random-forest.html"><a href="random-forest.html"><i class="fa fa-check"></i><b>12</b> Random Forest</a><ul>
<li class="chapter" data-level="12.1" data-path="random-forest.html"><a href="random-forest.html#how-does-it-work"><i class="fa fa-check"></i><b>12.1</b> How does it work?</a></li>
<li class="chapter" data-level="12.2" data-path="random-forest.html"><a href="random-forest.html#references-7"><i class="fa fa-check"></i><b>12.2</b> References</a></li>
</ul></li>
<li class="chapter" data-level="13" data-path="svm.html"><a href="svm.html"><i class="fa fa-check"></i><b>13</b> Support Vector Machine</a><ul>
<li class="chapter" data-level="13.1" data-path="svm.html"><a href="svm.html#support-vecotr-regression"><i class="fa fa-check"></i><b>13.1</b> Support Vecotr Regression</a><ul>
<li class="chapter" data-level="13.1.1" data-path="svm.html"><a href="svm.html#create-data"><i class="fa fa-check"></i><b>13.1.1</b> Create data</a></li>
<li class="chapter" data-level="13.1.2" data-path="svm.html"><a href="svm.html#tuning-a-svm-model"><i class="fa fa-check"></i><b>13.1.2</b> Tuning a SVM model</a></li>
<li class="chapter" data-level="13.1.3" data-path="svm.html"><a href="svm.html#discussion-on-parameters"><i class="fa fa-check"></i><b>13.1.3</b> Discussion on parameters</a></li>
</ul></li>
<li class="chapter" data-level="13.2" data-path="svm.html"><a href="svm.html#references-8"><i class="fa fa-check"></i><b>13.2</b> References</a></li>
</ul></li>
<li class="chapter" data-level="14" data-path="model-evaluation.html"><a href="model-evaluation.html"><i class="fa fa-check"></i><b>14</b> Model Evaluation</a><ul>
<li class="chapter" data-level="14.1" data-path="model-evaluation.html"><a href="model-evaluation.html#biais-variance-tradeoff"><i class="fa fa-check"></i><b>14.1</b> Biais variance tradeoff</a></li>
<li class="chapter" data-level="14.2" data-path="model-evaluation.html"><a href="model-evaluation.html#bagging"><i class="fa fa-check"></i><b>14.2</b> Bagging</a></li>
<li class="chapter" data-level="14.3" data-path="model-evaluation.html"><a href="model-evaluation.html#crossvalidation"><i class="fa fa-check"></i><b>14.3</b> Cross Validation</a></li>
</ul></li>
<li class="chapter" data-level="15" data-path="case-study-text-classification-spam-and-ham-.html"><a href="case-study-text-classification-spam-and-ham-.html"><i class="fa fa-check"></i><b>15</b> Case Study - Text classification: Spam and Ham.</a></li>
<li class="chapter" data-level="16" data-path="mushroom.html"><a href="mushroom.html"><i class="fa fa-check"></i><b>16</b> Case Study - Mushrooms Classification</a><ul>
<li class="chapter" data-level="16.1" data-path="mushroom.html"><a href="mushroom.html#import-the-data"><i class="fa fa-check"></i><b>16.1</b> Import the data</a></li>
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<li class="chapter" data-level="17.1" data-path="case-study-the-adults-dataset-.html"><a href="case-study-the-adults-dataset-.html#introduction-2"><i class="fa fa-check"></i><b>17.1</b> Introduction</a></li>
<li class="chapter" data-level="17.2" data-path="case-study-the-adults-dataset-.html"><a href="case-study-the-adults-dataset-.html#import-the-data-1"><i class="fa fa-check"></i><b>17.2</b> Import the data</a></li>
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<li class="chapter" data-level="19" data-path="final-words.html"><a href="final-words.html"><i class="fa fa-check"></i><b>19</b> Final Words</a></li>
