DRAFT WIP Project 3

EMP_DATA PRE WORK

Proposal_608

HW 1

DRAFT WIP Project 1

Lux Mar 22- Till Date

LUX

Week 14 Discussion

Country: name of the country LifeExp: average life expectancy for the country in years InfantSurvival: proportion of those surviving to one year or more Under5Survival: proportion of those surviving to five years or more TBFree: proportion of the population without TB. PropMD: proportion of the population who are MDs PropRN: proportion of the population who are RNs PersExp: mean personal expenditures on healthcare in US dollars at average exchange rate GovtExp: mean government expenditures per capita on healthcare, US dollars at average exchange rate TotExp: sum of personal and government expenditures.

Insurance Dataset

Sales Data Analysis

Using the “cars” dataset in R, build a linear model for stopping distance as a function of speed and replicate the analysis of your textbook chapter 3 (visualization, quality evaluation of the model, and residual analysis.) •Predictor: Speed •Response Variable: stopping distance

Discussion

Linear Regression

Project 4 : Using SVD USER ITEM, ITEM ITEM Recommendation

Medical Recommender System, Medical Recommender System Establishing a Medical Recommender System that can give recommendation with an excellent efficiency and accuracy based on diagnosis and symptoms.

Naive Bayes, TM, SVM, Use Mongo to save Model

April 24, 2019 Part 1 - Introduction Part 2 - Data Part 3 - Exploratory data analysis Part 4 - Inference Regression Part 5 - Conclusion References Appendix (optional)

Multiple linear regression

HW 8 Multiple Linear Regression

Working with Mongo DB, Upload, Query.and many more with mongolite package.

TidyVerse Recipe Part 2

TidyVerse Recipe "broom" "cli" "crayon" "dplyr" "dbplyr" "forcats" "ggplot2" "haven" "hms" "httr" "jsonlite" "lubridate" "magrittr" "modelr" "purrr" "readr" "readxl\n(>=" "reprex" "rlang" "rstudioapi" "rvest" "stringr" "tibble" "tidyr" "xml2" "tidyverse"

606 Problem

INTRODUCTION TO LINEAR REGRESSION

Google Search Recommendation

Introduction to linear regression, Sum of squared residuals

Working with TM and other text processing packages. Working with Train and Test concept for some part of the data. Working with SPAM and HAM dataset.

Inference for categorical data In August of 2012, news outlets ranging from the Washington Post to the Huffington Post ran a story about the rise of atheism in America. The source for the story was a poll that asked people, “Irrespective of whether you attend a place of worship or not, would you say you are a religious person, not a religious person or a convinced atheist?” This type of question, which asks people to classify themselves in one way or another, is common in polling and generates categorical data. In this lab we take a look at the atheism survey and explore what’s at play when making inference about population proportions using categorical data.

Inference for categorical data, Chi square, difference of two propotion

DATA 606 Data Project Proposal

Week 5 t-test, hypothesis testing.

hypotheses for testing

In this project, we breakdown how we built a database of influential people who are leading the charge in what the Harvard Business Review suggested to be the sexiest job field of the 21st century, that is Data Science. Because their skills and knowledge lead them to a successful data science career, with our database, we conducted a few data analysis to further discuss the many different paths that can lead you too to a lucrative, rewarding career as a data scientist. Data Science Thought Leadership DATA 607 Project 3 March 24, 2019 Team SPARC Santosh Cheruku Samantha Deokinanan Rajwant Mishra Priya Shaji

PPL IFRAME

Lux Event 22 Sep

Project 3

I am using Mongo DB/Local to store , read data back and forth from Mongo (online).. Read write and create HTML, XML, JSON.

sampling distribution, Sample Mean

`Sampling Distributions’, Confidence Interval, Critical value ,

Project For week 6 (3 Dataset) Analysis Disability Data Analysis using Entropy With Shiny App for countries

Project 2 of 3 Week6 : Data is from US disability, it list number of applications being submitted from online and offline . Objective is to find that if Online service has helped Gov. to get more application through online mode. * Data has lots of problem + Data is wide by Month, AND also Fiscal Year may Not equal to same year.

Project 3 of Week 6 : Entropy and Heart Data Analysis.

Analyse delay in Flight data

Using The normal distribution, QQPLOT, normal plot and GGPLOT density plot,

Presentation for 607

Using PNORM, QNORM,quantile,This is a Negative Binomial distribution, using choose, using dbinom(0, 100, .02), Calculate success and failure probability in draw, # Here 9 is number of failure before success , dgeom(9,0.02)

Self work on 607 project work

In this project, you’re given a text file with chess tournament results where the information has some structure. Your job is to create an R Markdown file that generates a .CSV file (that could for example be imported into a SQL database) with the following information for all of the players: Player’s Name, Player’s State, Total Number of Points, Player’s Pre-Rating, and Average Pre Chess Rating of Opponents. Example For the first player, the information would be: Gary Hua, ON, 6.0, 1794, 1605 Contributors: Santosh Cheruku Samantha Deokinanan Rajwant Mishra Priya Shaji https://github.com/Rajwantmishra/ms-project-607_1

PROBABILITY, 2.6 Dice rolls. 2.8 Poverty and language, 2.20 Assortative mating., Books on a bookshelf. , Baggage fees, Income and gender

Know understand probability and sample generations .Lab2 work for 606.

Know and understand probability Assignment 3 Lab work 606

Using string operations and extracting secret code from random text.

606 Assignment HW1

Lux Data

Working with standard deviation, mean . Analyzing box plot and other graph.

607 Assignment 2: Scrap data, clean data, create DB table, update DB, Read DB

606 Lab 0 : Data of Birth by year

study the dataset and the associated description of the data (i.e. “data dictionary”). You may need to look around a bit, but it’s there! You should take the data, and create a data frame with a subset of the columns in the dataset. You should include the column that indicates edible or poisonous and three or four other columns. You should also add meaningful column names and replace the abbreviations used in the data—for example, in the appropriate column, “e” might become “edible.” Your deliverable is the R code to perform these transformation tasks.

Analysis of data from YouTube 5 videos comments . Using different types of data transformation and string operation to build meaningful analysis.

Using Package Tidyverse stringr xml library(data.table)

Tables

Test 1st PUBLISH