cryptosky-report/PID.txt

Individual Project (CS3IP16)
Department of Computer Science
University of Reading

Project Initiation Document
PID Sign-Off
Student No.

24005432

Student Name

Andrew Sotheran

Email

andrew.sotheran@student.reading.ac.uk

Degree programme
(BSc CS/BSc IT)

BSc CS

Supervisor Name

Kenneth Boness

Supervisor Signature

Date

1

SECTION 1 – General Information
Project Identification
1.1

Project ID
(as in handbook)
N/A

1.2

Project Title

Cryptocurrency market and value prediction tracking
1.3

Briefly describe the main purpose of the project in no more than 25 words

To provide a means to predict the value of cryptocurrencies that will aid in investor decision making
in investment of the market

Student Identification
1.4

Student Name(s), Course, Email address(s)
e.g. Anne Other, BSc CS, a.other@student.reading.ac.uk
Andrew William Sotheran
BSc CS
Andrew.sotheran@student.reading.ac.uk

Supervisor Identification
1.5

Primary Supervisor Name, Email address
e.g. Prof Anne Other, a.other@reading.ac.uk

1.6

Secondary Supervisor Name, Email address
Only fill in this section if a secondary supervisor has been assigned to your project

Company Partner (only complete if there is a company involved)
1.7

Company Name

N/A
1.8

Company Address
N/A

1.9

Name, email and phone number of Company Supervisor or Primary Contact
N/A

2

SECTION 2 – Project Description
2.1

Summarise the background research for the project in about 400 words. You must include
references in this section but don’t count them in the word count.

To create a tool that aims to predict the price of cryptocurrencies that aids in investor decisions.
Research will need to be conducted into the following topics that surround data mining, machine
learning and artificial neural networks.
This research will consist along the lines of;
Natural Language processing and analysis – To analyse and process fed in data gathered through RSS
data feeds and social media feeds, through the underlying tasks of Natural language processing.
Content categorisation (search and indexing, duplication detection), Topic discovery and modelling
(Obtain meanings and themes within the data and perform analytic techniques), sentiment and
semantic analysis (which will identify the mood and opinions within the data), summariser (to
summarise a block of text and disregard the rest).
Machine learning algorithms: The three types of machine learning (Supervised, Unsupervised and
Reinforced)
The types of common algorithms used, each of these will be researched to identify the most suitable
for this project and only one will be used: (Linear Regression, Logistic Regression, Decision Tree,
SVM, Naive Bayes, kNN, K-Means, Random Forest, Dimensionality Reduction Algorithms,
Gradient Boosting algorithms (GBM, XGBoost, LightGBM, CatBoost).
Artificial Neural Networks: To identify the drawbacks and benefits of using them or other
computational models within machine learning. Recurrent Neural networks and 3rd generation
Neural Networks.
Data mining: To investigate the different techniques and algorithms used (Same as the ones listed
above for machine learning including C4.5, Apriori, EM, PageRanks, AdaBoost and CART) these
will be researched and the most appropriate identified.
To investigate techniques: for storing and processing large amount of data, such as Hadoop,
Elasticsearch utilities, Graphing and data modelling and visualisation.
To identify appropriate libraries for python or C for each of the topics above to aid in the creation of
this project. Libraries such as:
Natural Language Toolkit (NLTK) – python
Pandas - python
Sklearn - python
Numpy – python - scientific computation for working with arrays
Matplotlib - python - data visualisation
Investigate into types of databases. Sql and nosql for a storage medium between receiving data and
feeding it into the machine learning algorithm.
Investigate into the use of REST API and other web-service based technologies (GRPC,
Elasticsearch)
Investigate into frameworks for the thin client, such as Angular vs React, Nodejs, Leafelt.js, charts.js
Additionally Web scraping may be needed if certain website that don’t either have an API or JSON
for the data needed.
https://www.sas.com/en_gb/insights/analytics/what-is-natural-language-processing-nlp.html
https://blog.algorithmia.com/introduction-natural-language-processing-nlp/
https://gerardnico.com/data_mining/algorithm
https://www.analyticsvidhya.com/blog/2017/09/common-machine-learning-algorithms/
https://www.kdnuggets.com/2015/05/top-10-data-mining-algorithms-explained.html
https://www.datasciencecentral.com/profiles/blogs/artificial-neural-network-ann-in-machine-learning
http://scikit-learn.org/stable/index.html
https://grpc.io/docs/

