In this post I will build a web aspp that allows a company to predict a car’s auction price using a simple deep learning model. I will do this by using a Keras/Tensorflow backend and an Rshiny Front end. You can find the app here ! Set-up Environment library(tidyverse) library(tensorflow) library(keras) install_keras(tensorflow = "1.12") Clean Data First, we clean data and tokenize the auction data in order to be able to process the string data.
I have not been able to write for a while as the semester just started, but quite frankly that is a not an issue since no one other than me reads these posts :). Anyways, I wanted to do this week’s tidy tuesday as it was about French train delays which I got to get accustomated to while living in France. library(tidyverse) library(dynlm) library(gganimate) library(maptools) library(maps) library(lettercase) library(magrittr) library(ggfortify) library(pander) library(patchwork) library(kableExtra) trains_raw <- readr::read_csv("https://raw.
I criticized Turkish election polls a lot in the past for multiple reasons, so it is not a big surprise that my first blog post is about the performance of pollsters. (On a side note, Turkey is a country, where all major news outlets shared an election poll of samplesize ~100 based only on one village in Turkey because that village had historicaly voted close to the national vote result…) Election polls in Turkey tend to be very opaque since they generally are ordered by private parties who then share only the final results of the polls without any details on the methodolgies.