This project aims to develop a machine learning model for predicting cryptocurrency prices based on historical data. By leveraging algorithms like linear regression, time series forecasting, or deep learning, the model analyzes patterns and trends in the historical price data to make predictions about future price movements.
The project involves collecting reliable cryptocurrency price data, preprocessing and cleaning the data, and selecting relevant features for training the model. It utilizes a train-test split to evaluate the model's performance and employs evaluation metrics such as mean absolute error (MAE) and root mean squared error (RMSE).
Additionally, the project includes visualization of the predicted prices alongside the actual prices, allowing users to gain insights into the model's accuracy and the overall cryptocurrency price trends over time.
By exploring this project, developers can gain hands-on experience in cryptocurrency analysis, machine learning techniques, and time series forecasting. It provides a foundation for further research and experimentation with different algorithms and datasets, ultimately enhancing the understanding and predictive capabilities in the domain of cryptocurrency price prediction.