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

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

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

Forecast stock prices using machine learning approach. A time series analysis. Employ the Use of Predictive Modeling in Machine Learning to Forecast Stock Return. Approach Used by Hedge Funds to Select Tradeable Stocks (by kennedyCzar)
Stock Prediction predictive-modeling predictive-analysis guassian-processes svm-classifier knn-classifier adaboost decision-tree parameter-tuning gridsearchcv Pipeline random-forest quadratic-discriminant-analysis logistic-regression algorithmic-trading Stocks
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Empirical_Study_of_Ensemble_Learning_Methods

Training ensemble machine learning classifiers, with flexible templates for repeated cross-validation and parameter tuning (by timothygmitchell)
random-forest Extratreesclassifier conditional-inference-trees Xgboost knn-classification naive-bayes-classifier gaussian-processes elastic-net neural-network support-vector-machines ensemble-learning logistic-regression linear-discriminant-analysis quadratic-discriminant-analysis Splines
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STOCK-RETURN-PREDICTION-USING-KNN-SVM-GUASSIAN-PROCESS-ADABOOST-TREE-REGRESSION-AND-QDA Empirical_Study_of_Ensemble_Learning_Methods
3 1
116 10
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0.0 0.0
over 1 year ago over 3 years ago
Jupyter Notebook R
MIT License -
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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.

Empirical_Study_of_Ensemble_Learning_Methods

Posts with mentions or reviews of Empirical_Study_of_Ensemble_Learning_Methods. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2021-01-04.

What are some alternatives?

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

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..)

optuna - A hyperparameter optimization framework

pyGAM - [HELP REQUESTED] Generalized Additive Models in Python

psych-verbs - Research experiment design and classification of Romanian emotion verbs

vswift - Tools created for machine learning classification model evaluation

voice-gender - Gender recognition by voice and speech analysis

100-Days-Of-ML-Code - 100 Days of ML Coding

mljar-supervised - Python package for AutoML on Tabular Data with Feature Engineering, Hyper-Parameters Tuning, Explanations and Automatic Documentation

STOCK-RETURN-PREDICTION-USING-KNN-SVM-GUASSIAN-PROCESS-ADABOOST-TREE-REGRESSION-AND-QDA vs Intrusion-Detection-System-Using-Machine-Learning Empirical_Study_of_Ensemble_Learning_Methods vs optuna Empirical_Study_of_Ensemble_Learning_Methods vs pyGAM Empirical_Study_of_Ensemble_Learning_Methods vs psych-verbs Empirical_Study_of_Ensemble_Learning_Methods vs vswift Empirical_Study_of_Ensemble_Learning_Methods vs voice-gender Empirical_Study_of_Ensemble_Learning_Methods vs 100-Days-Of-ML-Code Empirical_Study_of_Ensemble_Learning_Methods vs mljar-supervised