JohnPaulinePineda

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/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.