MARALB83 / Boston_Housing_MLEN

Supervised learning regression project done as part of the Machine Learning Engineer Nanodegree at Udacity.

Boston_Housing_MLEN

Supervised learning regression project done as part of the Machine Learning Engineer Nanodegree at Udacity.

Disclaimer

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.

Data dependencies:

housing.csv

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

1. RM: average number of rooms per dwelling
2. LSTAT: percentage of population considered lower status
3. PTRATIO: pupil-teacher ratio by town

Target Variable 4. MEDV: median value of owner-occupied homes

Other dependencies:

Python 3 version was used to run the Notebook.

visuals.py

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.