This report covers: Part A: daily ATM cash forecasting for May 2010. Part B: monthly residential power forecasting for 2014. Bonus Part C: hourly waterflow aggregation, stationarity assessment, and one-week-ahead forecasting. Across all three parts, the modeling work depended on fixing data-format and structure problems before any forecasts could be trusted. The ATM file required Excel date conversion and removal of trailing non-series rows, the power file required interpolation of a missing monthly usage value, and the waterflow files required conversion from Excel serial timestamps into regular hourly series. Those cleanup steps were not cosmetic. Each one addressed a specific failure mode that would otherwise distort the time index, break the tsibble structure, or cause the forecasting functions to evaluate the wrong objects.

FPP3 Chapter 9 ARIMA Exercises

— Complete Worked R Markdown

Home work for DATA 624 problems 3.1, 3.2, and 3.3 in the Kuhn and Johnson book Applied Predictive Modeling.

Homework assignment for FPP3 chapter 5

Chapter 3 homework

Exploriing tsibble, facet_grid, ACF, etc

This analysis evaluates a Knowledge Graph-powered machine learning system for classifying hate incident terminology against human expert classifications.

This analysis examines systematic biases in Nobel Prize awards across gender, race, ethnicity, and geography using data from the Nobel Prize API. Our investigation reveals: Gender Bias: Only a small percentage of science Nobel laureates are women, with statistical evidence suggesting bias contributes to this gap. Racial Bias: There are no Black scientists who have won science Nobel Prizes in the historical record examined here. The Rosalind Franklin Case: A prominent example of uncredited contributions in the discovery of DNA’s double helix.

This document demonstrates sentiment analysis using tidy data principles. The primary example code is based on Chapter 2 of "Text Mining with R: A Tidy Approach" by Julia Silge and David Robinson【N:cmhhademz01cwoe0fwgxngx3b】.

Our project will analyze trends and contributing factors of school shootings in the United States by modeling the data in a normalized PostgreSQL database. We’ll explore how variables like location, school type, incident characteristics, and demographics relate to shooting frequency and severity.

A comparison of Data Formats using books I like

This analysis examines Wrexham AFC’s performance across three seasons spanning multiple divisions of English Football, from League Two (E3) through League One (E2) to the Championship (E1). The analysis focuses on identifying Wrexham’s closest competitors in each season and evaluating their competitive positioning.

An example of taking untidy data from https://glaad.org/trump-accountability-tracker/ and processing it into a form that makes analysis more accessible

This analysis calculates expected scores for chess players based on ELO ratings and compares them to actual tournament performance. The analysis uses the ELO expected score formula from Glickman’s “A Comprehensive Guide To Chess Ratings” paper.

This analysis examines a large dataset of political news articles to develop automated classification methods for identifying Trump-related and Fox News-related content. The dataset spans multiple years of political coverage during a period of significant media activity and political events. This work builds upon research in automated news categorization and applies scalable text processing techniques to handle large-scale news corpora. The methodology demonstrates efficient processing of substantial datasets while maintaining classification accuracy for media bias detection and political sentiment tracking.