Kaggle competition Airbnb Recruiting: New User Bookings

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Presentation transcript:

Kaggle competition Airbnb Recruiting: New User Bookings Advanced Network Database Lab Kaggle competition Airbnb Recruiting: New User Bookings Where will a new guest book their first travel experience?

Registration Site: https://www.kaggle.com/competitions Account: IKDD1(Group Number)

Airbnb AirBed&Breakfast https://www.airbnb.com.tw/ Book rooms with locals, rather than hotels https://www.airbnb.com.tw/

Airbnb Competition url: https://www.kaggle.com/c/airbnb-recruiting- new-user-bookings Data url: https://www.kaggle.com/c/airbnb-recruiting- new-user-bookings/data Leaderboard: https://www.kaggle.com/c/airbnb-recruiting- new-user-bookings/leaderboard

Data Attribute

Classification

Prediction

Decision Tree

Sklearn – Python tool Simple and efficient tools for data mining and data analysis! Decision tree url : http://scikit- learn.org/stable/modules/tree.html

Homework 1 Registration Apply a simple algorithm to build the classifier Use the classifier to predict the country a new user will make his or her first booking Submit the result to Kaggle Deadline: next Thursday (12/10)

Homework 2 Oral report Deadline: next Thursday (12/17)

Homework 3 Try different algorithms to build the best classifier Use the classifier to predict the survival passengers Submit the result to Kaggle

Final project Deadline: 12/23 23:59 Submission: Submit the results to kaggle Email your project to cwchang.ncku@gmail.com Project file content: code prediction result report

Report The details of the your best method The description of the methods that you tried The important attributes or surprised features you found

Grading Homework 1: 20% Homework 2: 10% Final Project : 70% The ranking: 30% Algorithm and coding : 30% Report: 10%

XGBoost General purpose gradient boosting library, including generalized linear model and gradient boosted decision tree SITE: http://dmlc.ml/

tslm A linear model with time series components SITE: http://www.inside- r.org/packages/cran/forecast/docs/tslm

H2o.randomForest Random Forest (RF) is a powerful classification tool. When given a set of data, RF generates a forest of classification trees, rather than a single classification tree. Each of these trees generates a classification for a given set of attributes. The classification from each H2O tree can be thought of as a vote; the most votes determines the classification. SITE: http://docs.h2o.ai/h2oclassic/datascience/rf.ht ml