This device is not compatible.
PROJECT
Predictive Time Series Analysis Using LSTM and Flask
In this project, we will create a robust LSTM model for forecasting future values based on historical data. We will gain hands-on experience in advanced machine learning, focusing on deploying a specialized univariate LSTM through a user-friendly web interface and DigitalOcean CLI.
You will learn to:
Build and train a univariate LSTM model for forecasting.
Create a Flask API to serve predictions from the LSTM model.
Dockerize the application for seamless deployment.
Deploy the dockerized Flask application using the DigitalOcean CLI.
Skills
Time Series Analysis
Data Cleaning
Machine Learning
Prerequisites
Good understanding of Python programming language
Experience with data preprocessing
Basic knowledge of the LSTM model
Basic understanding of the Flask framework
Basic knowledge of deployment on DigitalOcean
Technologies
Flask
Python
Docker
DigitalOcean
Project Description
This comprehensive project integrates advanced deep learning techniques with web application development to provide robust time series forecasting capabilities. We specifically employ publicly available Bitcoin price data from Yahoo Finance to demonstrate the entire process. The project is divided into two essential parts: model training and deployment.
Building on this foundation, we explore the details of training the model and understanding how to build and optimize a Long Short-Term Memory (LSTM) model. We’ll look into its structure, training process, and how to evaluate its performance. Next, we’ll smoothly integrate the trained model into a Flask web application and use Docker for a consistent application setup across different platforms.
The final step is deploying the complete setup on DigitalOcean, giving us practical experience in real-world applications.
Project Tasks
1
Initial Setup
Task 0: Get Started
Task 1: Import Packages
Task 2: Load the Data
2
Data Preparation
Task 3: Explore the Data
Task 4: Manipulate the Data
Task 5: Generate the Input-Output Pairs
3
Model Construction and Training
Task 6: Create and Compile the LSTM Model
Task 7: Train the LSTM Model
4
Prediction and Model Evaluation
Task 8: Predict the Training Data
Task 9: Generate Future Dates for Predictions
Task 10: Make Predictions for Future Dates
Task 11: Inverse Transform Predictions
5
Flask App Creation
Task 12: Create a Flask Application
6
Docker and Deployment
Task 13: Dockerize the Dash Web App
Task 14: Create a Droplet on DigitalOcean
Task 15: Transfer Files to the DigitalOcean Droplet
Task 16: Build and Run the Docker Container
Congratulations!
Relevant Courses
Use the following content to review prerequisites or explore specific concepts in detail.