3

2.2

Summarise the project objectives and outputs in about 400 words.
These objectives and outputs should appear as tasks, milestones and deliverables in your project plan.
In general, an objective is something you can do and an output is something you produce – one leads
to the other.
To produce a thin web client that provides a dashboard that provides tangible and useful information
to users such as; current price of a cryptocurrency (Updated every 5 minutes), exchange rates, network hashrates,
historical price data. It will also display statistics about sentiment analysis conducted on social media
about the currency, graphical predictions on what the price may be, in a given time, and will also
compare this to other currencies for aid in investment.
To produce significant research into the topics in and around data mining, machine learning and
Artificial Neural network and the underlying tasks and algorithms used, the efficiency, drawbacks
and advantages of each to identify the most suitable for the use in this project.
To produce a system that analyses a data set obtained through social media feeds and posts on news
sites regarding crypto currencies. It should perform sentiment analysis using Natural Language
processing and analysis techniques to identify features and identifies the type of sentiment in the data
and categorises it for machine learning.
To utilise machine learning techniques and algorithms to produce a system that learns from historical
data to predict to an extent the possible future price of a given currency. To compare this with the use
of an Artificial Neural Network and to analyse the drawbacks of both.

2.3

Initial project specification - list key features and functions of your finished project.
Remember that a specification should not usually propose the solution. For example, your project
may require open source datasets so add that to the specification but don’t state how that data-link
will be achieved – that comes later.
The finished project should provide a thin client single page application. This will provide a means to
users the ability to view various statistics on crypto currencies on a dashboard that incorporates text
analysis through natural language analysis, and will utilise various machine learning and data mining
techniques to provide price predictions to the users. The nature and level of this will depend on the
research conducted into the areas of data mining, machine learning, natural language processing and
artificial neural networks, along with the algorithms used.
The data set will be created from scratch for this project as it will require the gathering of data from
numerous sources and performing text analysis on them to for the data needed. Data sets for the
characteristic and data for the currencies can be obtained from pre-existing data sets such as:
https://www.kaggle.com/sudalairajkumar/cryptocurrencypricehistory
https://www.kaggle.com/jessevent/all-crypto-currencies
Web scraping may be included if certain news/social media websites do not provide an API or RSS
feed for the analysis engine to perform text analysis on
Additionally, there will be a server between the analysis/prediction engine and the thin client that will
maintain a database, either SQL or NoSQL, that will hold statistics about the currencies and data
about the price predictions about the currencies. It will not hold any of the data used in the analysis
engine, as this database will only hold data available to the end users.

4

2.4

Describe the social, legal and ethical issues that apply to your project. Does your project
require ethical approval? (If your project requires a questionnaire/interview for conducting
research and/or collecting data, you will need to apply for an ethical approval)
The project will not be handling any user related data, therefore it does not need ethical approval.

2.5

Identify and lists the items you expect to need to purchase for your project. Specify the cost
(include VAT and shipping if known) of each item as well as the supplier.
e.g. item 1 name, supplier, cost

None Needed

2.6

State whether you need access to specific resources within the department or the University e.g.
special devices and workshop

Possibly a server to host the database and analysis engine on to perform the computation necessary,
and a server to host the thin client.

5

SECTION 3 – Project Plan
3.1

Project Plan
Split your project work into sections/categories/phases and add tasks for each of these sections. It is
likely that the high-level objectives you identified in section 2.2 become sections here. The outputs from
section 2.2 should appear in the Outputs column here. Remember to include tasks for your project
presentation, project demos, producing your poster, and writing up your report.

Task No.

Task description

1

Background Research

1.1

Investigate into RPC frameworks and REST APIs

0.3

1.2

Research into Natural Language processing and analysis
techniques

0.5

1.3

Research into the use of machine learning – types and
algorithms

0.5

1.4

Research into the application of Neural Networks –
drawbacks and advantages of using them

0.3

1.5

Research techniques for storing and processing large
amount of data, such as Hadoop, spark or Elasticsearch
utilities.

1

1.6

Identify appropriate libraries for data modelling and
visualisation, NLP and Machine Learning

1

1.7

Investigate into frameworks for the front-end thin clients

0.3

1.8

Research web scraping techniques

0.3

2
2.1

Analysis and design
Resolve issues discovered by background research
Identify limitations discovered from research and
what is not feasible
UML Diagrams/ XUML
Wire frames for frontend
Data Flow
User Flow
Develop prototype
Develop thin client
Develop analysis Engine
Develop Prediction Engine
Develop Unit tests
Testing, evaluation/validation
Unit testing
Acceptance Testing
User testing
Assessments
write-up project report
produce poster
Log book

2.2
2.3
2.4
2.5
2.6
3
3.1
3.2
3.3
3.4
4
4.1
4.2
4.3
5
5.1
5.2
5.3

Effort
(weeks)

6

Outputs

To identify the type of API/RPC
framework that would be most
suitable
To get an understanding of how
NLP works and how it could be
used
To grasp how ML paradigms work
and how this project will use it
To identify whether there will be a
need for a neural network or ML
paradigms can be used instead
To understand the uses, application
and whether the use of these are
more viable solution than standard
ML practices
To identify what libraries will aid in
the construction of this project
To identify what frameworks the
thin client should be used with,
along with drawbacks and
advantages
To understand the application of
these techniques and learn how to
apply them

0.2

...

