shubham10divakar/Deep-Analysation-of-Airbnb-Data

This is the first project of my Udacity Data Scientist Nanodegree. For this project, you will pick a dataset. Inspired by Robert, there are a few public datasets from AirBnB available below, but you may also choose a dataset similar to what was used in the lessons, or an entirely different dataset. Using your dataset, you will choose 3 questions you aspire to answer from the data.

Dive Into Boston and Seattle Airbnb Data

Project Motivation

Based on Cross-Industry Standard Process of Data Mining (CRISP-DM), the Boston and Seatle Airbnb datasets were collected and investigated. Three bisinuess questions were asked and answered:

• Is there any noticeable difference between Seattle and Boston Airbnb?
• What are the most important features to estimate Airbnb rental price?
• What are the top amenities people needs most?

File Description

• A Descriptive Jupyter Notebook
• Two datasets: Detailed Listings data for Boston and Seattle which were collected Aug.19th. Dataset Link: http://insideairbnb.com/get-the-data.html

Results of the analysis

Results and discussion were published on Medium: Dive Into Boston and Seattle Airbnb Data from my blog

In this project, I dived into the most recent Airbnb Boston and Seattle dataset and found many interesting phenomenom:

• We gathered the Boston and Seattle Airbnb data, and compare the two dataset.
• We established a machine learning model to predict the rental price for both cities.
• We took a look at the feature importance of the trained model and check if they make sense.
• We list all the important amenities to get a better feeling how host can make more money by providing better services to meet customers’ need.