Crypto Currency Price Prediction Using Random Forest and Gradient Boosting Algorithms

Cryptocurrency prediction is a challenging task due to the volatile and unpredictable nature of the crypto market. However, machine learning algorithms such as Random Forest and Gradient Boosting can be employed to build models that offer insights and potential predictions. This paper presents a framework for utilizing these algorithms in cryptocurrency price prediction. The first step involves gathering historical cryptocurrency price data along with relevant features such as trading volume, market capitalization, social media sentiment, and technical indicators. Data preprocessing techniques are then applied to clean and normalize the data, handle missing values, and split it into training and testing sets.