?! IMPORTANT !? -> Problems downloading and compressing data, full documentation uploaded on drive and link-shared


We found it very challenging to do this challenge due to the complexity of data and being available to work with real world values, much more complex but interesting.

What it does

It predicts sale incomings for 3 weeks from now, the probability that a customer leaves the online buying process and we analyze some factors that may influence on this and some possibilities to fix it.

How we built it

Trying different Machine Learning / Deep Learning algorithms, like LSTM's and SVM, we built a time series predictor to obtain our results.

Challenges we ran into

Very difficult data to process, grouped by multiple-row users, high correlations that produce fake accuracies on training set and disasters when testing, and the difficult to process a time series related data.

Accomplishments that we're proud of

We've been able to do an implementation (at least a basic one, but very improvable with more working time) for a very complex problem without having deep knowledge on this topic.

What we learned

Deal with difficult and complex data

What's next for M.jar

Fix this project and try to improve the accuracy

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