Part 2 Exploring market food prices.

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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 […]

Summer BBQ in the park with DSS and Friends

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The smell of 🥩grilled meats and 🥕vegetables will fill the air…featuring some of the best grill-busting, wood-burning, sauce-smearing almost professional grill data geeks. 👨‍🍳
Join us for a BBQ at South Park, Tuesday, August 21th at 7:00pm, just to celebrate good food, good friends, the science and the BBQ Season! 🍖

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

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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

Summer School of Research Methods – Forecasting, Machine self-learning and AI

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This June Data Science Society is presenting to you the Summer School of Research Methods! We are involved in the action and will be able to provide you with exclusive videos from the lectures and workshops. If you are interested in Forecasting, Machine self-learning and AI this is a great opportunity for you to grab some awesome materials on the topics presented by our experts.

An Introduction to Machine Learning Theory and Its Applications: A Visual Tutorial with Examples

Posted Leave a commentPosted in Learn

What actually does Machine Learning mean and what types of problems does it solve? This Machine Learning tutorial introduces the basics of ML theory, laying down the common themes and concepts. The practical examples substituted with the mathematical functions of univariate linear regression, linear least squares and others make it easy to follow the logic and get comfortable with machine learning basics.

MEET THE WINNERS OF THE FIRST INTERNATIONAL ACADEMIA DATATHON 2018

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Between 27th and 29th April, 2018, the Data Science Society organized the world’s first online Academia Datathon – a global Data Science University competition. The event was held online and on-site at the participating Universities. Within 48 hours, a total of 130 participants from more than 6 countries and 10 universities experimented with exclusive cryptocurrency […]

CRYPTO CURRENCY PREDICTION

Posted 2 CommentsPosted in Datathons Solutions, Learn, Team solutions

TOOL USED R MICROSOFT AZURE MICROSOFT EXCEL SUMMARY The Dataset is time series data of crypto currency consisting of of 1869 observation and 21 features(each feature showing different crypto currency). Frequency of the observations is 5 min showing from date 18/01/18 to 24/01/18. BUSINESS UNDERSTANDING As the data belongs to the crypto exchange. Intraday short-term […]