While the Titanic disaster occurred just over 100 years ago, it still attracts researchers looking for understanding as to why some passengers survived while others perished. With the use of a modern data mining tools (Weka) and an available dataset we take a look at what factors or classifications of passengers have a persuasive relationship towards survival for passengers that took that fateful trip on April 10, 1912. The analysis looks to identify characteristics of passengers cabin class, age, point of departure and that relationship to the chance of survival for the disaster.
Deep Dive into Classification of Titanic Passenger Data and Chances of Surviving the Disaster.
While the Titanic disaster occurred just over 100 years ago, it still attracts researchers looking for understanding as to why some passengers survived while others perished. With the use of a modern data mining tools (Weka) and an available dataset we take a look at what factors or classifications of passengers have a persuasive relationship towards survival for passengers that took that fateful trip on April 10, 1912. The analysis looks to identify characteristics of passengers cabin class, age, point of departure and that relationship to the chance of survival for the disaster.
The Titanic was a ship disaster that on its maiden voyage sunk in the northern Atlantic on April 15, 1912, killing 1502 out of 2224 passengers and crew [2]. While there exists conclusions regarding the cause of the sinking, the analysis of the data on what impacted the survival of passengers continues to this date [2,3]. The approach taken is utilize a publically available data set from a web site known as Kaggle [4] and the Weka[5] data mining tool. We focused on decision tree based and cluster analysis after data review and normalization.
Kaggle offers businesses and other entities crowd-sourcing of data mining, machine learning, and analysis. Sometimes offering prizes (for example there had been a $200,000 prize being offered from GE through Kaggle in a competition [1]).
The Weka tool provides a collection of machine learning and data mining tools. Freely available built upon Java which allows it to run on platforms that support Java. It’s maintained and supported primarily by researchers at the University of Waikato.
The following is a representation of the test dataset provided in a comma separated value (CSV) format from Kaggle and 891 rows of data (a subset of the entire passenger manifest). The file structure with example rows is listed in the following 3 tables.
The dataset was modified to create nominal columns from some of the numeric columns in order to facilitate usage in Weka for Tree analysis and simple cluster analysis.
The modification is done to facilitate usage in Weka for tree analysis and simple cluster analysis. The following table identifies the conversions and other modifications.
Upon conversion, the final dataset utilized for the analysis in the Weka tool is illustrated below with the first few rows shown.
The table is then converted and saved into the Weka Attribute-Relation File Format (ARFF). The ARFF file used is represented in appendix E. The key characteristics of the ARFF file format in order to facilitate the data exploration in the Weka tool is the identification of the data types and within those fields the order of the nominal values.
Using Weka, we generated a J48[6] Tree (C.45 implementation) which resulted in the classifier output represented in appendix G -J48 Classifier Output. The J48 Tree diagram shown in figure 2 below illustrates the classification path that the data suggests.
Based upon the outcome of the J48 analysis it was clear that the most significant association with regards to survival was related to Sex; in that just being Female was the most significant classifier. We then reviewed the cluster analysis for further relationships.
Clustering the data based upon classifications and use of clustering analysis simple associations may be understood from the data. While an association might be strong through this analysis, the true cause and effect cannot be concluded.
For our cluster analysis, we chose the Simple K Means, just for simplicity. The Simple K Means text Output is included in appendix H. The visualizations are also shown in the following sections.
Using the cluster diagram we can visually analyze the clusters for relationships within the dataset. The strength of the classification and clustering is shown visually as well as within the text output. This clustering relationship may be used to conclude that some relationship exists, but not cause-and-effect.
Quite dramatically visually we see that sex of the passenger shows significant clustering around survival chances. This had been also shown in the J48 tree. Figure 2 below illustrates the significant clustering of Sex vs. chance of survival. Whether this is anticipated or not is something that would require further corollary analysis within social sciences as to why one Sex may fare better in these traumatic situations.
with more granular or genealogically defined age groups to draw any further potential relationships that might exist.
Perhaps not surprisingly, cabin class had significant clustering with the lower tiered cabins showing significant weight towards non-survival. This is shown in figure 3 below with a fairly dominant clustering for those in 3 rd class that did not survive. And somewhat clear clustering for those in 1 st class surviving. We can make conjectures about this result, perhaps the physical location or other facts about how passengers were able to freely or not freely move about the ship. However, we cannot drawl strong conclusions or inferences from this data alone.
Our data normalization arbitrarily bucketed passenger data into various nominal groups. Amongst these groups, again not surprisingly the adultsage 20 -49were amongst those that perished. Figure 4 below doesn’t have as great of a visual clustering as the prior twosex or age. Our approach to age buckets was a generalization. Further analysis could be made
Finally, the analysis identified that point of embarking of the passengers was also an indicator of survival rate, although not as strong. What
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