Stock Price Prediction And Forecasting Using Stacked LSTM- Deep Learning – YouTube

Stock Price Prediction And Forecasting Using Stacked LSTM- Deep Learning



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  • Video Views: 302811
  • Published On: 2020-05-25 20:01:27
  • Video Published/Author: Krish Naik
  • Video Duration: 00:36:33
  • Source: Watch on YouTube


A Machine Learning Model for Stock Market Prediction. Stock market prediction is the act of trying to determine the future value of a company stock or other financial instrument traded on a financial exchange
References: Jason Browniee Machine Learning Mastery Blogs
https://machinelearningmastery.com/time-series-prediction-with-deep-learning-in-python-with-keras/

github link: https://github.com/krishnaik06/Stock-MArket-Forecasting
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49 comments
  1. good morning kris , thank you so much for the videos , you are just amazing , need a help i deveoped a ANN and save the model using jupyter notebook , and i have taken that model in flask , the issue is , when i predict in flask , the data fromat will be scaled since while develping ANN we need to scale data before putting it in ANN , so this is the problem i face , please help me on unscale data in flask after predicting

  2. A good video indeed… But, as a beginner, I could not grasp a lot. It could be a great video if you explained it in more detail in a few series of videos. And of course, if it was in Hindi then it was certainly a great video. lol!!! A good video indeed.

  3. Hi Krish,
    I just have one query you transform the train_predict and test_predict back to their original form but you did not do the same with y_train and y_test and maybe that is giving such RMSE values.. am i right or wrong ???

  4. First of all , thanks for a wonderful session. One question about scaling though. Shouldn't the MinMaxScaler be used to fit_transform the training data and then use the "fitted" scaler to the test data ?

  5. NotImplementedError: Cannot convert a symbolic Tensor (lstm_6/strided_slice:0) to a numpy array. This error may indicate that you're trying to pass a Tensor to a NumPy call, which is not supported

    is anyone getting this error?if yes, how did you fix it? Thanks

  6. Important to note that real stock prediction takes in MANY different data sources, the time-series data of the stock price alone is not sufficient to make accurate predictions, you must take into account the sentiment of news around a company etc

  7. NotImplementedError: Cannot convert a symbolic Tensor (lstm_4/strided_slice:0) to a numpy array. This error may indicate that you're trying to pass a Tensor to a NumPy call, which is not supported

    How to solve this error?

  8. Hi ! Thank you for your video, very useful. I have a noob question though : if you want to predict the next day's value (I mean the first value of your lst_output). Which value do you have to rely on please ? df3[1259] (or lst_output[1]), correct ?

  9. Great! It is so fun to me as a beginner. I tried and almost got through the whole tutorial. Only I found an error for "x_input=x_input.reshape((1,n_steps,1))", the error message is : "ValueError: cannot reshape array of size 79 into shape (1,100,1)". I wonder if you would spend a few seconds to help. Anyway, thank you very much. I learned a lot already.

  10. when we work on huge dataset , the time step make 3 or 4 times huge than orginal dataset so memory is out of allocated
    and restart the kaggle notebook how can be overcome from this issue

  11. So far, this is the most comprehensive tutorial I have seen. Thank you Sir!
    I would like to ask a question:

    In this part of code, I have an error that says: "AttributeError: module 'numpy' has no attribute 'arrange'", how to fix this?

    day_new=np.arrange(1,51)

    day_pred=np.arrange(51,56)

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