Aviation Data Analysis for New Enterprise

1. Introduction

1.1 Understanding the Business

At Jungle Ltd, we specialize in Transportation of goods and services. The company was started in 2010 by John Doe and has grown from a 3 vehicle business to a 100 vehicle operating business . Our top of the line vehicles offer comfortable, luxurious and affordable transport from various places in the country. We pride ourselves on being on time and offering our clients the best experience

1.2 Business Problem

With the success of the business, The C.E.O has decided its time to take its interest further by investing in another branch business of flying people to their destinations. To ensure taking our clients arrive faster and safer, The C.E.O decided to acquire a fleet of airplanes for both private and commercial use. With no clue about how to run an airplane service, it has to be determined which are the best planes to buy with the lowest risk and high safety.

1.3 Purpose of Analysis and Objectives

The purpose of this project is to analyze the aviation dataset from the National Transport Safety Board (NTSB) to come up with reasonable actionable intelligence about the aviation industry where the objective is to start a new business in the airline business.

1.4 Limitation of dataset

We are operating under the assumption that the dataset is accurate with the required information required to make an informed decision

2.Data Understanding

Here we are going to use the necessary instruments to read the data from the National Transport Safety Board (NTSB).

2.1 Here we look at what our data looks like

Our data has 88889 rows and 31 columns

The columns are

(['Event.Id', 'Investigation.Type', 'Accident.Number', 'Event.Date','Location', 'Country', 'Latitude', 'Longitude', 'Airport.Code','Airport.Name', 'Injury.Severity', 'Aircraft.damage','Aircraft.Category', 'Registration.Number', 'Make', 'Model','Amateur.Built', 'Number.of.Engines', 'Engine.Type', 'FAR.Description','Schedule', 'Purpose.of.flight', 'Air.carrier', 'Total.Fatal.Injuries','Total.Serious.Injuries', 'Total.Minor.Injuries', 'Total.Uninjured','Weather.Condition', 'Broad.phase.of.flight', 'Report.Status','Publication.Date']

2.1 Data statistics

The statistics for the original data are as follows

Statistic	Number of Engines	Total Fatal Injuries	Total Serious Injuries	Total Minor Injuries	Total Uninjured
Count	82805.0000	77488.000000	76379.000000	76956.000000	82977.000000
Mean	1.146585	0.647855	0.279881	0.357061	5.325440
Std dev	0.446510	5.485960	1.544084	2.235625	27.913634
Min	0.000000	0.000000	0.000000	0.000000	0.000000
25%	1.000000	0.000000	0.000000	0.000000	0.000000
50%	1.000000	0.000000	0.000000	0.000000	1.000000
75%	1.000000	0.000000	0.000000	0.000000	2.000000
Max	8,000000	349.000000	161.000000	380.000000	699.000000

2.2 Data Quality

Our Data included some missing values in our columns which we are supposed to deal with before proceeding to analyse our data. Missing values will either be replace or the rows removed entirely.

2.3 Data analysis

Here we are going to use some key columns to analyse our data mainly

1. Amateur built

2. Make

3. Aircraft Damage

4. Total Fatal Injuries

5. Number of Engines

6. Engine Type

7. Weather condition

Tableau Dashboard**

Visualisatons can be found through https://public.tableau.com/app/profile/mark.muriithi6051/viz/tableauonnewAviatiandataanalysis/Dashboard1?publish=yes

3.Summary

1. Multiple engine plane are safer to use beacuse they have low fatalities and accident

2. Prioritise getting the planes with latest technology that can fly through any weather condition or help in an emergency.

3. Rigorius training and retraining of pilots in case of emergency like failure of instruments

In conclusion, more information is required like size of plane, weight etc for further analysis and a more informed analysis.