Can FTX Recover?

Ladies and gentlemen, I took the time to run some machine learning models of the dataset of the FTT coin from the last article I wrote earlier today.

I took to Python to see which models are the most efficient to predicting the future prices of the FTT coin.

I have utilized six models to compare:

• Linear Regression
• LASSO
• Elastic Net
• K-Nearest Neighbors
• Classification & Regression Trees
• Supporting Vector Machines

So far, the linear regression model has the most minimal error. Let's scale those comparisons below.

So far, the linear regression model still has the most minimal error, even when scaled. Let's try some ensemble comparisons with five of the following models with scale:

• AdaBoost
• Gradient Boosting Method
• Random Forest
• Extra Trees

Unfortunately, none of the ensemble models came close to the minimal error of the linear regressive model, which we will use to predict the FTT stock.

I've generated a prediction model with linear regression and plotted the model using a logarithmic plot, showing that the predictive model is approximately congruent with the actual model. Again, this is speculation barring an investigation from the Securities and Exchange Commission as well as the United States Department of Justice.

Have a wonderful Thanksgiving and enjoy the holiday weekend.

#ftx #ftxcrash #datascience #machinelearning #algorithmcomparison #thanksgiving