Boston-House-Prices-With-Regression-Machine-Learning-and-Keras-Deep-Learning
In this repository, a regression analysis is conducted using different machine learning models. The study is led on the prediction of median value of owner-occupied homes, from the Boston house pricing dataset. The project is carried out by selecting different regression models and finding the most suitable one. The procedure consists in different steps ranging from data visualization, features standardization, model tuning and hyperparameters search to find which model is the most accurate in terms of minimal mean squared error, for the comparison of predicted prices with the real prices.
Getting Started
These instructions will get you a copy of the project up and running on your local machine for development and testing purposes. See deployment for notes on how to deploy the project on a live system.
Prerequisites
You need Python 3.x to run the following code. You can have multiple Python versions (2.x and 3.x) installed on the same system without problems. Python needs to be first installed then SciPy, as there are dependencies on packages.
In Ubuntu, Mint and Debian you can install Python 3 like this:
sudo apt-get install python3 python3-pip
Alongside Python, the SciPy packages are also required. In Ubuntu and Debian, the SciPy ecosystem can be installed by:
sudo apt-get install python-numpy python-scipy python-matplotlib ipython ipython-notebook python-pandas python-sympy python-nose
For deep learning tasks, the Keras library can be installed using the pip package manager:
sudo pip install keras
Finally, the Jupyter Notebook which can be installed through Python's package manager:
pip3 install --upgrade pip
pip3 install jupyter
For other Linux flavors, OS X and Windows, packages are available at:
http://www.python.org/getit/ for Python
https://www.scipy.org/install.html for the SciPy ecosystem
https://keras.io/#installation for the Keras library
https://jupyter.readthedocs.io/en/latest/install.html for the Jupyter Notebook
File descriptions
"Boston_regression_analysis.ipynb" in which the complete procedure for the regression case study of Boston house pricing is explained. This covers different types of models, hyperparameters search, features visualization and standardization, for the choice of the most appropriate model in terms of lowest negative mean squared error.
"Boston_deeplearning_regression.ipynb" which treats of the Boston house price with deep learning neural networks using the Keras library. Standardization, and influence of the number of hidden layers, and neuron inside the hidden layers are covered, to give a direction towards the best performing network.
"housing.csv" contains the dataset of the Boston house prices in csv format.
Running the files
Both notebooks can directly be opened on GitHub. Alternatively, you can also use the Jupyter Notebook. This can be done by executing the following command at the Terminal (Mac/Linux) or Command Prompt (Windows):
jupyter notebook
This will print some information about the notebook server in your terminal, including the URL of the web application (by default, http://localhost:8888):
$ jupyter notebook
[I 11:47:00.830 NotebookApp] Serving notebooks from local directory: C:\Users\EC-PM-3
[I 11:47:00.830 NotebookApp] The Jupyter Notebook is running at:
[I 11:47:00.830 NotebookApp] http://localhost:8888/?token=d22181d47f4826316a37161bb8c8469d77a5851bf9ab2c1f
[I 11:47:00.830 NotebookApp] Use Control-C to stop this server and shut down all kernels (twice to skip confirmation).
It will then open your default web browser to this URL.
When the notebook opens in your browser, you will see the Notebook Dashboard, which will show a list of the notebooks, files, and subdirectories in the directory where the notebook server was started. The notebook can then be chosen by navigating in the Notebook Dashboard.
For more information on how to run a specific jupyter notebook, you can go to the following link.
Contributing
Please read CONTRIBUTING.md for details on our code of conduct, and the process for submitting pull requests to us.
Versioning
We use SemVer for versioning.
Authors
- David Cicoria - Initial work - DavidCico
See also the list of contributors who participated in this project.