Crypto-Currency price prediction using Decision Tree and Regression techniques
Crypto-currency such as Bitcoin is more popular these days among investors. In the proposed work, it is studied to forecast the Bitcoin price precisely considering different parameters that influence the Bitcoin price. This study first handles, it is identified the price trend on day by day changes in the Bitcoin price while it gives knowledge about Bitcoin price trends. The dataset till current date is taken with open, high, low and close price details of Bitcoin value. Exploiting the dataset machine learning module is introduced for prediction of price values. The aim of this work is to derive the accuracy of Bitcoin prediction using different machine learning algorithm and compare their accuracy. Experiment results are compared for decision tree and regression model.
How to represent the users' preference is one of the principle problems in widely used recommender systems. To address this problem, in this paper, the expressibility, space complexity and learning complexity of different kinds of preference models are investigated. The recommendation performances of these models are also compared on a real-life dataset. Considering both the expressibility and computational complexity, the quadric model is the most suited to recommender systems.