High performance implementation of the Naive Bayes algorithm in R

Identification and Classification of the Most Influential Nodes

Regular Expression Counts of Terms and Substrings

Dataset containing radio frequencies, geo-locations, and habitat types for Oregon frogs

prettyglm provides a set of functions which can easily create beautiful coefficient summaries which can readily be shared and explained.

Exploring consumer behaviour through Expedia sales.

As part of this project, I have developed algorithms from scratch using Gradient Descent method. The first algorithm developed will be used to predict the average GPU Run Time and the second algorithm will be used to classify a GPU run process as high or low time consuming process.

Fire Incident risk classification Data Mining project

Statistics class project aimed at studying the relationship between temperature and other attributes such as humidity, pressure, etc in Szeged, Hungary to build effective predictive models.

[Completed] Classification Report using K-Nearest Neighbors, Random Forests, and Logistic Regression.

This project was conducted at UT Tyler Data Analytics Lab with the goal of using historical patient data and neural networks to predict future opioid abuse.

Comparison of the logistic regression, decision tree, and random forest models to predict red wine quality in R.

To predict baby birthweight using regression and classify baby birthweight groupings (Low, Normal, Overweight) using classification with machine learning techniques.

Implementation of multiclass classification problems in R

Built a logistic regression model and a classification tree model for predicting the final status of a loan based on various variables available. Confusion matrix and misclassification rate for each model for a test dataset. Variables that appear to be important for predicting outcome. Plotted and described the ROC curves and AUC for the four mo…

Using Decision Tree to predict Employee Attribution

R, Classification, Stepwise Variable, A/B Testing

Using the 'neuralnet' package in R for machine learning classification

Class projects