added data and exercise version of tutorial 9

R/BIA_T09_Classification _exercise.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
+
+# Derive and investigate the dependent variable "number of residents"
+
+```
+
+```{r Detailed analysis of the independent variables}
+# Descriptive analysis of load traces -------------------------------------
+# Plot some load curves from households to get familiar with the data
+
+household <- 8
+
+```
+
+
+```{r Feature extraction}
+# Define and implement 10 features from SMD (e.g. mean consumption, mean 
+# consumption in the evening)
+
+
+
+#calculate the features for one household
+
+```
+
+
+```{r Feature selection}
+# Feature filtering  -------------------------------------------------------
+# Combine all features in one data frame and apply feature selection methods from the FSelector package. 
+# a) Which features are selected? 
+# b) Can you explain why those features might be selected?
+
+#combine all datasets
+
+
+#simple call of the feature selection function
+
+
+#correlation based filter (2 similar ways to call the method)
+
+#further feature filter
+
+```
+
+
+```{r Classification Basic evaluation approach}
+
+## decisoon tree
+
+#train the model
+
+#predict test cases
+
+#create confusion matrix and calculate accuracy
+
+## random forest
+
+#train the model
+
+#predict test cases
+
+#create confusion matrix and calculate accuracy
+
+## kNN
+
+# predict test cases from training data (lazy learning algoritm has no explicit training step!)
+
+#create confusion matrix and calculate accuracy
+
+## SVM
+
+#train the model
+
+#predict the test cases
+
+#create confusion matrix and calculate accuracy
+```
+
+
+
+
+