Publications by Anil Akyildirim
Computational Math Final
library(dplyr) ## ## Attaching package: 'dplyr' ## The following objects are masked from 'package:stats': ## ## filter, lag ## The following objects are masked from 'package:base': ## ## intersect, setdiff, setequal, union library(ggplot2) library(tidyr) library(purrr) library(ggcorrplot) 1 Problem 1 Using R, generate a random...
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Mortality Rates
Introduction In this assignment, we have been provided data around mortality from all 50 states and the District of Columbia. We will be using R shiny package. We already created an account in https://www.shinyapps.io/ and set our name, token and password. Question 1** As a researcher, you frequently compare mortality rates from particular cause...
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Document
0.0.1 Overview In this homework assignment, you will work through various classification metrics. You will be asked to create functions in R to carry out the various calculations. You will also investigate some functions in packages that will let you obtain the equivalent results. Finally, you will create graphical output that also can be used to...
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Recommender Systems Bias - Algorithmic Discrimination
Biases in Recommender systems and Algorithmic Discrimination Before we start giving examples on Bias and Algorithmic Discrimination, let’s define what Bias is. Based on Dr. Baeza-Yates (CTO of NTENT), we can categorize Bias in three categories. Statistical bias where there is a significant systematic deviation from prior distribution. Cultura...
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Project 2- Collaborative Filtering
1 Introduction In this assignment, we will work with MovieLens dataset, leveraging the course text’s recommender lab , perform certain data analysis and further implement Item-Item and User-User Collaborative Filtering recommendation system.The goal of the assignment is to figure out different ways to create and configure a recommender system a...
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Mitigating the Harm of Recommendation Engines
Mitigating the Harm of Recommender Systems About 7-8 years ago, I worked on a project for a mall chain where the objective was to build a digital Holiday Experience for kids in a shopping center. The idea was to build sections of digital and interactive events where kids can go through to meet Santa at the final stage for an Holiday Photo. The pr...
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Global-Baseline-Predictors
Introduction The recommender system that we will create in this assignment will recommend a product to a consumer through the business website based on previously collected product ratings data from other consumers. The use case of the recommender system is that, as a business we want the fastest purchase funnel in order to increase conversion. F...
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Music Recommendations Spotify
Music Recommendations at Scale with Spark (Spotify) The presentation from Christopher Johnson was interesting in terms of infrastructure and efficiency of execution of Collaborative filtering at Spotify. I was aware of the different ways to browse and discover songs and albums at Spotify however didn’t realize how these options help get persona...
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Matrix Factorization
1 Introduction In this project, we are going to review the Matrix Factorization methods with the same data set we have used in the earlier project (MovieLense). In the previous project, while we were creating the Movie Ratings Matrix, we used “realRatingMatrix” class“. More details on”realRatingMatrix" can be found here: https://www.rdocu...
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final-project-proposal
Introduction In our final project, we are going to continue to use the MovieLens dataset. We have implemented User to User and Item to Item Collaborative Filtering and Matrix Factrorization Singlular Valur Decomposition(SVD) methods for Recommenders System. With the Final Project, we will extract additional data and add implementation of Content ...
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