Predicting the price of a house in IOWA using Machine Learning (Neutral Network and Linear Regression)

Looking at the model performance

U.S. Military Casualty 1980-2010

Predicting a pH of ABC beverage Company

Exponential Smoothing

Module 3 Interactive Visualizations in R

The Forecaster’s Toolbox Forecasting: (Principles and Practice)[https://otexts.com/fpp3/] by Rob J Hyndman and George Athanasopoulos

Time

Intro to Simple linear regression

Set of Probability problems

Eigenvalue and images

Matrix , vectors, and spaces relations

Matrix

Collaborating and Providing peer to peer Feedback on TidyVerse Assignment

to analyze an existing recommender system that you find interesting

Talking about linear relationships, fitting a line, residuals, and correlation.

Due Date Saturday, October 31, 2020 11:59 PM Points Possible 50 In Text Mining with R, Chapter 2 looks at Sentiment Analysis. In this assignment, you should start by getting the primary example code from chapter 2 working in an R Markdown document. You should provide a citation to this base code. You’re then asked to extend the code in two ways:

Linear regression slope, intercept, R-squared, coefficient correlation, overestimate or understimate , independent, dependent

readr, stringr, dplyr

Studying students behaviors...How does study time impact students performance

The Human Freedom Index is a report that attempts to summarize the idea of “freedom” through a bunch of different variables for many countries around the globe. It serves as a rough objective measure for the relationships between the different types of freedom - whether it’s political, religious, economical or personal freedom - and other social and economic circumstances. The Human Freedom Index is an annually co-published report by the Cato Institute, the Fraser Institute, and the Liberales Institut at the Friedrich Naumann Foundation for Freedom. In this lab, you’ll be analyzing data from Human Freedom Index reports from 2008-2016. Your aim will be to summarize a few of the relationships within the data both graphically and numerically in order to find which variables can help tell a story about freedom.

This assignment of week 9 is about practicing web API calls. How to get web API in R. Approach: I signed up on New York Time to get an API key. there are various option group in category. I chose the “most”Most Popular" and played around to see what I can get for news. I used GET() for httr request functions on the most shared news within 7days on facebook. glimpse(NyTimes_newsJ) display all the 20 observations of 22 variables. Now I want to print out a link to let’s say a news

Every two years, the Centers for Disease Control and Prevention conduct the Youth Risk Behavior Surveillance System (YRBSS) survey, where it takes data from high schoolers (9th through 12th grade), to analyze health patterns. You will work with a selected group of variables from a random sample of observations during one of the years the YRBSS was conducted.

## Description: This assginment is about learning how to pull less-structured data from the web. To get started, I will get familiar with R and HTML, XML and JSON formats: For Rmarkdown,, we will create R-srcipt to generate file in HTML, XML and JSON formats. We will use 03 books of a choice and use the R-script to access information about this book and display them. These information include the title, authors, and 2-3 other attributes of three books, at least one having more than one author. This assignment will also give more string manipulation functions and regular expressions.We should have 03 distincts files of the sames books in each of these 03 formats.

p-value, confidence interval, hypothesis test

The goal of this assignment is to practice in preparing different datasets for downstream analysis work.

The normal distribution In this lab, you’ll investigate the probability distribution that is most central to statistics: the normal distribution. If you are confident that your data are nearly normal, that opens the door to many powerful statistical methods. Here we’ll use the graphical tools of R to assess the normality of our data and also learn how to generate random numbers from a normal distribution.

In this chapter, we discuss statistical distributions that frequently arise in the context of data analysis or statistical inference. We start with the normal distribution in the rst section, which is used frequently in later chapters of this book. The remaining sections will occasionally be referenced but may be considered optional for the content in this book.

Data extracting, manipulation, storing, importing , saving

Applying probability and computing in basket ball to unveil pattern in basket ball players...Example: The Hot Hand

Practice probability, sum, disjoint, independent Vs. dependent, sd , mean

In this lab we explore flights, specifically a random sample of domestic flights that departed from the three major New York City airports in 2013. We will generate simple graphical and numerical summaries of data on these flights and explore delay times. Since this is a large data set, along the way you’ll also learn the indispensable skills of data processing and subsetting.

Summarizing data from chapter 2...

Always struggle to knit at the end , causing a lot of frustration and creating other bugs

Week 1 Assignment... I had problem publishing this assignment...something with working directory..."Knit error. Object not found" ...So I have to comment out line in order to get it work.

Week1 assg DATA607