Publications by Christian Uriostegui
Data624_ExtraCredit
Objective In the Grocery Dataset, there contains rows of purchase receipts. Each line represents 1 receipt and items purchased. Using R, I will mine the data for association rules. Market Basket Analysis, or the study of relationships in transaction data sets seeks to discover patterns and associations between items. In the case of retail trans...
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Data624_Assignment9
Exercise 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" Part A Fit a random forest model to all of the predictors, then estimate the variable importance sc...
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Data624_Assignment8
Question 7.2 Friedman (1991) introduced several benchmark data sets create by sim ulation. One of these simulations used the following nonlinear equation to create data: \[ y =10sin(πx1x2) +20(x3 −0.5)2 +10x4 +5x5 +N(0,σ2) \] where the x values are random variables uniformly distributed between [0, 1] (there are also 5 other non-informativ...
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Data624_Project1
Part A Objective In part A, I will be forecasting the cash withdrawals from 4 different ATM machines for May 2010. Initial Exploration A few things that stand out is that the ATM and Date columns are in the wrong format. I also observed that the Cash column contains 19 nulls. summary(ATM) ## DATE ATM Cash ...
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Data 624 Assignment 6
Objective Assignment 6 involves answering questions 9.1, 9.2, 9.3, 9.5, 9.6, 9.7, 9.8 from the textbook Forecasting: principles and practice by Rob J Hyndman and George Athanasopoulos. Question 9.1 Figure 9.32 shows the ACFs for 36 random numbers, 360 random numbers and 1,000 random numbers. Explain the differences among these figures. Do they...
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Data 624 Assignment 4
Objective Assignment 4 involves answering questions 3.1, and 3.2 the book Applied Predictive Modeling by Kuhn and Johnson. Exercise 3.1 The UC Irvine Machine Learning Repository contains a data set related to glass identification. The data consist of 214 glass samples labeled as one of seven class categories. There are nine predictors, includin...
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Data 624 Assignment 3
Objective Assignment 3 involves answering questions 5.1, 5.2, 5.3, 5.4 and 5.7 from the textbook Forecasting: principles and practice by Rob J Hyndman and George Athanasopoulos. Exercise 5.1 Produce forecasts for the following series using whichever of NAIVE(y), SNAIVE(y) or RW(y ~ drift()) is more appropriate in each case: Australian Populat...
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Fraud Detection in Metaverse Financial Transactions Using Decision Trees and Support Vector Machines
Abstract For every new innovation or feature that the Open Metaverse brings, there are more venues & opportunities for scammers to exploit. Given the risks associated with this new world, companies are hesitant to bring business into the Metaverse for the fear of potential loses. This hesitancy by corporations may potentially delay the adoptio...
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Credit Card Customer Segmentation Using Clustering Techniques
Abstract To effectively target customers for financial or credit card services, the product should add value to the consumer. In the case of credit card services, consumers can be offered a range of options such as balance transfers, special introductory rate offers, and reward specific credit cards. Customer segmentation is a key strategy in...
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Data 624 Assignment 2
Objective Assignment 2 involves answering questions 3.1, 3.2, 3.3, 3.4, 3.5, 3.7, 3.8, 3.9 from the textbook Forecasting: principles and practice by Rob J Hyndman and George Athanasopoulos. Exercise 3.1 Consider the GDP information in global_economy. Plot the GDP per capita for each country over time. Which country has the highest GDP per capi...
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