Commit 358b71b4 authored by Weigert, Andreas's avatar Weigert, Andreas
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added data and exercise version of tutorial 9

parent e2e4cd88
---
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
```
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