###########################################
Author:
Andrew Wong
Email: andrewwongls@outlook.com
Medium Blog: https://medium.com/human-science-ai
###########################################
The goals of this project are four-folds.
FIRST This notebook designed to be a guide to the reader on a contemporary issue and debate of basic income - currently sweeping across the western world due to anxiety towards the future of work.
SECOND This project attempts to use a confluence data science, data journalism, and social science disciplines to understand and contextualize basic income. The OSMEIC framework is created for this purpose.
THIRD This project attempts to leverage on a combinations of Hypothesis Testing (drawing observations that resulting in real, significant effect - accepting alternative hypothesis), and Machine Learning - Predictive Analytic (use of data, statistical algorithms and machine learning techniques to identify the likelihood of future outcomes based on historical data). The results from these two data science methods will help us not only build higher confidence of the results, but also drawing new insights. The maxim of when two diferent things collide, something interesting bound to happen.
FOURTH The 3 key questions we are trying to answer - the summarized version to be discussed in the next section (Project Outcome).
-
Based on the Dalia Research 2016 survey dataset, and predictive analytics: What is the possibility of predicting whether some will vote for or against basic income?
-
Based on the 2016 survey dataset, and hypothesis testing: What key factors that have significant influence on basic income voting outcome?
-
Based on the above two results, and drawing from recent debates on basic income (on mainstream and social media): What is the future of basic income in the future of work?
From Dalia Research basic_income=pd.read_csv('basic_income_dataset_dalia.csv