Supervised learning regression project done as part of the Machine Learning Engineer Nanodegree at Udacity.
This project was done as part of Udacity's Machine Learning Engineer Nanodegree. It started as a template developed by Udacity which I completed with code of my own in order to uncover insights in the data and to answer the questions.
The modified Boston housing dataset consists of 489 data points, with each datapoint having 3 features. This dataset is a modified version of the Boston Housing dataset found on the UCI Machine Learning Repository.
Features
RM
: average number of rooms per dwellingLSTAT
: percentage of population considered lower statusPTRATIO
: pupil-teacher ratio by town
Target Variable
4. MEDV
: median value of owner-occupied homes
Python 3 version was used to run the Notebook.
This is a Python module that was made available by Udacity. It provides visualizations that help see the performance of a decision tree regressor on both the training and the testing datasets, across multiple 'max_depth' parameter.