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NFT VALUATOR
MACHINE LEARNING DEVELOPMENT CASE STUDY
About NFT Valuator
NFTValuator is a pioneering project dedicated to forecasting the optimal price of a Non-Fungible Token (NFT). Leveraging advanced Machine Learning (ML) algorithms, it delivers precise predictions. To enhance the accuracy, it employs a strategy of stacking multiple models. In predicting the value of a single NFT, it considers a broad array of data parameters, even incorporating Twitter statistics to understand the sentiments influencing an NFT purchase.
Project Development
Post-project evaluation, TokenMinds contributed to the NFTValuator project by implementing a machine learning strategy. Our focus was to develop an accurate prediction model for determining the optimal pricing of an NFT.
Custom Dataset Creation
We designed a comprehensive dataset with information extracted from Twitter interactions to decipher buyer sentiments. The dataset, covering parameters like createdAt_timestamp, rarity_Score, last_Sale_price, etc., encompassed around 40,000 NFT ids from 10 unique collections.
Exploratory Data Analysis
Our approach encompassed an in-depth analysis and scrutiny of the dataset to identify and comprehend any missing variables, patterns, or behavior. Visualization was an integral part of our process, facilitating understanding through detailed graphics. Here are some examples of our Exploratory Data Analysis (EDA) reports:
Model Comparison
In our approach, we compared various machine learning algorithms to identify the one that delivered superior results for the regression problem at hand. The range of algorithms we explored included, but was not limited to:
Machine Learning Development Examples
We have experienced significant improvements in R2 score through rigorous training of the model.
Finally, we attained a classic R2 score of 0.9+ on the training dataset.
Achievements
Developed a detailed dataset covering various Twitter engagement metrics.
Collected data for approximately 40,000 NFT ids from 10 collections.
Boosted R2 Score from 0.07 to 0.9.
Increased efficiency by 'pickling' our model to avoid redundant training.
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