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.