A LSTM Linear Regression model which is trained to predict the price of crypto currencies. The dataset used here was downloaded from finance.yahoo.com. The dataset contains five years of data. The crypto currency used here was BTC INR. The accuracy with the test data was above 95%.

Bitcoin Price Prediction using machine learning models including Random Forest Regressor, LSTM, a hybrid model (via Linear Regressor), and a meta-model (via Gradient Boosting Regressor).

Machine Learning project for predicting Bitcoin prices using historical time-series data. Includes data preprocessing, feature engineering, model training (Linear Regression, LSTM, etc.) and evaluation with RMSE, MSE and visualization of predictions.

The Bitcoin Price Prediction from Tweet Analysis project integrates historical price data with Twitter sentiment analysis to improve forecasting accuracy. Using LSTM and Linear Regression, it explores the influence of public sentiment on Bitcoin's price trends, revealing insights into how social media may impact cryptocurrency markets.

Bitcoin price prediction using machine learning with data preprocessing, visualization, and modeling. Implements Linear Regression, Decision Trees, and LSTM, evaluated with MAE and RMSE. Includes a Tkinter GUI for users to input parameters, view trends, and make accurate predictions intuitively.

A time-series forecasting project that predicts Bitcoin prices using LSTM and multiple regression models (Linear, Random Forest, SVR, KNN, Decision Tree). Models are evaluated with R² and MSE, and combined through an inverse-MSE weighted ensemble to improve overall prediction performance and robustness.