Housing Prices Prediction Project for Udacity Machine Learning Engineer Nanodegree

Project overview

This is Boston housing prices predicting project. In this project, I used SciKit-learn to apply basic machine learning concepts on data collected for housing prices in the Boston, Massachusetts area to predict the selling price of a new home.

• First, I explored the data to obtain important features and descriptive statistics about the dataset.
• Next, I split the data into testing and training subsets, and determined a suitable performance metric for this problem.
• Then, I analyzed performance graphs for a learning algorithm with varying parameters and training set sizes.
• This allowed me to pick the optimal model that best generalizes for unseen data.
• Finally, I tested this optimal model on a new sample and compared the predicted selling price to statistics.

Prerequisites

The code is Python in a Jupyter Notebook and it uses:

• SciKit-learn
• Pandas
• Matplotlib
• NumPy

You can install everything you need to run this project with Anaconda.

Installing

Clone this repository:

git clone https://github.com/rauf-mifteev/MLND_Boston_Housing.git

Run the Code

Navigate to the cloned directories location and start jupyter notebook with boston_housing.ipynb:

jupyter notebook boston_housing.ipynb