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CRYPTOCURRENCY PRICE ANALYSIS WITH ARTIFICIAL INTELLIGENCE

Project Code CB1048
Application Type WEB APP
Tech/Programming PYTHON

API:
NONE
Web Framework:
DJANGO
IDE:
VS CODE
Database:
MYSQL
DFD Diagram:
Available
Videos:
Not Available
E-R Diagram:
Available
UML Diagrams:
Available
Final PPT:
Available
SRS:
Available

Description

Cryptocurrency is playing an increasingly important role in reshaping the financial system due to its growing popular appeal and mechant acceptance. While many people are making investments in Cryptocurrency, the dynamical features, uncertainty, the predictability of Cryptocurrency are still mostly unknown, which dramatically risk the investments. It is a matter to try to understand the factors that infiuence the value formation. In this study, we use advanced artificial intelligence frameworks of fully connected Artificial Neural Network (ANN) and Long Short-Term Memory (LSTM) Recurrent Neural Network to analyze the price dynamics of Bitcoin, Ethereum, and Ripple. We find that ANN tends to rely more on long-term history while LSTM tends to rely more on short-term dynamics, which indicate the efficiency of LSTM to utilize useful information hidden in historical memory is stronger than ANN. However, given enough historical information ANN can achieve a similar accuracy, compared with LSTM. This study provides a unique demonstration that Cryptocurrency market price is predictable. However, the explanation of the predictability could vary depending on the nature of the involved machine-learning model.

Note

Project with Source Code, Documentation and PPT

1st Review PPT:
Available
2nd Review PPT:
Available
3rd Review PPT:
Available
Architecture Diagram:
Available

Literature Survey:
Available
Test Cases:
Available
Data Dictionary:
Available
Future Enhancement:
Available

SRS:
Available
All Modules:
Available
Documentation:
Available
Screen Shots:
Available

How to Install Softwares :Not Available
How to Execute Project :Not Available
Project Explanation Video :Not Available

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