John Pauline Pineda's repositories
johnpaulinepineda.github.io
Portfolio of data science projects in markdown and HTML formats as hosted on GitHub pages.
Portfolio_Project_13
Data science project which demonstrates various remedial procedures for handling imbalanced data for classification in R.
Portfolio_Project_42
Data science case study aimed at estimating cancer rate group probabilities across countries using classification algorithms with class imbalance treatment in Python.
Portfolio_Project_43
Data science case study aimed at discovering global patterns in cancer mortality across countries via clustering analysis in Python.
Portfolio_Project_44
Data science case study aimed at learning hierarchical features for predicting multiclass x-ray images using convolutional neural network model variations in Python.
Portfolio_Project_46
Data science project which demonstrates the implementation of the backpropagation algorithm in updating the weights of a neural network classification model in Python.
Portfolio_Project_47
Data science project which demonstrates the implementation of the batch, stochastic and mini-batch approaches to gradient descent in estimating the coefficients of a linear regression model in Python.
Portfolio_Project_48
Data science project which explores various activation functions and their effects on gradient updates during backpropagation for a neural network classification model in Python.
Portfolio_Project_49
Data science project which explores various optimization algorithms and their effects on parameter updates and loss function optimization for a neural network classification model in Python.
Portfolio_Project_50
Data science project which explores various regularization approaches to control model complexity by penalizing large parameter weights for a neural network classification model in Python.
Portfolio_Project_17
Data science project which demonstrates the implementation of recursive feature elimination in selecting informative predictors for a modelling problem in R.
Portfolio_Project_18
Data science project which demonstrates the implementation of univariate filters in selecting informative predictors for a modelling problem in R.
Portfolio_Project_19
Data science project which demonstrates the implementation of simulated annealing and genetic algorithms in selecting informative predictors for a modelling problem in R.
Portfolio_Project_20
Data science project which demonstrates various remedial procedures for handling skewed data with extreme outliers for classification in R.
Portfolio_Project_21
Data science project which demonstrates various dimensionality reduction algorithms for extracting information in R.
Portfolio_Project_22
Data science project which demonstrates various clustering algorithms for segmenting information in R.
Portfolio_Project_24
Data science project which demonstrates various dichotomization thresholding strategies for optimal classification in R.
Portfolio_Project_25
Data science project which demonstrates various density-based clustering algorithms for identifying multivariate outliers in R.
Portfolio_Project_26
Data science project which demonstrates various isolation forest-based anomaly detection algorithms for estimating outlier scores in R.
Portfolio_Project_27
Data science project which demonstrates various density and distance-based anomaly detection algorithms for estimating outlier scores in R.
Portfolio_Project_29
Data science project which demonstrates various metrics for evaluating survival model predictions in R.
Portfolio_Project_30
Data science project which demonstrates various oversampling and undersampling algorithms for handling class imbalance in R.
Portfolio_Project_31
Data science project which demonstrates various sample size and power calculations for tests comparing means in R.
Portfolio_Project_32
Data science project which demonstrates various sample size and power calculations for tests comparing proportions in R.
Portfolio_Project_33
Data science project which demonstrates various visualization strategies for extracted dimensions from exploratory multivariate data analyses algorithms in R.
Portfolio_Project_34
Data science project which demonstrates various penalized methods for modelling high-dimensional survival data in R.
Portfolio_Project_35
Data science case study aimed at characterizing life expectancy drivers across countries using model-agnostic interpretation methods for black-box models in R.
Portfolio_Project_37
Data science case study aimed at uncovering underlying constructs of chronic disease indicators across US states using exploratory and confirmatory factor analyses in R.
Portfolio_Project_38
Data science project which demonstrates various boosting, bagging and stacking ensemble models in R.
Portfolio_Project_51
Data science project which demonstrates various predictive modelling procedures for right-censored survival responses in Python.