Machine learning

1. R BHARGAVAN 20W91A05J2
SRI RAM HARSHIT 20W91A05L7
SYED JAWED ALI JAFFER 20W91A05M2
VARUN KUMAR 20W91A05P2
VEMULA BHARGAVI 20W91A05P4
GROUP MEMBERS :
UNDER THE GUIDANCE OF
Mr. T SATISH

2.  Abstract
 Existing System
 Existing System Limitations
 Proposed System
 Advantages
 Software And Hardware Requirements
 Software Architecture[UML Diagrams]
 Modules
INDEX :

3. • Cryptocurrency is one of the famous financial state in all over the world which cause several type of risks that
effect on the intrinsic assessment of risk auditors.
• From the beginning the growth of cryptocurrency gives the financial business with the wide risk in term of
presentation of money laundering. In the institution of financial supports such as anti-money laundering, banks
and secrecy of banks proceed as a specialist of risk, manager of bank and officer of compliance which has a
provocation for the related transaction through cryptocurrency and the users who hide the illegal funds.
• In this study, the Hierarchical Risk Parity and unsupervised machine learning applied on the cryptocurrency
framework. The process of professional accounting in term of inherent risk connected with cryptocurrency
regarding the occurrence likelihood and statement of financial impact.
• Determining cryptocurrency risks comprehended to have a high rate of occurrence likelihood and the access of
private key which is unauthorized.

4. • The professional cryptocurrency experience in transaction cause the lower risk comparing the less experienced one.
• The Hierarchical Risk Parity gives the better output in term of returning the adjusted risk tail to get the better risk
management result.
• The result section shows the proposed model is robust to various intervals which are re-balanced and the co-
variance window estimation.

5.  The Existing System uses the strategy of Hierarchical Risk Parity (HRP) on the multi-asset multi-factor allocation
which achieves good results on tail risk. Moreover, It applies the same strategy for the individual stocks to comport
the fifty indexes of NIFTY.
 It compares different variants of Hierarchical Risk Parity (HERC and HCCA) and evaluates the performance. It uses
the mean-variance framework to analyze the portfolios of cryptocurrency based on the Markowitz optimization
with a high ratio.
 The proposed relationship between cryptocurrencies based on the highest frequency. The presented system gives
the output of useful marketing insights and gives the allowance to the agent to improve the system stability.

6.  It demonstrates the estimation error in terms of return estimation rather than a naively diversified
(1/N) strategy. Similarly, they used the model of Black Litterman based on the variance constraints to
support the sophisticated portfolio technique for estimation control of the simple methods to
manage the cryptocurrency.
 It Applies the wavelet-based analysis for cryptocurrency multi-scale dynamic interdependence
between the liquid cryptocurrencies to count the trader’s and investors’ heterogeneous behavior. It
Compares the different rules of trading in terms of average oscillators to break out the range of
trading strategies.

7.  If an unauthorized party gets any access to the private key then all the cryptocurrency is stolen.
 Misrepresentation of the private key of an entity.
 The cryptocurrency contains the delay of transactions at the end of the period. It becomes difficult to record
the conditions and events for financial purposes.
 The transactions of cryptocurrency get recorded from an entity that has no identification possibility based on
the anonymity of the transactions in the blockchain.
 Sending the incorrect address from the entity which is not possible for recovery from cryptocurrency.

8.  Using the Hierarchical Risk Parity for the cryptocurrency portfolio based on the usage of machine
learning Algorithms
 Decision Tree Classifier
 Naïve Bayes
 SVM (Support Vector Machine)
 Logistic Regression
 The proposed system can examine professional accounting based on the associated risk of
cryptocurrency and the impact that is expected from the financial statements.
 Finding the intrinsic risks that are correlated negatively in the cryptocurrency.
 Ranking the exchange level control risk based on the likelihood evaluation.
 Finding the highest likelihood risk of the determined cryptocurrency.

9.  The proposed system implements a graph-based theory and using the
machine learning Technique’s like (Decision Tree Classifier, Naïve
Bayes, SVM , Logistic Regression the proposed system is processing in
the following way.
- Clustering datasets.
- Recursive bisection on datasets.
- Quasi-diagonalization on datasets

10. Operating system : Windows 7 Ultimate.
Coding Language : Python.
Front-End : Python.
Back-End : Django-ORM
Designing : HTML, CSS, JavaScript.
Data Base : MySQL (WAMP Server)

11. • Processor : Pentium –IV
• RAM : 4 GB (min)
• Hard Disk : 20 GB
• Keyboard : Standard Windows Keyboard
• Mouse : Two or Three Button Mouse
• Monitor : SVGA

13. UML Diagrams:
 UML stands for Unified Modeling Language. UML is a standardized general-purpose modeling language in the field
of object-oriented software engineering.
 The standard is managed, and was created by, the Object Management Group.
 The goal is for UML to become a common language for creating models of object-oriented computer software.
 In its current form UML is comprised of two major components: a Meta-model and a notation.
 In the future, some form of method or process may also be added to; or associated with, UML.
 The Unified Modeling Language is a standard language for specifying, Visualization, Constructing and documenting
the artifacts of software system, as well as for business modeling and other non-software systems.
 The UML is a very important part of developing objects-oriented software and the software development process. The
UML uses mostly graphical notations to express the design of software projects.

