Abstract
Bitcoin, being one of the most volatile cryptocurrencies, presents a major challenge for investors and traders in making informed decisions. Traditional forecasting approaches often fail to capture the complex, non-linear nature of cryptocurrency price movements. To address this, our project leverages Machine Learning (ML) techniques to predict Bitcoin stock prices using live data fetched from the Yahoo Finance server. The collected datasets consist of historical price data and technical indicators, which serve as input features for training the prediction model. We employ the Long Short-Term Memory (LSTM) algorithm, a deep learning model well-suited for time-series forecasting, to capture sequential dependencies and market trends. The trained system then predicts future Bitcoin stock prices, enabling users to assess whether to buy or hold. This solution provides an intelligent and data-driven decision support tool for cryptocurrency enthusiasts, traders, and investors.
Existing System
- Bitcoin stock prediction is the act of trying to determine the future value of a company bitcoin stock or other financial instrument traded on an exchange.
- The successful prediction of a bitcoin stock’s future price could yield significant profit.
- The future, like any complex problem, has far too many variables to be predicted. Quantitative models, historical models, even psychic models have all been tried and have all failed.
- The existing system is done with logistic regression technique which randomized the data and predicting the future.
- The problem with the existing system is its, prediction data which is not accurate or almost not matching the nearest values.
Proposed System
- Predicting how the bitcoin stock market will perform is one of the most difficult things to do. There are so many factors involved in the prediction.
- We need a clear, very strong prediction system which will have more accuracy in pinpointing the data for accuracy.
- Our system will take the normal data for consideration and in case of more accuracy raw data too can be taken for prediction.
- LSTM – Long Short Term Memory algorithm involves the data taken in very accurate.
- We built the model with the training data sets and the prediction involves verifying the testing data.
- The prediction is much better and accurate when compared to other machine learning algorithms and the existing system too.
Hardware Requirements
- Processor : Intel Core i3 (or above)
- System Type : 64-bit Operating System
- Storage : 500 GB HDD / SSD
- RAM : 4 GB (minimum)
Software Requirements
- Operating System : Windows 10 (or above)
- Software : Anaconda Navigator, Python IDE (e.g., Jupyter Notebook or VS Code)
- Python Libraries : Numpy, Matplotlib, Keras, Pandas
Architecture Diagram
Project Modules
- Get the stock quote
- Visualize the closing price hisory
- Scale the data
- Create the scaled training dataset
- Visualize the data
- Show the valid and prediction
Components of Project Report
- Abstract
- Table of Contents
- List of Tables
- List of Figures
- Chapters
- Introduction
- Literature review
- Problem definition and requirement analysis
- Design and Implementation
- Testing and deployment
- Future enhancements
- Summary
- References
Project Report Pages : 80
Can be used in : Data Science
Delivery Time : Within 2 hours.
Support / Query : Call +91-7449000533
Email [email protected]
