This project is a modified interpretation and implementation of the End-to-End Machine Learning Project in Chapter 2 of the book Hands-on Machine Learning with Scikit-Learn, Keras & TensorFlow by Aurelien Geron [1]. When compared to the original notebook, there are changes, varied observations and rearrangement of sections at my own discretion.
Here, we aim to build a machine learning model that performs the supervised univariate multiple regression task of predicting the price of a house based on a set of features obtained through the 1990 California census.
The dataset used is a slight modification (see [2] for modifications) of the original California Housing Prices Dataset from the Statlib repository [3].
[1] "Hands-On Machine Learning with Scikit-Learn, Keras, and Tensorflow", 2nd Edition, bu Aurelien Geron (O'Reilly). Copyright 2019 Aurelien Geron, 978-1-492-03264-9.
[2] "California Housing", README file from handson-ml2 github, Aurélien Geron.
[3] R. Kelley Pace, Ronald Barry, "Sparse spatial autoregressions", Statistics & Probability Letters, Volume 33, Issue 3, 1997, Pages 291-297, ISSN 0167-7152, https://doi.org/10.1016/S0167-7152(96)00140-X .