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Dogecoin Price Prediction Using Python & Machine Learning



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Dogecoin Price Prediction Using Python & Machine Learning

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19 thoughts on “Dogecoin Price Prediction Using Python & Machine Learning
  1. When i put this code

    y = np.array(df['Prediction'])

    y = y[:-predictions_days -1]

    print(y)

    I get some nan values so when i train the model i get "ValueError: Input contains NaN, infinity or a value too large for dtype('float32')."

  2. Please create and run on live data with paper trading. How to work with Live Data or by getting candlesticks every 5 min and then doing calculations?

  3. Random forest suffers from the same cons as any another regression model built on decision trees — the inability to extrapolate i.e. make correct prognostications for an X data that is out of the train dataset borders
    Also this model has no idea of trends of ascending or descending of prices because it actually just remembered the relations
    And, after all, to be honest, the final validation doesn't show a result nearly close to be good as the accuracy is like 78%. It isn't good even out of speaking in terms of costs like comissions that implies high requirements for model accuracy.

    I suppose there should be applied another model to solve the price prognosing problem

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