Datathon NSI Mentors’ Guidelines – Economic Time Series Prediction

Posted 1 CommentPosted in GD2018 Mentors, Mentors

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 […]

Website Guidelines

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1. Overview of the Data.Platform website 1.1. Register Register in our Datathon website. The link is: https://www.datasciencesociety.net/datathon/register/ and the registration will go through e-mail confirmation. After that You should fill-in Your profile, including an avatar (not necessary Your actual photo), Your name, Your data science interest, etc.     Besides going to our public landing […]

Datathon industry experts guidelines

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Dear colleague, we highly appreciate Your participation in Datathon 2018 as an industry expert and we are sure that Your contribution will make for another great Datathon. Please, see the mentor instructions for the Dathaton 2018. https://www.datasciencesociety.net/datathon/    1. Log in to the Data Chat Before starting to use the Data Chat read the instructions […]

Using Machine Learning to explain and predict the life expectancy of different countries

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The project tries to create a model based on data provided by the World Health Organization (WHO) to evaluate the life expectancy for different countries in years. The data offers a timeframe from 2000 to 2015. The data originates from here: https://www.kaggle.com/kumarajarshi/life-expectancy-who/data The output algorithms have been used to test if they can maintain their accuracy in predicting the life expectancy for data they haven’t been trained. Four algorithms have been used:

Linear Regression
Ridge Regression
Lasso Regression
ElasticNet Regression
Linear Regression with Polynomic features
Decision Tree Regression
Random Forest Regression

Stochastic Processes and Applications

Posted 1 CommentPosted in Probabilities

This notebook is a basic introduction into Stochastic Processes. It is meant for the general reader that is not very math savvy, like the course participants in the Math Concepts for Developers in SoftUni.
There is a basic definition. Some examples of the most popular types of processes like Random Walk, Brownian Motion or Weiner Process, Poisson Process and Markov chains have been given. Their basic characteristics and examples for some possible applications are stated. For all the examples there are simulations in Python, some are visualized.
The following packages have been used:

nympy
matplotlib.pyplot
random
scipy.stats
itertools
matplotlib.patches

Part 2 Exploring market food prices.

Posted Leave a commentPosted in Learn

Abstract¶We would like to see if there is any connection between the products (names) and price, as well as existing patterns. This is set a-priori. When we do the exploration further question will arise. Some of the data will be removed as it will not be used. There will be plots, groupings and hypothesis testing […]

Part 1 Exploring Food Data

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Abstract¶When we first look at the file we can see that it is the biggest one of all, most of the data will not be used, we will create samples. Lets set an a priory goal to see if there is a connection between the countries and some of the ingredients that don’t have that […]

Tiny smart data modelled with a not-so-tiny smart model – the Case of SAP

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Tiny smart data modelled with a not-so-tiny smart model Introduction Metadata Business Understanding Data Understanding Data Preparation Modelling Evaluation Deployment Conclusion Metadata Case: The SAP Case – Analyze Sales Team: Chameleon Project URL: https://github.com/Bugzey/Chameleon-SAP Memebers: Stefan Panev (stephen.panev@gmail.com), Metodi Nikolov (metodi.nikolov@gmail.com), Ivan Vrategov (ivanvrategov@gmail.com, Radoslav Dimitrov (rdimitrov@indeavr.com) Mentors: Alexander Efremov(aefremov@gmail.com) Agamemnon Baltagiannis (agamemnon.baltagiannis@sap.com) Team Toolset: […]

Datathon Kaufland Solution – Team Total Kaputt! – Why da faQ the machine broke down?

Posted 1 CommentPosted in Prediction systems

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.