Udacity机器学习入门 / 进阶 预测波士顿房价 Branch master / v1

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).

Used machine learning techniques to predict the prices of houses in the Boston housing market dataset.

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.

Repository for Assignment 1 for CS 725

Predicting Boston house prices with Machine Learning.

Implementing linear regression on Boston Housing dataset using scikit-learn

Udacity MLND P1: Predicting Boston Housing prices

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.

MA Housing Assistance APP

Predicting house prices of the "Boston Housing" dataset.

This repo contains projects developed in Machine Learning Foundation Nanodegree.

Regression Analysis into the Boston Housing in-demand pricing in 1978

Visualizing rent affordability in East Boston

Boston Housing Price prediction using regressions