What we tried to do to solve the Kaufland case for the Global Datathon 2018. This article just contains our exploratory data analysis in the form of many plots and some explanations. There isn’t any modeling stage described here.
In this article the mentors give some preliminary guidelines, advice and suggestions to the participants for the case. Every mentor should write […]
Dear participants, we highly appreciate your participation in the Datathon as challengers and we are sure that your contribution will make for […]
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In an attempt to make a case which is to be somewhat universally understandable by various types of students, the case is financial time-series prediction, while making it more engaging with the hot topic of cryptocurrencies. The case integrates knowledge from various sources – Crypto Currencies, Quantitative Finance and Machine learning. At the same time, the case is stratified as the teams solving it could complete various levels – as far as they could solve it.
Introduction Data provided consists of 3 years of weekly volume of sales, price of product in question, prices of main competitors […]
Prediction of cryptocurrency prices (5 min period) with AR, ARIMA and Neural Network models using R and Python.
Techonnology and methods used: R – plyr, dplyr, tidyverse, stringr, data.table, geohash, ggmap, maps, robustbase, geosphere, pracma, Hmisc, ggplot2, tidyquant, reshape2, pastecs Python […]
