Crypto Forecasting using LSTM RNN in a Python Streamlit App | Is it any good?

In this video, we show you how to build a Streamlit app that uses an LSTM RNN model to predict cryptocurrency prices. The app is the centerpiece of this project, turning a complex machine learning pipeline into a user-friendly tool for real-time crypto forecasting.

Why the Streamlit App Stands Out:
The Streamlit app makes advanced crypto forecasting accessible to everyone. With a clean, intuitive interface, users can:
• Choose their crypto ticker symbol to analyze.
• Set the prediction horizon (number of days ahead).
• See real-time results with automated model retraining and predictions.
All the technical processes—data preparation, model training, and forecasting—happen seamlessly behind the scenes.

What the Streamlit App Offers:
1️⃣ Dynamic Inputs:
• Enter your preferred cryptocurrency ticker (e.g., BTC-USD) and the number of days ahead to forecast.
2️⃣ Automated Model Workflow:
• The app dynamically loads the data, preprocesses it for the LSTM model, and retrains the model for your selected crypto.
3️⃣ Real-Time Predictions:
• Displays the latest actual price and the predicted future price for the chosen ticker.
4️⃣ Interactive Visualizations:
• Plots showing:
o Actual historical prices.
o Predictions on the training and testing data.
o Forecasted prices for the selected prediction horizon.
5️⃣ Simplicity and Usability:
• The app provides a front-end interface that hides all the complexity of deep learning, letting users focus on insights instead of code.

Why Watch?
If you’re looking to deploy deep learning models in a way that’s practical, interactive, and accessible, this video is for you. The Streamlit app brings crypto forecasting to life, offering an easy-to-use tool that combines advanced LSTM RNNs with a sleek interface.

Subscribe for More! Learn how to build powerful machine learning models and turn them into interactive apps with Python and Streamlit. Let’s forecast the future together! 💹

🔗 Chapters:
00:00 – Intro
02:20 – Libraries & App Settings
03:50 – Sidebar Inputs
04:37 – Raw data & Data preprocessing
05:40 – Building LSTM Model
06:56 – Forecasting ahead
07:57 – Cards
08:50 – Final Plot
10:16 – Deploying the App
12:37 – Testing the App

Python Part 1:    • LSTM RNN for Forecasting Crypto Values – I...  
Streamlit Part 2:    • Crypto Forecasting using LSTM RNN in a Pyt...  

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