Analysis from Airbnb, Seattle and Boston listing data for gaining some business related insights
To run the project in your local machine using python 3.6 just run the following command in your virtual environment
pip install -r requirements.txt
This projects explored the Airbnb boston dataset by CRISP-DM methodology and analysed few aspects inside data what it can offer benefits to hotel owners, people who visits to boston. The below are the questions which i analyzed in this project
1. What time of the year is peak season in Boston?
2. Analyse people reviews, what they think about the Airbnb?
3. What are the important factors that influence price in Boston ?
listings.csv -- this file has more information about price, reviews, type of house, neighbourhood etc.,
reviews.csv -- this file collects the information from websites what tenants thinking about their hotels.
calender.csv -- this file talks about price of each hotels on each day.
boston.ipnyb -- this notebook explained each question with analysis,how to clean data, what to do with missing data,
how to transform certain features and how to apply imputation methods
From the analysis, we found that rents tend to increase more in summer season, people like to visit Airbnb hotels more often and prefered the home with more space, rich in amenities.
The blog post can be found here(https://medium.com/@kowshik226/insights-of-airbnb-boston-home-data-e32da62f1351)
Thanks to the Udacity team for making my learning path fascination, Licensing related to data can be found in Kaggle