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<div id="case-study---the-adults-dataset." class="section level1">
<h1><span class="header-section-number">Chapter 17</span> Case study - The adults dataset.</h1>
<div id="introduction-2" class="section level2">
<h2><span class="header-section-number">17.1</span> Introduction</h2>
<p>The adult data set is another famous one from the <strong>UCI - machine learning repository</strong>.<br />
The idea is to predict whether income exceeds $50K/yr based on census data. Also known as “Census Income” dataset. Extraction was done by Barry Becker from the 1994 Census database.</p>
<p>Load the libraries</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">library</span>(stringr)</code></pre></div>
</div>
<div id="import-the-data-1" class="section level2">
<h2><span class="header-section-number">17.2</span> Import the data</h2>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">df <-<span class="st"> </span><span class="kw">read_csv</span>(<span class="st">"dataset/adult.csv"</span>)
<span class="kw">glimpse</span>(df)</code></pre></div>
<pre><code>## Observations: 32,561
## Variables: 15
## $ AGE <dbl> 39, 50, 38, 53, 28, 37, 49, 52, 31, 42, 37, 30, 23…
## $ WORKCLASS <chr> "State-gov", "Self-emp-not-inc", "Private", "Priva…
## $ FNLWGT <dbl> 77516, 83311, 215646, 234721, 338409, 284582, 1601…
## $ EDUCATION <chr> "Bachelors", "Bachelors", "HS-grad", "11th", "Bach…
## $ EDUCATIONNUM <dbl> 13, 13, 9, 7, 13, 14, 5, 9, 14, 13, 10, 13, 13, 12…
## $ MARITALSTATUS <chr> "Never-married", "Married-civ-spouse", "Divorced",…
## $ OCCUPATION <chr> "Adm-clerical", "Exec-managerial", "Handlers-clean…
## $ RELATIONSHIP <chr> "Not-in-family", "Husband", "Not-in-family", "Husb…
## $ RACE <chr> "White", "White", "White", "Black", "Black", "Whit…
## $ SEX <chr> "Male", "Male", "Male", "Male", "Female", "Female"…
## $ CAPITALGAIN <dbl> 2174, 0, 0, 0, 0, 0, 0, 0, 14084, 5178, 0, 0, 0, 0…
## $ CAPITALLOSS <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ HOURSPERWEEK <dbl> 40, 13, 40, 40, 40, 40, 16, 45, 50, 40, 80, 40, 30…
## $ NATIVECOUNTRY <chr> "United-States", "United-States", "United-States",…
## $ ABOVE50K <dbl> 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0,…</code></pre>
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<div id="tidy-the-data-1" class="section level2">
<h2><span class="header-section-number">17.3</span> Tidy the data</h2>
<p>Let’s check the level of missing data</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">map_dbl</span>(df, <span class="cf">function</span>(.x) <span class="kw">sum</span>(<span class="kw">is.na</span>(.x)))</code></pre></div>
<pre><code>## AGE WORKCLASS FNLWGT EDUCATION EDUCATIONNUM
## 0 0 0 0 0
## MARITALSTATUS OCCUPATION RELATIONSHIP RACE SEX
## 0 0 0 0 0
## CAPITALGAIN CAPITALLOSS HOURSPERWEEK NATIVECOUNTRY ABOVE50K
## 0 0 0 0 0</code></pre>
<p>No missing data! That’s great news.</p>
<p>Before we change the <chr> variables into factors, let’s see what type of levels we have.</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">df <span class="op">%>%</span><span class="st"> </span><span class="kw">select_if</span>(is.character) <span class="op">%>%</span><span class="st"> </span><span class="kw">map_if</span>(is.character, unique)</code></pre></div>
