PricePrediction
In this project we perform some data analysis, and build some machine learning models to predict the price of a real-estate property.
The whole program is in a Jupyter Notebook running Python 3. I choose to use a jupyter notebook because it's easier to explain my line of thought and choices that I have made in the process of building the models.
To run the notebook, it's necessary to have numpy, pandas, matplotlib, seaborn, and scikit-learn installed on the machine. I have tested the code only on Ubuntu 16.04, with all libraries provided by Anaconda.
During the feature engineering step and construction of the machine learning models it's necessary to have at least 16 GB of memory.
To open the notebook, just open the terminal located where the project folder is and open the notebook as default:
jupyter notebook
To better visualize the notebook online, try Jupyter NBViewer.