Author: stephen

Posted junior, stephen, zenpanik, alex-efremov, pepe, tomislavk, liad, preslav, tony, laura, mladensavov, anenov, metodinikolov, drceenish, leomrsic, boryana, mcneto, jjagusch, drsubhabahapal, sist, jans, gkmitov, metodinikolov, gloria, quintshekhar1 CommentPosted in GD2018 Mentors, MentorsGD2018 Mentors, Mentors

Datathon NSI Mentors’ Guidelines – Economic Time Series Prediction

In this article the mentors give some preliminary guidelines, advice and suggestions to the participants for the case. Every mentor should write […]

Posted stephenLeave a commentPosted in Prediction systemsPrediction systems

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

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

Posted stephen1 CommentPosted in ProbabilitiesProbabilities

Stochastic Processes and Applications

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