STOCK-RETURN-PREDICTION-USING-KNN-SVM-GUASSIAN-PROCESS-ADABOOST-TREE-REGRESSION-AND-QDA VS bitcoin_price_prediction

Compare STOCK-RETURN-PREDICTION-USING-KNN-SVM-GUASSIAN-PROCESS-ADABOOST-TREE-REGRESSION-AND-QDA vs bitcoin_price_prediction and see what are their differences.

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STOCK-RETURN-PREDICTION-USING-KNN-SVM-GUASSIAN-PROCESS-ADABOOST-TREE-REGRESSION-AND-QDA bitcoin_price_prediction
3 1
116 10
- -
0.0 0.0
over 1 year ago about 3 years ago
Jupyter Notebook Jupyter Notebook
MIT License MIT License
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.

STOCK-RETURN-PREDICTION-USING-KNN-SVM-GUASSIAN-PROCESS-ADABOOST-TREE-REGRESSION-AND-QDA

Posts with mentions or reviews of STOCK-RETURN-PREDICTION-USING-KNN-SVM-GUASSIAN-PROCESS-ADABOOST-TREE-REGRESSION-AND-QDA. We have used some of these posts to build our list of alternatives and similar projects.

bitcoin_price_prediction

Posts with mentions or reviews of bitcoin_price_prediction. We have used some of these posts to build our list of alternatives and similar projects.

What are some alternatives?

When comparing STOCK-RETURN-PREDICTION-USING-KNN-SVM-GUASSIAN-PROCESS-ADABOOST-TREE-REGRESSION-AND-QDA and bitcoin_price_prediction you can also consider the following projects:

Empirical_Study_of_Ensemble_Learning_Methods - Training ensemble machine learning classifiers, with flexible templates for repeated cross-validation and parameter tuning

mlnotify - 🔔 No need to keep checking your training - just one import line and you'll know the second it's done.

Intrusion-Detection-System-Using-Machine-Learning - Code for IDS-ML: intrusion detection system development using machine learning algorithms (Decision tree, random forest, extra trees, XGBoost, stacking, k-means, Bayesian optimization..)

NYU-DLSP20 - NYU Deep Learning Spring 2020

TrainInvaders - 👾 Jupyter Notebook + Space Invaders!?

mix_merkletree - Usefull Merkle Tree Example

Python-Bible - Resourceful Python Collection

Animender - An AI that recommends anime based on personal history.

it-salary-analysis - 💰 Analysis of Salaries in IT Roles: DevOps, Cyber Security, and AI

biggestwar_ai

peacasso - UI interface for experimenting with multimodal (text, image) models (stable diffusion).

Anomaly_Detection_Tuto - Anomaly detection tutorial on univariate time series with an auto-encoder