The decentralization of cryptocurrencies has greatly reduced the level of central control over them, impacting international relations and trade. Further, wide fluctuations in cryptocurrency price indicate an urgent need for an accurate way to forecast this price. This project proposes a method to predict fluctuations in the prices of cryptocurrencies, which are increasingly used for online transactions worldwide. This project proposes a novel method to compare the efficiency of five different models predicting the cryptocurrency price by considering various factors such as market cap, volume, circulating supply, and maximum supply based on deep learning techniques such as the Linear Regression, Support vector regression (SVR), Auto Regressive Integrated Moving Average (ARIMA), Long Short-Term Memory (LSTM) and a hybrid model of ARIMA and LSTM which are effective learning models for training data. The hybrid model outperforms the LSTM and ARIMA model after comparing RMSE values. The proposed approach is implemented in Python and validated for benchmark datasets.