CSIT6000O Advanced Cloud Computing
Group 17
When NFT Market History meets Spark
Panoramic analysis of NFT ecology: customers, collections and transaction history
* Statistics from 2021-04-01 to 2021-09-25
Which collections have the largest number?
Ens
145303
Sorare
206037
Uniswap
132087
What is the percentage of value each account owns?
We would like to see how much value NFT each account owns. After summing all the NFT values until 2021 September 25th, the total market value of the NFT is 5.277165e+24 owned by 625354 accounts. We predict that most of the value is owned by a small number of accounts. The following plot shows that the market value is heavily skewed.
We would like to see how much value NFT each account owns. After summing all the NFT values until 2021 September 25th, the total market value of the NFT is 5.277165e+24 owned by 625354 accounts. We predict that most of the value is owned by a small number of accounts. The following plot shows that the market value is heavily skewed.
The distribution of the market share
The entire market share is heavily skewed. The majority of high-value NFTs are owned by a small number of accounts. We aggregate the top 100 accounts by the total market value and see the distribution of the market share. The top 10 accounts own 21% of the entire NFT value.
The entire market share is heavily skewed. The majority of high-value NFTs are owned by a small number of accounts. We aggregate the top 100 accounts by the total market value and see the distribution of the market share. The top 10 accounts own 21% of the entire NFT value.
How many NFTs are owned by each account?
After investigating the NFT values, we would like to see the number of NFTs owned by each account. There are 6937402 NFTs owned by 625354 accounts as of September 25th, 2021. The distribution of the owned NFT is similar to the NFT market value distribution.
After investigating the NFT values, we would like to see the number of NFTs owned by each account. There are 6937402 NFTs owned by 625354 accounts as of September 25th, 2021. The distribution of the owned NFT is similar to the NFT market value distribution.
How many transactions per month?
After data classification and preprocessing, we mainly focus on the time and volume of transactions. The data we have contain transactions from April to September. Therefore, the first statistic we made is the trading volume of each month. However, the data for September is not completed, only the data from September 1st to September 25st was contained. So, we decided to do analyze first to see what we can do for the result. By observing and sorting through the data, we can see that the trading volume is constantly increasing. Although September's transaction value is not as large as August's, this is because our dataset does not include all the data of September. We believe the number of transactions of September will greater than August’s. So, trading volume is an overall uptrend.
After data classification and preprocessing, we mainly focus on the time and volume of transactions. The data we have contain transactions from April to September. Therefore, the first statistic we made is the trading volume of each month. However, the data for September is not completed, only the data from September 1st to September 25st was contained. So, we decided to do analyze first to see what we can do for the result. By observing and sorting through the data, we can see that the trading volume is constantly increasing. Although September's transaction value is not as large as August's, this is because our dataset does not include all the data of September. We believe the number of transactions of September will greater than August’s. So, trading volume is an overall uptrend.
How many transactions per week?
After we have the monthly transaction analysis, we found that the pie chart is not a good way to show the trend and magnitude of the growth. So, we decided to present our data in another way. We add the data for each week together so that there won’t be too many columns to obscure trends, or too few columns to show too little data on the chart. Through such data processing and analysis, we can clearly see how the data grows on the line chart. The reason why we care so much about the number of transactions is we believe that we can find the relationship between data and events by analyzing what happens in reality and how the trend of the data changed. After we have such a relationship, we can try to make predictions.
After we have the monthly transaction analysis, we found that the pie chart is not a good way to show the trend and magnitude of the growth. So, we decided to present our data in another way. We add the data for each week together so that there won’t be too many columns to obscure trends, or too few columns to show too little data on the chart. Through such data processing and analysis, we can clearly see how the data grows on the line chart. The reason why we care so much about the number of transactions is we believe that we can find the relationship between data and events by analyzing what happens in reality and how the trend of the data changed. After we have such a relationship, we can try to make predictions.
What are the top 10 accounts with trades in frequency?
After data classification and preprocessing, we mainly focus on the time and volume of transactions. The data we have contain transactions from April to September. Therefore, the first statistic we made is the trading volume of each month. However, the data for September is not completed, only the data from September 1st to September 25st was contained. So, we decided to do analyze first to see what we can do for the result. By observing and sorting through the data, we can see that the trading volume is constantly increasing. Although September's transaction value is not as large as August's, this is because our dataset does not include all the data of September. We believe the number of transactions of September will greater than August’s. So, trading volume is an overall uptrend.
After data classification and preprocessing, we mainly focus on the time and volume of transactions. The data we have contain transactions from April to September. Therefore, the first statistic we made is the trading volume of each month. However, the data for September is not completed, only the data from September 1st to September 25st was contained. So, we decided to do analyze first to see what we can do for the result. By observing and sorting through the data, we can see that the trading volume is constantly increasing. Although September's transaction value is not as large as August's, this is because our dataset does not include all the data of September. We believe the number of transactions of September will greater than August’s. So, trading volume is an overall uptrend.
