Bitcoin Price Prediction using Machine Learning in Python

Price prediction with machine learning involves using algorithms and statistical models to forecast the future prices of various assets, products, or financial instruments.
This can be applied to a wide range of fields, including stock markets, real estate, cryptocurrencies, commodities, and more.
I explained how to analyze historical data and predict price movement with machine learning for Bitcoin proce.
Then I demonstrated how to implement price prediction models in Python.
The historical Bitcoin price data can be downloaded from
www.kaggle.com...
You are welcome to provide your comments and subscribe to my UA-cam channel.
The Python code is uploaded into github.com/AIM...

[@Mcchagualo]

Are u millionarie ? Great results

[@statisticsandriskmodeling5477]

on my target 😄

[@ryangermain5716]

nice model, i created one with a gru. Have you tried making 3 classifications based on buy hold and sell?

[@polishcow6419]

Train/test split is only 10%. Maybe you encountered overfitting. Did you tested the model on real time data?

[@BeforeTomorrowBegins]

what % do you use?

[@ryangermain5716]

I have been running the code on Ethereum daily data and the performance is no where near the performance in this video. Im using daily data from 2017 to now.
I have also tried using bitcoin daily data and i am still getting the same performance metrics.

[@ryangermain5716]

(2048, 3) (228, 3)
LogisticRegression() :
Training Accuracy : 0.5536501574577727
Validation Accuracy : 0.5320705320705321
SVC(kernel='poly', probability=True) :
Training Accuracy : 0.4671218627731654
Validation Accuracy : 0.44375144375144376
XGBClassifier(base_score=None, booster=None, callbacks=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=None, device=None, early_stopping_rounds=None,
enable_categorical=False, eval_metric=None, feature_types=None,
gamma=None, grow_policy=None, importance_type=None,
interaction_constraints=None, learning_rate=None, max_bin=None,
max_cat_threshold=None, max_cat_to_onehot=None,
max_delta_step=None, max_depth=None, max_leaves=None,
min_child_weight=None, missing=nan, monotone_constraints=None,
multi_strategy=None, n_estimators=None, n_jobs=None,
num_parallel_tree=None, random_state=None, ...) :
Training Accuracy : 0.9397762191048763
Validation Accuracy : 0.4949179949179949