added first part of tutorial 9

R/BIA_T09_Classification.Rmd

+---
+title:  'Tutorial 9: Classification'
+output: html_notebook
+editor_options: 
+  chunk_output_type: inline
+---
+
+This file is part of the lecture Business Intelligence & Analytics (EESYS-BIA-M), Information Systems and Energy Efficient Systems, University of Bamberg.
+
+
+```{r Load libraries}
+library(FSelector) #for feature selection
+library(party) #for classification algorithm decision trees
+library(class) #for classification algorithm kNN
+library(e1071) #for classification algorithm SVM
+library(randomForest) #further random forest
+```
+
+
+
+```{r Load and prepare data}
+# Load data
+load("../data/classification.RData")
+
+# Derive and investigate the dependent variable "number of residents"
+adults <- as.integer(ifelse(customers$residents.numAdult=="5 oder mehr",
+                            "5",customers$residents.numAdult))
+children <- as.integer(ifelse(customers$residents.numChildren=="5 oder mehr",
+                              "5",customers$residents.numChildren))
+
+table(ifelse(is.na(children), adults, adults+children))
+# think in classes. we have some very rare classes of number of residents (>5)
+
+customers$pNumResidents <- sapply(ifelse(is.na(children), adults, adults+children), 
+                                       function(a) {
+  if(a==0 || is.na(a)){
+    return(NA)
+  } else if(a==1){
+    return("1 person")
+  } else if(a==2){
+    return("2 persons")
+  } else if(a<=5){
+    return("3-5 persons")
+  } else {
+    return(">5 persons")
+  }
+})
+
+customers$pNumResidents <- ordered(customers$pNumResidents, 
+                                      levels=c("1 person", "2 persons", 
+                                               "3-5 persons", ">5 persons"))
+table(customers$pNumResidents)
+```
+