Commit 65c9d0fe by Weigert, Andreas

### added first part of tutorial 9

parent d479b63b
 --- 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) ```
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