Titanic-ML-Competition

Titanic Competition is one the most popular data science challenge on Kaggle platform:

https://www.kaggle.com/c/titanic The goal of the challenge is simple: predict which people survived the Titanic disaster.

• Of course, the Titanic dataset is an open dataset which you can reach on many repositories and websites. However, I used dataset from Kaggle. Also, Kaggle easily measure the accuracy of your predictions.

In this competition, you obtain two similar datasets: train.csv and test.csv.

1. train.csv contains the details of a subset of the 891 passengers on board and importantly, will reveal whether they survived or not.
2. test.csv dataset contains similar data for another 418 passengers but does not have survival information - it’s your job to predict these outcomes.

The training set is used to build a machine learning model, while test dataset is used to see how high is the accuracy of created model. Kaggle also includes gender_submission.csv, a set of predictions that assume all and only female passengers survive, as an example of what a submission file should look like.

Variables in dataset with definition:

• PassengerId - unique ID
• Survived - Survival -> 0 = No, 1 = Yes
• Pclass - Ticket Class -> 1 = 1st, 2 = 2nd, 3 = 3rd
• Name - Name and Title of a passenger
• Sex - Sex of a Passenger
• Age - Age of a Passenger
• SibSp - Number of Passenger's Siblings/Spouses aboard
• Parch - Number of Passenger's Parents/Children aboard
• Ticket - Tictek number
• Fare - Passenger fare
• Cabin - Cabin number
• Embarked - Port of Embarkation -> C = Cherbourg, Q = Queenstown, S = Southampton

Workflow of a project

My work is divided into 3 parts:

• Part 1 - Data Cleaning
• Part 2 - EDA
• Part 3 - Decision Tree Model

R (RStudio)

R version 4.1.1 (2021-08-10)

R packages:

• ggplot2
• dplyr
• gridExtra
• rpart
• rpart.plot