Publications by Anthony Conrardy
DATA 622-Assignment #3
Assignment Perform an analysis of the dataset used in Homework #2 using the SVM algorithm. Compare the results with the results from previous homework. ## education marital housing contact duration month age balance campaign pdays ## 1 tertiary married yes unknown 261 may 58 2143 1 -1 ## 2 secondary single ...
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Assignment #3 R Code
Assignment #3 R Code # Categorical to Factors categorical_vars <- c('job', 'marital', 'education', 'default', 'housing', 'loan', 'contact', 'month', 'poutcome', 'y') df[categorical_vars] <- lapply(df[categorical_vars], as.factor) # Feature Engineering df$previously_contacted <- ifelse(df$pdays == -1, "no", "yes") df$pr...
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Story-3: Obesity and Healthcare Spending
Instructions: Assignment: Investigating the Relationship Between Healthcare Spending and Adult Obesity Public health outcomes often reflect differences in policy priorities and resource allocation. This assignment uses state-level adult obesity prevalence data to explore whether differences in healthcare spending correlate with obesity rates. ...
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Exploratory Data Analysis-Assignment #1 DATA 622
Dataset A Portuguese bank conducted a marketing campaign (phone calls) to predict if a client will subscribe to a term deposit The records of their efforts are available in the form of a dataset. The objective here is to apply machine learning techniques to analyze the dataset and figure out most effective tactics that will help the bank in nex...
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Homework #3-DATA 605
Problem 1-Transportation Safety Scenario: You are a data analyst at a transportation safety organization. Your task is to analyze the relationship between the speed of cars and their stopping distance using the built-in R dataset cars. This analysis will help in understanding how speed affects the stopping distance, which is crucial for improvi...
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Homework #2-Data 605
Problem 1 (Bayesian): A new credit scoring system has been developed to predict the likelihood of loan defaults. The system has a 90% sensitivity, meaning that it correctly identifies 90% of those who will default on their loans. It also has a 95% specificity, meaning that it correctly identifies 95% of those who will not default. The default r...
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Document
Question #1 Customer monthly movement between your product and competitors is represented by the following monthly market transition matrix. Assume you and your two competitors have 100 customers each on the first month. About how many customers would you expect to have at the beginning of the third month. You are represented by the first row, ...
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TLX-Analysis
Read in Excel File with Task Load Index (TLX) Data Read in the Excel file and change column names for easier analysis. Create a Team Size measure. tlx_df <- read_excel("D:/Documents/R_Working_Directory/TLX Data Analysis/TLI Data(working).xlsx") colnames(tlx_df)[colnames(tlx_df)=="Number"] <- "Team" tlx_df <- tlx_df |> group_by(Team, Scenario) |>...
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Final Project Part I
Introduction While the intent of this project was to link two very disparate data sources, it turned out to be much more complicated than expected. The data sources used for this project came from the student clinical evaluation Jotform data source located as a report on their site, and from a manual extraction of the testing data from the Plat...
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Final Project Part II
Introduction In Part II of the this final project, we will import the sterilized data sets from Part I that are located on the GitHub repository in the locations below. We will then merge the data sets into a unified set containing the required elements for further investigation and analysis. We will rename the variables to be something easier ...
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