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seattle-airbnb's Introduction

seattle-airbnb

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.

  • Find what features affect an Airbnb price based on the features in the listings.csv
  • Find what is the trend of price change during an year.
  • Find what is the trend of prices change during a week.

Getting Started

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

Prerequisites

Requirements

  • Anaconda 3
  • Python 3.7.3
  • matplotlib
  • seaborn
  • numpy
  • pandas

Running the notebook

  • add the dependencies in your environment.yml and install it in a conda environment
  • Run the commands below in your working directory to open the project in jupyter lab:
    git clone https://github.com/arjun-pp/seattle-airbnb.git
    
    jupyter notebook 
    
    
    
  • run seattle-airbnb.ipynb: This notebook includes the preliminary work, explorations.

Summary

Price depends on 'accommodates', 'bathrooms', 'bedrooms', 'beds', 'guests_included', 'neighbourhood_group_cleansed', 'property_type', 'room_type', 'bed_type', 'cancellation_policy', 'zipcode'. An r2 score of 0.65 was achieved in this experiment using Linear Regression.

Price also varies during an year and is highest during Jun and July. Also there is a pattern within a week and price is highest on Fridays and Saturdays.

You can read more about it here. https://medium.com/@arjun-pp/hosting-an-airbnb-pricing-is-the-key-68c6f7fbcbe9

Acknowledgements

  • Kaggle
  • Stackoverflow

seattle-airbnb's People

Contributors

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Watchers

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