Introduction to Time Series in R_R-Tutorial_udemy. The topics/implementation covered include ETS, ARIMA, forecast packages functionalities, VAR and NNAR. Four small time series projects are also done covering topics such as multivariate time analysis, irregular time series handling etc

AutoML is a function in H2O that automates the process of building a large number of models, with the goal of finding the “best” model without any prior knowledge or effort by the Data Scientist. This is a comprehensive example of AutoML functionality in H2O R Package

Complete Machine Learning and Deep Learning With H2O in R

Tidy Data Processing using tidyr and dplyr inR Udemy course by Minerva Singh

The following R code shows how to create a random Secret Santa list by two different methods.(I have shamelessly lifted the concept from others and hence not my original work).

Create hundreds of dataset + model combinations, store data & model objects neatly in one tibble, and post process programatically. Purrr package enables succinct functional programming in which a lot gets done with just a few lines of code. Also other similar iterative methods bench marked and compared.Ending the article with Forecasting multi timeseries in couple of lines of code using purrr .....WIP

Classifying messages as Spam or Ham using a simple Naive Bayes classifier and some natural language processing.

This is a small attempt in creating dashboard using R (Flexdashboard package). This dashboard allows one to explore trends in Infosys stock prices from 2007 onwards. We will also forecast Infosys prices using many timeseries algorithms as well as ML models including deep learning frameworks......Just playing around....

This document is Week 1 of Applied Time-Series Analysis (MOOC) Course offered by Prof. Arun K. Tangirala, IIT Madras through NPTEL during Jan-April 2017........

This is my first attempt to build my own local R package ......YaY!....feels good to progress from an R user to R package creator albeit a small beginning....

This is the fourth MOOC in the PH525x series - Biomedical Data Science offered by HarvardX on edX. I have tried to capture the entire offering in the markdown as a single document.This covers first two weeks module--WIP

Christmas Tree & HNY in R.

This is just a basic overview of parallel computing framework in R.....just to get the feel of parallelisation......

Packrat is an R package that implements a dependency management system for R: Maintains two components of your project: The project private library, containing R packages your project is using, and The last ‘snapshotted’ state, which can be used to save and restore the state of the private library. Packrat enhances your project directory by storing your package dependencies inside it, rather than relying on your personal R library that is shared across all of your other R sessions. Packrat lets you snapshot the state of your private library, which saves to your project directory whatever information packrat needs to be able to recreate that same private library on another machine.

This is the third MOOC in the PH525x series - Biomedical Data Science offered by HarvardX on edX. I have tried to capture the entire offering in the markdown as a single document.

This is the second MOOC in the PH525x series - Biomedical Data Science offered by HarvardX on edX. I have tried to capture the entire offering in the markdown as a single document.

This is just a big mashed up effort to deal with data which has lot of categorical variables along with number of missing values.This is just an attempt to gain some comfort level while visualising various plots by grouping them according to levels of the categorical variable.Its a work in progress......

This is an analysis covering basics of univariate time series analysis using Eviews.

This is the first MOOC in the PH525x series - Biomedical Data Science offered by HarvardX on edX. I have tried to capture the entire offering in the markdown as a single document.

This is a demo presentation about basics of Simple Linear Regression

This is an Interactive tutorial to introduce basic to intermediate plotting capabilities of base R graphics. The codes here are entirely reproduced from here: https://gist.github.com/noamross/027e1f63343ba223b9cb

This is the week 1 module assignment on Simple Linear Regression consisting of answers .This forms part of the MOOC on Coursera: Econometrics: Methods and Applications offered by Erasmus University Rotterdam.

This is detailed document covering various aspects of Normal distribution and various functions in R to deal with it. Visualising the distribution is the best way to understand and interpret a probability distribution and hence graphing of many aspects of normal distribution was generously dealt with here.

This is a rudimentary example of plotting time series data of stocks .Here use of lubridate as well as manipulate package is shown .

This is just the basic exploration of the salaries of US Universities faculties .

Assignment 2: Installing/Loading packages and some basic descriptive statistics

This shiny pitch is intended to display the Central Limit Theorem (CLT) in the Coursera's Developing Data Products module using Slidify

This shiny pitch is intended to display the Central Limit Theorem (CLT) in the Coursera's Developing Data Products module using R presentation

This is the first assignment of the Certificate in Computer Applications in Economic Analysis(CAEA) offered by Gokhale Institute of Politics & Economics(GIPE),Pune.Thisis a basic exploratory analysis of the data set

These are the detailed course notes by Nishant Upadhyay for The_Analytics_Edge_edX MIT15.071x June2015_6 Unit 7: Visualization

These are the detailed course notes by Nishant Upadhyay for The_Analytics_Edge_edX_MIT15.071x_June2015 Unit6: Clustering.

These are the detailed course Notes by Nishant Upadhyay for The_Analytics_Edge_edX MIT15.071x June2015 Unit 5: Text Analytics

Simple demo of Chi-square Goodness of Fit test in R

This is the peer assignment of Coursera's Introduction to Statistics for Social Sciences by Marco Steenbergen. This assignment being open ended in terms of research question and statistical test to be employed, I used the Chi-square test on Austin City Limits dataset.

These are the detailed course Notes by Nishant Upadhyay for The_Analytics_Edge_edX MIT15.071x June2015 Unit4:Trees .

Course Notes for the The_Analytics Edge edX MIT15.071x_June2015 Unit 1: An Introduction to Analytics & Unit 2: Linear Regression.

Course Note for Unit 3:Logistic Regression of edX's The Analytics Edge_MIT offering June 2015