There should be no necessary libraries to run the code here beyond the What is provided in Kaggle notebooks. The code should run with no issues using Python versions 3.*.
However, if you are setting up in your own environment, the following libraries need to be installed with python 3.*.:
- Pandas
- Numpy
- Scikit-learn
- Seaborn
- Matplotlib
To begin Clone this repo and extract the dataset.zip file which contains the dataset.
Open the juptyer file and Run the Notebook
Make Sure to update the directory of the dataset in the notebook
Or
Click here to direct you to the notebook on kaggle and Run it. No need to download the dataset and install libraries.
This project centered on using the Seattle Airbnb dataset to find out how new hosts can enter the market and start earning fast on Airbnb. To better understand, the following questions were used in the analysis:
-
What type of property type are most people interested in?
-
What are the characteristics of listing that influence the price of listings?
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When is the best time to host a listing for booking?
- crisp-dm-process-on-seatle-airbnb-data.ipynb - Notebook for the analysis
- dataset.zip - Seattle Airbnb dataset
- calender.csv
- listings.csv
- reviews.csv
The main findings of the code can be found at the post below Link to blogpost CRISP-DM process on Airbnb Seatle Dataset
Credit goes to Airbnb for the data. You can find the Licensing for the data and other descriptive information at the Kaggle link available here. Otherwise, feel free to use the code here as you would like!