Commit 65c9d0fe authored by Weigert, Andreas's avatar Weigert, Andreas
Browse files

added first part of tutorial 9

parent d479b63b
title: 'Tutorial 9: Classification'
output: html_notebook
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
# Derive and investigate the dependent variable "number of residents"
adults <- as.integer(ifelse(customers$residents.numAdult=="5 oder mehr",
children <- as.integer(ifelse(customers$residents.numChildren=="5 oder mehr",
table(ifelse(, adults, adults+children))
# think in classes. we have some very rare classes of number of residents (>5)
customers$pNumResidents <- sapply(ifelse(, adults, adults+children),
function(a) {
if(a==0 ||{
} else if(a==1){
return("1 person")
} else if(a==2){
return("2 persons")
} else if(a<=5){
return("3-5 persons")
} else {
return(">5 persons")
customers$pNumResidents <- ordered(customers$pNumResidents,
levels=c("1 person", "2 persons",
"3-5 persons", ">5 persons"))
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