Non-Nonvaluable-Tool — How can the value of NFT’s be measured and predicted using EDA of market data and social media interactions?


The goal of the project is to analyze the relevance, interaction and market value of NFT-collections (Non Fungible Token) using various metrics. Therefore, we asked ourselves whether there is a correlation between social interactions and the market development of selected Ethereum-based NFTs and to what extent interactions on specific NFTs in social networks (in our case on the Twitter platform) are related to price development and demand. For this, in our project the value of different NFTs is being measured and predicted using exploratory data analysis of market data and social media interaction. The project results in an analysis tool to predict potential buy and sell signals on NFTs and a dashboard that clearly and concisely shows the relevant metrics around NFTs and thus provides a way for easier comparisons of different NFTs.


The concept arose from the current hype surrounding NFTs (Non Fungible Tokens) and chatter of very rapid money. It has been our personal experience that predicting or identifying potentially successful ventures is extremely tough. There is an abundance of data, some of which is abstract and difficult to measure and compare. Furthermore, there are various ways to uncover prospective new blue chip NFTs, a rug pull (scam), or simply to spot the next hype and price pump. The following factors are crucial in the market, which is also known as the “wild west” of the crypto world:


In the initial phase, we looked for data sources that were relevant for our project. Therefore, we examined various sources of information, including NFT groups, market databases, and social media. Because there was no current data that we could use, the initial step of our approach was to locate relevant data sources before collecting data on our own.


The next step after completing the analyses would be to integrate the visualizations into a dashboard. Unfortunately, we weren’t able to do this due to time constraints, so we put it together manually for now. So, for each NFT collection, there is a Twitter analytics dashboard that shows KPIs like the intensity of tweets around the keyword or a word cloud that provides content about Twitter interactions. To analyze the interactions around the tweets, it also provides statistics about the number of retweets and likes for the tweets. The second part of the analysis is related to the NFT trading data that we analyzed from the Ethereum blockchain. These were also summarized in a dashboard with the absolute KPI’s and visualizations of these over time. By comparing the Twitter KPI’s and the NFT data from the Ethereum Blockchain, we were able to see a correlation between Twitter activity and actual transactions of NFT collections. It is possible that market behavior is influenced by both targeted sales/transactions and targeted Twitter marketing. Both analysis components can influence each other in a decoupled manner and have a direct correlation at the same time.

The team

Colin Borremans Data Science Track (LinkedIn)
Marie Cermann Data Science Track (LinkedIn)
Fabienne Gadorosi Data Science Track (LinkedIn)
Sven Kreciszewski Data Science Track (LinkedIn)

Roles inside the team

Colin Borremans
- Conceptual design , Data Preparation , Definition & Identification of KPI´s, NFT Market Analysis and Data Visualization (NFT Market)
Marie Cermann
- Twitter Scraping, Data Preparation, Tweets Analysis, Definition & Identification of KPIs and Data Visualization (Tweets)
Fabienne Gadorosi
- Data Preparation, Tweets Analysis, Definition & Identification of KPIs and Data Visualization (Tweets)
Sven Kreciszewski
- Conception of a possible dashboard, Data cleaning, Presentation construction, Definition & elaboration of KPIs and NFT Market Analysis


Marcus Cramer



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