Model Evaluation and Validation

Predicting Boston Housing Prices

A model to predict the value of a given house in the Boston real estate market using various statistical analysis tools. Best price identified that a client can sell their house for, utilizing machine learning.

Install

This project requires Python 2.7 and the following Python libraries installed:

• NumPy
• Pandas
• matplotlib
• scikit-learn

This project is executed in a Jupyter Notebook

It is highly recommended that you install the Anaconda distribution of Python, which already has the above packages and more included. Make sure that you select the Python 2.7 installer and not the Python 3.x installer.

Code

Template code is provided in the boston_housing.ipynb notebook file. The visuals.py Python file and the housing.csv dataset file will be requiered to complete the project.

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:

ipython notebook boston_housing.ipynb

or

jupyter notebook boston_housing.ipynb

This will open the Jupyter Notebook software and project file the browser. The contents can be executed by using Shift-Enter.

Data

The modified Boston housing dataset consists of 490 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-student ratio by town

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