NFT Datathon 2022
Team(Daniel Pavlov, Martin Nenov, Aleksandar Svinarov)
Technology we use:
What was our approach:
Our approach with the NFT sales dataset and the NFT traits type dataset:
NFT traits dataset:
we made a function to generate a new trait dataset containing only the rarity score for each trait on each NFT. We can later use this data to train our model
NFT sales dataset:
We wanted to add more features to our sales dataset so we added a ‘time_diff’ feature which gives information about how much time has elapsed before the next transaction takes place.
we added a ‘price_diff’ feature that gives us information abut the difference between each transaction.
Filtering of sales dataset:
we made a function to filter and remove 3 consecutive transactions with a total sum of ‘time_diff’ more or equal to 24 hours which we think would be bad for the training model.
We removed transactions with zero loss/profit.
the python file that includes our functions for filtering and generating new datasets:
In our data preparation we will consider some data from the filtered and improved NFT sales dataset and the newly generated NFT rarity score dataset.
what data we decided to exclude from the NFT sales dataset: hash, from and to address currency for ETH and amount because we will use amountUSD.
we will make use of the from and to addresses by using the newly added feature ‘time_diff’.
features will will use: timestamp, token_id, gas_price,block_number, amountUSD,time_diff,price_diff, and( rarity score for each trait)
We merge the data from the rarity score dataset and the new sales dataset into a new dataset that will feed into our models.
What was our approach with training and testing models:
the dataset will be using:
First we use a MinMaxScaler to scale our data.
we checked the correlation between our features and it does not look good.
we have a function so we can test and evaluate the performance of each model and decide what is the best approach:
What we can improve: