A fun and very useful competition as well.
Congratulations to the winners and good luck to everyone.
here is my solution in simple details:
First, we group the data by date and source, then sum up the Kilowatt * Hr. of each day for each source, finally we perform the division into 5 days feature and label samples.
The model can be divided into two main parts:
- the first part is the parallel neural network models with the two different inputs entering to it
- the second part is the concatenation layer and then the Dense layer for getting output
did you receive any mail from Zindi ?
Yes, I recieved the code submission
Thank you for sharing
Nice solution. Thanks for sharing. The cross validation for me - very intuitive !
Curious to know, how many samples did you end up with in total?
After aggregating the target variable by date and source, did you try dropping duplicates and retaining just first or last data points?
Thank you so much!
I had around 130,000 samples,
nope I didn't try dropping duplicates
Interesting. Thank you for shedding light on that
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الله يبارك فيك 3> 3> .