The rise of meme coins on various blockchain platforms has been nothing short of meteoric. However, with this rise has come an increase in the number of “rugs” — fraudulent coins that are created with the sole purpose of scamming investors.
In this article, we explore how artificial intelligence (AI) can be used to build a probabilistic model for predicting which new meme coins on chain like base are likely to be rugs.
## The Data
The first step in building any AI model is to gather and prepare the data. In this case, we need a large dataset of prior rugs on chain like base. This data can be obtained from various sources, such as blockchain explorers and community forums. Once we have this data, we can begin to identify the variables that are common among these rugs.
## The Model
With our data in hand, we can now begin to build our probabilistic model. This model will take in various inputs, such as the coin’s age, trading volume, and liquidity, and output a probability that the coin is a rug. The exact details of the model will depend on the data and the variables that we have identified as being important.
One approach could be to use a machine learning algorithm, such as a decision tree or a neural network, to learn the relationships between the input variables and the output probability. These algorithms can be trained on our dataset of prior rugs to learn what characteristics are common among these fraudulent coins.
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Laika AI is working to make predictive modelling in blockchain space a reality. By using advanced AI techniques, Laika AI is developing a probabilistic model that can help investors avoid rugs on chain like base and many other chains
AI can be a powerful tool for predicting which new meme coins on chain like base are likely to be rugs. By gathering a large dataset of prior rugs and using machine learning algorithms to learn the relationships between various variables and the likelihood of a coin being a rug, we can build a probabilistic model that can help investors avoid these scams
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