Our project aims to utilise XG (eXtra Gradient) Boost to forecast future sales figures for the companies provided, so that the companies are able to make informed strategic decisions, optimise inventory management, and allocate resources efficiently
How we built it
We used multiple Python libraries, such as pandas for handling our .csv file and matplotlib for data visualisation
Challenges we ran into
The process of data filtering was slightly complicated, as there was a lot of data to work with.
Accomplishments that we're proud of
Thankfully, we were able to filter out which columns were not useful in our analysis, as well as one-hot encode other forms of data
What we learned
We learnt about the existence and workings of XG Boost
Log in or sign up for Devpost to join the conversation.