Decision trees

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Table of contents

1 Introduction 
1.1 Motivation
1.2 Onslip
1.3 Purpose
1.4 Research questions
1.5 Limitations
2 Theory 
2.1 Automated forecasting
2.2 Data mining and machine learning
2.3 Decision trees
2.4 Decision forests
2.5 Gradient boosting
2.6 XGBoost
2.7 Related work
2.7.1 Weather’s effects on sales
2.8 Forecast accuracy metrics
2.8.1 Mean absolute error
3 Method 
3.1 Raw data
3.2 Receipt data
3.3 Sales history data
3.4 External data collection
3.4.1 Weather data
3.4.2 Google Maps data
3.4.3 Product data
3.4.4 Calendar data
3.5 The combined data sets
3.6 Company selection
3.7 Model implementation
3.8 Evaluation
4 Results 
4.1 Model training
4.2 Forecast accuracy
4.3 Feature importance
5 Discussion 
5.1 Model training
5.2 Result
5.2.1 Restaurant variables
5.2.2 Calendar variables
5.2.3 Sales history variables
5.2.4 Weather variables
5.3 This work in a wider context
6 Conclusion 
6.1 Future work
References
A Appendix
SMHI forecast parameters
SMHI historical parameters
Product sales data set
Total sales data set
Individual and collective training comparison
Product sales MAE
Total sales MAE
Feature importance and Pearson correlation coefficient