Predicting-Boston-Housing-Prices

Model Evaluation & Validation Project [Udacity Machine Learning Engineer Nanodegree]

Project Overview

Project Description

Develop a model to predict Boston housing prices. Utilize sklearn techniques for training, testing, evaluating and optimizing models.

Project Procedure

• Explore the data
• Develop a model using decision tree algorithm
• Optimize model parameters using grid search
• Predicting selling prices for 3 clients

Project Results

• Predicted sellling price

  	home price
  client 1	$409,100.00
  client 2	$285,600.00
  client 3	$957,218.18

• Applicability of the constructed model

  Still have a big room to improve before being used in the real-world for below reasons.

  • The mdoel learn from a old dataset which has quite different value of money from current, so it will not able to make a accurate prediction for house price nowadays.
  • More features relevant to house prices should be considered such as location, economic growth and interest rate, etc.

Getting Started

Prerequisites

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.

Run

In a terminal or command window, 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 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