Benefit Computing Using Python & Ml

Stock Price Prediction Using Python & Machine Learning (LSTM).
In this video you will learn how to create an artificial neural network called Long Short Term Memory to predict the future price of stock.

NOTE: In the video to calculate the RMSE I put the following statement:
rmse=np.sqrt(np.mean((predictions- y_test)**2))

When in fact I meant to put :
rmse=np.sqrt(np.mean(((predictions- y_test)**2)))

You can use the following statements to calculate RMSE:
1. rmse =np.sqrt(np.mean(((predictions- y_test)**2)))
2. rmse = np.sqrt(np.mean(np.power((np.array(y_test)-np.array(predictions)),2)))
3. rmse = np.sqrt(((predictions – y_test) ** 2).mean())

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►Article :
https://medium.com/@randerson112358/stock-price-prediction-using-python-machine-learning-e82a039ac2bb

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20 Antworten auf „Benefit Computing Using Python & Ml“

  1. This is an awesome tutorial. How did you learn how to do all that? I always have to copy off other people, I wouldn't have been able to guess how to set up a model. Off the top of your head you know to "from keras.models import Sequential", how would I guess that I needed that?

  2. Thank you a lot for this sharing of knowledge. A question: …….. did you predict two years (2018-2020) at once? …… OR …….. you have predicted day by day (or week) every day during two years ???????

  3. How does the program know the precise address of the Yahoo website if no exact address was given here?
    df = web.DataReader('AAPL', data_source='yahoo', start='2012-01-01', end='2019-12-17')
    Can we try other websites instead of data_source='yahoo'?

  4. Thank you for the video. Very useful to create its own model. All the videos on stock prediction are on single stock. If i want to predict like 10 or 50 stocks, it would be great to have a video to show how to code that. Possible ? thanks

  5. Great Video, but he calculated RMSE wrong. You square the differences and then take the mean of the squared differences, not just square the mean of the differences

  6. Brother, A Question, at the end of your amazing code, you predict the value for one day, how can I predict for one week?

  7. predictions = model.predict(x_test)
    predictions = scaler.inverse_transform(predictions)

    the following lines are giving an error:
    ValueError: Error when checking input: expected lstm_18_input to have 3 dimensions, but got array with shape (400, 60)
    please help!!!!

  8. This video does not teach anything useful. There's tons of material on the internet teaching how to use 'just' LSTM, Keras, Pandas, data loaders… This video would have much more value if the guy had presented some graph of how the network looks and what it actaully does. May a comparison with classic pure NN, and where the backpropagation comes in, and what recurrent means etc etc.

  9. Jan 1, 2012 is a holiday and must have been Saturday bc there is no data for Jan 2, which would have been Sunday. Meaning Jan 3 would be Monday.

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