A Secure Block chain-Based Crypto currency Trading and Prediction System Using Django and Machine Learning

Abstract

The rapid evolution of digital finance has significantly transformed traditional financial
systems, with crypto currencies emerging as a dominant form of decentralized digital
assets. This project presents a comprehensive Block chain-Based Crypto currency
Trading and Prediction System developed using the Django web framework. The
system is designed to facilitate secure transactions between users and agents while
maintaining transparency through block chain ledger technology. Additionally, it
integrates machine learning techniques to provide predictive insights for crypto currency
trends. The proposed system introduces three major modules: Admin, Agent, and User.
The admin module manages user registrations, monitors agents, updates crypto currency
exchange rates, and oversees the block chain ledger. The agent module acts as an
intermediary that buys crypto currencies and sells them to users. The user module allows
customers to purchase crypto currencies, track transaction history, and analyze prediction
results. A key feature of this system is the Block chain Ledger, which records every
transaction in a tamper-proof structure. This ensures transparency, traceability, and
security of financial operations. The ledger maintains transaction details such as buyer,
seller, crypto currency type, quantity, and block chain charges, providing an auditable
system.
The application also incorporates a crypto currency price update mechanism, where
admins can dynamically adjust currency values based on percentage increments or
decrements. These updates are logged for historical tracking and performance analysis.
Furthermore, the system includes a machine learning prediction module, where
datasets are processed to generate predictions using advanced algorithms like LSTM
(Long Short-Term Memory), enabling users to make informed investment decisions.
Security is maintained through authentication mechanisms for users and agents, session
management, and controlled access levels