Predictive Modelling Solve a Business Problem - PDF

1RUNNINGHEAD: Predictive modelling solve a business problem
Table of Contents
1. Business Objective...................................................................................................1
1.1 Introduction.............................................................................................................1
1.2 Business Problem....................................................................................................2
Two sets of business questions will be addressed:........................................................3
2. Data preparation.....................................................................................................4
2.1 Data description.......................................................................................................4
2.2 Data cleaning...........................................................................................................4
i. Histogram...................................................................................................................6
ii. Scatter-plot................................................................................................................7
3. Data Sampling........................................................................................................13
3.1.1 Building the model.............................................................................................13
4. Model building.......................................................................................................15
4.1 Developing a multiple linear regression model.....................................................15
5. Deploying the model..............................................................................................17

2RUNNINGHEAD: Predictive modelling solve a business problem
1. Business Objective
1.1 Introduction
Since the inception of the art of business, risk has always been present making it
eminent for a better part of business as a whole and its operations component. This
range from the probability of losses to making decisions on what strategy to adopt.
Placing the business executive under constant need to come up with solutions that
befit not only the business problem at hand but also enables it to predict the
probability of such a problem occurring in both the near and far future. As such, the
business is then set to meet its responsibility to various of its stakeholders, who
include:
i. Shareholders
ii. Customers
iii. Government
iv. Business partners
v. Employees
vi. Community
The business world is prone to uncertainty and therefore prediction is crucial to
making sustainable decisions that aid in propelling the business further and
cementing the confidence of stakeholders in the ability of the business to sustain
itself and be equally profitable.
In this project, we will explore the groups or types of people who were likely to
survive the accident of the Titanic establish whether altruism (placing other humans'
welfare before yours) did play a part in the survivors of the Titanic.

3RUNNINGHEAD: Predictive modelling solve a business problem
1.2 Business Problem
In order to project the probable cost of an life insurance policy on passengers in a
cruise ship. An insurance company may choose to examine the kind of passengers
likely to survive a catastrophic incident such as that of the “Titanic”. A cruise ship
accident that killed approximately 1500 passengers of the 2,225 aboard.
According to Elinder and Erixson (2012), the common fallacy that women and
children have a higher survival rate in cases of maritime accidents stands to be
disproved. In their research, they establish that, captains and men have comparatively
higher advantages of survival than women and children. Additionally the argue that
Captains actually have a higher chance of surviving than passengers.
In this paper, using historical data on the maritime tragedy of the sinking of the
Titanic, we will explore and investigate the truth of the statements: “Women and
children have a higher survival chance than men” and “Captains and ship crew have a
lower survival rate than the passengers.”
Business question
Two sets of business questions will be addressed:
i. Do women and children have a higher chance of survival than men in sea-time
accidents?
ii. Do captains crew members give priority to the survival of passengers hence
leading to their low survival chance?

4RUNNINGHEAD: Predictive modelling solve a business problem
iii. Do passengers in better travelling class have better surviving chances than those
of lower classes?

5RUNNINGHEAD: Predictive modelling solve a business problem
2. Data preparation
2.1 Data description
Data used for this project is obtained from historical data on the sinking of the
Titanic. It is sampled into:
i. Train data- used for training the prediction model
ii. Test data- used to test the build model.
The data has 12 variables with 276 entries. The variables include:
 Passenger ID
 Passenger traveling class
 Name
 Sex
 Age
 Number of siblings/spouses aboard
 Number of parents/ children aboard
 Ticket number
 passenger fare
 Cabin number

6RUNNINGHEAD: Predictive modelling solve a business problem
 Port of embarking
The data has 177 missing values.
2.2 Data cleaning
Vidhya (2017) suggests the process of data cleaning as involving:
i. Exploratory analysis also involves filtration of data following a given condition
ii. Data visualization
iii. Checking for errors, which involves:
 Selecting- picking of variables of interest
 Imputation/dealing with errors- treating of missing and duplicate data entries
 summarize- conducting of data analysis
2.2.1 Exploratory analysis
#reading data into R
precdata<-read.table("D:/train.csv", header=TRUE, sep=",")
tesdat<-read.table("D:/test2.csv", header=TRUE, sep=",")
library(dplyr)
head(precdata)
PassengerId Survived Pclass
1 1 0 3
2 2 1 1
3 3 1 3
4 4 1 1
5 5 0 3
6 6 0 3
Name Sex Age SibSp Parch
1 Braund, Mr. Owen Harris male 22 1 0
2 Cumings, Mrs. John Bradley (Florence Briggs Thayer) female 38 1 0
3 Heikkinen, Miss. Laina female 26 0 0
4 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35 1 0
5 Allen, Mr. William Henry male 35 0 0
6 Moran, Mr. James male NA 0 0