Price Prediction Case Study predicting the Bitcoin price and the Google stock price using Deep Learning, RNN with LSTM layers with TensorFlow and Keras in Python. (Includes: Data, Case Study Paper, Code)

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.

No description available

This project uses a Long Short-Term Memory (LSTM) model, a type of recurrent neural network, to predict the future price of Bitcoin.

: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.

In this project, I created prediction model for predict bitcoin price with Gated Recurrent Unit Model. GRU is a gating mechanism in recurrent neural networks (RNN) similar to a long short-term memory (LSTM), GRU have more simple computation and faster than LSTM because have fewer number of gates.

Bitcoin Price Prediction model - LSTM | Multivariable (Price&Polarity) Time Series Forecasting with NLP for Twitter Sentiments aka my Master's Thesis

LSTM-based Bitcoin price prediction using PyTorch. Explore deep learning for time-series forecasting with data preprocessing and model training. Ideal for cryptocurrency enthusiasts and those interested in financial forecasting.

Python

Bitcoin_Price_Prediction_with_LSTM - Code for my YouTube Video

Building a Recurrent Neural Network with Long Short Term Memory (LSTM) layers to predict the price of Bitcoin that yields prediction results

Bitcoin price prediction with LSTM models using TensorFlow and Pandas. Train and evaluate models; visualize forecasts and compare predictions to actual prices 🐙

Predict Bitcoin prices with ML & DL models! 📈 Uses Ridge, Lasso, Random Forest, MLP, RNN & LSTM with hyperparameter tuning. 📊 Visualizes predictions & ROC curves. 🚀 Fetch data via yfinance, evaluate with MSE/R2. Perfect for crypto enthusiasts! 💸

Hybrid Bitcoin price forecasting model combining Hidden Markov Models (HMM) for market regime detection and LSTM networks for next-day price prediction. Uses daily BTC data (2019–2024) and integrates hidden states with sequence learning to capture structural market shifts in financial time-series.

No description available

prediction of bitcoin price with LSTM

Hybrid crypto algo trading system using LSTM price prediction and reinforcement learning (PPO) with leverage-based backtesting on Bitcoin.

🚀 Bitcoin price prediction using RNN & LSTM neural networks with 30-day forecasting. Complete ML pipeline with data extraction, preprocessing, and time series analysis.

Crypto Price Prediction with LSTM uses machine learning and LSTM neural networks to forecast Bitcoin (BTC-USD) prices based on historical data. This project is for educational purposes only and should not be used for actual financial decisions due to cryptocurrency market volatility.

A Streamlit-based web app for 14-day Bitcoin price prediction using LSTM+CNN and XGBoost models. The app visualizes real BTC prices, ML-generated forecasts, and a moving average baseline. Automated daily updates with GitHub Actions.

Explore Bitcoin price prediction with this concise guide. Learn data preprocessing, visualization, and deep learning models (Dense, LSTM, 1D CNN). Proper dataset splitting, model evaluation, and N-BEATS algorithm replication included. Save best models and make accurate forecasts. #TimeSeries #BitcoinPrediction #DeepLearning

In this project, I’ve combined the power of data analysis and deep learning to predict Bitcoin prices. Using historical price data, I explored trends, calculated moving averages, and built an LSTM (Long Short-Term Memory) model to forecast future price movements.

Bitcoin stock price prediction with LSTM Recurrent Neural Network and XGBoost.

Bitcoin price prediction using Machine Learning (XGBoost & LSTM) with feature engineering and visualization.

CorrWeighted-LSTM is a hybrid deep learning approach that combines traditional technical indicators (RSI, MACD, Volatility) with correlation-based weighting to enhance Bitcoin price trend prediction. The project demonstrates a data-driven pipeline with feature engineering, LSTM-based sequence modeling, and evaluation metrics.

This project analyzes 4 years of Bitcoin (BTC-USD) historical data, visualizes trends using moving averages, and predicts future prices with an LSTM model. It also integrates the Fear & Greed Index for multi-feature cryptocurrency prediction, enhancing model accuracy and insights.

Bit Predict is a bitcoin price predictor which predicts the price of bitcoin using previous timeseries data (Timeseries Forecasting). Evaluated the results of a series of deep learning model such as sequence models (LSTM,1D CNN), ensemble model, dense model, multivariate model, also replicated the N-BEATS algorithm using TensorFlow layer subclassing and made prediction with the best performing model.

No description available

No description available

This project implements a Long Short-Term Memory (LSTM) neural network with PySpark Library to forecast Bitcoin (BTC/USD) prices using historical time series data.