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In this project, we will create a classification model to predict energy consumptions of buildings(EUI-High/Low) using Decision tree. We aim to use XGBoost to further improve accuracy of the model.

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arunsivakumar5/XGBoost-Decision-tree-for-Predicting-Energy-consumption

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XGBoost-Decision-tree-for-Predicting-Energy-consumption

In this project, we will create a classification model to predict energy consumptions of buildings(EUI-High/Low) using XGBoost and Decision tree . We aim to use XGBoost to further improve accuracy of the model.

Libraries:
Sklearn
Numpy
Pandas

Programming Languages:
Python

Dataset Source:
This Energy consumption dataset Contains information from https://www.kaggle.com/claytonmiller/annual-energy-consumption-from-singapore-buildings accessed on 07/08/2021 from https://data.gov.sg/dataset/building-energy-performance-data which is made available under the terms of the Singapore Open Data Licence version 1.0 https://data.gov.sg/open-data-licence

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In this project, we will create a classification model to predict energy consumptions of buildings(EUI-High/Low) using Decision tree. We aim to use XGBoost to further improve accuracy of the model.

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