Project: Predicting Boston Housing Prices

Install

This project requires Python and the following Python libraries installed:

• NumPy
• Pandas
• matplotlib
• scikit-learn

You will also need to have software installed to run and execute a 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 the above packages and more included.

Files

boston_house_price_Predictor.ipynb: notebook file with coding and explanation about the project. housing.csv: Dataset file obtained from UCI Machine Learning Repository.

Run

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

jupyter notebook boston_house_price_Predictor.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