If a person asks a home buyer to describe their ideal home, they are unlikely to start with the basement ceiling height or the closeness to an east-west railroad. However, the dataset from this project demonstrates that there are many more factors that impact price negotiations than the amount of beds or a white-picket fence. This project forecasts the ultimate price of each property using 79 explanatory factors that describe (nearly) every element of residential dwellings in Ames, Iowa.
Start by downloading the project and run "SourceCode.ipynb" file in ipython-notebook.
You need to have installed following softwares and libraries in your machine before running this project.
1. Python 3
2. Google Colaboratory
- ipython-notebook - Python Text Editor
- scikit-learn, Tensorflow - Machine learning library
- Seaborn, matplotlib.pyplot, - Visualization libraries
- NumPy - number python library
- Pandas - data handling library
- Andrew Thenedi
- COMP4433 (Data Mining and Data Warehousing) Course