Homework 9

Homework 8

Week 10 hw 7

Project 1

Forecasting Principles and Practice

Data 624 Week 6 Homework 5

Applied Predictive Modeling

Analysis of NYC 311 complaints and twitter sentiment analysis of nyc 311

Generalized metadata search and discovery tool.

Regression analysis

Donor Matching Recommender System

Donor Matching Recommender System: Project Proposal

DATA 612 Project 4 | Accuracy and Beyond

Evaluating and comparing different approaches, using different algorithms, normalization techniques, similarity methods, neighborhood sizes, etc. and building a basic movie recommender system.

Choose one commercial recommender and describe how you think it works (content-based, collaborative filtering, etc). Does the technique deliver a good experience or are the recommendations off-target?

In this first assignment, we’ll attempt to predict ratings with very little information. We’ll first look at just raw averages across all (training dataset) users. We’ll then account for “bias” by normalizing across users and across items.

Adolescent Pregnancy Analysis

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

Extend an Existing Example. Using one of your classmate’s examples, extend his or her example with additional annotated code.

For this assignment, you should take information from a relational database and migrate it to a NoSQL database of your own choosing. For the relational database, you might use the flights database, the tb database, the "data skills" database your team created for Project 3, or another database of your own choosing or creation. For the NoSQL database, you may use MongoDB (which we introduced in week 7), Neo4j, or another NoSQL database of your choosing. Your migration process needs to be reproducible. R code is encouraged, but not required. You should also briefly describe the advantages and disadvantages of storing the data in a relational database vs. your NoSQL database.

"Dplyr", one of the tidyverse package, is used to explore a dataset "drug_use_by_age" and a programming sample “vignette” is executed that demonstrates how to use one or more of the capabilities of the selected TidyVerse package. dataset source: fivethirtyeight.com

Homework Chapter 7

In this lab, we’ll be looking at data from all 30 Major League Baseball teams and examining the linear relationship between runs scored in a season and a number of other player statistics. Our aim will be to summarize these relationships both graphically and numerically in order to find which variable, if any, helps us best predict a team’s runs scored in a season.

Data 606 Project Proposal

Our task is to choose one of the New York Times APIs, construct an interface in R to read in the JSON data and transform it into an R data frame.

Inference for Numerical Data

Who are today’s “thought leaders” in data science? What are the topics that data scientists care most about? How do these change over time, and across geographical location?

Pick three of your favorite books on one of your favorite subjects. At least one of the books should have more than one author. For each book, include the title, authors, and two or three other attributes that you find interesting. Take the information that you’ve selected about these three books, and separately create three files which store the book’s information in HTML (using an html table), XML, and JSON formats (e.g. “books.html”, “books.xml”, and “books.json”). To help you better understand the different file structures, I’d prefer that you create each of these files “by hand” unless you’re already very comfortable with the file formats. Write R code, using your packages of choice, to load the information from each of the three sources into separate R data frames. Are the three data frames identical?

Preparing different datasets for downstream analysis work

In this project, you’re given a text file with chess tournament results where the information has some structure. The task 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 For the first player, the information would be: Gary Hua, ON, 6.0, 1794, 1605 1605 was calculated by using the pre-tournament opponents’ ratings of 1436, 1563, 1600, 1610, 1649, 1663, 1716, and dividing by the total number of games played.

Exercises_Data607

Analyzing two similar datasets but differed on their scale count, using basic R commands.

In this publication, we learn about different transformation tasks performed on the given mushroom dataset. Transformation Tasks help us to analyze the factors affecting the data and also to rename abbreviations and column names to make it more user-friendly and effective.