[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
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Updated
Dec 3, 2017 - Python
[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 project consists in competing in the following Kaggle competition: https://www.kaggle.com/c/house-prices-advanced-regression-techniques
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.
Kaggle House Prices: Advanced Regression Techniques.Public Leaderboard Score 0.12076.
All my Kaggle Notebooks that I've published
Deep Learning using Tensorflow for the "House Prices: Advanced Regression Techniques" Kaggle competition.
All of mine ML projects
Data playground for improving machine learning skills using Kaggle datasets. Work in Progress: Listed here are Kaggle competitions I am working on, not necessarily finished.
Kaggle project using regression models to predict housing price.
Repository for source code of Kaggle competition: House Prices: Advanced Regression Techniques
My Data Mining Training Repository
🏘 Ames house dataset modelled and explained
Kaggle House Prices Problem
Predict sales prices and practice feature engineering, RFs, and gradient boosting
A write-up on Kaggle's Titanic and AMES housing competitions
Kaggle's data science competition for students about predicting final prices of residential homes in Iowa.
Advanced Regression Techniques to predict housing prices.
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