Publications by Erinda Budo
Implementing a Recommender System on Spark
Introduction Adapt one of your recommendation systems to work with Apache Spark and compare the performance with your previous iteration. Consider the efficiency of the system and the added complexity of using Spark. You may complete the assignment using PySpark (Python), SparkR (R) , sparklyr (R), or Scala. #Required libraries library(recommen...
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Project 1
This system will recommend Books to readers. Libraries: library(kableExtra) 1. Reading in the Data. get_url <- read.csv(file="https://raw.githubusercontent.com/ErindaB/Data-612/master/BookRatings.csv", header=TRUE, sep=",") Book_R <- get_url[1:5,] Book_R %>% kable(caption = "Book Ratings") %>% kable_styling("striped", full_width = TRUE) Book ...
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Data 612-Project2
Introduction Start with an existing dataset of user-item ratings, such as our toy books dataset, MovieLens, Jester or another dataset of your choosing. Implement at least two two of these recommendation algorithms: Content-Based Filtering User-User Collaborative Filtering Item-Item Collaborative Filtering # Loading libraries library(recommende...
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Data 612-Project 4
Instructions The goal of this assignment is give you practice working with accuracy and other recommender system metrics. As in your previous assignments, compare the accuracy of at least two recommender system algorithms against your offline data. Implement support for at least one business or user experience goal such as increased serendipity,...
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Blog 2
Leaps: Regression Subset Selection regsubsets() function in the R package leaps. The basic idea of the all possible subsets approach is to run every possible combination of the predictors to find the best subset to meet some pre-defined objective criteria such as Cp and adjusted R^2. It is hoped that that one ends up with a reasonable and useful...
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Data 608 -HW1
Libraries Used require(dplyr) require(tidyr) require(knitr) require(kableExtra) require(kable) require(ggplot2) Principles of Data Visualization and Introduction to ggplot2 I have provided you with data about the 5,000 fastest growing companies in the US, as compiled by Inc. magazine. lets read this in: inc <- read.csv("https://raw.githubus...
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