Crappy model for bitcoin price prediction using sklearn module's regressor and decision trees. The model is trained over past 4 years data fetched using the coindesk API. The trained model objects are stored in /trained_objects as pickle formatted objects. flask is used to deploy the models online and retrieve the prediction.

The depended variables are :-

• Price on xx/xx/2014
• Price on xx/xx/2015
• Price on xx/xx/2016
• Price on xx/xx/2017

The independent variable (to be predited) is:-

• Price on xx/xx/2018

Hence, we are limiting the predictions for year 2018, anything after 12/30/2018 won't be, rather can't be predicted using this crappy model.

flask_app.py is used to deploy the model online and retrieve prices by calculating predictions over the already trained models (no dynamic data) flask webapp arguments :-

• model : dt, rr, mll_rr, rfr
• data: date in MM/DD/YYYY format for prediction

• Temporal data yet i've used randomized train-test sample which gives insights into the future
• Not enough dependend variables, dates give much more insight than just the UNIX epoch, columns shall be divided into day of month, day of year, day of week etc
• For other cryptos than bitcoin, bitcoin price should be one of the dependted variable, based on the generaly community phrase "bitcoin is the highway, other cryptos are the roads'
• No feature engineering whatsoever was done, tch tch tch...
• Many more...