Skip to content
View Elizzzza's full-sized avatar
  • University of Washington
  • Seattle, WA

Block or report Elizzzza

Block user

Prevent this user from interacting with your repositories and sending you notifications. Learn more about blocking users.

You must be logged in to block users.

Maximum 250 characters. Please don't include any personal information such as legal names or email addresses. Markdown supported. This note will be visible to only you.
Report abuse

Contact GitHub support about this user’s behavior. Learn more about reporting abuse.

Report abuse

Pinned Loading

  1. Multiple-linear-regression-models-on-country-level-mortality-rate-over-time Multiple-linear-regression-models-on-country-level-mortality-rate-over-time Public

    Repository for Applied Biostatistics on Poisson regression models, effect modification, and hypothesis testing

    R

  2. Prediction-of-Breast-Cancer-Diagnosis Prediction-of-Breast-Cancer-Diagnosis Public

    Using classification techniques (logistic regression model and random forest) to predict the diagnosis of breast tumor tissues as either malignant or benign based on ten cytological features.

    1

  3. Survival-anaylsis-on-time-to-first-cancer-recurrence Survival-anaylsis-on-time-to-first-cancer-recurrence Public

    Repository for Applied Biostatistics on log-rank test, survival function, and Cox proportional hazard models

    R

  4. Correlation-between-tobacco-use-and-chronic-health-in-US-residents Correlation-between-tobacco-use-and-chronic-health-in-US-residents Public

    Apply multivariable logistic regression analyses to examine tobacco usage as a potential predictor of chronic health and oral health of the US residents and potentially study the association of chr…

    R

  5. Stochastic-gradient-descent-exercise Stochastic-gradient-descent-exercise Public

    Computational skills for Biostatistics

    R

  6. S3-method-shallow-neural-network-and-C-implementation S3-method-shallow-neural-network-and-C-implementation Public

    Using S3 object-oriented system to refactor the shallow neural network code for binary classification. Re-implement in C++ the computation of the gradient of the parameter theta of the neural network.

    R