[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 notebook explores the housing dataset from Kaggle to predict Sales Prices of housing using advanced regression techniques such as feature engineering and gradient boosting.
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.
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.
🏘 Ames house dataset modelled and explained
My Data Mining Training Repository
Repository for source code of Kaggle competition: House Prices: Advanced Regression Techniques
Kaggle House Prices Problem
A write-up on Kaggle's Titanic and AMES housing competitions
Predict sales prices and practice feature engineering, RFs, and gradient boosting
my attempt to train a model on Ames housing dataset with scikit-learn and XGBoost .
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