This is the repository for the project of Nonparametric Statistics course held at Politecnico di Milano during academic year 2020/2021. The aim of this work is to give an insight on 2020 US presidential election, in particular in understanding which factors may have infuenced the election.
A first part of the analysis is devoted to making some preliminary inference on different socio-economic and demographic factors at our disposal. We then focus on the fitting of a GAM prediction model using a part of our data as training set. We will compare it with respect to other possible models through a validation set, and, in the end, we will evaluate its predictive performances through the remaining observations, used as test set.
For further details and for a formal mathematical explanation please refer to the report.
The code is strucured as follows:
/data: folder containing all the data used for the analysis/code: folder containing the codes for the analysis, further divided into:/data clean: codes for cleaning and merging data-sets/Inference: codes for the Inference part, on county level/Model: codes for prediction part: GAM, SVM, Random Forest/Nations: codes for the Inference part, on state levelinstall.R: file that provides automatic installation of required R packages
/Pics: folder containing most of the pics present in the reportNPS-Report-Ajroldi-Lurani-Marchionni.pdf: report for the analysis
The file install.R provides automatic installation of the required packages.
Computation were performed using
R Core Team (2020). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/.
For all the R-packages used in the codes please refer to section Reference in the report.
Niccolò Ajroldi - Politecnico di Milano
Agostino Lurani - Politecnico di Milano
Edoardo Marchionni - Politecnico di Milano
Master of Science in Mathematical Engineering students at Politecnico di Milano.
