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 […]
Data preparation The first step in our analysis was to see if there are any observations missing. Some dates were not even present in the provided file, so we decided to add them and consider the price for that certain day, hour or minute as NA. The next step was to differentiate the data. At […]
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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.
Team members: Ana Popova, @anie Izabella Taskova, @ izabellataskova Kamelia Kosekova, @kameliak Kameliya Lokmadzhieva, @kameliyalokmadzhieva Nikolay Bojurin, @nikolay Mentors: @boryana @alex-efremov @pepe Team name: DAB PANDA Team logo: NB!!!! OUR NOTEBOOKS ARE AVAILABLE HERE: DAB PANDA Rmds Data Understanding and Preparation You may see our code with results and brief comments if you […]
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 […]
