User Guide to Run Jupyter Notebook This guide presents the necessary steps to execute the Jupyter Notebook provided in this directory.
Prerequisites Have a version of Python installed on your machine Have selected a Jupyter environment Installation of necessary packages To run the notebook, it is necessary to install certain Python packages. You can do this using the following commands:
pip install -U scikit-learn pip install seaborn pip install xgboost
Executing the notebook Once the packages are installed, you can run the notebook by opening the file Projet_Machine_Learning.ipynb in Jupyter Notebook.
Predicting Data If you wish to predict data, you can run the file Projet_Machine_Learning_trained_models.ipynb in Jupyter Notebook.
We have saved our two most performant models (one for regression and one for classification).
You can enter house features in two different ways:
If you want to predict the price of a single house, you can fill in the necessary characteristics of that house in the np.array of the cells associated with the models.
If you want to predict the price of multiple houses, you can fill in the data_to_predict.csv file in the data folder, then run the cells associated with the models.
A data_description.txt file is available to help you select characteristic values to estimate the price of your house(s).
Happy predictions!