This project focuses on predicting the prices of Bitcoins, the most in-demand cryptocurrency of today's world.

Python Bitcoin is widely used cryptocurrency for digital market. It is decentralised that means it is not own by government or any other company.Transactions are simple and easy as it doesn’t belong to any country.Records data are stored in Blockchain.Bitcoin price is variable and it is widely used so it is important to predict the price of it for making any investment.This project focuses on the accurate prediction of cryptocurrencies price using neural networks. We’re implementing a Long Short Term Memory (LSTM) model using keras; it’s a particular type of deep learning model that is well suited to time series data (or any data with temporal/spatial/structural order e.g. movies, sentences, etc.).We have used different activation function for analysing the efficiency of the system.Instead of historical data we are using live streaming data for better accuracy.

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.

This project leverages the Binance API to acquire real-time data for Bitcoin (BTC) and Ethereum (ETH). The data is then used to train a Long Short-Term Memory (LSTM) neural network model for predicting cryptocurrency prices.

Project shows that real-time Twitter data can be used to predict market movement of Bitcoin Price. The goal of this project is to prove whether Twitter data relating to cryptocurrencies can be utilized to develop advantageous crypto coin trading strategies. By way of supervised machine learning techniques, have outlined several machine learning pipelines with the objective of identifying cryptocurrency market movement. The prominent alternative currency ex- amined in this paper is Bitcoin (BTC). Our approach to cleaning data and applying supervised learning algorithms such as logistic regression, Decision Tree Classifier, and LDA leads to a final prediction accuracy exceeding 70%. In order to achieve this result, rigorous error analysis is employed in order to ensure that accurate inputs are utilized at each step of the model.

Predicted the Price of the Cryptocurrency(Bitcoin) using the past time-series data, Twitter Sentiments(Polarity and Sensitivity), Currency's Fundamentals and Technical Indicators like RSI and SMA on LSTM. The Notebook contains the Exploratory data analysis(with important links) and the astounding result at the end of it

Predicting Cryptocurrency prices (Bitcoin & ZCOIN)

Predicting the price of future cryptocurrencies based on the historical data of ethereum, iota, bitcoin etc

This repository contains implementations of Long Short-Term Memory (LSTM) models for predicting the closing prices of three major cryptocurrencies: Bitcoin (BTC), Solana (SOL), and XRP. The project leverages historical OHLC data to train deep learning models capable of forecasting future price trends.

The Python code employs historical Bitcoin and Ethereum prices to train a neural network model for cryptocurrency trading, executing buy or sell decisions based on predicted spreads. It evaluates the strategy's performance, visualizes training progress, and prints executed trades with associated probabilities.

BitPredictor - A cutting-edge machine learning-based solution for predicting cryptocurrency prices. Harnessing the power of advanced algorithms and data analysis techniques, this system aims to provide accurate and timely forecasts for Bitcoin and other cryptocurrencies.

My attempt at using recurrent neural networks to predict the prices of cryptocurrencies such as bitcoin.

Capstone Project: Predict Price of Bitcoin cryptocurrency using machine learning algorithms.

focuses on predicting cryptocurrency price movements (e.g., Bitcoin, Ethereum) and modeling their market volatility using time series forecasting techniques like LSTM and ARIMA, and financial risk models like GARCH

Bitcoin, the most famous decentralised digital currency, is the largest cryptocurrency globally in market capitalisation. It was introduced to the world for the first time in January 2009 and has had a volatile trading history ever since. The transactions occur via a Peer-to-Peer (P2P) network and are recorded in the blockchain, a public ledger. Here, an attempt has been made to predict, using regression (particularly LSTM), the closing price of Bitcoin at the end of each hour from 1st March 2016 to 24th November 2018 based on the available hourly data. It is multivariate because the closing price depends on the different prices, the volume traded, as well as trend, i.e. the popularity of the terms associated and time series as the values vary across time.

