Table of Contents

1. Installation
2. Project Motivation
3. File Descriptions
4. Results
5. Licensing, Authors, and Acknowledgements

" class="reference-link">Installation

This project uses Python 3, along with Jupyter Notebook. The following libraries are necessary for running the notebook:

• Pandas
• Numpy
• MatplotLib
• Seaborn
• Scikit-Learn

" class="reference-link">Project Motivation

For this project, I was interested in exploring the AirBnB dataset from Seattle to better understand the following questions:

1. Do the prices vary by location?
2. Do the prices vary from older to newer hosts?
3. Can we predict the price using a model?

" class="reference-link">File Descriptions

The main code for this project is included in the notebook science_blog.ipynb. The notebook walks through all the steps of the CRISP-DM Process for analyzing the dataset to answer the above three questions. The code and results are also posted on Medium as a @issanllo/introduction-to-machine-learning-using-python-the-practical-way-a2c338ac2378">blog post.

Data for the project is not included because of large file sizes. To properly run the notebook, it must be placed in data. The directory should have the following files:

• calendar.csv
• listings.csv
• reviews.csv
• neighbours.csv
• neighbours.geojson

" class="reference-link">Results

The main findings of the code can be found at the post available @issanllo/introduction-to-machine-learning-using-python-the-practical-way-a2c338ac2378">here.

" class="reference-link">Licensing, Authors, Acknowledgements

Credit to AirBnB for providing the data. You can find the Licensing for the data and other descriptive information at the Kaggle link available here. This code is free to use. If so please refer a contact to @IsraelLlorens