Analysis of Singapore real estate market in the period: July 2015 to July 2016. Data contains real estate prices (from actual transactions) for the several selected districts of Singapore. It is obtained from the Urban Redevelopment Authority of Singapore. Analysis is carried out in Python.
Following Python packages have been employed: Numpy, Scipy, Pandas, Seaborn, Folium, Scikit-learn, TensorFlow.
Analysis features exploration of the dataset of actual real estate transactions, using pandas, seaborn and folium. Real estates are geolocated and presented on an interactive folium map. Analysis of real estates, based on their distance from the Metro stations, using data from MyTransport Singapore, is provided. A simple attempt at using a Machine Learning to predict unit prices (in $ per square meter) for real estates, based on the features found in the dataset, is provided as well.
The Jupyter Notebook can be seen rendered on nbviewer here