Bayesian Statistics Python R

Exploration student performarmance in maths, and prediction of final exam scores. Feature Selection using correlation. Comparison between standard machine learning regression algorithms(Random Forest, Linear Regression, Extra Trees, SVM, Gradient Boosted, ElasticNet Regression, vs Baseline. And Bayesian Regression (Normal distribution)

Exploration student performarmance in portuguese, and prediction of final exam scores. Feature Selection using correlation. Comparison between standard machine learning regression algorithms(Random Forest, Linear Regression, Extra Trees, SVM, Gradient Boosted, ElasticNet Regression, vs Baseline. And Bayesian Regression (Normal distribution)

Exploration student performarmance in portuguese, and prediction of final exam scores. Feature Selection using correlation. Comparison between standard machine learning regression algorithms(Random Forest, Linear Regression, Extra Trees, SVM, Gradient Boosted, ElasticNet Regression, vs Baseline. And Bayesian Regression (T-distribution)

Exploration student performarmance in portuguese, and prediction of final exam scores. Feature Selection using correlation. Comparison between standard machine learning regression algorithms(Random Forest, Linear Regression, Extra Trees, SVM, Gradient Boosted, ElasticNet Regression, vs Baseline. Preprocessing(standartization) ,And Bayesian Regression (Poisson distribution)

PyMC3 Model for MCMC (Markov Chain Monte Carlo), classification Awake and Asleep models from Garmin datasets Import required libraries and viisualize both datasets (sleep and waking data) Sleep and Wake data are from Garmin indicating weather a person is asleep or awake based on motion sensing and heart rate My objective is to use this data to create a model that returns the probability I am asleep at a given time. The final goal can be mathematically expressed as: 𝑃(awake|time)

99_R_Datacamp_1_fundamentals_of_Bayesian_data_analysis.Rmd 99_R_Datacamp_2_bayesian_regression_modeling_with_rstanarm.Rmd