Switch from volitility to price tomorrow
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@@ -17,7 +17,7 @@ def Predict():
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df = features.CleanDF(df)
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# Drop our predictor
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df.drop('Volatility_5', axis=1, inplace=True)
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df.drop('Target_Close_Tomorrow', axis=1, inplace=True)
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# Lazy load this so it doesnt interfere with yfinance
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from keras.models import load_model
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@@ -34,6 +34,6 @@ def Predict():
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# 'predictions' will be a 2D array, flatten it if you want a simple list
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flat_predictions = predictions.flatten()
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print(f"Predicted Volatility: {flat_predictions}")
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print(f"Predicted Target_Close_Tomorrow: {flat_predictions}")
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return flat_predictions
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