Publications by Kristin Lussi
DATA 622 Project 4
Project 4: MNIST using KNN and Neural Networks Author Team: I Love Lucy Published December 11, 2024 Project 4 Github: [Quarto Presentation] [Python] | Projects: [4] [3] [2] [1] from IPython.display import display, HTML from lussi.mnist import * X_train, X_test, y_train, y_test, X_train_unscaled = load_data() Execut...
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Project 4 draft
Project 4: MNIST using KNN and Neural Networks Author Team: I Love Lucy Published December 8, 2024 Project 4 Github: [Quarto Presentation] [Python] | Projects: [4] [3] [2] [1] from IPython.display import display, HTML from lussi.mnist import * X_train, X_test, y_train, y_test, X_train_unscaled = load_data() Executi...
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Project 4 Draft
Project 4: MNIST using KNN and Neural Networks Author Team: I Love Lucy Published December 8, 2024 Project 4 Github: [Quarto Presentation] [Python] | Projects: [4] [3] [2] [1] from IPython.display import display, HTML from lussi.mnist import * X_train, X_test, y_train, y_test, X_train_unscaled = load_data() Executi...
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DATA 622 Project 2
Project 2: Decision Trees vs Random Forest: Predicting IMDB Ratings Author Team: I Love Lucy Published November 18, 2024 github | web presentation Project Overview In this project, we set out to use Random Forest on the IMDB non-commercial dataset to predict movie ratings. While the dataset lacks financial performance metrics like box ...
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DATA 622 Project 3
Project 3: IMDB Ratings, comparing Random Forest to Support Vector Machines Author Team: I Love Lucy Published November 12, 2024 github | project 2 | project 3 Executive Summary This project compares two machine learning approaches—Random Forest and Support Vector Machines (SVM)—for predicting IMDB movie ratings. Through multipl...
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Project 3 draft
Project 3: IMDB Ratings, comparing Random Forest to Support Vector Machines Author Team: I Love Lucy Published November 12, 2024 github | project 2 | project 3 Executive Summary This project compares two machine learning approaches—Random Forest and Support Vector Machines (SVM)—for predicting IMDB movie ratings. Through multipl...
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DATA 622 Project 1
Project 1: Pay and our industry Author Team: I Love Lucy Published October 20, 2024 github | web presentation Data Sets This project analyzes two datasets: one from ZipRecruiter and the other from Stack Overflow. • ZipRecruiter Dataset: A heavily curated, clean, and well-structured dataset. It offers a tight focus, making it easy to ...
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Test123
Project 1: Pay and our industry Author Team: I Love Lucy Published October 10, 2024 Data Introduction This project covers two datasets, one we’ll call ZipRecruiter, and the the other we’ll call Stack. Zip Recruiter First, let’s view a sample of the Zip Recruiter dataset: State Annual Salary Monthly Pay Weekly Pay Hourly Wage Job Title...
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DATA 605 Homework 15
Week 15 Homework Exercise 1 Find the equation of the regression line for the given points. Round any final values to the nearest hundredth, if necessary. \((5.6,8.8), (6.3,12.4), (7,14.8), (7.7,18.2), (8.4,20.8)\) Solution x_vals <- c(5.6,6.3,7,7.7,8.4) y_vals <- c(8.8,12.4,14.8,18.2,20.8) # calculate the mean of x values xbar <- sum(x_vals)/len...
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DATA 605 Discussion Week 15
Discussion Week 15 Page 711 Exercise 32: Find \(f_x\), \(f_y\), \(f_z\), \(f_{yz}\), and \(f_{zy}\). \(f(x,y,z)=x^3y^2 +x^3z+y^2z\) Solution \[ f_x = \frac{\partial}{\partial x} (x^3 y^2 + x^3 z + y^2 z)\\ f_x = 3x^2 y^2 + 3x^2 z\\ f_y = \frac{\partial}{\partial y}(x^3 y^2 + x^3 z + y^2 z)\\ f_y = 2x^3 y + 2yz\\ f_z = \frac{\partial}{\partial z}(...
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