This project aims to implement and compare variant machine learning techniques for predicting house prices. It is an expanded project from Kaggle House Prices: Advanced Regression Techniques, such that the project is going to use the same dataset provided under this Kaggle competition. The machine learning techniques in this project include Linear Regression, Random Forest, Adaptive Boosting, Support Vector Regression, Gradient Boosted Decision Tree, K Nearest Neighbors, and Neural Network.

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Haiyang Shi

I am a Ph.D. student in Computer Science and Engineering at The Ohio State University (OSU), advised by Dr. Xiaoyi Lu. My professional interests involve high-performance interconnects and protocols, erasure coding, in-network computing, and distributed systems. In my ongoing and recent projects, which are mainly developed in C++ and Java, I focus on implementing RDMA (Remote Direct Memory Access)-based communication protocols, optimizing I/O performance by exploiting RDMA for data transmission, enabling distributed systems to leverage heterogeneous hardware-optimized erasure coders in parallel, and designing coherent in-network EC to gain better overlapping and parallelism on HPC clusters. In the meantime, I have participated in characterizing deep learning over big data stacks recently, and have understandings of deep learning training on distributed systems and modern data centers. Prior to graduate school, I worked as a Software Engineer at MiningLamp and Weibo, China. I finished my Bachelor’s degree at Tianjin University, China.

Graduate Research Associate

Columbus, OH