Kaggle Kernel for House Prices competition https://www.kaggle.com/massquantity/all-you-need-is-pca-lb-0-11421-top-4

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

This is Kaggle project for the house price prediction

A light-weight transformer model for Kaggle House Prices Regression Competition

An example project that predicts house prices for a Kaggle competition using a Gradient Boosted Machine.

Repository for source code of kaggle competition: House Prices: Advanced Regression Techniques

[Done] Master version: developed the stacked regression (score 0.11, top 5%) based on (xgboost, sklearn). Branch v1.0: developed linear regression (score 0.45) based on Tensorflow

This repo has been developed for the Istanbul Data Science Bootcamp, organized in cooperation with İBB and Kodluyoruz. Prediction for house prices was developed using the Kaggle House Prices - Advanced Regression Techniques competition dataset.

This project consists in competing in the following Kaggle competition: https://www.kaggle.com/c/house-prices-advanced-regression-techniques

Kaggle House Prices: Advanced Regression Techniques.Public Leaderboard Score 0.12076.

In this Data science project I tried to create Jupyter notebooks for EDA and feature engineering of Advanced House Price Prediction Dataset from Kaggle Competition.

问题定义，下载数据，观察结构，找最相关部分特征； 除去异常值，填充缺省值，部分特征正态化，特征编码向量化，增加新特征； 定义评估标准，选择表现好的几个模型，模型参数最优化，stacking模型融合。最终误差降低到0.101左右。详细介绍见https://zhuanlan.zhihu.com/p/89923216

This notebook explores the housing dataset from Kaggle to predict Sales Prices of housing using advanced regression techniques such as feature engineering and gradient boosting.

Iowa House Prices Kaggle (top 5%)

All my Kaggle Notebooks that I've published

Predict sales prices and practice feature engineering, RFs, and gradient boosting

Deep Learning using Tensorflow for the "House Prices: Advanced Regression Techniques" Kaggle competition.

A Linear regression project to predict the selling price of a house. Data set contained features of house using which we predicted the price. Dataset was taken from kaggle.

基于随机采样与精度加权的Stacking算法，用于kaggle竞赛House Price的房价预测，比传统stacking算法有所提升

My solution to House-Prices Advanced Regression Techniques, A beginner-friendly project on Kaggle.

https://www.kaggle.com/c/house-prices-advanced-regression-techniques

Machine Learning Regression with Kaggle House Prices

An Artificial Neural Network model to predict house prices over a kaggle dataset.

The famous Housing Price Advanced Regression competition on Kaggle. The dataset contains of training and testing sets each with about 1.46K rows and 81 features pertaining to a house. I have first performed an exhaustive EDA to identify the underlying trends in the data. I have also removed outliers to make the regression models more robust. Also proper missing values treatment has been done with imputation being done wherever needed. Lastly I have deployed various regression models like Lasso,Ridge etc... from scikit and have also tuned their parameters from the GridSearchCV module. Finally achieved a RMSE of little more than 0.12 which is pretty decent.

House Prices: Advanced Regression Techniques - Kaggle competition

Kaggle challenge of House Prices: Advanced Regression Techniques is solved using ANN models with only low-level APIs of TensorFlow. The predicted test-result scored 0.1190 in Kaggle leaderboard.

Kaggle Competition - House Prices: Advanced Regression Techniques

All of mine ML projects

Predicting house prices using Python with data provided by kaggle

This is a streamlit app created by using the Ames Housing dataset on Kaggle (https://www.kaggle.com/competitions/house-prices-advanced-regression-techniques/overview).