Predicts real-time bitcoin price using twitter and reddit sentiment, and sends out notifications via SMS.

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.

Bitcoin price prediction using twitter sentiment analysis

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.

:mortar_board: Social media text data scraping for sentiment analysis (Vader, Bert), correlation study, and Bitcoin price prediction using LSTM and XGBoost, with a comparative performance analysis across various media sources like forums and group chats.

Natural language processing on 3 million tweets contain #Bitcoin tags or related. Machine learning model to predict the bitcoin price from the sentiment analysis.

Bitcoin price prediction using sentiment analysis

Masterthesis repository - Bitcoin Price Prediction using Sentiment Analysis of Twitter Data

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Bitcoin price prediction using Sentiment Analysis on tweets(From twitter API) and technical analysis on historical candlestick data(From Binance API) using Bollinger Bands and Relative Strength Index.

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 aims to forecast Bitcoin prices by analyzing Twitter sentiment data. By combining machine learning techniques with sentiment analysis, we can identify leading indicators that correlate with Bitcoin price movements. The project uses various natural language processing models and forecasting methods to enhance prediction accuracy.

Money Talks is a web-based crypto prediction app using Flask and XGBoost to forecast prices of Bitcoin, Ethereum, BNB, and Cardano. It includes live price updates, news with sentiment analysis, a chatbot, portfolio tracking, and price alerts for a smart and interactive crypto experience.

Cryptocurrencies have been trending for a while now, this project aims to help businesses and individuals access the risks and rewards of entering the markets, by giving them a tool to forecast the price of Bitcoin using sentiment analysis and price prediction using historical data. The proposed method combined Historical Data Analysis (Investing.com) and Sentiment Analysis (Twitter) to predict future trend of cryptocurrency.

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Predictive models for Bitcoin price fluctuations based on Twitter sentiment analysis

This project has been done as a part of a Masters Degree at National University of Computer and Emerging Sciences

Our aim is to create a model which would show the relation between social signals and Bitcoin price, namely through news and other major statements put out by influential companies/ individuals.

Bitcoin price prediction using sentiment analysis

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Bitcoin Price Prediction Using Twitter Sentiment Analysis

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This code is a comprehensive script for predicting Bitcoin (BTC) prices using historical price data, sentiment analysis, technical indicators, and machine learning models (XGBoost) combined with volatility modeling (GARCH). It performs data collection, feature engineering, model training, evaluation, and generates future price predictions.

Bitcoin Price Prediction using Sentiment Analysis of Bitcoin tweets

Bitcoin Price Prediction using Sentiment Analysis of Bitcoin tweets

This repository contains the code and resources for a project focused on predicting Bitcoin price fluctuations through sentiment analysis of tweets and machine learning models. The project integrates natural language processing (NLP) techniques, financial data analysis, and machine learning algorithms