Using the Consumer Expenditure Microdata to examine how regression trees and linear regression differ in predicting total expenditures.

Assignments, Projects and other course related material.

Simple Implementation of Gradient Boosted Trees

What factors influence runners

Various techniques applied for the prediction of median home value were- Generalized Linear Regression, Regression Tree, Generalized Additive Model and Neural Networks.

Machine Learning course term project, collaborated with Dr. Daniel Chen, and Dr. Elliot Ash

A real life case study of property price prediction based on data of New York City.

Generic decision trees for rust

An R package that implements several methods for growing regression trees with functional and multivariate outputs

An implementation of 4 machine learning algorithms from scratch

Use regression tree to predict firearm death rate with firearm law & CDC firearm death rate data.

The objective here is to predict the purchase of energy efficient home appliances using various methods like Neural Network, Logistic regression, SVM, Random Forest etc. and then compare the prediction performance in the test data. For estimation, the library sklearn has been used here. This is part of a class project for EconS 514 in Spring 201…

Hybrid of Regression Trees & Linear Regression

This is a presentation I gave to the Econometrics IV class. I described a paper by Patrick Bajari and coauthors. I also described some of the methods the applied in simple terms.

Exporting trained boosted trees to executable code in plain C / Python

Moody's Bond Rating Classifier and USPHCI Economic Activity Forecast Modeling

Statistical Learning in R

Building Decision Trees From Scratch In Python

⚡️⚡️⚡️《机器学习实战》代码（基于Python3）🚀