maneeshd / boston-housing

Predicting Boston Housing Prices

Udacity: Machine Learning Foundation Nanodegree

Model Evaluation and Validation Project: Predicting Boston Housing Prices

Project Report

Report can be viewed at https://maneeshd.github.io/boston-housing/.

Install

This project requires Python and the following Python libraries installed:

• NumPy
• Pandas
• matplotlib
• seaborn
• scikit-learn
• Jupyter Notebook

If you do not have Python installed yet, it is highly recommended that you install the Anaconda distribution of Python, which already has most of the above packages and more included or if you don't want to install a huge number of packages then you can try Miniconda and then install the above packages.

Code

Code is in the boston_housing.ipynb notebook file. Also required is the included visuals_md.py python file which contains some modified code for model visualizations and the housing.csv dataset file.

Run

In a terminal or command window, navigate to the top-level project directory boston_housing/ (that contains this README) and run one of the following commands:

jupyter notebook boston_housing.ipynb

or

ipython notebook boston_housing.ipynb

This will open the Jupyter Notebook software and project file in your browser.

Data

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