Predict bitcoin values using social sentiments (e.g. news and reddit posts)

Implementation of various Neural Network using Keras for predicting Stocks Market (BM&F Bovespa future contracts of dollar - Mini Dolar) and Bitcoin.

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

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

Predicting Market Price for Bitcoin using regressors like KNN repressor, linear regression, Ridge, Lasso, polynomial regression, SVM

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/.

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, in collaboration with the RAO Lab at EPFL, focuses on predicting Bitcoin price trends using deep learning models, including LSTMs and Transformers. By leveraging historical price data and market indicators, the model aims to capture temporal dependencies and improve forecasting accuracy.

In recent years,Bitcoin ecosystem has gained the attention of consumers, businesses, investors and speculators alike.As a result of blockchain-networkbased feature engineering ,macro-economic factors of actual market and machine learning algorithms optimization, we can obtain up-down Bitcoin price movement classification accuracy of roughly 55 percent. This research is concerned with predicting the price of Bitcoin using machine learning. The goal is to ascertain with what accuracy can the direction of Bitcoin price in USD can be predicted. The price data is sourced from the Bitcoin Price Index . The task is achieved with varying degrees of success through the implementation of a Bayesian optimised recurrent neural network (RNN) and Long Short Term Memory (LSTM) network.

Linear regression Prediction model for predicting market price of bitcoin

A comprehensive MVP for analyzing crypto campaigns, predicting Bitcoin prices, analyzing market sentiment, generating investment plans, and simulating trading strategies.

This topic involves predicting the price of Bitcoin (BTC) using data analysis and machine learning models. The goal is to analyze historical price data, market trends, and other relevant factors to forecast future Bitcoin price movements.

Machine Learning-based Early Warning System (EWS) for predicting Bitcoin market crashes (defined as ≥−20% return over 7 days). Utilizes multi-domain data: sentiment, on-chain, and macroeconomic indicators.

CryptoForecasting is a machine learning and deep learning project aimed at predicting cryptocurrency prices for Bitcoin (BTC) and Ethereum (ETH). The project leverages advanced models to provide accurate forecasts based on historical market data.

By Astama

Simple Linear Regression

Predicting bull or bear market of bitcoin prices

based on tweets)

This project uses machine learning in Python to analyze and predict Bitcoin and gold price trends from historical data. Using tools like NumPy, Pandas, and Scikit-learn, the models explore market behavior in both digital and traditional assets.

Predicting Bitcoin Direction with Market Structure and Machine Learning Features

Predicting Bitcoin future prices based on the current prices of bitcoin in the market.

A Simple code to help in predicting future trends in Bitcoin Market

Orchestrated project where we Analyzed Market Activity over time and the Sentiments between various User Tweets' on Twitter for Bitcoin, Gold and Inflation via Twint (a WebScapper). We analyze the overall Composite Sentiment for each category. We used the Sentiment Analyzers - NLTK Vader and Textblob. For our hypothesis, we are predicting that there is a direct correlation between the number of Tweets, the Sentiments and Market Activity for Bitcoin. The results from the Sentiment Scores in EmoTrader to evaluate a market indicator for algorithmic trading to incorporate in automated trading strategies. Libraries: Pandas, Pathlib.

Jupyter Notebooks with Time-Series Machine Learning algorithms for predicting S7P500 and Bitcoin market conditions

Predicting Bitcoin daily price using Linear Regression with historical market data. Includes preprocessing, feature engineering, and model evaluation.

Machine learning models predicting Bitcoin price movements using economic indicators and market data. Combines Binance API data with ForexFactory economic events for comprehensive cryptocurrency market analysis.

Predicting Bitcoin price using Multiple Linear Regression based on market indicators. A simple yet effective ML approach to model cryptocurrency trends.

Studying how public opinion influences the prices of Bitcoin by identifying the correlation between social media sentiments and market sentiments and thus predicting the Bitcoin prices in future.

🚀 AI-powered Bitcoin Price Prediction | Predicting BTC market trends using Machine Learning & Python 📊 | Data-driven insights, forecasting, and visualizations for crypto enthusiasts & researchers.

The goal of the project is predicting the bitcoin market price, employing the past values of the own time series as well as other variables.