Use historical pricing data to predict future cryptocurrency prices of Bitcoin, Ethereum, Litecoin, and Zcash in Python

Time Series Forecasting: Predicting Bitcoin Price The cryptocurrency market has seen its rise and fall in the past few years. With a variety of coins being exchanged for real money, it is important to know the trend in the coin price. In this article, we will build a fairly simple LSTM network to predict or forecast the prices of Bitcoin. Obtaining Bitcoin Data There are plenty of open sources available on the internet to extract historical data of Bitcoin prices. The one that I have used below is from https://coinmarketcap.com/.

Attempting to predict Bitcoin price movement given sentiment of prominent members of the Cryptocurrency space through their Tweets

Cryptocurrency markets, particularly Bitcoin, exhibit high volatility, making them challenging to predict. This project employs machine learning techniques to forecast Bitcoin prices, leveraging historical data and various predictive feature

A deep learning-based time series project that predicts Bitcoin prices using RNN and LSTM models by analyzing historical cryptocurrency data.

Market volatility leads to unpredictable losses, traditional trading methods struggle to analyze vast data. We Hybrid LSTM-Transformer-CNN model for predicting cryptocurrency prices (specifically Bitcoin) using historical data

This project leverages the Binance API to acquire real-time data for Bitcoin (BTC) and Ethereum (ETH). The data is then used to train a Long Short-Term Memory (LSTM) neural network model for predicting cryptocurrency prices.

LSTM Stock Predictor: Due to the volatility of cryptocurrency speculation, investors will often try to incorporate sentiment from social media and news articles to help guide their trading strategies. One such indicator is the Crypto Fear and Greed Index (FNG) which attempts to use a variety of data sources to produce a daily FNG value for cryptocurrency. You have been asked to help build and evaluate deep learning models using both the FNG values and simple closing prices to determine if the FNG indicator provides a better signal for cryptocurrencies than the normal closing price data. In this assignment, you will use deep learning recurrent neural networks to model bitcoin closing prices. One model will use the FNG indicators to predict the closing price while the second model will use a window of closing prices to predict the nth closing price.

This document summarizes how to use ARIMA model, why do we use ARIMA?, the assumptions of ARIMA model with hypothesis test, and the algorithm of time series ARIMA model implementing in daily bitcoin price with computed volatility for predicting values of its cryptocurrency in the future.

Forecasting the future has always been a difficult task. The importance of predicting the future has been getting a primary role in the field of computer science and information technology. Developing several algorithms and building it to forecast future events has been paid a significant amount of atention since the past decade. Here, we use one of the latest and most advanced technologies i.e. Deep Learning. The main aim of this project is to showcase how a trained machine model can forecast the future fluctuations in the rise of cryptocurrencies like bitcoin. It shows how a trained machine model can give the price of a cryptocurrency like bitcoin if we give the right amount of data and sufficient computational power. What we expect as the ouput is a graph with the predicted values. The most popular and widely used technology is the kind of technological solution that could help the humankind to predict the future events. Here, we collect the previous values of bitcoin from various bitcoin exchanges. So, with the vast amount of data generated, we segregate the values and try to forecast the bitcoin prices in the form of a graph. We use Recurrent Neural Networks(RNN) for this deep learning methodology. This can be achieved by using several machine learning techniques and methodologies.

Cryptocurrency enthusiasts, explore Bitcoin's future with machine learning! Predict, analyze, and visualize Bitcoin prices. Dive in now.

This project focuses on predicting the prices of Bitcoins, the most in-demand cryptocurrency of today's world.

This project focuses on predicting the prices of Bitcoins, the most in-demand cryptocurrency of today's world.

A Streamlit-based machine learning web app to predict Bitcoin (BTC) closing price using cryptocurrency market indicators like USDT and BNB prices and volumes.

CryptoForecasting flask project aimed at predicting cryptocurrency prices for Bitcoin (BTC) and Ethereum (ETH) using machine learning and deep learning.