[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
datascienv is package that helps you to setup your environment in single line of code with all dependency and it is also include pyforest that provide single line of import all required ml libraries
Data Science algorithms and topics that you must know. (Newly Designed) Recommender Systems, Decision Trees, K-Means, LDA, RFM-Segmentation, XGBoost in Python, R, and Scala.
An stacked regression based on xgboost, keras for Kaggle house price competition
Description: implement a stacked regression using xgboost, keras linear regression. The score (0.11) is much better than using Tensorflow (0.45) in branch v1.0.
Development environment:
Anaconda 4.4, Python 3.6
Xgboost 0.6
Window 10.
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