Price Prediction model is used to develop an LSTM model to predict the future market price of Bitcoin and Ethereum.

Overview

Price-Prediction-Model

This project’s goal is to develop a machine learning model that can predict a cryptocurrency's future market price.

A LSTM model is trained on historical price data that is pulled in through an API and stored in a relational database.

The model attempts to predict prices for a chosen time window, for both Bitcoin and Ethereum.

Our app is deployed using Heroku:

https://price-prediction-model.herokuapp.com/

Dataset:

The daily crypto price data has been pulled in through an API on CryptoCompare:

https://min-api.cryptocompare.com/documentation?key=Historical&cat=dataHistoday

The pricing information includes: timestamp, high, low, open, volumefrom, volumeto, and close. We will most likely save all the data, but only use one of the pricing metrics to train the model.

ETL proccess

The API data includes timestamp, high, low, open, volumefrom, volumeto, and close. In addition to these columns, we've created a coin, date, and year column.

Data storage

  • We used Heroku Postgres to store data for our app.
  • The database updates only when needed, based on the current and last unix timestamp in the db Database updates up to once daily, when index page loads, based on 00:00 GMT time zone.
  • Time units were daily only.
  • Data for both coins was stored in 1 table, due to limitations of a free Heroku Postgres database.

Long Short Term Memory (LSTM) Model

The Long Short Term Model (LSTM) has been used to do the price forecasting. LSTM is a slightly more sophisticated version of a Recurrent Neural Network (RNN) which incorporates long term memory. The model will be trained on historical price data and used to predict the next value in the series. (Time window for predictions, tbd)

Visualization

HTML/CSS/Plotly has been used to do the visualization and plots. Here are the final plots and Welcome page:

Welcome Page:

Bitcoin PricePerformance Plot and Table:


Bitcoin Candlestick chart:

Bitcoin Price Prediction Model:

Bitcoin Price Acceleration Plot:

Ethereum Price Performance Plot and Table:

Ethereum Candlestick Plot:

Bitcoin vs Ethereum Comparison Table and Plot:


Team Members:

Anna Weeks
Hima Vissa
Jacob Trevithick
Lekshmi Prabha
MCML is a toolkit for semi-supervised dimensionality reduction and quantitative analysis of Multi-Class, Multi-Label data

MCML is a toolkit for semi-supervised dimensionality reduction and quantitative analysis of Multi-Class, Multi-Label data. We demonstrate its use

Pachter Lab 26 Nov 29, 2022
A data preprocessing package for time series data. Design for machine learning and deep learning.

A data preprocessing package for time series data. Design for machine learning and deep learning.

Allen Chiang 152 Jan 07, 2023
Machine-learning-dell - Repositório com as atividades desenvolvidas no curso de Machine Learning

📚 Descrição Neste curso da Dell aprofundamos nossos conhecimentos em Machine Learning. 🖥️ Aulas (Em curso) 1.1 - Python aplicado a Data Science 1.2

Claudia dos Anjos 1 Jan 05, 2022
Distributed Tensorflow, Keras and PyTorch on Apache Spark/Flink & Ray

A unified Data Analytics and AI platform for distributed TensorFlow, Keras and PyTorch on Apache Spark/Flink & Ray What is Analytics Zoo? Analytics Zo

2.5k Dec 28, 2022
Cool Python features for machine learning that I used to be too afraid to use. Will be updated as I have more time / learn more.

python-is-cool A gentle guide to the Python features that I didn't know existed or was too afraid to use. This will be updated as I learn more and bec

Chip Huyen 3.3k Jan 05, 2023
Test symmetries with sklearn decision tree models

Test symmetries with sklearn decision tree models Setup Begin from an environment with a recent version of python 3. source setup.sh Leave the enviro

Rupert Tombs 2 Jul 19, 2022
Scikit-Learn useful pre-defined Pipelines Hub

Scikit-Pipes Scikit-Learn useful pre-defined Pipelines Hub Usage: Install scikit-pipes It's advised to install sklearn-genetic using a virtual env, in

Rodrigo Arenas 1 Apr 26, 2022
A demo project to elaborate how Machine Learn Models are deployed on production using Flask API

This is a salary prediction website developed with the help of machine learning, this makes prediction of salary on basis of few parameters like interview score, experience test score.

1 Feb 10, 2022
Python package for concise, transparent, and accurate predictive modeling

Python package for concise, transparent, and accurate predictive modeling. All sklearn-compatible and easy to use. 📚 docs • 📖 demo notebooks Modern

Chandan Singh 983 Jan 01, 2023
Simple linear model implementations from scratch.

Hand Crafted Models Simple linear model implementations from scratch. Table of contents Overview Project Structure Getting started Citing this project

Jonathan Sadighian 2 Sep 13, 2021
This is an implementation of the proximal policy optimization algorithm for the C++ API of Pytorch

This is an implementation of the proximal policy optimization algorithm for the C++ API of Pytorch. It uses a simple TestEnvironment to test the algorithm

Martin Huber 59 Dec 09, 2022
fastFM: A Library for Factorization Machines

Citing fastFM The library fastFM is an academic project. The time and resources spent developing fastFM are therefore justified by the number of citat

1k Dec 24, 2022
A high-performance topological machine learning toolbox in Python

giotto-tda is a high-performance topological machine learning toolbox in Python built on top of scikit-learn and is distributed under the G

giotto.ai 632 Dec 29, 2022
Deep Survival Machines - Fully Parametric Survival Regression

Package: dsm Python package dsm provides an API to train the Deep Survival Machines and associated models for problems in survival analysis. The under

Carnegie Mellon University Auton Lab 10 Dec 30, 2022
Time series changepoint detection

changepy Changepoint detection in time series in pure python Install pip install changepy Examples from changepy import pelt from cha

Rui Gil 92 Nov 08, 2022
Machine Learning e Data Science com Python

Machine Learning e Data Science com Python Arquivos do curso de Data Science e Machine Learning com Python na Udemy, cliqe aqui para acessá-lo. O prin

Renan Barbosa 1 Jan 27, 2022
A simple and lightweight genetic algorithm for optimization of any machine learning model

geneticml This package contains a simple and lightweight genetic algorithm for optimization of any machine learning model. Installation Use pip to ins

Allan Barcelos 8 Aug 10, 2022
Implementations of Machine Learning models, Regularizers, Optimizers and different Cost functions.

Linear Models Implementations of LinearRegression, LassoRegression and RidgeRegression with appropriate Regularizers and Optimizers. Linear Regression

Keivan Ipchi Hagh 1 Nov 22, 2021
Free MLOps course from DataTalks.Club

MLOps Zoomcamp Our MLOps Zoomcamp course Sign up here: https://airtable.com/shrCb8y6eTbPKwSTL (it's not automated, you will not receive an email immed

DataTalksClub 4.6k Dec 31, 2022
Python module for machine learning time series:

seglearn Seglearn is a python package for machine learning time series or sequences. It provides an integrated pipeline for segmentation, feature extr

David Burns 536 Dec 29, 2022