ivombi/NY-Taxi-Forecast

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ivombi/NY-Taxi-Forecast

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Titanic-Machine-Learning-from-Disaster

use machine learning to create a model that predicts which passengers survived the Titanic shipwreck

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US-Shootings

Time Series Analysis of the number of people shoot dead per week by the police in the USA

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House-price-prediction

Ask a home buyer to describe their dream house, and they probably won't begin with the height of the basement ceiling or the proximity to an east-west railroad. But this playground competition's dataset proves that much more influences price negotiations than the number of bedrooms or a white-picket fence. With 79 explanatory variables describing (almost) every aspect of residential homes in Ames, Iowa, this competition challenges you to predict the final price of each home.

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Generalized-Linera-Models

At the end of this course, the student should have a profound knowledge of generalized linear models and basic knowledge of some extensions, including Part I Standard descriptive and inferential methods for multiway contingency t ables (odds ratios, conditional independence, Cochran-Mantel-Haenszel procedures,...) Components of a generalized linear model (GLM) GLM for binary data: logistic regression Building and applying logistic regression models Overdispersion and quasi-likelihood Conditional logistic regression and exact distributions Part II Extensions to multinomial responses (baseline category, cumulative link, partial odds ratio,...) Extensions to clustered binary (GEE, random effects) Extensions to clustered & multinomial data Loglinear models Models for matched pairs The student should be able to apply such models and methods using appropriate software (SAS, R).

NY-Taxi

Visualisation project for the New York Yellow Taxi

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Data-visualisation

As data becomes easier and cheaper to generate, we are moving from a hypothesis-driven to data-driven paradigm in scientific research. As a result, we don't only need to find ways to answer any questions we have, but also to identify interesting questions/hypotheses in that data in the first place. In other words: we need to be able to dig through these large and complex datasets in search for unexpected patterns that - once discovered - can be investigated further using regular statistics and machine learning. Interactive data visualization provides a methodology for just that: to allow the user (be they domain expert or lay user) to find those questions, and to give them deep insight in their data. Content Background and context of data visualization and visual data analysis Design as a process: framing the problem, ideation, sketching, design critique, ... Programming visualizations: static and dynamic Project: visualization of expert dataset

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