Project 2019 21

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This document discusses using Bayesian inference and causality analysis to estimate the causal effect of attributes on house prices using the Boston housing dataset. It introduces the Boston housing dataset which contains various attributes of houses and towns in the Boston area like crime rates, proportions of various land types, nitric oxide concentrations, room counts, ages of homes and more. It then discusses using Bayesian linear regression and the DoWhy library to perform a causal analysis on this dataset to determine the causal relationships between attributes and median home prices. The document concludes by thanking the reader and mentioning the team that worked on this analysis.

Data & Analytics

P21 - Bayesian inference,
Causality, Machine Learning

Our goal was to do a causal analysis and to estimate a
causal effect of some attribute on the price of the house
We switched from London real estate dataset to a famous
Boston dataset from scikit learn

Boston house prices dataset
CRIM per capita crime rate by town
ZN proportion of residential land zoned for lots over 25,000 sq.ft.
INDUS proportion of non-retail business acres per town
CHAS Charles River dummy variable (= 1 if tract bounds river; 0 otherwise)
NOX nitric oxides concentration (parts per 10 million)
RM average number of rooms per dwelling
AGE proportion of owner-occupied units built prior to 1940
DIS weighted distances to five Boston employment centres
RAD index of accessibility to radial highways
TAX full-value property-tax rate per $10,000
PTRATIO pupil-teacher ratio by town
B 1000(Bk - 0.63)^2 where Bk is the proportion of blacks by town
LSTAT % lower status of the population
PRICE Median value of owner-occupied homes in $1000's

The team!
Mentor: Max Sklar
Team: Kateryna Borshchuk, Vladyslav Herasymenko, Klaudia Mur,
Oleg Dats, Oleh Buhrii, Vitaliia-Anna Oliinyk
And don’t forget to listen to the Local Maximum!

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