## Recently Published

##### KEC-Titanic Demo

Predict who is most likely to survive

##### Knowledge Exchange Conference 2018

Sample R script

##### RootCon 2017 Extra Codes

Some tips based on the book Data Driven Security

##### MLR-Section39-XGBoost

XGBoost

##### MLR-Section38-GridSearch

GridSearch

##### MLR-Section38 XValidation

x Fold Validation

##### MLR-Section37-KernelPCA

Kernel PCA

##### MLR-Section36-LDA

Linear Discriminant Analysis

##### MLR-Section34 PCA

Principal component analysis

##### MLR-Section31-ANN

Advanced Neural Networks

##### MLR-section25 UpperConfidenceBoundary

Multi arm bandit

##### ML using R section 17 Decision Tree

Decision Tree

##### ML using R Section 16 Naive Bayes

Naïve Bayes

##### ML Using R Section 15 Kernel SVM

Kernel SVM for non linear dataset

##### ML Using R Section 14 SVM

Support Vector Machine

##### ML Using R Section 13 KNN

KNearest Neighbor

##### ML Using R Section 12

Logistic Regression

##### ML Using R Section 9

Random Forest

##### ML Using R Section 8

Decision Tree Regressions

##### ML Using R Section 7

Support Vector Regression

##### ML Using R Section 6

Polynomial Regression

##### ML Using R Section 5

Multiple Linear Regression

##### ML Using R

Linear Regression

##### R for Data Science Model Building

Chapter 19

##### R For Data Science- Markdown Output

Chapter 23

##### R for Data Science R Markdown

Chapter 23

##### R for Data Science Chapter 18

Introduction to Modeling

##### R for Data Science Chapter 14 Magritter %>%

Library(magrittr)
%>% pipes

##### R For Data Science Chapter 13 Lubridate

All about dates and times

##### R For Data Science Chapter 12

Factors with forcats package

##### R For Data Science Chapter 11

Strings with Stringr

##### R for Data Science Chapter 10

Relational Data with Tidyr

##### R for Data Science Chapter 09

Tidy Data with Tidyr

##### R for Data Science Chapter 8

Data Import with readr

##### R For Data Science Chapter 6 Workflow Projects

Workflow Projects

##### R For Data Science Chapter 5

Exploratory Data Analysis

##### R for Data Science Chapter 4

Chapter 4 of R4DS

##### R for Data Science-Chapter 3

Data Transformation with dplyr

##### R for Data Science Chapter 2

Workflow: Basics

##### Chapter 15 Elementary Statistics

One of the hardest chapters to follow.
I wish the author made it with plain english

##### Document

test

##### Chapter 13 Basic Statistics

centrality: mean, median, mode
correlations, variance, covariance, standard deviation,
summary
iqr, quantiles

##### Chapter 10 Conditions and Loops

if, else, ifelse, nesting, stacking,switch, for loops, nesting, while, apply, next, break, repeat

##### Chapter 9 Writing Functions

Scoping, Matching

##### Chapter 7 Basic Plotting with R

Chapter 7 of The book of R

##### Chapter 6

The Book of R chapter 6

##### Document1

test

##### The Book of R Chapter 4

Lab for Chapter 4

##### Lab Notes on "The Book of R"

My lab notes on following the examples in the excellent book "The Book of R" by Tilman Davies

##### Plot2

ggplotsample2

##### Plot Test R programming Class

Singapore Jan 2017