Data analysis using common Machine Learning Algorithm named Linear Regression
The dataset is describing the heating load and cooling load requirements of buildings (that is, energy efficiency) as a function of building parameters. The dataset was taken from UCI, a Machine Learning repository. Link to the dataset will be provided in the acknowledgement. To be able to run and modify the project, please make sure you have jupyter notebook, python 3, pandas, numpy and sklearn (scikit-learn). These can be done either through pip or conda. See Installing for more detailed information and links.
Required stack of programs and libraries are Jupyter notebook, Python 3, Pandas, Numpy and Sklearn (scikit-learn)
Make sure to install conda or pip. I have personally used conda, for which first I had to download Anaconda distribution - The World's most popular Python/R Data science platform, as it is more suitable for data analysis and it includes libraries from other programming languages such as C, C++, R. Go to terminal
conda --version
If the code above returns some version you are good to go, otherwise follow the link below to install anaconda: https://www.anaconda.com/distribution/
After the anaconda installation is complete, open the terminal and follow with the required packages installation
conda install -c conda-forge jupyterlab
Create a new environment for this project
conda create -n yourEnvironmentName
Switch to the newly created environment
source activate yourEnvironmentName
Install required packages
conda install python=3.6 numpy pandas scikit-learn
After all the above installations are successfully completed clone the repo
git clone thisRepoName
And finally open the jupyter notebook by just typing
jupyter notebook
You will see the current folder being opened in the browser, navigate to the Energy_Efficiency.ipynb file and open it. Click play button to execute each cell of code or comments
This project is licensed under the MIT License - see the LICENSE.md file for details
Dataset was created by Angeliki Xifara (angxifara@gmail.com, Civil/Structural Engineer) and was processed by Athanasios Tsanas (tsanasthanasis@gmail.com, Oxford Centre for Industrial and Applied Mathematics, University of Oxford, UK)
- dataset by Kory Becker: https://archive.ics.uci.edu/ml/datasets/Energy+efficiency