KAR-NG / Predicting-House-Prices-in-Boston_UniqueVersion

Extracted statistical relationships between house prices and many factors, applicationised the 90% R2 Random Forest model that outcompeted MLR, Lasso, PLS, KNN, and DT into production.

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Predicting House Prices in Boston by Machine Learning (Regression)

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RShiny Application

Visit this link to use the online app to make predictions with your favourite numbers: https://karhou.shinyapps.io/boston/

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Reference

Boston thumbnail picture By King of Hearts - Own work, CC BY-SA 4.0, https://commons.wikimedia.org/w/index.php?curid=62981160

Belsley D.A., Kuh, E. and Welsch, R.E. (1980) Regression Diagnostics. Identifying Influential Data and Sources of Collinearity. New York: Wiley.

Brownlee J 2016, How to Work Through a Regression Machine Learning Project in Weka, viewed 26 September 2021, https://machinelearningmastery.com/regression-machine-learning-tutorial-weka/

Harrison, D. and Rubinfeld, D.L. (1978) Hedonic prices and the demand for clean air. J. Environ. Economics and Management 5, 81–102.

James, Gareth, Daniela Witten, Trevor Hastie, and Robert Tibshirani. 2014. An Introduction to Statistical Learning: With Applications in R . Springer Publishing Company, Incorporated.

Minitab Blog Editor 2013, Enough Is Enough! Handling Multicollinearity in Regression Analysis, viewed 25 September 2021, https://blog.minitab.com/en/understanding-statistics/handling-multicollinearity-in-regression-analysis

Sivakumar C 2017, https://rpubs.com/chocka314/251613, viewed 28 September 2021, https://rpubs.com/chocka314/251613

https://cran.r-project.org/web/packages/MASS/MASS.pdf

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Extracted statistical relationships between house prices and many factors, applicationised the 90% R2 Random Forest model that outcompeted MLR, Lasso, PLS, KNN, and DT into production.


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Language:R 100.0%