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 their name and chat name in the beginning of their texts, so that there are no mix-ups with the other menthors. By rules it is essential to follow CRISP-DM methodology (http://www.sv-europe.com/crisp-dm-methodology/). The DSS […]
Dear participants, we highly appreciate your participation in the Datathon as challengers and we are sure that your contribution will make for another great Datathon. During the event your task would be to develop a data based solution to a chosen case study. Before the event Register into the Datathon Platform Register in Datathon website […]
Dear Society, you should register for the Global Datathon 2018 – http://bit.ly/2wadU9C in order to see the case descriptions! 🙂
You must be a registered user for the #AcademiaDatathon to see this content.
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 and promotion calendar for a FCMG product. Data is provided by SAP. The task is to identify the volume uplift drivers, measure the promotional effectiveness and measure the cannibalization effect from 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 – s3fs, pandas, numpy, matplotlib, plotly, geohash2, folium, geopy OLS Regression, Ridge Regression, Decision Trees Introduction Air pollution beyond the norms is a common problem in many locations. Examining the causes behind and being able to predict […]