Airbnb-New-User-Bookings

Capstone Project 1 Proposal

Problem: Where will a new guest book their first travel experience?

New users on Airbnb can book a place to stay in 34,000+ cities across 190+ countries. By accurately predicting where a new user will book their first travel experience, Airbnb can share more personalized content with their community, decrease the average time to first booking, and better forecast demand. Airbnb is the client of this project, the goal is to predict in which country a new user will make his or her first booking so to better forecast demand and therefore increase revenue.

Data are provided by Airbnb on Kaggle’s competition platform:

train_users.csv - the training set of users

test_users.csv - the test set of users

sessions.csv - web sessions log for users

countries.csv - summary statistics of destination countries in this dataset and their locations

age_gender_bkts.csv - summary statistics of users' age group, gender, country of destination

sample_submission.csv - correct format for submitting your predictions

My Approach to this project:

1. Data collecting: downloading all the data provided by Airbnb to local.

2. Data Wrangling: data cleaning, seek mistakes in data, look for peculiar behavior, fix problematic/missing data.

3. Data exploration: use of classification, inferential statistics and data visualization to find interesting trends and identify significant features in the data set.

4. Data Analysis: data manipulation and modeling.

5. Complete and submit final deliverables : report paper explaining my approach and findings of this data exploration.

My Deliverables:

Final deliverables will include code on Github repository,

final report paper explaining approach and findings on Github,

slide deck to present on Github, and a blog post to share online.