BITCOIN Price prediction using machine learning .pptx

this is the project ppt can be used for btech and mtech students

1. NOBLE INSTITUTE OF SCIENCE AND TECHNOLOGY DEPARTMENT OF COMPUTER SCIENCE BITCOIN PRICE PREDICTION USING LONG SHORT TERM MEMORY Submitted by Gembali sai dinesh Registered number 32225620016 Project guide P.Kavitha

2. CONTENTS abstract Introduction Why this project What is lstm Problem statement Hardware &Software requirement Step wise procedure Outputs conclusion

3. ABSTRACT The volatile nature of Bitcoin prices necessitates the exploration of effective prediction methods. This project investigates the application of Long Short-Term Memory (LSTM) networks, a type of recurrent neural network (RNN), for forecasting Bitcoin prices. LSTMs excel at analyzing time series data like historical Bitcoin prices due to their ability to capture long-term dependencies.

4. INTRODUCTION Bitcoin is an innovative payment network and a new kind of money or a cryptocurrency, where cryptocurrency is a digital asset designed to work as a exchange medium that uses strong cryptography to secure financial transactions, control the creation of additional units, and verify the transfer of assets. BITCOIN, the first cryptocurrency was introduced in a paper published in 2008 by an author under pseudonym of SATOSHI NAKAMото.We aim to incorporate machine learning to analyse past fluctuations in currency prices, and attempt to decipher a trend in prices, this is because we can't predict accurately and sentiment analysis of bitcoin from twitter tweets.

5. WHY THIS PROJECT As we reach,in the final year,we will soon be rsposible,earning Individuals ,who will want to save moneyand invest it properly to Gain huge benefits Since stocks and cryptocurrencytrading are in trend, we choose to Help common man to learn how this works,and enable them ti invest Judiously by studying this trend analysis

6. Long short-term memory (LSTM)[1] is a type of recurrent neural network (RNN) aimed at dealing with the vanishing gradient problem[2] present in traditional RNNs. Its relative insensitivity to gap length is its advantage over other RNNs, hidden Markov models and other sequence learning methods. It aims to provide a short-term memory for RNN that can last thousands of timesteps, thus "long short-term memory".[1] The name is made in analogy with long-term memory and short-term memory and their relationship, studied by cognitive psychologists since early 20th century. WHAT IS LSTM

7. PROBLEM STATEMENT To develop a model which can help us to predict the price of the crypto currency used (in this case: Bitcoin), with low errorrate and a high precision of accuracy. The model will not tellthe future, but it might forecast the general trend and thedirection to expect the prices to move.

8. HARDWARE AND SOFTWARE REQUIREMENTS Software requirements • Programming language (Backend): python • Data analysis and ML libraries: panda , numpy , Tensor flow • Data visualisation : Matplotlib, Seaborn • IDE:Jupyter Notebook & Pycharm Hardware requirements • Processor intel core i5 or equivalent • RAM: 8 GB or higher • Storage : 10 GB SSD or higher (for faster data processing)

9. Importing libraries Loading data set EDA Building model Prediction Output Diagram kuda steps pdf lo undi METHODOLOGY

10. CONCLUSION This project contributes to the ongoing research in cryptocurrency price prediction by evaluating the effectiveness of LSTMs. The price of Bitcoin can be predicted with the deep learning method using LongShort Term Memory (LSTM) with an accuracy rate of 97.48%, testdata MSE 1327.70 and test data RMS 1756.27 with several inpuparameters , namely the number of input layer 2, the number of epochs 200, the number of hidden layer 32, and activation using Softmax . Prediction using deep learning method using LSTM is a regression approach. The stages begin with dataset preparation, normalization, modeling, training, testing, and evaluation of predicted results.