Titanic - Machine Learning from Disaster

data
Author

Tony Duan

Published

January 1, 2023

Code 
library(plotly)
library(tidyverse)
library(stringr)
library(doFuture)
library(tidymodels)

1. download data

2. read data

Code 
list.files("data")
[1] "gender_submission.csv" "test.csv"              "train.csv"
Code 
train <- read.csv("data/train.csv")
test <- read.csv("data/test.csv")
gender_submission <- read.csv("data/gender_submission.csv")

3. data Dictionary

Code 
glimpse(train)
Rows: 891
Columns: 12
$ PassengerId <int> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17,…
$ Survived    <int> 0, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1…
$ Pclass      <int> 3, 1, 3, 1, 3, 3, 1, 3, 3, 2, 3, 1, 3, 3, 3, 2, 3, 2, 3, 3…
$ Name        <chr> "Braund, Mr. Owen Harris", "Cumings, Mrs. John Bradley (Fl…
$ Sex         <chr> "male", "female", "female", "female", "male", "male", "mal…
$ Age         <dbl> 22, 38, 26, 35, 35, NA, 54, 2, 27, 14, 4, 58, 20, 39, 14, …
$ SibSp       <int> 1, 1, 0, 1, 0, 0, 0, 3, 0, 1, 1, 0, 0, 1, 0, 0, 4, 0, 1, 0…
$ Parch       <int> 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 1, 0, 0, 5, 0, 0, 1, 0, 0, 0…
$ Ticket      <chr> "A/5 21171", "PC 17599", "STON/O2. 3101282", "113803", "37…
$ Fare        <dbl> 7.2500, 71.2833, 7.9250, 53.1000, 8.0500, 8.4583, 51.8625,…
$ Cabin       <chr> "", "C85", "", "C123", "", "", "E46", "", "", "", "G6", "C…
$ Embarked    <chr> "S", "C", "S", "S", "S", "Q", "S", "S", "S", "C", "S", "S"…
Code 
glimpse(test)
Rows: 418
Columns: 11
$ PassengerId <int> 892, 893, 894, 895, 896, 897, 898, 899, 900, 901, 902, 903…
$ Pclass      <int> 3, 3, 2, 3, 3, 3, 3, 2, 3, 3, 3, 1, 1, 2, 1, 2, 2, 3, 3, 3…
$ Name        <chr> "Kelly, Mr. James", "Wilkes, Mrs. James (Ellen Needs)", "M…
$ Sex         <chr> "male", "female", "male", "male", "female", "male", "femal…
$ Age         <dbl> 34.5, 47.0, 62.0, 27.0, 22.0, 14.0, 30.0, 26.0, 18.0, 21.0…
$ SibSp       <int> 0, 1, 0, 0, 1, 0, 0, 1, 0, 2, 0, 0, 1, 1, 1, 1, 0, 0, 1, 0…
$ Parch       <int> 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
$ Ticket      <chr> "330911", "363272", "240276", "315154", "3101298", "7538",…
$ Fare        <dbl> 7.8292, 7.0000, 9.6875, 8.6625, 12.2875, 9.2250, 7.6292, 2…
$ Cabin       <chr> "", "", "", "", "", "", "", "", "", "", "", "", "B45", "",…
$ Embarked    <chr> "Q", "S", "Q", "S", "S", "S", "Q", "S", "C", "S", "S", "S"…
Code 
glimpse(gender_submission)
Rows: 418
Columns: 2
$ PassengerId <int> 892, 893, 894, 895, 896, 897, 898, 899, 900, 901, 902, 903…
$ Survived    <int> 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 1…

4. data cleanning

6. model

Reference

https://www.kaggle.com/competitions/titanic/overview

https://github.com/Kaggle/kaggle-api

https://github.com/mkearney/kaggler