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as 07-08-2024 3:32pm EST

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Nasdaq

GoldMining Inc is a mineral exploration company with a focus on the acquisition, exploration, and development of projects in Colombia, Brazil, the United States, Canada, and Peru. The principal projects are its La Mina Gold project and its Titiribi Gold-Copper project, located in Colombia, Crucero project, located in Peru, Sao Jorge, Batistao, Boa Vista, located in Brazil, Yellowknife Gold Project, located in the Northwest Territories, Canada, and other projects. The company earns maximum revenue from Canada.

More About Our GLDG ML Model...

What kind of parameters do you use to train GLDG stocks ML model?

To train our stocks GLDG ML model, we use historical data with over 25 parameters, including volume indicators, volatility indicators, momentum indicators, trend indicators, and other metrics.

How often do you update GLDG ML model?

We update our ML model either weekly or biweekly, depending on the market capitalization of the stocks, using TensorFlow, typically over the weekend.

Why is the accuracy of your GLDG model low?

Unfortunately, our current data provider offers limited historical data, which impacts the accuracy of our ML model. However, as we continue to gather more data over time and add more indicators, we expect the accuracy of our model to improve.

How can I provide and share more data with you to increase your ML model accuracy?

We would greatly appreciate your contribution. Please send an email to [email protected]

Do you offer an ML model for shorter time periods, such as 5 minutes or 15 minutes?

Yes, we offer it for strategy purposes only, with intervals: 1 minute, 2 minutes, 5 minutes, 15 minutes, and 30 minutes etc.

Can I rely on your ML model to make financial decisions regarding buying or selling stocks, or is it only intended for learning purposes?

Our ML model is primarily designed for educational purposes and is not intended to provide financial advice. We strongly recommend consulting with a financial advisor before making any buying or selling decisions.

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