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Kushan

Kushan De Silva

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Data Visualization
Meta analysis of prediction models
Meta analysis of diagnostic and prognostic models
Data Handling in R
R 101
clustering techniques iv
clustering techniques iii
clustering techniques ii
clutering techniques i
Network Analysis iii
Network Analysis ii
Network Analysis in R
Forecasting
Time series analysis
Time series modelling
Genomic Data Analysis: Marker Association Testing
Bioinformatics
statistics for bioinformatics
Clinical Trial Data
RCT Data Analysis part 1
Epidemiological Analyses in R
Genomic Data Analysis
GWAS Analysis
Genetic risk score analyses
Modelling and prediction on genetic data
Mendelian Randomization
ML in R
Machine Learning Variable Importance and Performance comparison
ML ROC Performance comparison
LDA CV MLR
ANN SVM Black box Methods
Regression Tree
Decision Tree classifier
Naive Bayes Classifier
KNN classifier Lazy Learning
feature selection for modelling
Feature selection with Boruta
feature selcetion ii
Feature selection for machine learning
Credit Risk Modelling with Machine Learning
AUC ROC Multi-class ROC
Cut Points ROC AUC
ML
Machine Learning
Generalized Linear Mixed Model
Multilevel modelling over 3 levels
Multilevel modelling R
Bayesian network meta analysis with pcnetmeta
Bayesian network meta analysis with gemtc
RF LR MARS Algorithm
Classification and Regression Tree Algorithm use in Genetic Association
Genetic Association Handling Unobservable Phase
Genetic Association Multiple Comparison
Genetic Association
Network Meta Analysis using bmeta package; A Bayesian Approach
Network Meta Analysis using netmeta package; a frequentist approach
GLMM
Mixed models for longitudinal data
Linear Mixed Model with multiple random effects
Linear Mixed Model
Network Meta Analyses
Multivariate Meta Analysis
Missing Data Handling in Meta Analyses
Adjustment for missing Data Sensitivity Analysis Techniques -Fixed equal -Fixed opposite -Random equal -Random uncorrelated Multiple Imputation Algorithm
Publication bias and small study effects handling in meta analyses
funnel plot tests for small study effect Modified tests for binary outcome Adjusting for small study effect
Heterogeneity and meta regression
Measures of heterogeneity Tests for subgroup differences Meta regression with categorical/continuous covariate
Out of sample prediction
Model selection Capstone
Meta analyzing binary outcome
Binary effect measures: OR,RR,RD, Arc sine Difference Estimating with sparse data points, Peto OR, FE model; inverse variance method, Mantel Haenzel method OR,RR,RD, Peto method, REmodel; DeSimonian-Laird method, Heterogeneity, subgroup analyses
Meta analysis core
Fixed effects and random effects model, modelling continuous outcomes, respective effect measures MD and SMD, Hartung-Knapp adjustment, prediction intervals, heterogeneity, subgroup analysis, meta-analyzing survival data, cross-over trials, and adjusted treatment effects are demonstrated using hypothetical/ simulated data.
Meta analysis of continuous data
MANOVA multivariateregression and robust regression
Text Mining
Data Mining sequences
Data Mining Clustering Algorithms Association Rule Mining etc
Multivariate Data PCA PCoA CA
Linear model Logistic and GLM
Summaries Tables and Merging Data
Probability Inference Regression
Regression models and EDA on housing data; Capstone Week 4
Data Mining:Association Rule Mining using Groceries Dataset
Principal Components Analysis
Cluster analysis techniques in R
Deep Learning with H20
Neural networks
Random forests classification
Machine learning:random forests regression
Machine learning:classification tree with Glass Identification Data
Machine learning:regression tree with Boston Housing Data
SVM modelling with parameter tuning and feature selection using Pima Indians Data
Machine learning;KNN algorithm with Pima Indians Diabetes Data
Advanced feature slection in linear model with Boston housing data;best subsets, ridge,lasso regressions, elastic net and lasso with CV
A comparison of feature selection methods for the linear model including best subsets regression, ridge regression, lasso regression, elastic net, and lasso with cross validation.
EDA Housing Data Capstone Assignment Week 2
Machine learning with MLR, LDA, QDA, ROC curve, AUC, cross validation and model selection
A data science and machine learning project using the Pima Indians Diabetes Data
Machine learning with Wisconsin Breast Cancer Data
Predictive modelling with MLR
meta analyses meta regressions and diagnostic plots with BCG dataset
A meta analysis
Document
special data types
Data manipulation in R
EDA and missing data imputation
master statistics with R course: Bayesian Regression modelling
master statistics with R course: statistical inference
machine learning model with logistic regression
This was created for a project on Coursera. Probably because the provided data had been simulated, a grossly overfitted model was resulted which fitted both train and test data with 100% accuracy. Despite the limitations, it provided leeway for experimenting with modelling including best-subsets selection in logistic regression models based on the AIC.