Pinned Repositories
Applied-Data-Analytics-Capstone-Final-Assignment
Final Assignment for the Applied Data Analytics Capstone module under the IBM Data Analyst Professional Certificate program
Applied-Data-Science-Capstone-Final-Assignment
Final Assignment for the Applied Data Science Capstone module under the IBM Data Science Professional Certificate program
Data-Analysis-With-Python-Final-Assignment
Final Assignment for the Data Analysis with Python module under the IBM Data Science Professional Certificate program
Data-Visualization-With-Python-Final-Assignment
Final Assignment for the Data Visualization with Python module under the IBM Data Science Professional Certificate program
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_51
Data science project which demonstrates various predictive modelling procedures for right-censored time-to-event responses in Python.
Portfolio_Project_52
Data science project which implements shapley additive explanations for interpreting feature contributions in penalized cox regression models in Python.
Portfolio_Project_53
Data science project which explores different parametric accelerated failure time models for estimating lifetimes in survival data using Python.
JohnPaulinePineda's Repositories
JohnPaulinePineda/johnpaulinepineda.github.io
Portfolio of data science projects in markdown and HTML formats as hosted on GitHub pages.
JohnPaulinePineda/Portfolio_Project_13
Data science project which demonstrates various remedial procedures for handling imbalanced data for classification in R.
JohnPaulinePineda/Portfolio_Project_51
Data science project which demonstrates various predictive modelling procedures for right-censored time-to-event responses in Python.
JohnPaulinePineda/Portfolio_Project_52
Data science project which implements shapley additive explanations for interpreting feature contributions in penalized cox regression models in Python.
JohnPaulinePineda/Portfolio_Project_53
Data science project which explores different parametric accelerated failure time models for estimating lifetimes in survival data using Python.
JohnPaulinePineda/JohnPaulinePineda
JohnPaulinePineda/Portfolio_Project_1
Data science project which demonstrates various data quality assessment, preprocessing and exploration methods in R.
JohnPaulinePineda/Portfolio_Project_10
Data science project which demonstrates various predictive modelling procedures for dichotomous categorical responses in R.
JohnPaulinePineda/Portfolio_Project_11
Data science project which demonstrates various predictive modelling procedures for multiclass categorical responses in R.
JohnPaulinePineda/Portfolio_Project_12
Data science project which demonstrates various evaluation procedures for hyperparameter tuning strategies and resampling distributions in R.
JohnPaulinePineda/Portfolio_Project_14
Data science project which demonstrates various cost-sensitive learning procedures for handling class imbalance in R.
JohnPaulinePineda/Portfolio_Project_15
Data science project which demonstrates various model-independent feature importance metrics for predictors with numeric responses in R.
JohnPaulinePineda/Portfolio_Project_16
Data science project which demonstrates various model-independent feature importance metrics for predictors with dichotomous categorical responses in R.
JohnPaulinePineda/Portfolio_Project_17
Data science project which demonstrates the implementation of recursive feature elimination in selecting informative predictors for a modelling problem in R.
JohnPaulinePineda/Portfolio_Project_18
Data science project which demonstrates the implementation of univariate filters in selecting informative predictors for a modelling problem in R.
JohnPaulinePineda/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.
JohnPaulinePineda/Portfolio_Project_2
Data science project which demonstrates various data quality assessment, preprocessing and exploration methods in R.
JohnPaulinePineda/Portfolio_Project_22
Data science project which demonstrates various clustering algorithms for segmenting information in R.
JohnPaulinePineda/Portfolio_Project_23
Data science project which demonstrates the implementation of the gradient descent algorithm in estimating the coefficients of a linear regression model in R.
JohnPaulinePineda/Portfolio_Project_24
Data science project which demonstrates various dichotomization thresholding strategies for optimal classification in R.
JohnPaulinePineda/Portfolio_Project_25
Data science project which demonstrates various density-based clustering algorithms for identifying multivariate outliers in R.
JohnPaulinePineda/Portfolio_Project_26
Data science project which demonstrates various isolation forest-based anomaly detection algorithms for estimating outlier scores in R.
JohnPaulinePineda/Portfolio_Project_28
Data science project which demonstrates various robust methods for handling logistic regression quasi-complete separation in R.
JohnPaulinePineda/Portfolio_Project_3
Data science project which demonstrates various treatment comparison tests applied to data with a single factor variable (2-level) and a single response variable (numeric) in R.
JohnPaulinePineda/Portfolio_Project_4
Data science project which demonstrates survival analysis and descriptive modelling steps for a two-group right-censored data with time-independent variables using the Cox Proportional Hazards Model in R.
JohnPaulinePineda/Portfolio_Project_5
Data science project which demonstrates survival analysis and descriptive modelling steps for a three-group right-censored data with time-independent variables using the Cox Proportional Hazards Model in R.
JohnPaulinePineda/Portfolio_Project_6
Data science project which demonstrates various missing data pattern analyses, imputation method evaluations and post-imputation diagnostics in R.
JohnPaulinePineda/Portfolio_Project_7
Data science project which demonstrates the best practices when developing and evaluating prognostic models for clinical research using R.
JohnPaulinePineda/Portfolio_Project_8
Data science project which demonstrates various resampling procedures as applied during model hyperparameter tuning and internal validation in R.
JohnPaulinePineda/Portfolio_Project_9
Data science project which demonstrates various predictive modelling procedures for numeric responses in R.