The main goal of our project is to encourage individuals to invest in the proper NFTs based on our data because many people find it difficult or harder to choose the right NFTs for their wallet.
The chain ethereum is covered in this application with general information and predictions. Volume exchanges per day, unique wallets per day, collection-wise unique wallets and volume, and so on are all included in the forecast. This will provide a clear statistic to NFT traders and buyers. As a result, they will be able to invest according to market conditions. This also provides a graphical view of how the chain works over time and how the market fluctuates. The home page represents the total volume and wallet prediction across the chain ethereum by line chart and includes a few key metrices like Total volume, Average daily volume, and Average weekly volume. Time series forecasting model was built here to predict the number(LSTM). The collection page depicts the overall volume and wallet prediction across all collections in the chain ethereum by line chart; for example, we've provided a few key metrics and predictions of volume and wallet over the period of time for BAYC. The metrices includes total volume, average daily volume, and average weekly volume. The Token page displays a list of unique tokens, tokens that are frequently traded, tokens that are sold in large quantities, and so on. We've also included the current owner of the token id, as well as the characteristics of the Top 4 most sold tokens by volume throughout each collection. The wallet page displays a list of unique wallets, as well as the top seller by volume, tokens top seller by volume, and total transactions, among other information. We've also compiled a list of the top 100 wallet-to-wallet transactions in the chain ethereum. Duplicate and forgery page shows the near duplicate token minted in the same nft collections or other. Discussion page stores the idea of the people in IPFS storage. The state page includes all of the collectios across the chain ethereum, along with the market cap, transaction count, and floor price,etc.
How it's made
we have used steamlit to built front end and for the backend we used python to integrate with the API's. We have used API's from covalent for listing transcation details, collection details,etc. We have also used IPFS storage. to stores the keyfindings of the people. This will provide a clear statistic to NFT traders and buyers. As a result, they will be able to invest according to market conditions. This also provides a graphical view of how the chain works over time and how the market fluctuates.