Predicting Housing Prices

Description

Building and optimizing a machine learning model to predict housing prices.

Install

This project requires Python 3.7 and the following Python libraries installed:

• Jupyter Notebook (IPython) 4.10.0
• NumPy 1.16.4
• Pandas 0.24.2
• matplotlib 2.2.4
• scikit-learn 0.19.2

If you do not have Python installed yet, it is highly recommended that you install the Anaconda distribution of Python, which already has the above packages and more included.

Code

The code is provided in the predicting_housing_pricesg.ipynb notebook file. You will also be required to use the included visuals.py Python file and the housing.csv dataset file to complete your work.

Run

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

ipython notebook predicting_housing_prices.ipynb

or

jupyter notebook predicting_housing_prices.ipynb

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

Data

The modified 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