Home prices in Ames,Iowa

For this project, I analyzed a dataset from kaggle, which consists of 79 explanatory variables that describe various features of residential homes in Ames,Iowa. The challenge is to identify which features affect sale prices and predict home prices of each of these homes. I used Gradient Boost and XGBM algorithms for this.

Packages: tidyverse, ggplot2, caret, gbm, xgboost

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Predicting Fraud in Health Insurance claims

For this project, I analyzed 3 datasets (~550k observations) that consist of health insurance claims (inpatient and outpatient) and beneficiary information. The goal is to identify features from these claims that can be help us characterize the behavior of fraudulent providers. I used Logistic regression, Random Forest, and XGBM algorithms for this.

Packages: tidyverse, ggplot2, caret, xgboost

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