<pre><code>## $WORKCLASS
## [1] "State-gov" "Self-emp-not-inc" "Private"
## [4] "Federal-gov" "Local-gov" "?"
## [7] "Self-emp-inc" "Without-pay" "Never-worked"
##
## $EDUCATION
## [1] "Bachelors" "HS-grad" "11th" "Masters"
## [5] "9th" "Some-college" "Assoc-acdm" "Assoc-voc"
## [9] "7th-8th" "Doctorate" "Prof-school" "5th-6th"
## [13] "10th" "1st-4th" "Preschool" "12th"
##
## $MARITALSTATUS
## [1] "Never-married" "Married-civ-spouse" "Divorced"
## [4] "Married-spouse-absent" "Separated" "Married-AF-spouse"
## [7] "Widowed"
##
## $OCCUPATION
## [1] "Adm-clerical" "Exec-managerial" "Handlers-cleaners"
## [4] "Prof-specialty" "Other-service" "Sales"
## [7] "Craft-repair" "Transport-moving" "Farming-fishing"
## [10] "Machine-op-inspct" "Tech-support" "?"
## [13] "Protective-serv" "Armed-Forces" "Priv-house-serv"
##
## $RELATIONSHIP
## [1] "Not-in-family" "Husband" "Wife" "Own-child"
## [5] "Unmarried" "Other-relative"
##
## $RACE
## [1] "White" "Black" "Asian-Pac-Islander"
## [4] "Amer-Indian-Eskimo" "Other"
##
## $SEX
## [1] "Male" "Female"
##
## $NATIVECOUNTRY
## [1] "United-States" "Cuba"
## [3] "Jamaica" "India"
## [5] "?" "Mexico"
## [7] "South" "Puerto-Rico"
## [9] "Honduras" "England"
## [11] "Canada" "Germany"
## [13] "Iran" "Philippines"
## [15] "Italy" "Poland"
## [17] "Columbia" "Cambodia"
## [19] "Thailand" "Ecuador"
## [21] "Laos" "Taiwan"
## [23] "Haiti" "Portugal"
## [25] "Dominican-Republic" "El-Salvador"
## [27] "France" "Guatemala"
## [29] "China" "Japan"
## [31] "Yugoslavia" "Peru"
## [33] "Outlying-US(Guam-USVI-etc)" "Scotland"
## [35] "Trinadad&Tobago" "Greece"
## [37] "Nicaragua" "Vietnam"
## [39] "Hong" "Ireland"
## [41] "Hungary" "Holand-Netherlands"</code></pre>
<p>Allright, so maybe there were no NA, but there are quite a few “?”</p>
<p>The “?” should probably be replaced with NAs.</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">df <-<span class="st"> </span><span class="kw">read_csv</span>(<span class="st">"dataset/adult.csv"</span>, <span class="dt">na =</span> <span class="kw">c</span>(<span class="st">"NA"</span>, <span class="st">"?"</span>))
<span class="co"># Let's redo a check on the NA now</span>
<span class="kw">map_int</span>(df, <span class="cf">function</span>(.x) <span class="kw">sum</span>(<span class="kw">is.na</span>(.x)))</code></pre></div>
<pre><code>## AGE WORKCLASS FNLWGT EDUCATION EDUCATIONNUM
## 0 1836 0 0 0
## MARITALSTATUS OCCUPATION RELATIONSHIP RACE SEX
## 0 1843 0 0 0
## CAPITALGAIN CAPITALLOSS HOURSPERWEEK NATIVECOUNTRY ABOVE50K
## 0 0 0 583 0</code></pre>
<p>Let’s now rework the column names to better fit our naming conventions</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">colnames</span>(df) <-<span class="st"> </span><span class="kw">c</span>(<span class="st">"age"</span>, <span class="st">"working_class"</span>, <span class="st">"final_weight"</span>, <span class="st">"education"</span>, <span class="st">"education_num"</span>, <span class="st">"marital_status"</span>,
<span class="st">"occupation"</span>, <span class="st">"relationship"</span>, <span class="st">"race"</span>, <span class="st">"gender"</span>, <span class="st">"capital_gain"</span>, <span class="st">"capital_loss"</span>, <span class="st">"hours_per_week"</span>,
<span class="st">"native_country"</span>, <span class="st">"above_50k"</span>)</code></pre></div>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">df2 <-<span class="st"> </span>df <span class="op">%>%</span><span class="st"> </span><span class="kw">mutate_if</span>(is_character, as.factor)
<span class="kw">levels</span>(df2<span class="op">$</span>working_class)</code></pre></div>
<pre><code>## [1] "Federal-gov" "Local-gov" "Never-worked"
## [4] "Private" "Self-emp-inc" "Self-emp-not-inc"
## [7] "State-gov" "Without-pay"</code></pre>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">summary</span>(df2)</code></pre></div>
<pre><code>## age working_class final_weight
## Min. :17.00 Private :22696 Min. : 12285
## 1st Qu.:28.00 Self-emp-not-inc: 2541 1st Qu.: 117827
## Median :37.00 Local-gov : 2093 Median : 178356
## Mean :38.58 State-gov : 1298 Mean : 189778
## 3rd Qu.:48.00 Self-emp-inc : 1116 3rd Qu.: 237051
## Max. :90.00 (Other) : 981 Max. :1484705
## NA's : 1836
## education education_num marital_status
## HS-grad :10501 Min. : 1.00 Divorced : 4443
## Some-college: 7291 1st Qu.: 9.00 Married-AF-spouse : 23
## Bachelors : 5355 Median :10.00 Married-civ-spouse :14976
## Masters : 1723 Mean :10.08 Married-spouse-absent: 418
## Assoc-voc : 1382 3rd Qu.:12.00 Never-married :10683
## 11th : 1175 Max. :16.00 Separated : 1025
## (Other) : 5134 Widowed : 993
## occupation relationship race
## Prof-specialty : 4140 Husband :13193 Amer-Indian-Eskimo: 311
## Craft-repair : 4099 Not-in-family : 8305 Asian-Pac-Islander: 1039
## Exec-managerial: 4066 Other-relative: 981 Black : 3124
## Adm-clerical : 3770 Own-child : 5068 Other : 271
## Sales : 3650 Unmarried : 3446 White :27816
## (Other) :10993 Wife : 1568
## NA's : 1843
## gender capital_gain capital_loss hours_per_week
## Female:10771 Min. : 0 Min. : 0.0 Min. : 1.00
## Male :21790 1st Qu.: 0 1st Qu.: 0.0 1st Qu.:40.00
## Median : 0 Median : 0.0 Median :40.00
## Mean : 1078 Mean : 87.3 Mean :40.44
## 3rd Qu.: 0 3rd Qu.: 0.0 3rd Qu.:45.00
## Max. :99999 Max. :4356.0 Max. :99.00
##
## native_country above_50k
## United-States:29170 Min. :0.0000
## Mexico : 643 1st Qu.:0.0000
## Philippines : 198 Median :0.0000
## Germany : 137 Mean :0.2408
## Canada : 121 3rd Qu.:0.0000
## (Other) : 1709 Max. :1.0000
## NA's : 583</code></pre>
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