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Udacity机器学习入门 / 进阶 预测波士顿房价 Branch master / v1
2018 [Julia v1.0] machine learning (linear regression & kernel-ridge regression) examples on the Boston housing dataset
We apply basic machine learning concepts on data collected for housing prices in the Boston, Massachusetts area to predict the selling price of a new home.
GPoFM: Gaussian Process Training with Optimized Feature Maps for Shift-Invariant Kernels
I created a machine learning regression model that predicts housing prices in Boston. This model is created using supervised learning from a dataset created by UCI (University of California, Irvine).
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.
Used machine learning techniques to predict the prices of houses in the Boston housing market dataset.
Udacity MLND P1: Predicting Boston Housing prices
Repository for Assignment 1 for CS 725
Predicting Boston house prices with Machine Learning.
This repository showcases projects that I have done as a partial fulfillment for Udacity's Machine-Learning Nano Degree program. The projects involve Supervised Machine Learning, Unsupervised Machine Learning , Reinforcement Learning as tools to solve the data driven problems coming from different real life situations.
This repo contains projects developed in Machine Learning Foundation Nanodegree.
Predicting house prices of the "Boston Housing" dataset.
Implementing linear regression on Boston Housing dataset using scikit-learn
MA Housing Assistance APP
Projects completed in the Urban Informatics masters degree program at Northeastern University - All projects completed in R
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.
Exploratory Data Analysis of Boston Housing Dataset Using R
Projects of Udacity's Machine Learning Specialization Nanodegree program.
This repository contains code and associated files for deploying ML models using AWS SageMaker. This repository consists of a number of tutorial notebooks for various coding exercises, mini-projects, and project files that will be used to supplement the lessons of the Nanodegree.
Code and associated files created or filled in, used in my learning during the Machine Learning Engineer Udacity Nanodegree Program
Visualizing rent affordability in East Boston
Regression Analysis into the Boston Housing in-demand pricing in 1978
This code file predicts the House prices (MEDV) based on given features.
Predicting housing prices using machine learning and the widely available Boston Housing dataset.
Boston Housing Price prediction using regressions