Titanic: Machine Learning from Disaster

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Presentation transcript:

Titanic: Machine Learning from Disaster Kaggle Competition Titanic: Machine Learning from Disaster

kaggle What is Kaggle? A data science competitions : Upload your predictions. Scores your solution Shows your score on the leaderboard

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

Titanic Competition url: https://www.kaggle.com/c/titanic Data url: https://www.kaggle.com/c/titanic/data Leaderboard: https://www.kaggle.com/c/titanic/leaderboard

Classification

Prediction

Titanic Attribute Description:

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

Provided by Kaggle gendermodel - python genderclassmodel - python myfirstforest - python

Homework 1 Registration Apply a simple algorithm to build the classifier Use the classifier to predict the survival passengers Submit the result to Kaggle Deadline: next Thursday (11/19)

Homework 2 Oral report The illustration of x-level decision tree Deadline: next Thursday (11/26)

Final project Registration Try different algorithms to build the best classifier Use the classifier to predict the survival passengers Submit the result to Kaggle

Final project Deadline: 12/2 23:59 Submission: Submit the results to kaggle Email your project to sydang.ncku@gmail.com Project file content: code prediction result report

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

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

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

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 tree can be thought of as a vote; the most votes determines the classification. SITE: http://www.r-bloggers.com/a-brief-tour-of-the- trees-and-forests/

Important attribute Pclass Sex Fare Embarked

Important attribute Title ('Capt', 'Don', 'Major', 'Sir’,'Dona', 'Lady', 'the Countess', 'Jonkheer’) Mother (Sex='female' & Parch>0 & Age>18 & Title!='Miss') Child (Parch>0 & Age<=18) FamilyNum (Parch+SibSp+1) Pclass (Pclass & age & sex)