Stock Price Prediction using Transformer Deep Learning Architecture
Project description
TrendMaster: Stock Price Prediction using Transformer Deep Learning Architecture
TrendMaster leverages advanced Transformer deep learning architecture to provide highly accurate stock price predictions, enabling informed investment decisions.
Utilizing a wealth of data and sophisticated algorithms, TrendMaster stands out as a top-tier tool for financial forecasting.
Installation
To get started with TrendMaster, run the following installation command:
pip install TrendMaster
Usage
Here's how to integrate TrendMaster into your Python projects:
from trendmaster import TrendMaster
#Initialize the TrendMaster object
tm = TrendMaster()
#Load your data
data = tm.load_data('path_to_your_data.csv')
#Train the model
tm.train(data, transformer_params={'num_layers': 3, 'dropout': 0.1})
#Perform inference
predictions = tm.infer('path_to_trained_model.pth')
print(predictions)
Star History
Our Transformer-based prediction model is trained on a large dataset of historical stock prices, giving it the ability to identify patterns and trends that would be impossible for a human to discern. The model's predictions are also highly accurate, with a mean average error of just a few percentage points.
In addition to stock price prediction, TrendMaster also offers a range of other features, such as real-time data visualization and a user-friendly interface. With TrendMaster, you'll have all the information you need to make smart investment decisions.
So why wait? Try TrendMaster today and see the difference for yourself!
📫 How to reach me
Checkout Cool GitHub Other Repositories:
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- transformers_stock_prediction
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- AutoCut
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