ai_hack_2021

Link to website: https://namiyousef.github.io/ai_hack_2021/

Link to blogpost: https://techcommunity.microsoft.com/t5/educator-developer-blog/mech-eng-defectors-a-hackathon-story-ai-hack-2021/ba-p/2178670

Project Abstract

The Boston Housing Market dataset is ubiquitous but imperfect: with problems like small size, inconsistent definitions, incorrect coordinates and many many. However, it is still a very rich dataset containing informative geographical information, powerful socioeconomic indicators, and continuous levels of Nitrogen Oxides (NOx). This report explores the effect of developing low income neighbourhoods on NOx. This involves three logical steps: 1) Verifying that the dataset is rich enough to form clusters of economic class, 2) train a regressor for predicting NOx values, and finally 3) creating synthetic data simulating ‘improved’ low income neighbourhoods by bootstrapping values from higher income classes, while keeping geographical constraints fixed. The evidence suggests that improving low income neighbourhoods does indeed decrease overall NOx levels, giving non-humanitarian reasons for supporting social uplifting policy. This project also corrects erroneous longitude and latitude values of the Boston dataset using Google’s geocoder API.

Contents of repository

Files

• environment.yml: conda environment file to reproduce work

Directories

• PythonScripts: a folder containing Python scripts used througout this project

• data: a folder containing data pertaining project (inlcuding images)

• data_exploration: a folder containing data exploration notebooks

• modelling: a folder containing modelling work (includes clustering)

• reports: a folder containing profile reports or actual project reports

• UsefulDocs: AI hack welcome info and links