• Shreya Kashid
• Tejas Machkar
• Stephanie Kedari
• Harshika Mishra
• Dr. Archana Kale

Bitcoin is a cryptocurrency founded in 2008. It is a new currency that is recognized as a smart and intelligent payment network. Bitcoin uses peer-to-peer technology to operate without central authority or banks; managing transactions and issuing bitcoins is done jointly by the network. The open source code structure of Bitcoin allows it to be uncontrolled and uncontrolled by any organization with bitcoin limited and isolated. It has a central entertainment and tracking system for all transactions and authentication of all payments is protected using public key encryption. Bitcoin prices fluctuate at high prices making it difficult to predict, this is the main reason for this study. The study focuses on the pricing of popular bitcoin currencies using various neural network methods namely Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) as well as ten times cross verification. Analysis of various styles from the bitcoin market is performed and important factors are considered and daily price changes are measured by the neural network model. Live streaming data, as well as the database, is considered a test function from a website called coinmarketcap. Mean Absolute Error (MAE) is considered as a comparative parameter in analyzing the performance of the proposed model with the existing ones. The experiment led to testing hyperparameters to increase the accuracy of the prediction which was significantly lower than the predicted results.

Bitcoin, Recurrent Neural Network, Long Short Term Memory, K-fold cross validation, Machine learning,Prediction.

"A Machine Learning Approach in Predicting Bitcoin Prices Using LSTM and 10-Fold Cross Validation", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.9, Issue 3, page no.a174-a181, March-2022, Available :http://www.jetir.org/papers/JETIR2203021.pdf

"A Machine Learning Approach in Predicting Bitcoin Prices Using LSTM and 10-Fold Cross Validation", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.9, Issue 3, page no. ppa174-a181, March-2022, Available at : http://www.jetir.org/papers/JETIR2203021.pdf