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The sinking of the RMS Titanic is one of the most infamous shipwrecks in history. On April 15, 1912, during her maiden voyage, the Titanic sank after colliding with an iceberg, killed 1503 out of 2222 passengers and crew. This sensational tragedy shocked the international community and led to better safety regulations for ships. One of the reasons that the shipwreck led to such loss of life was that there were not enough lifeboat equipment for the passengers and crew. In particular, we are going to apply the tools of machine learning to predict which passengers survived the tragedy.
Why: Problem statement
Titanic was constructed with sixteen watertight compartments doctrine. Each compartment had doors that were designed to close automatically if the water level rose above a certain height. The doors could also be electronically closed from the bridge. Titanic was able to stay afloat if any two compartments or the first four became flooded. Shortly after Titanic hit the iceberg it was revealed that the first six compartments were flooded. The bottom of the ship contains boiler room, turbine engine room, and reciprocating engine room. Open front bulkheads were there.
The cargo holds and boiler rooms flooded after the hull is pierced by an iceberg. Since the bulkheads were not closed, water pours over the top of the deck above flooding the entire hull.
How: Solution description
The general analysis says the majority of the passengers traveled in titanic died. This occurred because of three major reasons which are lifeboat equipment were not sufficient which led to chaos and confusion and there was no legislation. The second reason was the ship sank because of the broken bottom, and the bulkheads were not closed. The bottom was broken because of hitting the iceberg and the hull was not strong enough to bare the hit. And the third reason was the rescue boats were not on time which made the people rush for boarding. So this analysis clearly shows the major reasons for the accident.
The tool I used for analysis is python. Python is increasingly being used as a scientific language. Basic libraries of python are Numpy, pandas, and Matplotlib. I started analyzing all the features with respect to the target. Example: I have drawn the graph of Age vs Survived, gender vs Survived, Passenger class vs Survived, Survival at the Embark town, Analysis based on the Honorific title.
I analyzed that, 61.616% of people sank and lost their lives. And 38.383% were survived. Due to this accident, the majority of the people died. Here the Men, Women, and Children are calculated by the gender and Age features. The percentage of male survived after travel is 74.20. The percentage of female survived after travel is 18.89. From the graph, we can see that the woman show a high rate of survival.
The port of embarkation accounts very less for the passenger’s survival. In P class 1, 62.96% of the people survived. In P class 2, 47.28% of the people survived. In P class 3, 24.23% of the people survived. So we found that most of the people were got survived in P Class 1.
Nearly 120 people in the P-class 1, 170 people in the P-class 2 and 350 people in the P-class 3 are from the Southampton. Around 85 people in the P-class 1, 20 people in the P-class 2 and 70 people in the P-class 3 are from the Cherbourg. Around 3 people in the P-class 1, 7 people in the P-class 2 and 80 people in the P-class 3 are from the Queenstown.
From my prediction, the data inferred is Out of 2222 passengers, 1503 have been killed. That is 67.64% of the people died. From the data analysis, the prediction says, if 5000 passengers traveled, the death count might be 3350 to attain 67.64%.
How is it different from competition
In particular, I applied the tools of machine learning to predict which passengers survived the tragedy.
Who are your customers
Ship manufacturer company can use this project. Once they studied about the sinking, they will concentrate more in building good bulkheads.
Data Scientists can use this for study and literature survey.