To continue in the knowledge journey, I will continue with other projects I have, like the Bitcoin Price Predictor program using Machine Learning and Python. I think I am going to use Support Vector Regression function, this is a supervised learning algorithm that analyzes data for regression analysis. This SVM was proposed by Christopher J. C. Burges, Vladimir N. Vapnik, Harris Drucker, Alexander J. Smola and Linda Kaufman. The model produced depends only on a subset of the training data, this is, the cost functionfor building the model ignores any training data close to the model prediction. The pros in this case are: –The effectiviness in high dimensional spaces. –Works well with clear margin of separation. – When the number of dimensions are greater than the samples it's really effective. Some cons would be: –That the dataset needs to be small, cause this does no't perform so well in large datasets. –The Low perfomance if there is a large amount of meaningless info.