Build A Bitcoin Price Prediction Program Using Machine Learning And Python
Disclaimer: The material in this article is purely educational and should not be taken as professional investment advice. Invest at your own discretion.
It is extremely hard to try and predict the direction of the stock market and stock price, but in this article I will give it a try. Even people with a good understanding of statistics and probabilities have a hard time doing this. So, please keep this in mind while reading through this article.
In this article I will show you how to build your own Python program to predict the price of Bitcoin (BTC) using a machine learning technique called Support Vector Machine. So you can start trading and making money ! Actually this program is really simple and I doubt any major profit will be made from this program, but it may be slightly better than guessing!
In the program we will use the Support Vector Regression function which is a type of Support Vector Machine. A Support Vector Regression (SVR) is a type of supervised learning algorithm that analyzes data for regression analysis. In 1996, this version of SVM for regression was proposed by Christopher J. C. Burges, Vladimir N. Vapnik, Harris Drucker, Alexander J. Smola and Linda Kaufman. The model produced by SVR depends only on a subset of the training data, because the cost function for building the model ignores any training data close to the model prediction.
Support Vector Machine Pros:
- It is effective in high dimensional spaces.
- It works well with clear margin of separation.
- It is effective in cases where number of dimensions is greater than the number of samples.
Support Vector Machine Regression Cons:
- It does not perform well, when we have large data set.
- Low performance if the data set is noisy ( a large amount of additional meaningless information).
Types Of Kernel:
- radial basis function (rbf)