0.1

…

0.2
0.1
0.1
0.1
2
4
3
2
1
0.8
0.8
2
0.5
0.5

Project Report
Poster

TOTAL

Sum of total effort in weeks

7

21.9

SECTION 4 - Time Plan for the proposed Project work
For each task identified in 3.1, please shade the weeks when you’ll be working on that task. You should also mark target milestones, outputs and key decision points.
To shade a cell in MS Word, move the mouse to the top left of cell until the curser becomes an arrow pointing up, left click to select the cell and then right click and
select ‘borders and shading’. Under the shading tab pick an appropriate grey colour and click ok.
START DATE: 10/2018

<enter the project start date here>

Project Weeks
0-3
3-6

9-12

6-9

12-15

Project stage
1 Background Research
Investigate into RPC frameworks and REST
APIs

Research into Natural Language processing
and analysis techniques
Research into the use of machine learning –
types and algorithms
Research into the application of Neural
Networks – drawbacks and advantages of
Research techniques for storing and
using them
processing large amount of data, such as
Identify appropriate libraries for data
Hadoop, spark or Elasticsearch utilities.
modelling and visualisation, NLP and
Investigate into frameworks for the frontMachine Learning
end thin clients
Research web scraping techniques
2 Analysis/Design
Resolve issues discovered by background
research
Identify limitations discovered from
research and what is not feasible
UML Diagrams/ XUML
Wire frames for frontend
Data Flow
User Flow

8

15-18

18-21

21-24

24-27

27-30

30-33

33-36

36-39

3 Develop prototype.
Develop thin client
Develop analysis Engine
Develop Prediction Engine
Develop Unit tests
4 Testing, evaluation/validation
Unit testing
Acceptance Testing
User testing
5 Assessments
write-up project report
produce poster
Log book

9

RISK ASSESSMENT FORM
Assessment Reference No.

Area or activity
assessed:

Assessment date
Persons who may be affected by
the activity (i.e. are at risk)

Andrew Sotheran

SECTION 1: Identify Hazards - Consider the activity or work area and identify if any of the hazards listed below are significant (tick the boxes that apply).

1.

2.

Fall of person (from
work at height)
Fall of objects

3.

Slips, Trips &
Housekeeping

4.

Manual handling
operations

5.

Display screen
5
equipment
5

6.

7.

Lighting levels

Heating &
ventilation

11.

Use of portable
tools / equipment

12.

Fixed machinery or
lifting equipment

Layout , storage,

X 8. space, obstructions
9.

13.

Welfare facilities
Electrical

X 10. Equipment

14.

X

15.

Pressure vessels

Noise or Vibration

16.

21.

17.

Outdoor work /
extreme weather

22.

Hazardous
biological agent

27.

18.

Fieldtrips / field
work

23.

Confined space /
asphyxiation risk

28.

24.

Condition of
Buildings & glazing

29.

19.

Fire hazards &
flammable material

10

Hazardous fumes,

Vehicles / driving
at work

20.

Radiation sources

Work with lasers

25.

chemicals, dust

Food preparation

26.

Occupational stress

Violence to staff /
verbal assault
Work with animals
Lone working /
work out of hours
Other(s) - specify

30.

X

SECTION 2: Risk Controls - For each hazard identified in Section 1, complete Section 2.
Hazard
No.

3

5

Hazard Description

Tripping over wires

Eye strain from
looking at a
monitor

Existing controls to reduce risk

Risk Level (tick one)

Further action needed to reduce risks

High

(provide timescales and initials of person responsible)

Med

Cable management is at a minimum, none are
currently properly cable managed and kept out
of way
Current screen contrast and brightness is
acceptable

x

x

SIGNED

Name of Assessor(s)
Review date

11

Low

Sufficient cable management needed, cables
tied together and moved out of way of feet

To have periodic breaks from the screen

Health and Safety Risk Assessments – continuation sheet

Assessment Reference No
Continuation sheet number:

SECTION 2 continued: Risk Controls
Hazard
No.

Hazard Description

Existing controls to reduce risk

Risk Level (tick one)

Further action needed to reduce risks

High

(provide timescales and initials of person responsible for
action)

Med

SIGNED

Name of Assessor(s)
Review date

12

Low