Publications by Melissa Bowman
HW4 Data 622
Problem Description: The problem at hand involves building predictive models to recognize hand-written digits from the MNIST dataset. Handwritten digit recognition is a classic problem in the field of computer vision and machine learning, with applications ranging from postal automation to digital document processing. The primary goal is to acc...
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HW3 Data622
Analyzing the Titanic Dataset: SVM and Decision Tree Models Comparision. https://www.kaggle.com/competitions/titanic/data?select=test.csv In the prior Titanic survival prediction analysis, we relied on decision trees for modeling. Now, we’ll assess the performance of both random forest decision trees and SVM models using the same dataset. ETA...
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HW9 Data 624
Do problems 8.1, 8.2, 8.3, and 8.7 in Kuhn and Johnson. library(caret) library(tidyverse) library(AppliedPredictiveModeling) library(corrplot) library(e1071) library(mlbench) library(randomForest) library(rpart) library(party) library(gbm) library(Cubist) library(partykit) Question 8.1 Recreate the simulated data from Exercise 7.2: set...
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HW8 Data 624
Do problems 7.2 and 7.5 in Kuhn and Johnson. library(caret) library(tidyverse) library(AppliedPredictiveModeling) library(corrplot) library(e1071) library(mlbench) Question 7.2 Friedman (1991) introduced several benchmark data sets create by simulation. One of these simulations used the following nonlinear equation to create data: where the...
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HW2 Data622
Analyzing the Titanic Dataset: Decision Tree and Random Forest Models The decision tree chosen for analysis pertains to a Kaggle competition aimed at predicting which passengers survived the Titanic shipwreck. This particular dataset is divided into a training set and a testing set. Initially, a thorough examination of the variables’ summary ...
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HW7 Data 624
In Kuhn and Johnson do problems 6.2 and 6.3. library(caret) library(tidyverse) library(AppliedPredictiveModeling) library(corrplot) library(e1071) Question 6.2 Developing a model to predict permeability (see Sect. 1.4) could save significant resources for a pharmaceutical company, while at the same time more rapidly identifying molecules tha...
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Data 624 Project1 Part B: Residential Power Forecast
Part B comprises a straightforward dataset detailing residential power consumption from January 1998 to December 2013. The task involves modeling this dataset and generating a monthly forecast for 2014. The data is provided in a single file, with the variable ‘KWH’ representing power consumption in Kilowatt hours, while the remaining variab...
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HW6 Data 624
Do the exercises 9.1, 9.2, 9.3, 9.5, 9.6, 9.7, 9.8 in Hyndman. (https://otexts.com/fpp3/) library(fpp3) library(tidyverse) library(gridExtra) library(urca) 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 all indicate that the data...
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HW1 Data622
Essay Exploratory Data Analysis and Model Building for Profit Prediction Exploratory data analysis (EDA) is a crucial step in understanding and preparing data for predictive modeling. In this essay, we delve into the exploration of two datasets: one containing 100 sales records (small data) and the other with 1,000,000 sales records (big data)....
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HW5 Data 624 Predictive Analytics 2024 Spring Term
Do exercises 8.1, 8.5, 8.6, 8.7, 8.8, 8.9 in Hyndman. (https://otexts.com/fpp3/) library(fpp3) library(tidyverse) Question 8.1 Consider the the number of pigs slaughtered in Victoria, available in the aus_livestock dataset. a.Use the ETS() function to estimate the equivalent model for simple exponential smoothing. Find the optimal values of a a...
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