Trying out first Kaggle dataset exploration.

• feature engineering .
• Blending
• Stacking
• Ensembling
• Voting
• Stacking/Ensembling guide
• Random Forest Lot more of them....

We need to predict for each testing data whether the person was able to survive or not based on the model learnt from training data.

1. survival Survival (0 = No; 1 = Yes)
2. pclass Passenger Class (1 = 1st; 2 = 2nd; 3 = 3rd)
3. name Name
4. sex Sex
5. age Age
6. sibsp Number of Siblings/Spouses Aboard
7. parch Number of Parents/Children Aboard
8. ticket Ticket Number
9. fare Passenger Fare
10. cabin Cabin
11. embarked Port of Embarkation (C = Cherbourg; Q = Queenstown; S = Southampton)

1. Pclass is a proxy for socio-economic status (SES) 1st ~ Upper; 2nd ~ Middle; 3rd ~ Lower

2. Age is in Years; Fractional if Age less than One (1) If the Age is Estimated, it is in the form xx.5

3. With respect to the family relation variables (i.e. sibsp and parch) some relations were ignored. The following are the definitions used for sibsp and parch.

Sibling: Brother, Sister, Stepbrother, or Stepsister of Passenger Aboard Titanic Spouse: Husband or Wife of Passenger Aboard Titanic (Mistresses and Fiances Ignored) Parent: Mother or Father of Passenger Aboard Titanic Child: Son, Daughter, Stepson, or Stepdaughter of Passenger Aboard Titanic

4. Other family relatives excluded from this study include cousins, nephews/nieces, aunts/uncles, and in-laws.

5. Some children travelled only with a nanny, therefore parch=0 for them. As well, some travelled with very close friends or neighbors in a village, however, the definitions do not support such relations.