Bitcoin price prediction using both traditonal machine learning and deep learning techniques, based on historical price and sentiment extracted from Twitter posts. Fear of missing out analysis after Elon Musk tweeted about Dogecoin.

Identify the software project, create business case, arrive at a problem statement. REQUIREMENT: Window XP, Internet, MS Office, etc. Problem Description: - 1. Introduction of AI and Machine Learning: - Artificial Intelligence applies machine learning, deep learning and other techniques to solve actual problems. Artificial intelligence (AI) brings the genuine human-to-machine interaction. Simply, Machine Learning is the algorithm that give computers the ability to learn from data and then make decisions and predictions, AI refers to idea where machines can execute tasks smartly. It is a faster process in learning the risk factors, and profitable opportunities. They have a feature of learning from their mistakes and experiences. When Machine learning is combined with Artificial Intelligence, it can be a large field to gather an immense amount of information and then rectify the errors and learn from further experiences, developing in a smarter, faster and accuracy handling technique. The main difference between Machine Learning and Artificial Intelligence is , If it is written in python then it is probably machine learning, If it is written in power point then it is artificial intelligence. As there are many existing projects that are implemented using AI and Machine Learning , And one of the project i.e., Bitcoin Price Prediction :- Bitcoin (₿ ) (founder - Satoshi Nakamoto , Ledger start: 3 January 2009 ) is a digital currency, a type of electronic money. It is decentralized advanced cash without a national bank or single chairman that can be sent from client to client on the shared Bitcoin arrange without middle people's requirement. Machine learning models can likely give us the insight we need to learn about the future of Cryptocurrency. It will not tell us the future but it might tell us the general trend and direction to expect the prices to move. These machine learning models predict the future of Bitcoin by coding them out in Python. Machine learning and AI-assisted trading have attracted growing interest for the past few years. this approach is to test the hypothesis that the inefficiency of the cryptocurrency market can be exploited to generate abnormal profits. the application of machine learning algorithms to the cryptocurrency market has been limited so far to the analysis of Bitcoin prices, using random forests , Bayesian neural network , long short-term memory neural network , and other algorithms. 2. Applications/Scope of AI and Machine Learning :- a) Sentiment Analysis :- It is the classification of subjective opinions or emotions (positive, negative, and neutral) within text data using natural language processing. b) It is Characterized as a use of computerized reasoning where accessible data is utilized through calculations to process or help the handling of factual information. BITCOIN PRICE PREDICTION USING AI AND MACHINE LEARNING: - The main aim of this is to find the actual Bitcoin price in US dollars can be predicted. The chance to make a model equipped for anticipating digital currencies fundamentally Bitcoin. # It works the prediction by taking the coinMarkup cap. # CoinMarketCap provides with historical data for Bitcoin price changes, keep a record of all the transactions by recording the amount of coins in circulation and the volume of coins traded in the last 24-hours. # Quandl is used to filter the dataset by using the MAT Lab properties. 3. Problem statement: - Some AI and Machine Learning problem statements are: - a) Data Privacy and Security: Once a company has dug up the data, privacy and security is eye-catching aspect that needs to be taken care of. b) Data Scarcity: The data is a very important aspect of AI, and labeled data is used to train machines to learn and make predictions. c) Data acquisition: In the process of machine learning, a large amount of data is used in the process of training and learning. d) High error susceptibility: In the process of artificial intelligence and machine learning, the high amount of data is used. Some problem statements of Bitcoin Price Prediction using AI and Machine Learning: - a) Experimental Phase Risk: It is less experimental than other counterparts. In addition, relative to traditional assets, its level can be assessed as high because this asset is not intended for conservative investors. b) Technology Risks: There is a technological risk to other cryptocurrencies in the form of the potential appearance of a more advanced cryptocurrency. Investors may simply not notice the moment when their virtual assets lose their real value. c) Price Variability: The variability of the value of cryptocurrency are the large volumes of exchange trading, the integration of Bitcoin with various companies, legislative initiatives of regulatory bodies and many other, sometimes disregarded phenomena. d) Consumer Protection: The property of the irreversibility of transactions in itself has little effect on the risks of investing in Bitcoin as an asset. e) Price Fluctuation Prediction: Since many investors care more about whether the sudden rise or fall is worth following. Bitcoin price often fluctuates by more than 10% (or even more than 30%) at some times. f) Lacks Government Regulation: Regulators in traditional financial markets are basically missing in the field of cryptocurrencies. For instance, fake news frequently affects the decisions of individual investors. g) It is difficult to use large interval data (e.g., day-level, and month-level data) . h) The change time of mining difficulties is much longer. Moreover, do not consider the news information since it is hard to determine the authenticity of a news or predict the occurrence of emergencies.

