A Case Study using Pyspark on Big Data from Udacity
Introduction
In this project, we work on a Dataset of a fictive on-demand music streaming service named “Sparkify” provided by Udacity. Sparkify is a service similar to industry leaders like Spotify or Deezer.
The goal of the project is to predict, which users on Sparkify are likely to churn, i.e. leave the service or cancel the subscription. This is a huge problem in the industry. If we can successfully predict, which users may churn in the future, we could offer these specific users incentives to stay at the platform. This knowledge could lead to significant revenue increases for the service providers.
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I looked at AirBnB data to explore rental prices and their correlation with the Oktoberfest in Munich
Introduction
As part of the Udacity Data Science Nanodegree Program, I was encouraged to write my first Data Science Blog Post. For the analysis I choose the publicly available Airbnb Dataset on rentals in Munich. The post answers the following 3 questions:
- Which neighbourhoods are the most expensive in Munich?
- The “Oktoberfest effect”: Is there a time in the year when rentals in Munich are very expensive?
- Is this effect the same for all rentals in all neighborhoods in Munich?
The Munich Aibnb data is an open dataset and can be accessed here. The analysis will follow the Cross Industry Standard…
Markus Grüneis
Data Scientist / Data Engineer
