hey @Naman-Taneja-2420178691408660 ,
your code isn’t wrong , it works the way you made it.
Things to focus on and can be improved :
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Preprocessing the data, you are normalizing X data , but i guess normalizing the output Y data can also be useful , you can try that too.
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lag time that you are using , currently its 19 , but you can try other values too. Or even prepare data with multiple lags , like 7,14,21,35 and pass all them to model.
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Preparing model architecture , the most important thing in Deep learning. You need to work a lot and improve it. You can try different combinations of those layers , adding a dropout after a LSTM is not always useful and using large number is also not useful sometimes , so thing and implement accordingly.
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Metric is R2 , not accuracy. So prepare a model that works for this metric the best.
I guess these points can be a lot useful for you in this task.
I hope this helps.
Thank You .