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.

a small python scraper to scrape historical data from the CoinMarketCap website and convert it to csv files . This is an initial step for a data mining process to develop a predictive model of cryptocurrencies prices.

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.

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.

Check different cryptocurrencies prices and predict their future price.

A python based project to predict the future prices of the top 10 trending cryptocurrencies using ML Algorithms like SVR, Decision Tree and LSTM with an interactive frontend using streamlit. Analysis using PowerBi and has DBMS connectivity.

Multivariate time series forecasting cryptocurrencies price with LSTMs in Keras

A neural network built using Python which can be used to predict future prices of stocks and cryptocurrencies.

This repository implements a Temporal Convolutional Network (TCN) model for predicting financial instrument prices, including currencies, stocks, and cryptocurrencies. It uses advanced techniques like gradient boosting to improve prediction accuracy and handle diverse datasets effectively.

The goal of this study is to predict prices for Cryptocurrencies using Time series analysis and machine learning techniques. The purpose of this project is to take a sneak peek into the future by forecasting the next 30 days' average daily Realized Volatility (RV) of ETH-BTC using 2 different approaches - the traditional econometric approach to volatility prediction of financial time series GARCH and state-of-the-art LSTM Neural Networks. Quantitative research methodology was used in this study and the The dataset Consist the historical data values of any any crypto-pair such as Open/Close/High/Low prices of any interval such as 15-minutes, Hourly, 1-day interval weekly, monthly. Dataset were obtained using the Binance API .

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.

This project aims to compare the performance of various open-source Large Language Models (LLMs) in predicting the price movements of cryptocurrencies and stocks. The goal is to assess how well these models handle price prediction tasks in financial markets with different levels of volatility, such as cryptocurrencies and traditional stocks.

Solana Stock Predict that users can place bets on the price movements of stocks, cryptocurrencies, and tokens, with fast transactions, low fees, and transparent results powered by Solana smart contracts

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.

This project aims to demonstrate how to use the yfinance library to obtain historical cryptocurrency data and use it to train an LSTM model to predict future closing prices. The Ethereum-USD (ETH-USD) pair is used as an example, but the same approach can be applied to other cryptocurrencies and financial assets.

• STOCK/CRYPTO PREDICTION WEB APP USING PYTHON (for everyone experienced and unexperienced both because it doesn’t require any pre-knowledge, user friendly, user-defined) A Web App with the help of python which can predict the future price of stock/crypto. The user has to enter the information of stock or crypto which they have to search the prediction & prediction time then my model will be providing all the future predictions and analyses with the help of graphs and datasets. With the help of M.L (fbprophets), the web app is able to plot the future graph of certain stock or cryptocurrencies. Here we are predicting the future of certain (user-defined) stock or crypto

Crawl data from Binance.com for predict price cryptocurrencies

Regression project - predict future stock price for the most popular cryptocurrencies.

Buys and Sell CryptoCurrencies using technical and Breakout strategy on Testnet Binance environment. Also predicts the cryptocurrency prices of next specified days using LSTM algorithm

Predicting the price of cryptocurrencies is one of the popular case studies in the data science community. The prices of stocks and cryptocurrencies don’t just depend on the number of people who buy or sell them. Today, the change in the prices of these investments also depends on the changes in the financial policies of the government regarding any cryptocurrency. The feelings of people towards a particular cryptocurrency or personality who directly or indirectly endorse a cryptocurrency also result in a huge buying and selling of a particular cryptocurrency, resulting in a change in prices.

This is the project on Cryptocurrency price prediction usin machine learning using a hybrid approach Abstract: To predict Cryptocurrency price at different frequencies using machine learning techniques, we first download the dataset from a trusted website which keeps all the data of various cryptocurrencies then we classify various Cryptocurrencies by the dataset that is according to the available price. We extract the basic trading features acquired from a cryptocurrency exchange are used for 1 month price prediction. Machine learning algorithms including ARIMA and SVR models for Cryptocurrency’s daily price prediction with high-dimensional features achieve an accuracy of 93% and 94% respectively, outperforming more complicated machine learning algorithms. Compared with benchmark results for daily price prediction, we achieve a better performance, with the highest accuracy of the machine learning algorithm of 97%. Our Hybrid Machine learning model including Support Vector Regression and Autoregressive integrated moving average for One month’s Cryptocurrency price prediction is superior to other Machine learning methods, with accuracy reaching 97%. Our investigation of Cryptocurrency price prediction can be considered a pilot study of the importance of the sample dimension in machine learning techniques. Extract the rar file for detailed project.

Cryptocurrencies classification and regression using Deep Learning - Tensorflow

Predict if cryptocurrencies are affected by 24-hour or 7-day price changes

This repository implements an XGBoost model for predicting the prices of financial instruments, such as stocks and cryptocurrencies. Using gradient boosting techniques, it aims to capture patterns in price movements, enhancing prediction accuracy across various datasets.

This repository contains code for training LSTM models to predict future prices of various cryptocurrencies based on historical data. The project utilizes TensorFlow and Python for model training and prediction. ## Project Structure - **`main.py`**: Main script to fetch historical data, train LSTM models, and predict future prices. -

Time Series Price Prediction using Gated Recurrent Units (GRU) for financial assets. This project predicts open, high, low, and close prices of assets like cryptocurrencies, forex, and commodities using machine learning. Includes data pre-processing, GRU model construction, and performance evaluation with metrics and visualizations.

This is a machine learning project where we will be predicting the future price of top cryptocurrencies using time series forecasting and along with it perform sentiment analysis to know how favorable the market is.

Quant AI is an dapp predicts the Ethereum trading price. This tool analyzes time series and trains deep learning models based on AI algorithm to forecast real-time trends and implement automatic trading of cryptocurrencies.