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Bitcoin Price Prediction with Python using Machine Learning
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All rights reserved. Want to join? If you have pip installed, all you need to do to get Prophet is open a console and type. On Windows I ran into some Anaconda-related dependency issue and had to run first. Quandl is an amazing repository with a massive number of extremely varied datasets. First download the Bitcoin market data from Quandl and get rid of the zero values the price data contains zeros in the initial rows for some reason. This puts the price data in a pandas dataframe called bitcoin. To inspect the first few rows type.
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Now we would like to plot logged data on a linear chart the reason will become apparent later , so make a new column in bitcoin for the logged price values. The first thing we need to do is a little house keeping. Most of what Prophet does takes place behind the scenes, but there are some hyperparameters that allow us to easily fine tune our models. Changepoints are basically points where the trend makes a sudden change in direction, e.
Prophet can find these points for you automatically, although it is possible to define them yourself, though for large and idiosyncratic datasets this is impractical. Likewise underfitting can also produce models that generalise poorly.
So we will use this one hyperparameter to do all our fine tuning. The higher the value the tighter the fit, and likewise the lower the value the looser the fit.
Prophet PROPHET price
For this demo I have chosen to values 0. At this point you should have a list of two prophet objects and a list of labels for plotting. The next step is to use those prophet objects to generate forecast objects. If all went well, you should have generated a list of two forecast objects really just pandas dataframes. The goal here was simply to mess around with the Prophet package and I thought Bitcoin would make an interesting dataset. Just by eyeballing, I think both predictions are pretty sane from a Bitcoin maximalist point of view, by which I mean they match what many bullish investors believe will happen to the price.
The blue fit is perhaps slightly underfitting but you can see that the uncertainty on the estimate remains fairly constant over time, even into the future. In general, it offers more scope for generalisation but it may respond too slowly to trend changes. Hence I think it is unreliable.
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I would not expect the price to maintain that trend for long. The red fit on the other hand seems to overfit the data. It follows the historical price action tightly and responds quickly to trend changes, however the uncertainty really blows up into the future making this prediction unreliable too, although the red trend is somewhat more believable. Personally I do not feel that this kind of price prediction is particularly useful, but it will be interesting to follow this and see how the Facebook algorithms pan out.