kaggle_tatanic_classification

1. Problem Definition

I will go through the whole process of creating a machine learning model on the famous Titanic dataset, which is used by many people all over the world. It provides information on the fate of passengers on the Titanic. By using it, I will predict whether a passenger on the titanic would have been survived or not.

2. Data Dictionary from Kagle

Variable	Definition	Key	Veriable Notes
Survival	Survival	0 = No, 1 = Yes
pclass	Ticket class	1 = 1st, 2 = 2nd, 3 = 3rd	1st = Upper, 2nd = Middle, 3rd = Lower
sex	sex
Age	Age in years		Age is fractional if less than 1. If the age is estimated, is it in the form of xx.5
sibsp	# of siblings / spouses aboard the Titanic		The dataset defines family relations
parch	# of parents / children aboard the Titanic		The dataset defines family relations
ticket	Ticket number
fare	Passenger fare
cabin	Cabin number
embarked	Port of Embarkation	C = Cherbourg, Q = Queenstown, S = Southampton

3. Data Correlation Analysis

Find out how much each Feature is influencing Survived.

Null Data Checking
Pclass -Sex
Sex and Pclass
Age
Age and Pclass
Pclass, Sex and Age
Embarked
Family
Fare

4. Data preproecessing

Name Replacing
Fill Null in Age
Fill Nill in Embarked
Change Initial, Embarked and Sex (string to numerical)
One-hot encoding on Initial and Embarked
Drop columns
Build Model and predict using the trained model
Feature Importance

5.Build Model and Train

RandomForestClassifier

Random forest builds multiple decision trees and merges them together to get a more accurate and stable prediction.