What it does

This project analyzes a dataset containing relevant data on sales of a brewery. It cleans and takes the more relevant data and trains a Neural Network to predict the sales of the following months. We have a API where data can be sent and it will return the predicted data and a frontend app to show this process

How I built it

The Neural Network and cleaning of the data is done in Python using Pandas, Tensorflow and Keras. The API is done in Python using Flask and the frontend is done using HTML-CSS-Javascript and Plotly to plot the charts. We used Tableau to see how the data affected the result.

Challenges I ran into

The data was huge and hard to manipulate and take the relevant parts of it. Also the NN was hard to train because of the same problem.

Accomplishments that I'm proud of

Approaching the relation of the sales and the dates using our predictions.

What's next for #73 - jda

If we had more time we could implement a better NN to have a better prediction.

Predictions can be found on output_data.csv

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