Publications by Albina Gallyavova, Michael Gankhuyag, Joby John
Project 2
In this project we will implment a content based and user to user colloborations. For user colloboration we will use the MovieLense and MovieLenseMeta dataset. MovieLense dataset contains 943 rows and 1664 columns MovieLenseMeta dataset contains 1664 rows and 22 columns Table and histogram below displays ratings frequencies. We will exclude movie...
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DATA624_HW3
library(readxl, quietly = TRUE, warn.conflicts = FALSE, verbose = F) library(fpp2,quietly = TRUE, warn.conflicts = FALSE, verbose = F) library(ggplot2) library(gridExtra) library(seasonal) Q1 Excercise 6.2 The plastics data set consists of the monthly sales (in thousands) of product A for a plastics manufacturer for five years. a. Plot th...
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DATA612_RD1
Recommender System : Amazon Amazon probably has one of the best recommendation systems of all US retailers. These recommendations are based on the order and search history. Target Users: Amazon customers Customers get exposed to new products otherwise they wouldn’t know about. We have bought many items based on these, but at times these recomm...
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Project 1
Dataset This dataset contains ratings for some of the new movies. Ratings are on a scale of 1 to 10. Some of the ratinsg are from actual sites and others were made up. Below table displays user movie ratings. ratings <- read.table('Reviews.txt',header = T, sep = ',', na.strings = T) kable_styling (kable(ratings),bootstrap_options = c("striped", ...
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HW#1
library(readxl, quietly = TRUE, warn.conflicts = FALSE, verbose = F) library(fpp2,quietly = TRUE, warn.conflicts = FALSE, verbose = F) Q1 Excercise 2.1 2.1a gold contains daily morning gold prices in US dollars. 1 January 1985 – 31 March. The plot below shows, gold was in an uptrend until around '670' followed by a downward trend. ...
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Document
library(readxl, quietly = TRUE, warn.conflicts = FALSE, verbose = F) library(fpp2,quietly = TRUE, warn.conflicts = FALSE, verbose = F) library(ggplot2) library(gridExtra) Q1 Excercise 3.1 For the following series, find an appropriate Box-Cox transformation in order to stabilise the variance. usnetelec pre <- autoplot(usnetelec) lambda <- ...
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DATA624_Assignment4
library(readxl, quietly = TRUE, warn.conflicts = FALSE, verbose = F) library(fpp2,quietly = TRUE, warn.conflicts = FALSE, verbose = F) library(ggplot2) library(gridExtra) library(mlbench) library(caret) library(corrplot) library(dplyr ) library(kableExtra) library(e1071) Q1 Excercise 3.1 The UC Irvine Machine Learning Repository6 cont...
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HW9
8.1 Recreate the simulated data from Exercise 7.2: set.seed(200) simulated <- mlbench.friedman1(200, sd = 1) simulated <- cbind(simulated$x, simulated$y) simulated <- as.data.frame(simulated) colnames(simulated)[ncol(simulated)] <- "y" a Fit a random forest model to all of the predictors, then estimate the variable importance scores: model1...
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624Final
beverages <-read.csv("StudentData.csv", header=TRUE, sep=",",stringsAsFactors = F) data_list <- list(beverages) rm(data_list) summary(beverages) ## Brand.Code Carb.Volume Fill.Ounces PC.Volume ## Length:2571 Min. :5.040 Min. :23.63 Min. :0.07933 ## Class :character 1st Qu.:5.293 1st Qu.:23....
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