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Titanic: Machine Learning from Disaster
- 1. INFSCI – 2725 DATA ANALYTICS Team Name: Data Warriors Team Members: Sushma Anand Akoju BharathKumar Inbasekaran Kaggle –Titanic: Machine Learning from Disaster Source: http://img.src.ca/2012/04/06/635x357/120406_g08if_betcie_titanic_iceberg_sn635.jpg
- 3. Source: https://www.kaggle.com/c/titanic Titanic: Machine Learning from Disaster • Goal –Which passenger survived the tragedy? • Kaggle “Getting Started” competition • No point / tiers awarded in Kaggle
- 6. Pclass, Age and Sex The survival of young passengers is not dependent on Pclass and Sex. Source: http://i.imgur.com/geNnKff.png
- 7. MissingValues The age for many male passengers who are travelling alone are missing Source: https://lh3.googleusercontent.com/6Ow3-1kbm2-lpabohQ4eKrcbg3DnWddCsqwpyjRiz2g=w402-h311-p-no
- 8. SurvivalVs Fare Passengers who have paid more fare for tickets had higher survival rate. Source: http://s24.postimg.org/7kp796lmd/Fare_Price_And_SRate.png
- 10. Using Decision tree with single attribute Title, Sex and Pclass are the major deciding factor. Source: http://s13.postimg.org/55y2aslnr/single_attribute_scores.jpg
- 11. Combining attributes Combining Sex and Fare yields good result Sourcehttps://http://s11.postimg.org/jc28ezt1f/sex_plus_attribute_scores.png
- 12. Missing Data CompletionTechniques • 236 missing values • Two approaches: • Median value • DecisionTree age • Two missing value • Substituted with “S” as many passengers had embarked from Southamptonembarked • One NA value • Two approaches: • Replaced with the median value. • DecisionTree fare Source: http://trevorstephens.com/post/72916401642/titanic-getting-started-with-r
- 13. Given Attributes Pclass, Age, Sex, Fare, Sibsp, Parch, Cabin Engineered Attributes Title, Surname, Mother, Father, Extended relation Attributes Considered
- 14. Algorithms C Forest + Boosting Naïve Bayes K - SVM GLM Random Forest GBM
- 15. Accuracy •0.77 Learning •Limitation with the number of factors affected the accuracy Random Forest
- 16. Accuracy •0.77 Learning •All the attributes do not follow standardized distribution Generalized Linear Model
- 17. Accuracy •0.79 Learning •Less accuracy because all the attributes could not be factored K – Fold SVM
- 18. Naïve Bayes Accuracy • 0.77 Learning • Considering the conditional and individual probabilistic dependence of each variable on survival rate.
- 19. Accuracy • 0.81 Learning • More factors were considered and the accuracy has improved over Random forest. Conditional Inference Forest (C Forest)
- 20. Accuracy •0.83 Learning •Important attributes like title, fare and pclass were correctly identified Gradient Boosted Regression Model (GBM)
- 23. Other Approaches and Observations • Used all attributes for prediction in decision tree, but ended up with the issue of overfitting • Used Bayesian Search in Genie to implement Naïve Bayes • GBM model predictionVs Random Forest predictionVs Bayesian Dependence • We found importance of attributes in GBM or conditional dependence in Bayesian Search were similar • Used brute force methods: • All Survived - .37 accuracy • All Perished - .63 accuracy • Used stacking and voting – achieved .85 accuracy • FrequencyTables and Conditional Probability
- 24. Manual Prediction Every one correct prediction gives a 0.2 percent increase in accuracy http://rstudio-pubs-static.s3.amazonaws.com/53109_cd4baa0d9ad54bfb94598a67f79a79a6.html
- 25. Experience and Learning Each algorithm gave different results No algorithm can predict with 100% accuracy The accuracy of a model as determined by confusion matrix is different from the accuracy obtained from kaggle
- 26. We were able to reach top 10 in kaggle !