14. GOALS:
The Primary goal in the design of the UML are as follows:
 Provide users a ready-to-use, expressive visual modeling Language so that they can develop and exchange meaningful
models.
 Provide extendibility and specialization mechanisms to extend the core concepts.
 Be independent of particular programming languages and development process.
 Provide a formal basis for understanding the modeling language.
 Encourage the growth of OO tools market.
 Support higher level development concepts such as collaborations, frameworks, patterns and components.
 Integrate best practices.

15.  A use case diagram in the Unified Modeling Language (UML) is a type of behavioral diagram defined by and created
from a Use-case analysis.
 Its purpose is to present a graphical overview of the functionality provided by a system in terms of actors, their goals
(represented as use cases), and any dependencies between those use cases.
 The main purpose of a use case diagram is to show what system functions are performed for which actor. Roles of the
actors in the system can be depicted.

16. opload osh face dataset
precross dataset
bulid VGG _19 model
upload test data&predict osh
user
accuracy comparison graph

17.  In software engineering, a class diagram in the Unified Modeling Language (UML) is a type of static structure
diagram that describes the structure of a system by showing the system's classes, their attributes, operations (or
methods), and the relationships among the classes. It explains which class contains information.

18.  A sequence diagram in Unified Modeling Language (UML) is a kind of interaction diagram that shows how processes
operate with one another and in what order. It is a construct of a Message Sequence Chart.
 Sequence diagrams are sometimes called event diagrams, event scenarios, and timing diagrams.
user dataset
upload osh face dataset
preprocess dataset
bulid VGG 19 model
upload test data & predict osh
accuracy comparsion graph

19.  Activity diagrams are graphical representations of workflows of stepwise activities and actions with support for
choice, iteration and concurrency.
 In the Unified Modeling Language, activity diagrams can be used to describe the business and operational step-by-
step workflows of components in a system.
 An activity diagram shows the overall flow of control.
user dataset
1: upload osh face dataset
2: preprocess dataset
3: bulid VGG 19 model
4: upload test data & predict osh
5: accuracy comparsion graph

20. :
 Scikit-learn provides a range of supervised and unsupervised learning algorithms via a
consistent interface in Python.
 It is licensed under a permissive simplified BSD license and is distributed under many
Linux distributions, encouraging academic and commercial use.
 It provides a wide range of machine learning algorithms, Such as :
•Logistic Regression
•Support Vector Machines (SVM)
•Decision Trees Classifier
•K-Nearest Neighbors

21. TensorFlow:
 TensorFlow is a free and open-source software library for dataflow and differentiable
programming across a range of tasks.
 It is a symbolic math library and is also used for machine learning applications such
as neural networks .
 TensorFlow uses a computation graph paradigm, where operations are
represented as nodes in a graph. This allows for efficient computation.
 It is known for its flexibility, scalability, and support for a wide range of machine
learning and deep learning tasks.

22.  NumPy is a general-purpose array-processing package. It provides a high-performance multidimensional array
object, and tools for working with these arrays.
 NumPy provides a large number of functions and operators that can be used to perform computations on
arrays. These operations are highly optimized and much faster than using loops over Python lists.
 NumPy arrays use less memory compared to Python lists, especially when dealing with large datasets
 NumPy has a powerful linear algebra library that allows you to perform various linear algebra operations like
matrix multiplication, decomposition, solving linear systems, etc..
 Besides its obvious scientific uses, NumPy can also be used as an efficient multi-dimensional container of
generic data.
 Arbitrary data-types can be defined using NumPy which allows NumPy to seamlessly and speedily integrate
with a wide variety of databases.

23.  Pandas is an open-source Python Library providing high-performance data manipulation and analysis tool
using its powerful data structures.
 Pandas provides a wide range of functions to clean and preprocess data. This includes handling missing
values, dealing with duplicates, transforming data, and more.
 Pandas is designed for memory-efficient data storage and operations. It's optimized for performance, making
it suitable for handling large datasets
 Using Pandas, we can accomplish five typical steps in the processing and analysis of data, regardless of the
origin of data load, prepare, manipulate, model, and analyze.
 Python with Pandas is used in a wide range of fields including academic and commercial domains including
finance, economics, Statistics, analytics, etc.