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HousePricing旨在提供房价的可视化预测，帮助用户更好的评估房产和预测未来的价格(dev)

Predicting house prices using Linear Regression and GBR

房价预测完整项目：1.爬取链家网数据 2.处理后，用sklearn中几个逻辑回归机器学习模型和keras神经网络搭建模型预测房价 最终结果神经网络效果更好，R^2值0.75左右

A web app that helps you figure out whether buying or renting a home makes you richer.

Linear Regression Model for Real State House Price Prediction

Udacity capstone project: Kaggle competition on house prices prediction using advanced regression techniques

This is Kaggle project for the house price prediction

This project demonstrates the use of machine learning for predicting house prices based on various features like crime rate, number of rooms, property tax rate, etc.

Built house price prediction model using linear regression and k nearest neighbors and used machine learning techniques like ridge, lasso, and gradient descent for optimization in Python

The "House Price Prediction" project focuses on predicting housing prices using machine learning techniques. By leveraging popular Python libraries such as NumPy, Pandas, Scikit-learn (sklearn), Matplotlib, Seaborn, and XGBoost, this project provides an end-to-end solution for accurate price estimation.

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北京二手房价数据分析

This repository contains files for Udacity's Machine Learning Nanodegree Project: Boston House Price Prediction

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kaggle上的一个房屋价格预测比赛

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Worked on Real Estate Price data analysis by scraping website from www.99acres.com to help the housing domain as well as estimate the sale value of various houses and open plots.

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This repo contains 4 different projects. Built various machine learning models for Kaggle competitions. Also carried out Exploratory Data Analysis, Data Cleaning, Data Visualization, Data Munging, Feature Selection etc

Model deployment with flask api, using Linear Regression to predict the price value. Deploy ML Models Using Flask to take your models from python to production.

Understand the relationships between various features in relation with the sale price of a house using exploratory data analysis and statistical analysis. Applied ML algorithms such as Multiple Linear Regression, Ridge Regression and Lasso Regression in combination with cross validation. Performed parameter tuning, compared the test scores and suggested a best model to predict the final sale price of a house. Seaborn is used to plot graphs and scikit learn package is used for statistical analysis.

This is a regression problem to predict california housing prices.

Predicting house prices using skicit learn in python.

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Data Analysis & Visualization - Predict the future price of houses

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