This was the final project for my data analytics course at Vanderbilt University with Dr. Scott Crossley. Morphological and summary level transcript data obtained from kaggle was analyzed in R Studio through the use of a principal component analysis that was then analyzed using a machine learning approach to logistic regression to see what predicted speech language impairment in a sample of 534 children. Data Source: https://www.kaggle.com/datasets/dgokeeffe/specific-language-impairment R Packages Required: tidyverse, ggplot2, dplyr, caret, car, corrplot, relaimpo, PerformanceAnalytics, glmnet, psych, stringr
-
Notifications
You must be signed in to change notification settings - Fork 0
Morphological and summary level transcript data obtained from kaggle was analyzed through the use of a principal component analysis that was then analyzed using a machine learning approach to logistic regression to see what predicted speech language impairment in a sample of 534 children's transcripts.
Mary-E-Rodgers/FinalProject_PCA_ML_LogisticRegression_TranscriptData
Folders and files
| Name | Name | Last commit message | Last commit date | |
|---|---|---|---|---|
Repository files navigation
About
Morphological and summary level transcript data obtained from kaggle was analyzed through the use of a principal component analysis that was then analyzed using a machine learning approach to logistic regression to see what predicted speech language impairment in a sample of 534 children's transcripts.
Resources
Stars
Watchers
Forks
Releases
No releases published
Packages 0
No packages published