In this data science project, I used decision tree algorithm to predict the survivors in the Titanic tragedy with an accuracy of 83%. I also used conditional probabilities to answer some specific questions such as whether the high survival rate among females was due to gender or wealth or why the survival rate among passengers who embarked at Cherbourg is higher than the survival rate at the two other locations. The software used is the R package

1. TITANIC
TITANIC PROJECT (R software used)

2. Cleaning the data
 Handling missing values
 Choosing theright attributes
 Converting the target to categorical type
 Adding a new column (“Survived”) to the testing data

3. Handling missing values
 Cabin column with very few data values removed from the data
 Blank space converted into NA
 NA in Age column filled out with the mean of column (since the
distribution of ages is roughly symmetric: the mean is a good
measure of center)

4. Choosing the appropriate attributes
Use logistic regression to examine the
relationship between the target variable
(“Survived”) and each one of the other
attributes one at a time. The p-value is a
good indicator of how much an attribute
impacts the target.
Example: the p-value in the relationship
"Survived” vs “Fare” is 9.79e-12 (very
small). “Fare” has an impact in
“Survived”
Another indicator could be a stacked bar
graph such as the one to the right
showing that a female has a much higher
probability of surviving than a male (gray
area shows the proportion of survivors)

5. Converting attribute types
The following columns: “Pclass”, “Survived” are converted into
categorical variables using the as.factor function

6. Adding a new column
A new column “Survived” added to the testing dataset so we can make
predictions using the model built from the training set

7. How to approach this
problem?
One would think that age and gender would be very important criteria
in saving passengers as well as fare and class.

8. What data analysis shows
Most females survived (74%)

9. What data analysis shows
Most passengers who embarked
at Cherbourg survived (55%) while
only 34% of passengers who
embarked at Southampton did.

10. What data analysis shows
Survival rate is highest in class1(63%)
and lowest in class 3 (only 24%)

11. Question 1
The survival rate being higher among females and also among first class
passengers may be understandable (Special treatment).
But why Cherbourg passengers have a higher survival rate?

12. What data shows
Fifty percent of people who
embarked at Cherbourg are first
class passengers. This explains
why the survival rate among
Cherbourg passengers is higher
than among passengers who
embarked at Southampton or
Queenstown

13. Question2
Why is the survival rate much higher among females? Is it because of
gender or wealth?

14. How did we solve this question?
• Find the survival rate among female in general (74%) against 19% for
males

15. How did we solve this question?
• Find the probability that a female survive given that she is a first class
passenger.
• The probability that a woman survives is 74% against 19% (previous
slide) for a man but the probability that a woman survive given that
she is in class1 is almost 97%, which is consistent with the high
survival rate in Pclass1 (63%) and higher percentage of female in
Pclass1 than male (30% vs 20%).
• Conclusion: The high survival rate among females is a combination of
gender and wealth

16. Structure

17. Decision tree using the classification
tree(ctree) of party library in R

18. Accuracy on training set

19. Predictions on testing set

20. Any suggestions, please share
THANK YOU