How to Predict Stock Prices with Scikit-learn (Python tutorial)




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In this Python tutorial, Caelan will show you how to use Scikit-learn to predict Tesla’s stock price by training and testing a long short-term memory (LSTM) neural network model. This same machine learning technique can be used to predict tomorrow’s stock price with only minor modifications.

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Comment List

  • Kite
    November 11, 2020

    6:25

    window_size = 20
    to_split = df['price'].rolling(window_size).mean()

  • Kite
    November 11, 2020

    line 10 is incorrect

  • Kite
    November 11, 2020

    He never defined T. What is T, his train data?

  • Kite
    November 11, 2020

    Super cool, thanks for sharing!

  • Kite
    November 11, 2020

    Quality Content!

  • Kite
    November 11, 2020

    How do I add you on LinkedIn?

  • Kite
    November 11, 2020

    You open your mouth so wide when you talk

  • Kite
    November 11, 2020

    The min max scaler is being fitted on the entire dataset. This means that you have essentially leaked information about the test data.

    The proper way to do this would be to first split your data into train and test set. Fit the scaler on train set only. Transform train and test set afterwards.

  • Kite
    November 11, 2020

    thanks. should not you make the time series stationary first? When you reshape X_train should not it be ( shape[0], shape[1],1)
    Great channel and tool by the way

  • Kite
    November 11, 2020

    Great Content

  • Kite
    November 11, 2020

    Thank u Kite for making learning Fun and Easy

  • Kite
    November 11, 2020

    Trust me everyone, you can learn more here

    instead of other tutorials on internet

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