GPoFM: Gaussian Process Training with Optimized Feature Maps for Shift-Invariant Kernels
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Updated
May 5, 2017 - Python
GPoFM: Gaussian Process Training with Optimized Feature Maps for Shift-Invariant Kernels
Repository for Assignment 1 for CS 725
Boston Housing Analysis: This repo presents an in-depth analysis of the Boston Housing dataset using Linear, Lasso, and Ridge Regression models. It explores data, preprocesses features, visualizes relationships, and evaluates model performance.
This project uses Mini-learn on Boston's housing data-set. Mini-learn is a miniature version of tensor-flow which I made to play around with neural nets. See https://github.com/Satyaki0924/minilearn for more information.
Boston Housing Price prediction using regressions
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