UDACITY Data Scientist Nanodegree CRISP-DM PROJECT

This project is about:

*Coming up with three questions

*Extracting the necessary data to answer these questions.

*Performing necessary cleaning, analysis, and modeling.

*Evaluating results.

*Sharing insights with stakeholders.

I have used Seattle AirBnB dataset(https://www.kaggle.com/airbnb/seattle) to answer the following questions and find predictive variables for price.

• What are the peak days in Seattle?
• Which areas are more pricy than others?
• What are the major factors that determine prices?

Medium Blog Post: https://medium.com/@elifsurmelif/seattle-airbnb-market-price-analytics-97196545da3a If you have difficulty in displaying .ipynb files please go to https://nbviewer.jupyter.org/ and paste the link that you're trying to display the notebook such as https://github.com/elifinspace/udacity_crispdm/blob/master/explore_raw.ipynb

Getting Started

Instructions below will help you setup your local machine to run the copy of this project.

Prerequisites

Software Requirements

• Anaconda 3

• Python 3.7.3

• textblob (https://pypi.org/project/textblob/)

• folium (https://pypi.org/project/folium/)

• branca (https://pypi.org/project/branca/)

• geopandas (https://pypi.org/project/geopandas/)

Running the notebooks

• First install all the packages stated above.
• Run the commands below in your working directory to open the project in jupyter lab:

  git clone https://github.com/elifinspace/udacity_crispdm.git
  
  jupyter lab
  
  

• explore_raw.ipynb: This notebook includes the preliminary work, explorations.
• map_visualisations.ipynb : This notebook includes map visualisations of some analysis on listings data.
• main.ipynb : This notebook makes use of the findings from the explore_raw.ipynb to cleanse and process the data. It is sufficient to run this notebook standalone to cleanse the data, generate predictions and evaluate the results.

Authors

• Elif Surmeli