The study explores the connection between Reddit sentiment and Bitcoin market dynamics. Through graphical analysis and deep learning models, it examines the correlation between Reddit sentiment and Bitcoin's price and trading volume. While the results reveal a limited correlation, the study suggests avenues for further model refinement and explorat

Deep learning models predicting the Bitcoin stock using technical stock market indicators and google news article sentiments

Repository containing work for w251 final project. The project is in regards to exploring sentiment analysis and price prediction of bitcoin using deep learning and twitter data.

This project is an intelligent Reinforcement Learning (RL) Bitcoin trading bot. It utilizes Deep Q-Network (DQN) variants, advanced market indicators, and sentiment analysis for trading, featuring comprehensive backtesting and performance analytics.

# Bitcoin Sentiment-Based Automated Trading Bot This project is an advanced crypto trading bot that generates real-time buy/sell signals for Bitcoin by combining **sentiment analysis from social media** with **technical indicators** such as RSI and MACD. It uses LSTM deep learning models and integrates with Binance API for automated trading.

Crypto News Sentiment Analyzer. Real time Crypto News Analysis and Price Prediction SignalForge, an AI based tool that processes cryptocurrency news headlines and foresees the price movements of Bitcoin with more than 74% accuracy. The technology employs cutting edge NLP and deep learning to deliver instantaneous analysis of market sentiment.

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Bitcoin Tweet Sentiment Analysis using Deep Learning

Bridging Social Media and Cryptocurrency: A Deep Learning-based Twitter Sentiment Analysis for Bitcoin Market Predictions

bitcoin cryptocurrency price-prediction deep-learning lstm transformer sentiment-analysis finbert python

Deep Learning project: Estimate bitcoin value using historical data social media sentiment analysis.

Bitcoin price predicter using historical market data and news feeds, utilising deep learning and sentiment analysis

Bitcoin price forecasting project using hybrid sentiment analysis and deep learning approaches. Combines historical Bitcoin market data with social media sentiment to enhance prediction accuracy. Implemented using Python, deep learning models, and NLP techniques.

Available codes of my thesis work which is titled "Sentiment Analysis of Bitcoin-Related Tweets Using Deep Learning Methods".

A RAG system that combines deep learning, sentiment analysis, and real-time data retrieval to provide comprehensive Bitcoin price predictions and market analysis.

Testing various approaches for Bitcoin price prediction: timestamp patterns, LSTM deep learning, and sentiment analysis. Sentiment analysis using Fear & Greed Index shows moderate correlation (0.49 for 7-day predictions).

Repository for the article "Improving Bitcoin Price Prediction accuracy by integrating On-Chain analysis and Twitter Sentiment into a Deep Learning approach"

A bitcoin price prediction project as part of the Topics in Deep Learning (TDL) elective using an LSTM and VADER Sentiment Analysis

Comprehensive sentiment analysis of Bitcoin-related tweets using multiple Natural Language Processing (NLP) techniques. From lexicon-based methods to deep learning and transformer models.

AI and Machine Learning tool that uses sentiment analysis from a database of over 2.6 million financial tweets plus historical data / deep learning to predict the price of Bitcoin

Sentiment Analysis on Bitcoin tweets using LSTM networks. VADER sentiment analysis provided compound sentiment scores. The deep learning model, with convolutional, pooling, bidirectional LSTM layers, achieved high accuracy (0.8580), precision (0.8849), recall (0.8345), and F1 score (0.8590), indicating its effectiveness.

This project predicts Bitcoin prices using a time series approach. First, sentiment analysis is performed on Twitter data using the RoBERTa model. The resulting sentiment scores are then used as input for deep learning models to forecast future prices.

This repository is a source code for the published paper entitled "Bitcoin price prediction based on financial data, technical indicators, and news headlines sentiment analysis using CNN and GRU deep learning algorithms".

Created a deep learning model to forecast Bitcoin prices. Sentiment analysis of related tweets was done by fine-tuning FinBERT model and final price prediction was done by LSTM model tuned with Grey Wolf optimization.

This project is part of my Fintech homework in which I use a deep learning model to predict the price of bitcoin, given pricing data and sentiment analysis as an indicator (in ML terms, an additional feature).

Developed a Bitcoin price prediction model using ML, leveraging time series analysis and deep learning to analyze trends. It incorporates key indicators like trading volume and market sentiment for accurate forecasts. Built with Python, TensorFlow, and scikit-learn, showcasing AI’s potential in cryptocurrency analysis.